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Claudio TURCHETTI

Pubblicazioni

Claudio TURCHETTI

 

173 pubblicazioni classificate nel seguente modo:

Nr. doc. Classificazioni
85 4 Contributo in Atti di Convegno (Proceeding)
49 1 Contributo su Rivista
28 2 Contributo in Volume
7 5 Altro
2 7 Curatele
1 3 Libro
1 6 Brevetti
Anno Risorsa
2019 A manifold learning approach to dimensionality reduction for modeling data
INFORMATION SCIENCES
Autore/i: Turchetti, Claudio; Falaschetti, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Manifold learning has gained in recent years a great attention in facing the problem of dimensionality reduction of high-dimensional data. This technique is based on the assumption that data are embedded in a nonlinear manifold of lower dimension. In this context the dimension of the embedding is a key parameter, therefore is of paramount importance for dimensionality reduction to discover the appropriate dimensionality of the reduced feature space, that is the intrinsic dimension (ID) of data. The purpose of this paper is to derive a manifold learning approach to dimensionality reduction for modeling data coming from either causal or noncausal signals. The approach is based on some theoretical results that aim first at giving a practical method for the estimation of the intrinsic dimension and then at deriving a local parametrization of data. Besides, an explicit nonlinear mapping relationship from data to the reduced space can be obtained as the regression of a nonlinear function. Several experiments on both synthetic and real data for the two classes of causal and noncausal signals have been conducted to validate the proposed approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/264728 Collegamento a IRIS

2019 Activity Monitoring and Phase Detection Using a Portable EMG/ECG System
Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2018.
Autore/i: Scherz, Wulhelm Daniel; Seepold, Ralf; Martínez Madrid, Natividad; Crippa, Paolo; Biagetti, Giorgio; Falaschetti, Laura; Turchetti, Claudio
Editore: Springer
Luogo di pubblicazione: Cham
Classificazione: 2 Contributo in Volume
Abstract: The investigation of stress requires to distinguish between stress caused by physical activity and stress that is caused by psychosocial factors. The behaviour of the heart in response to stress and physical activity is very similar in case the set of monitored parameters is reduced to one. Currently, the differentiation remains difficult and methods which only use the heart rate are not able to differentiate between stress and physical activity, without using additional sensor data input. The approach focusses on methods which generate signals providing characteristics that are useful for detecting stress, physical activity, no activity and relaxation.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266304 Collegamento a IRIS

2019 Fabrication of flexible high-performance organic field-effect transistors using phenacene molecules and their application toward flexible CMOS inverters
JOURNAL OF MATERIALS CHEMISTRY. C
Autore/i: Pompei, Emanuela; Turchetti, Claudio; Hamao, Shino; Miura, Akari; Goto, Hidenori; Okamoto, Hideki; Fujiwara, Akihiko; Eguchi, Ritsuko; Kubozono, Yoshihiro
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266550 Collegamento a IRIS

2019 Recognition of Daily Human Activities Using Accelerometer and sEMG Signals
Intelligent Decision Technologies 2019
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Luzzi, Simona; Turchetti, Claudio
Editore: Springer, Singapore
Luogo di pubblicazione: Singapore
Classificazione: 2 Contributo in Volume
Abstract: Human activity recognition (HAR) is an important technology for ambient-assisted living, sport and fitness activities, and health care of elderly people. HAR is usually achieved in two steps: acquisition of body signals and classification of performed activities. This paper presents an investigation on the optimal setup for recognizing daily activities using a wearable system designed to acquire surface electromyography (sEMG) and accelerometer signals through wireless sensor nodes placed on the upper limbs of the human body. To evaluate the optimal number of accelerometer and sEMG signals for detecting the user’s activities, data recorded from a few subjects were used to train and test an automatic classifier for recognizing the type of exercise being performed. In this evaluation, that was performed on eight different exercises executed by four subjects, the automatic classifier achieved an overall accuracy ranging from 10.6% to 93.0% according to different selections and combinations of the signals acquired from the sensing nodes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266885 Collegamento a IRIS

2019 Classification of Alzheimer’s Disease from Structural Magnetic Resonance Imaging using Particle-Bernstein Polynomials Algorithm
Intelligent Decision Technologies 2019
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Luzzi, Simona; Santarelli, Riccardo; Turchetti, Claudio
Editore: Springer Singapore
Luogo di pubblicazione: Singapore
Classificazione: 2 Contributo in Volume
Abstract: Automated structural magnetic resonance imaging (MRI) classification has gained popularity for the early detection of mild cognitive impairment (MCI), the first stage of dementia condition with an increased risk of eventually developing Alzheimer’s disease (AD). In general, an MRI diagnosis system requires some fundamental activities: MRI processing, features selection, data classification. The aim of this paper is twofold: (i) first, a high-performance classification algorithm based on particle-Bernstein polynomials (PBPs), recently proposed for nonlinear regression of input–output data that combines low complexity and good accuracy, has been developed, (ii) second, an MRI-based computer-aided diagnosis (CAD) system for the classification of AD has been derived. Several experiments on a dataset from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and comparisons with the state-of-the-art establish the performance of the method.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266886 Collegamento a IRIS

2019 Reduced complexity algorithm for heart rate monitoring from PPG signals using automatic activity intensity classifier
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Photoplethysmography (PPG) is a well-studied and promising technique to detect heart rate (HR) using cheap, non-invasive, wrist-wearable sensors that sense the amount of light reflected by the skin, related to the blood flow beneath. Still, the main issue is the high sensitivity to motion, which produces severe artifacts in the signal, often impeding accurate HR tracking. In this paper we present a method that combines an automatic activity intensity classifier, to select the proper amount of artifact cleaning that needs to be performed on the signal, with a geometric-based signal subspace approach to estimate the HR component of the PPG signal. Experimental evaluation is performed over a widely available dataset and the results are compared to an ECG-derived golden standard.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266197 Collegamento a IRIS

2019 Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring
DATA IN BRIEF
Autore/i: Biagetti, Giorgio; Carnielli, Virgilio Paolo; Crippa, Paolo; Falaschetti, Laura; Scacchia, Valentina; Scalise, Lorenzo; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: We introduce a dataset to provide insights into the relationship between the diaphragm surface electromyographic (sEMG) signal and the respiratory air flow. The data presented had been originally collected for a research project jointly developed by the Department of Information Engineering and the Department of Industrial Enginering and Mathematical Sciences, Polytechnic University of Marche, Ancona, Italy. This article describes data recorded from 8 subjects, and includes 8 air flow and 8 surface electromyographic (sEMG) signals for diaphragmatic respiratory activity monitoring, measured with a sampling frequency of 2 kHz.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/269289 Collegamento a IRIS

2019 A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal
SENSORS
Autore/i: Manoni, Lorenzo; Turchetti, Claudio; Falaschetti, Laura; Crippa, Paolo
Classificazione: 1 Contributo su Rivista
Abstract: Wearable devices offer a convenient means to monitor biosignals in real time at relatively low cost, and provide continuous monitoring without causing any discomfort. Among signals that contain critical information about human body status, electromyography (EMG) signal is particular useful in monitoring muscle functionality and activity during sport, fitness, or daily life. In particular surface electromyography (sEMG) has proven to be a suitable technique in several health monitoring applications, thanks to its non-invasiveness and ease to use. However, recording EMG signals from multiple channels yields a large amount of data that increases the power consumption of wireless transmission thus reducing the sensor lifetime. Compressed sensing (CS) is a promising data acquisition solution that takes advantage of the signal sparseness in a particular basis to significantly reduce the number of samples needed to reconstruct the signal. As a large variety of algorithms have been developed in recent years with this technique, it is of paramount importance to assess their performance in order to meet the stringent energy constraints imposed in the design of low-power wireless body area networks (WBANs) for sEMG monitoring. The aim of this paper is to present a comprehensive comparative study of computational methods for CS reconstruction of EMG signals, giving some useful guidelines in the design of efficient low-power WBANs. For this purpose, four of the most common reconstruction algorithms used in practical applications have been deeply analyzed and compared both in terms of accuracy and speed, and the sparseness of the signal has been estimated in three different bases. A wide range of experiments are performed on real-world EMG biosignals coming from two different datasets, giving rise to two different independent case studies.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/269351 Collegamento a IRIS

2018 Classifier level fusion of accelerometer and sEMG signals for automatic fitness activity diarization
SENSORS
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: The human activity diarization using wearable technologies is one of the most important supporting techniques for ambient assisted living, sport and fitness activities, healthcare of elderly people. The activity diarization is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a technique for data fusion at classifier level of accelerometer and sEMG signals acquired by using a low-cost wearable wireless system for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications. To demonstrate the capability of the system of diarizing the user’s activities, data recorded from a few subjects were used to train and test the automatic classifier for recognizing the type of exercise being performed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259769 Collegamento a IRIS

2018 A comparative study of machine learning algorithms for physiological signal classification
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference, KES-2018, Belgrade, Serbia
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Tanoni, Giulia; Turchetti, Claudio
Editore: Elsevier B.V.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The present work aims at the evaluation of the effectiveness of different machine learning algorithms on a variety of clinical data, derived from small, medium, and large publicly available databases. To this end, several algorithms were tested, and their performance, both in terms of accuracy and time required for the training and testing phases, are here reported. Sometimes a data preprocessing phase was also deemed necessary to improve the performance of the machine learning procedures, in order to reduce the problem size. In such cases a detailed analysis of the compression strategy and results is also presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261277 Collegamento a IRIS

2018 An acquisition system of in-house parameters from wireless sensors for the identification of an environmental model
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference, KES-2018, Belgrade, Serbia
Autore/i: Biagetti, Giorgio; Coccia, Diego; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Editore: Elsevier B.V.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a system for the acquisition of in-house parameters, such as temperature, pressure, humidity and so on, that can be used for the intelligent control of a building. The main objective of this work is to determine an environmental model of an in-house room using machine learning techniques. The system is based on a low data-rate network of sensing and control nodes to acquire the data, realized with a new protocol, called ToLHnet, that is able to employ both wired and wireless communication on different media. Several standard machine learning techniques, namely linear regression, classification and regression tree algorithm, support vector machine, have been used for the regression of the input-output thermal model. Additionally, a recently proposed new technique named particle-Bernstein polynomial has been successfully applied. Experimental results show that this technique outperforms the previous techniques, for both accuracy and computation time.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261274 Collegamento a IRIS

2018 Speaker identification in noisy conditions using short sequences of speech frames
Smart Innovation, Systems and Technologies - 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer Science and Business Media Deutschland GmbH
Luogo di pubblicazione: Heidelberg, Berlin
Classificazione: 2 Contributo in Volume
Abstract: The application of speaker recognition technologies on domotic systems, cars, or mobile devices such as tablets, smartphones and smartwatches faces with the problem of ambient noise. This paper studies the robustness of a speaker identification system when the speech signal is corrupted by the environmental noise. In the everyday scenarios the noise sources are highly time-varying and potentially unknown. Therefore the noise robustness must be investigated in the absence of information about the noise. To this end the performance of speaker identification using short sequences of speech frames was evaluated using a database with simulated noisy speech data. This database is derived from the TIMIT database by rerecording the data in the presence of various noise types, and is used to test the model for speaker identification with a focus on the varieties of noise. Additionally, in order to optimize the recognition performance, in the training stage the white noise has been added as a first step towards the generation of multicondition training data to model speech corrupted by noise with unknown temporal-spectral characteristics. The experimental results demonstrated the validity of the proposed algorithm for speaker identification using short portions of speech also in realistic conditions when the ambient noise is not negligible.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/249348 Collegamento a IRIS

2018 Human activity recognition using accelerometer and photoplethysmographic signals
Smart Innovation, Systems and Technologies - 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer Science and Business Media Deutschland GmbH
Luogo di pubblicazione: Heidelberg, Berlin
Classificazione: 2 Contributo in Volume
Abstract: This paper presents an efficient technique for real-time recognition of human activities by using accelerometer and photoplethysmography (PPG) data. It is based on singular value decomposition (SVD) and truncated Karhunen-Loève transform (KLT) for feature extraction and reduction, and Bayesian classification for class recognition. Due to the nature of signals, and being the algorithm independent from the orientation of the inertial sensor, this technique is particularly suitable for implementation in smartwatches in order to both recognize the exercise being performed and improve the motion artifact (MA) removal from PPG signal for accurate heart rate (HR) estimation. In order to demonstrate the validity of this methodology, it has been successfully applied to a database of accelerometer and PPG data derived from four dynamic activities.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/249349 Collegamento a IRIS

2018 tfelm: a TensorFlow Toolbox for the Investigation of ELMs and MLPs Performance
Proceedings of the 2018 International Conference on Artificial Intelligence ICAI'18
Autore/i: Castellani, A.; Cornell, S.; Falaschetti, L.; Turchetti, C.
Editore: CSREA Press
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Extreme learning machine (ELMs) and gradient-based multilayer perceptrons (MLPs) have competing performance, in terms of learning speed and model accuracy. Thus, with reference to a specific application, it is of interest to establish whether the performance of ELMs are better than those achieved with MLPs. To this end this paper proposes a unified framework for the investigation of ELMs and MLPs performance in real data sets. The tool is based on Google’s TensorFlow architecture and is able to overcome the lack of previously available ELMs libraries. A wide experimentation on both toy and benchmark datasets shows the usefulness of the tool.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259396 Collegamento a IRIS

2018 HMM speech synthesis based on MDCT representation
INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Hidden Markov model (HMM) based text-to-speech (TTS) has become one of the most promising approaches, as it has proven to be a particularly flexible and robust framework to generate synthetic speech. However, several factors such as mel-cepstral vocoder and over-smoothing are responsible for causing quality degradation of synthetic speech. This paper presents an HMM speech synthesis technique based on the modified discrete cosine transform (MDCT) representation to cope with these two issues. To this end, we use an analysis/synthesis technique based on MDCT that guarantees a perfect reconstruction of the signal frame from feature vectors and allows for a 50% overlap between frames without increasing the data vector, in contrast to the conventional mel-cepstral spectral parameters that do not ensure direct speech waveform reconstruction. Experimental results show that a sound of good quality, conveniently evaluated using both objective and subjective tests, is obtained.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261944 Collegamento a IRIS

2018 Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes
BIOMEDICAL ENGINEERING ONLINE
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: The human activity monitoring technology is one of the most important technologies for ambient assisted living, surveillance-based security, sport and fitness activities, healthcare of elderly people. The activity monitoring is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a low-cost wearable wireless system specifically designed to acquire surface electromyography (sEMG) and accelerometer signals for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/262186 Collegamento a IRIS

2018 A GPU Parallel Algorithm for Non Parametric Tensor Learning
Proc. of 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
Autore/i: Turchetti, Claudio; Falaschetti, Laura
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: One rich source of large data sets is the high dimensionality of the data formats known as tensors. Compared to the vector use, learning with tensors is inherently more complex and requires high-performance computing. The aim of this paper is to investigate tensor-based algorithms for regression and classification, i.e. tensor learning, that are suitable to be implemented in parallel architecture to handle large data sets. To this end a tensor learning model based on a general theoretical framework for approximating a generic tensor function has been established. Then a parallel version of the model has been derived to benefit the GPU resources. Finally, extensive experiments on large data sets that use both CPU and GPU have been carried out to validate the proposed approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/263844 Collegamento a IRIS

2018 Sigma-Delta Based Modulation Method for Matrix Converters
Conference Proceedings 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe)
Autore/i: Orcioni, Simone; Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a new modulation method for matrix converters control, based on Sigma-Delta modulation. The method employs a Sigma-Delta modulator, equipped with a quantizer with time-variable reference levels. A new filter type is also presented that reduces filter quality factor with losses that are present only at resonance.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261348 Collegamento a IRIS

2017 Homomorphic Deconvolution for MUAP Estimation from Surface EMG Signals
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Autore/i: Giorgio, Biagetti; Paolo, Crippa; Simone, Orcioni; Claudio, Turchetti
Classificazione: 1 Contributo su Rivista
Abstract: This paper presents a technique for parametric model estimation of the motor unit action potential (MUAP) from the surface electromyography (sEMG) signal by using homomorphic deconvolution. The cepstrum-based deconvolution removes the effect of the stochastic impulse train, which originates the sEMG signal, from the power spectrum of sEMG signal itself. In this way only information on MUAP shape and amplitude were maintained and then used to estimate the parameters of a time-domain model of the MUAP itself. In order to validate the effectiveness of this technique, sEMG signals recorded during several biceps curl exercises have been used for MUAP amplitude and time scale estimation. The parameters so extracted as functions of time were used to evaluate muscle fatigue showing a good agreement with previously published results.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/234480 Collegamento a IRIS

2017 Machine learning regression based on particle Bernstein polynomials for nonlinear system identification
Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Polynomials have shown to be useful basis functions in the identification of nonlinear systems. However estimation of the unknown coefficients requires expensive algorithms, as for instance it occurs by applying an optimal least square approach. Bernstein polynomials have the property that the coefficients are the values of the function to be approximated at points in a fixed grid, thus avoiding a time-consuming training stage. This paper presents a novel machine learning approach to regression, based on new functions named particle-Bernstein polynomials, which is particularly suitable to solve multivariate regression problems. Several experimental results show the validity of the technique for the identification of nonlinear systems and the better performance achieved with respect to the standard techniques.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/252355 Collegamento a IRIS

2017 A machine learning method to determine intrinsic dimension of time series data
GlobalSIP 2017 - Proceedings of the 2017 5th IEEE Global Conference on Signal and Information Processing
Autore/i: Turchetti, Claudio; Falaschetti, Laura
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The estimation of Intrinsic Dimension (ID) of data is particularly crucial in the unsupervised learning of nonlinear time series, as it essentially represents the minimum number of parameters to describe the data. The aim of this paper is to give both a new theoretical contribution and a machine learning algorithm that can be used for the ID estimation of time series. Several experimental results validate the proposed approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/251876 Collegamento a IRIS

2017 A Portable Wireless sEMG and Inertial Acquisition System for Human Activity Monitoring
Bioinformatics and Biomedical Engineering, 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part II
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: This paper presents a low-cost portable wireless system specifically designed to acquire both surface electromyography (sEMG) and accelerometer signals for healthcare applications, sport, and fitness activities. The system, consists of several ultralight wireless sensing nodes that acquire, amplify, digitize, and transmit the sEMG and accelerometer signals to one or more base stations through a 2.4GHz radio link using a custom-made communication protocol designed on top of the IEEE 802.15.4 physical layer. Additionally, the system can be easily configured to capture and process many other biological signals such as the electrocardiographic (ECG) signal. Each base station is connected through a USB link to a control PC running a user interface software for viewing, recording, and analysing the data.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/246923 Collegamento a IRIS

2017 An Investigation on the Accuracy of Truncated DKLT Representation for Speaker Identification With Short Sequences of Speech Frames
IEEE TRANSACTIONS ON CYBERNETICS
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Speaker identification plays a crucial role in biometric person identification as systems based on human speech are increasingly used for the recognition of people. Mel frequency cepstral coefficients (MFCCs) have been widely adopted for decades in speech processing to capture the speech-specific characteristics with a reduced dimensionality. However, although their ability to decorrelate the vocal source and the vocal tract filter make them suitable for speech recognition, they greatly mitigate the speaker variability, a specific characteristic that distinguishes different speakers. This paper presents a theoretical framework and an experimental evaluation showing that reducing the dimension of features by applying the discrete Karhunen-Loève transform (DKLT) to the log-spectrum of the speech signal guarantees better performance compared to conventional MFCC features. In particular with short sequences of speech frames, with typical duration of less than 2 s, the performance of truncated DKLT representation achieved for the identification of five speakers are always better than those achieved with the MFCCs for the experiments we performed. Additionally, the framework was tested on up to 100 TIMIT speakers with sequences of less than 3.5 s showing very good recognition capabilities.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238482 Collegamento a IRIS

2016 An Algorithm for Automatic Words Extraction From a Stream of Phones in Dictionary-Based Large Vocabulary Continuous Speech Recognition Systems
Proceedings of the 15th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2015)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: As improvements on acoustic modeling have rapidly progressed in recent years thanks to the impressive gains in performance obtained using deep neural networks (DNNs), language modeling remains a bottleneck for high performance large vocabulary continuous speech recognition (LVCSR) systems. In this paper an algorithm for automatic words extraction from a stream of phones is suggested to be used in a dictionary-based LVCSR system, to overcome the limitations of current LVCSR systems. Experimental results show the effectiveness of this approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230501 Collegamento a IRIS

2016 Surface EMG Fatigue Analysis by Means of Homomorphic Deconvolution
Mobile Networks for Biometric Data Analysis
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Cham
Classificazione: 2 Contributo in Volume
Abstract: In this paper we use homomorphic deconvolution to obtain the power spectrum of the motor unit action potential (MUAP) from the surface electromyography (sEMG) signal. This spectrum is then used to extract the parameters of a time-domain model of the MUAP itself, in particular its amplitude and time scale. The analysis of the extracted parameters leads to the estimation of cadence and muscle fatigue. The methodology is tested with a sEMG signal recorded during biceps curl exercises.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236334 Collegamento a IRIS

2016 Motion artifact reduction in photoplethysmography using Bayesian classification for physical exercise identification
ICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: SciTePress
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Accurate heart rate (HR) estimation from photoplethysmography (PPG) recorded from subjects' wrist when the subjects are performing various physical exercises is a challenging problem. This paper presents a framework that combines a robust algorithm capable of estimating HR from PPG signal with subjects performing a single exercise and a physical exercise identification algorithm capable of recognizing the exercise the subject is performing. Experimental results on subjects performing two different exercises show that an improvement of about 50% in the accuracy of HR estimation is achieved with the proposed approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/235876 Collegamento a IRIS

2016 Distributed Speech and Speaker Identification System for Personalized Domotic Control
Mobile Networks for Biometric Data Analysis
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Cham
Classificazione: 2 Contributo in Volume
Abstract: This paper presents a combined speech recognition/speaker identification system that can be efficiently used for personalized domotic control. The proposed system works as a distributed framework and it is designed to identify a speaker in home environments in order to provide user access to customized options. Human speech signals contain both language and speaker dependent information. Using this information the system realizes a personalized control in home environments and this approach can also be applied in more generic scenarios such as car customization settings. The system was optimized with the aim to allow an immediate use only with the addition of small and cheap audio front-ends that will capture commands spoken by the user. Meanwhile a remote server performs the speech recognition as well as user identification and combines these informations to provides user specific settings which are sent back to the desired actuator at home.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236331 Collegamento a IRIS

2016 An Analog Front-End for Combined EMG/ECG Wireless Sensors
Mobile Networks for Biometric Data Analysis
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Cham
Classificazione: 2 Contributo in Volume
Abstract: In this work we describe a combined wireless sensor, able to capture either the electromyographic (EMG) or the electrocardiographic (ECG) signal. Since the two signals differ mainly because of their bandwidths, with the ECG being shifted towards lower frequencies, a simple and inexpensive circuit solution has been developed to allow an optional software-based bypass of the high-pass filtering action incorporated in the EMG signal amplifier, without sacrificing neither signal quality nor bandwidth in the much more demanding EMG path.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236332 Collegamento a IRIS

2016 Learning HMM State Sequences from Phonemes for Speech Synthesis
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 20th International Conference KES-2016
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Elsevier
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a technique for learning hidden Markov model (HMM) state sequences from phonemes, that combined with modified discrete cosine transform (MDCT), is useful for speech synthesis. Mel-cepstral spectral parameters, currently adopted in the conventional methods as features for HMM acoustic modeling, do not ensure direct speech waveforms reconstruction. In contrast to these approaches, we use an analysis/synthesis technique based on MDCT that guarantees a perfect reconstruction of the signal frame feature vectors and allows for a 50% overlap between frames without increasing the data rate. Experimental results show that the spectrograms achieved with the suggested technique behave very closely to the original spectrograms, and the quality of synthesized speech is conveniently evaluated using the well known Itakura-Saito measure.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238344 Collegamento a IRIS

2016 Discrete Bessel Functions for Representing the Class of Finite Duration Decaying Sequences
Proocedings of the 2016 24th European Signal Processing Conference (EUSIPCO)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Bessel functions have shown to be particularly suitable for representing certain classes of signals, since using these basis functions may results in fewer components than using sinusoids. However, as there are no closed form expressions available for such functions, approximations and numerical methods have been adopted for their computation. In this paper the functions called discrete Bessel functions that are expressed as a finite expansion are defined. It is shown that in a finite interval a finite number of such functions that perfectly match Bessel functions of integer order exist. For finite duration sequences it is proven that the subspace spanned by a set of these functions is able to represent the class of finite duration decaying sequences.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238345 Collegamento a IRIS

2016 Optimizing linear routing in the ToLHnet protocol to improve performance over long RS-485 buses
EURASIP JOURNAL ON EMBEDDED SYSTEMS
Autore/i: Alessandrini, Michele; Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: As the adoption of sensing and control networks rises to encompass the most diverse fields, the need for simple, efficient interconnection between many different devices will become ever more pressing. Though wireless communication is certainly appealing, current technological limits still prevent its usage where high reliability is needed or where the electromagnetical environment is not really apt to let radio waves through. In these cases, a wired link, based on a robust and well-consolidated standard such as an RS-485 bus, might prove to be a good choice. In this paper, we present an extension to the routing strategy originally implemented in the recently proposed “tree or linear hopping network” (ToLHnet) protocol, aimed at better handling the special but important case of linear routing over a (possibly very long) wired link, such as an RS-485 bus. The ToLHnet protocol was especially developed to suit the need of low complexity for deployments on large control networks. Indeed, using it over RS-485 already makes it possible to overcome many of the traditional limitations regarding cable length, without requiring segmenting the bus to install repeaters. With the extension here proposed, it will also be possible to simultaneously reduce latency (i.e., increase throughput, should it matter) for short-distance communications over the same cable, largely increasing the overall network efficiency, with a negligible increase in the complexity of the nodes’ firmware.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236329 Collegamento a IRIS

2016 Wireless surface electromyograph and electrocardiograph system on 802.15.4
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: This paper presents a flexible low-cost wireless system specifically designed to acquire fitness metrics both from surface electromyographic (sEMG) and electrocardiographic (ECG) signals. The system, that can be easily extended to capture and process many other biological signals as well as the motion-related body signals, consists of several ultralight wireless sensing nodes that acquire, amplify, digitize, and transmit the biological or mechanical signals to one or more base stations through a 2.4 GHz radio link using a custom-made communication protocol designed on top of the IEEE 802.15.4 physical layer. The number of wireless nodes the base stations can handle depends on the type of signal being acquired. Each base station is connected through an USB link to a control PC running a user interface software for viewing, recording, and analyzing the data. The system for acquiring signals from wearable nodes in combination with a smartphone application provides a complete platform for monitoring fitness metrics extracted from the signals.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238948 Collegamento a IRIS

2016 Robust Speaker Identification in a Meeting with Short Audio Segments
Intelligent Decision Technologies 2016 - Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016) – Part II
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: The paper proposes a speaker identification scheme for a meeting scenario, that is able to answer the question "is somebody currently talking?", if yes, "who is it?". The suggested system has been designed to identify during a meeting conversation the current speaker from a set of pre-trained speaker models. Experimental results on two databases show the robustness of the approach to the overlapping phenomena and the ability of the algorithm to correctly identify a speaker with short audio segments.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/235877 Collegamento a IRIS

2016 An Efficient Technique for Real-Time Human Activity Classification Using Accelerometer Data
Intelligent Decision Technologies 2016 - Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016) – Part I
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: Accurate estimation of biometric parameters recorded from subjects’ wrist or waist, when the subjects are performing various physical exercises, is often a challenging problem due to the presence of motion artifacts. In order to reduce the motion artifacts, data derived from a triaxial accelerometer have been proven to be very useful. Unfortunately, wearable devices such as smartphones and smartwatches are in general differently oriented during real life activities, so the data derived from the three axes are mixed up. This paper proposes an efficient technique for real-time recognition of human activities by using accelerometer data that is based on singular value decomposition (SVD) and truncated Karhunen-Loève transform (KLT) for feature extraction and reduction, and Bayesian classification for class recognition, that is independent of the orientation of the sensor. This is particularly suitable for implementation in wearable devices. In order to demonstrate the validity of this technique, it has been successfully applied to a database of accelerometer data derived from static postures, dynamic activities, and postural transitions occurring between the static postures.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/235879 Collegamento a IRIS

2016 Multivariate Direction Scoring for Dimensionality Reduction in Classification Problems
Intelligent Decision Technologies 2016 - Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016) – Part I
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: Dimensionality reduction is the process of reducing the number of features in a data set. In a classification problem, the proposed formula allows to sort a set of directions to be used for data projection, according to a score that estimates their capability of discriminating the different data classes. A reduction in the number of features can be obtained by taking a subset of these directions and projecting data on this space. The projecting vectors can be derived from a spectral representation or other choices. If the vectors are eigenvectors of the data covariance matrix, the proposed score is aimed to take the place of the eigenvalues in eigenvector ordering.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/235878 Collegamento a IRIS

2015 CARMA: A Robust Motion Artifact Reduction Algorithm for Heart Rate Monitoring from PPG Signals
Proceedings of the 2015 23rd European Signal Processing Conference (EUSIPCO 2015)
Autore/i: Bacà, Alessandro; Biagetti, Giorgio; Camilletti, Marta; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Rossini, Luca; Tonelli, Dario; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Photoplethysmography (PPG) is a non invasive measurement of the blood flow, that can be used instead of electrocardiography to estimate heart rate (HR). Most existing techniques used for HR monitoring in fitness with PPG focus on slowly running alone, while those suitable for intensive physical exercise need an initialization stage in which wearers are required to stand still for several seconds. This paper present a novel algorithm for HR estimation from PPG signal based on motion artifact removal (MAR) and adaptive tracking (AT) that overcomes limitations of the previous techniques. Experimental evaluations performed on datasets recorded from several subjects during running show an average absolute error of HR estimation of 2.26 beats per minute, demonstrating the validity of the presented technique to monitor HR using wearable devices which use PPG signals.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227752 Collegamento a IRIS

2015 Speaker Identification with Short Sequences of Speech Frames
Proceedings of the 4th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2015)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Orcioni, Simone; Turchetti, Claudio
Editore: SCITEPRESS (Science and Technology Publications,Lda.)
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In biometric person identification systems, speaker identification plays a crucial role as the voice is the more natural signal to produce and the simplest to acquire. Mel frequency cepstral coefficients (MFCCs) have been widely adopted for decades in speech processing to capture the speech-specific characteristics with a reduced dimensionality. However, although their ability to de-correlate the vocal source and the vocal tract filter make them suitable for speech recognition, they show up some drawbacks in speaker recognition. This paper presents an experimental evaluation showing that reducing the dimension of features by using the discrete Karhunen-Loève transform (DKLT), guarantees better performance with respect to conventional MFCC features. In particular with short sequences of speech frames, that is with utterance duration of less than 1 s, the performance of truncated DKLT representation are always better than MFCC.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227759 Collegamento a IRIS

2015 Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM-FM Decomposition
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Sport, fitness, as well as rehabilitation activities, often require the accomplishment of repetitive movements. The correctness of the exercises is often related to the capability of maintaining the required cadence and muscular force. Failure to maintain the required force, also known as muscle fatigue, is accompanied by a shift in the spectral content of the surface electromyography (EMG) signal towards lower frequencies. This paper presents a novel approach for simultaneously obtaining exercise repetition frequency and evaluating muscular fatigue, as functions of time, by only using the EMG signal. The mean frequency of the amplitude spectrum (MFA) of the EMG signal, considered as a function of time, is directly related to the dynamics of the movement performed and to the fatigue of the involved muscles. If the movement is cyclic, MFA will display the same pattern and its average will tend to decrease. These two effects have been simultaneously modeled by a two-component AM-FM model based on the Hilbert transform. The method was tested on signals recorded using a wireless system applied to healthy subjects performing dumbbell biceps curls, dumbbell lateral rises, and bodyweight squats. Experimental results show the excellent performance of the proposed technique.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/225251 Collegamento a IRIS

2015 Improvement of RS-485 Performance Over Long Distances Using the ToLHnet Protocol
2015 12th International Workshop on Intelligent Solutions in Embedded Systems (WISES)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Ortolani, Nicola; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a technique to extend the transmission range over a single RS-485 cable, without requiring installation of expensive and cumbersome RS-485 repeaters, to virtually unlimited distances, while simultaneously improving transmission speeds between closer nodes. This was accomplished by leveraging the routing capabilities embedded into each node that implements the recently released and extremely lightweight ToLHnet protocol. With it, each network node can act as a sort of "smart repeater" only when there is need to, optimizing the overall network throughput. The key ideas underlying the routing strategies are here described, together with details of a prototype node and experimental results demonstrating transmission at distances well above the traditional limit.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230502 Collegamento a IRIS

2015 Multi-class ECG beat classification based on a Gaussian mixture model of Karhunen-Loève transform
INTERNATIONAL JOURNAL OF SIMULATION: SYSTEMS, SCIENCE & TECHNOLOGY
Autore/i: Crippa, Paolo; Curzi, Alessandro; Falaschetti, Laura; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Cardiovascular diseases are one of the main causes of death around the world. Automatic classification of electrocardiogram (ECG) signals is of paramount importance in the unmanned detection of a wide range of heartbeat abnormalities. In this paper an effective multi-class beat classifier, based on a statistical identification of a minimum-complexity model, is presented. This methodology extracts from the ECG signal the multivariate relationships of its natural modes, by means of the separation property of the Karhunen-Loève transform (KLT). Then, it exploits an optimized expectation maximization (EM) algorithm to find the optimal parameters of a Gaussian mixture model, with the focus being in reducing the number of parameters. The resulting statistical model is thus based on the estimation of the multivariate probability density function (PDF) that characterizes each beat type. Based on the above statistical characterization a multi-class ECG classification was performed. The experiments, conducted on the ECG signals from the MIT-BIH arrhythmia database, demonstrated the validity and, considering the reduced model size, the excellent performance of this technique to classify the ECG signals into different disease categories.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/232454 Collegamento a IRIS

2015 Distributed Speech Recognition for Lighting System Control
Intelligent Decision Technologies - Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: This paper presents a distributed speech recognition (DSR) system for home/office lighting control by means of users' voice. In this scheme a back-end processes audio signals and transforms them into commands, so that they can be sent to the desired actuators of the lighting system. This paper discusses in detail the solutions and strategies we adopted to improve recognition accuracy and spotting command efficiency in home/office environments, i.e. in situations that involve distant speech and great amounts of background noise or unrelated sounds. Suitable solutions implemented in this recognition engine are able to detect commands also in a continuous listening context and the used DSR strategies greatly simplify the system installation and maintenance. A case study that implements the voice control of a digital addressable lighting interface (DALI) based lighting system has been selected to show the validity and the performance of the proposed system.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227753 Collegamento a IRIS

2015 A Rule Based Framework for Smart Training Using sEMG Signal
Intelligent Decision Technologies - Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: The correctness of the training during sport and fitness activities involving repetitive movements is often related to the capability of maintaining the required cadence and muscular force. Muscle fatigue may induce a failure in maintaining the needed force, and can be detected by a shift towards lower frequencies in the surface electromyography (sEMG) signal. The exercise repetition frequency and the evaluation of muscular fatigue can be simultaneously obtained by using just the sEMG signal through the application of a two-component AM-FM model based on the Hilbert transform. These two features can be used as inputs of an intelligent decision making system based on fuzzy rules for optimizing the training strategy. As an application example this system was set up using signals recorded with a wireless electromyograph applied to several healthy subjects performing dumbbell biceps curls.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227754 Collegamento a IRIS

2014 A Multi-Class ECG Beat Classifier Based on the Truncated KLT Representation
Proceedings of the 2014 UKSim-AMSS 8th European Modelling Symposium (EMS 2014)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Orcioni, Simone; Turchetti, Claudio
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Automatic classification of electrocardiogram (ECG) signals is of paramount importance in the detection of a wide range of heartbeat abnormalities as aid to improve the diagnostic achieved by cardiologists. In this paper an effective multi-class beat classifier, based on statistical identification of a minimum-complexity model, is proposed. The classifier is trained by extracting from the ECG signal a multivariate random vector by means of a truncated Karhunen-Loève transform (KLT) representation. The resulting statistical model is thus estimated using a robust and efficient Expectation Maximization (EM) algorithm to find the optimal parameters of a Gaussian mixture model. Based on the above statistical characterization a multi-class ECG classifier was derived. The experiments, conducted on the ECG signals from the MIT-BIH arrhythmia database, demonstrated the excellent performance of this technique to classify the ECG signals into different disease categories, with a reduced model complexity.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/224519 Collegamento a IRIS

2014 Parameter estimation of surface EMG MUAP by means of power cepstrum deconvolution
Proceedings of the International Workshop on Mobile Networks for Biometric Data Analysis (mBiDA)
Autore/i: Giorgio Biagetti; Paolo Crippa; Simone Orcioni; Claudio Turchetti
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper we use power cepstrum deconvolution to obtain the power spectrum of the motor unit action potential (MUAP) from the surface electromyog- raphy (sEMG) signal. This spectrum is then used to extract the parameters of a time- domain model of the MUAP itself, in particular its amplitude and time scale. The methodology is tested with a sEMG signal recorded during biceps curl exercises. Extraction of these parameters as a function of time can lead to pace calculation and muscle fatigue estimation.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/222515 Collegamento a IRIS

2014 Multi-port de-embedding methodology based on exponential mapping
European Microwave Week 2014: "Connecting the Future", EuMW 2014 - Conference Proceedings; EuMIC 2014: 9th European Microwave Integrated Circuits Conference
Autore/i: Ballicchia, Mauro; Turchetti, Claudio; Orcioni, Simone
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper proposes a methodology that, starting from a set of calibration measurements picked up at the external ports, allows the de-embbeding of a multi-port transition and the determination of its representative matrix. With this methodology the coupling between internal versus external ports are supposed symmetrical. This hypothesis together with the use of exponential mapping and Baker-Campbell-Hausdorff allows the use of only three standards for the characterization of the multi-port transition. The proposed methodology is applied to the identification of the QFN16 package.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/222513 Collegamento a IRIS

2014 ToLHnet: A low-complexity protocol for mixed wired and wireless low-rate control networks
Proceedings of the 2014 6th European Embedded Design in Education and Research Conference (EDERC 2014)
Autore/i: Giorgio Biagetti; Paolo Crippa; Alessandro Curzi; Simone Orcioni; Claudio Turchetti
Editore: Texas Instruments
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: ToLHnet (which stands for 'tree or linear hopping network') is a powerful yet simple networking protocol we developed in order to support the creation of mixed networks, employing wired and wireless connections over different media among thousands of nodes. It is based on tree routing, with special care to support the degenerate case of linear routing, to keep implementation on nodes simple and protocol overhead low. This paper describes the essentials of the protocol and presents a case study detailing its implementation and performance on a Texas Instruments TM4C123GH6PMI microcontroller, with the addition of a power-line-communication modem and a 433 MHz radio.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/222514 Collegamento a IRIS

2014 A Methodology for RF modeling of packages with external pin measurements
INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING
Autore/i: Ballicchia, Mauro; Farina, Marco; Morini, Antonio; Turchetti, Claudio; Orcioni, Simone
Classificazione: 1 Contributo su Rivista
Abstract: This article presents a methodology that allows the determination of the matrix representation of a package. The methodology is based on a set of calibration measurements, picked up at the external pins of the package, of a set of known integrated circuits. The representation of the package can be used both for a correct measurement of the embedded devices and to improve the design of integrated circuits. The effectiveness of the technique is demonstrated by the de-embedding of a packaged passive integrated inductor and the design of a low noise amplifier. The measurement sets were obtained through computer simulations. The effects of added noise on data, like measurement errors, are also investigated, and an approximate methodology, able to reduce these effects, is suggested.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/182702 Collegamento a IRIS

2014 A Speech Interaction System for an Ambient Assisted Living Scenario
Ambient Assisted Living
Autore/i: M. Alessandrini; G. Biagetti; A. Curzi; C. Turchetti
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: In this work we describe a speech recognition system aimed at controlling various apparatus of an intelligent home. The system is especially tailored, and ad-hoc optimizations and strategies have been implemented, to make it suitable to operate unobtrusively in the ambient, requiring that the user only installs small and cheap audio front-ends that will capture his spoken commands. A recognition back-end, running either as a network service reached over the Internet or on a PC in the user’s home, performs the hard work of processing the data and turning it into commands, which are sent back to the desired actuator in the home. A case study involving the voice control of a DALI lighting system is presented, together with ideas and results on how to improve recognition accuracy and command spotting efficiency of a system which, by its very nature, might have to deal with audio captured from a distance and great amounts of background noise and unrelated sounds.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/205829 Collegamento a IRIS

2014 A distributed speaker identification system for personalized home control
Proceedings of the International Workshop on Mobile Networks for Biometric Data Analysis (mBiDA)
Autore/i: Biagetti G.; Crippa P.; Curzi A.; Falaschetti L.; Orcioni S.; Turchetti C.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a combined speaker identification/speech recognition system that can be efficiently used for personalized home control. The proposed system works as a distributed framework and is designed to identify a speaker in home environments in order to provide user access to customized options. Human speech signals contain both language and speaker dependent information. Using this information the system realizes a personalized control in home environment and this approach can also be applied in more generic scenarios such as car customization settings. The system was optimized with the aim to allow an immediate use only with the addition of small and cheap audio front-ends that will capture his spoken commands. Meanwhile a remote server performs the speech recognition as well as user identification and combines these informations to provides user specific settings which are sent back to the desired actuator at home.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/224517 Collegamento a IRIS

2014 An inexpensive circuit solution for the acquisition of the ECG signal with wireless EMG sensors
Proceedings of the International Workshop on Mobile Networks for Biometric Data Analysis (mBiDA)
Autore/i: Biagetti G.; Crippa P.; Orcioni S.; Turchetti C.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this work we describe a combined wireless sensor, able to capture either the electromyographic (EMG) or the electrocardiographic (ECG) signal. Since the two signals differ mainly because of their bandwidths, with the ECG being shifted towards lower frequencies, a simple and inexpensive circuit solution has been developed to allow an optional software-based bypass of the high-pass filtering action incorporated in the EMG signal amplifier, without sacrificing neither signal quality nor bandwidth in the much more demanding EMG path.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/224518 Collegamento a IRIS

2013 Iterative Constrained MLLR Approach for Speaker Adaptation
Proceedings of the 10th IASTED International Conference on Signal Processing, Pattern Recognition, and Applications (SPPRA 2013)
Autore/i: G. Biagetti; A. Curzi; M. Mercuri; C. Turchetti
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper an effective technique for speaker adaptation on the feature domain is presented. This technique starts from the well known maximum-likelihood linear regression (MLLR) auxiliary function to obtain the constrained MLLR transformation in an iterative fashion. The proposed approach is particularly suitable to be implemented on the client side of a distributed speech recognition scheme, due to the reduced number of iterations required to reach convergence. Extensive experimentation using the CMU Sphinx 4 ASR system along with a preliminarily trained speaker-independent acoustic model for the Italian language, in a setting designed for large-vocabulary continuous speech recognition, demonstrates the effectiveness of the approach even with small amounts of adaptation data.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/97462 Collegamento a IRIS

2013 A Speech Interaction System for an Ambient Assisted Living Scenario
Atti del 4º Forum Italiano per l'Ambient Assisted Living (FORITAAL 2013)
Autore/i: Michele Alessandrini; Giorgio Biagetti; Alessandro Curzi; Claudio Turchetti
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/163548 Collegamento a IRIS

2013 A garbage model generation technique for embedded speech recognisers
Proceedings of the 2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA 2013)
Autore/i: Michele Alessandrini; Giorgio Biagetti; Alessandro Curzi; Claudio Turchetti
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper we present a simple but effective technique to help the designer of a voice-operated appliance add out-of-grammar command rejection capabilities, with a minimal effort and without overly degrading the recognition accuracy. Given the desired operational grammar of the appliance, and starting from a generic pre-trained acoustic model and comprehensive dictionary, we use a speech recogniser to identify suitable decoys to be added to the target grammar. These decoys will capture most of the spoken out-of-vocabulary words, and with appropriate changes to the desired grammar, will make the rejection of unintended commands quite easy. An evaluation of the performance of the proposed approach has been carried out on a sample appliance we developed, and tested with several users, under different acoustic conditions, in a command-spotting scenario. The reported results show that the proposed approach largely outperforms the standard phone loop-based approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/158106 Collegamento a IRIS

2011 A Methodology for RF Modeling of Packages Using IC Known-Loads
2011 IEEE 20th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS-2011
Autore/i: Ballicchia M.; Farina M.; Morini A.; Rozzi T.; Turchetti C.; Orcioni S.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The present paper proposes a methodology that, starting from a set of calibration measurements picked up only at the external pins of the package, allows the determination of its representative matrix. Such a matrix can be used both for a correct measurement of the embedded device and in view of improving its design, by accounting for the effect of the package. The technique is demonstrated over a packaged passive integrated inductor.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/63204 Collegamento a IRIS

2011 Semi-automatic acoustic model generation from large unsynchronized audio and text chunks
Proceedings of the 12th Annual Conference of the International Speech Communication Association
Autore/i: Alessandrini M.; Biagetti G.; Curzi A.; Turchetti C.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper an effective technique to train an acoustic model from large and unsynchronized audio and text chunks is presented. Given such a speech corpus, an algorithm to automatically segment each chunk into smaller fragments and to synchronize those to the corresponding text is defined. These smaller fragments are more suitable to be used in standard model training algorithms for usage in automatic speech recognition systems. The proposed approach is particularly suitable to bootstrap language models without relying neither on specialized training material nor borrowing from models trained for other similar languages. Extensive experimentation using the CMU Sphinx 4 recognizer and the SphinxTrain model generator in a setting designed for large-vocabulary continuous speech recognition shows the effectiveness of the approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/62801 Collegamento a IRIS

2010 Unsupervised identification of nonstationary dynamical systems using a Gaussian mixture model based on EM clustering of SOMs
Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS)
Autore/i: G. BIAGETTI; P. CRIPPA; A. CURZI; C. TURCHETTI
Editore: IEEE
Luogo di pubblicazione: Piscataway
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper an effective unsupervised statistical identification technique for nonstationary nonlinear systems is presented. This technique extracts from the system outputs the multivariate relationships of the system natural modes, by means of the separation property of the Karhunen-Loève transform (KLT). Then, it applies a Self-Organizing Map (SOM) to the KLT output vectors in order to give an optimal representation of data. Finally, it exploits an optimized Expectation Maximization (EM) algorithm to find the optimal parameters of a Gaussian mixture model. The resulting statistical system identification is thus based on the estimation of the multivariate probability density function (PDF) of system outputs, whose convergence towards that computed by kernel estimation has also been proved by verifying the asymptotically vanishing of Kullback-Leibler divergences. A large number of simulations on ECG signals demonstrated the validity and the excellent performance of this technique along with its applicability to noninvasive diagnosis of a large class of medical pathologies originated by unknown, unpractical to measure, physiological factors.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50613 Collegamento a IRIS

2010 Piecewise linear second moment statistical simulation of ICs affected by non-linear statistical effects
INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS
Autore/i: BIAGETTI G; P. CRIPPA; CURZI A; ORCIONI S; TURCHETTI C
Classificazione: 1 Contributo su Rivista
Abstract: This paper presents a methodology for statistical simulation of non-linear integrated circuits affected by device mismatch. This simulation technique is aimed at helping designers maximize yield, since it can be orders of magnitude faster than other readily available methods, e.g. Monte Carlo. Statistical analysis is performed by modeling the electrical effects of tolerances by means of stochastic current or voltage sources, which depend on both device geometry and position across the die. They alter the behavior of both linear and non-linear components according to stochastic device models, which reflect the statistical properties of circuit devices up to the second order (i.e. covariance functions). DC, AC, and transient analyses are performed by means of the stochastic modified nodal analysis, using a piecewise linear stochastic technique with respect to the stochastic sources, around a few automatically selected points. Several experimental results on significant circuits, encompassing both the analog and the digital domains, prove the effectiveness of the proposed method.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51084 Collegamento a IRIS

2009 Information theoretical algorithm based on statistical models for blind identification of nonstationary dynamical systems
Proceedings of 2009 IEEE International Joint Conference on Neural Networks (IJCNN 2009)
Autore/i: CRIPPA, Paolo; GIANFELICI, Francesco; TURCHETTI, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents an effective blind statistical identification technique for nonstationary nonlinear systems based on an information theoretical algorithm. This technique firstly extracts, from the output signals, the multivariate relationships in the Hilbert spaces by exploiting the separability properties of the signal outputs transformed by the Karhunen-Loeve transform (KLT). Then, the algorithm methodologically clusters the stochastic surfaces in the Hilbert spaces using the self-organizing maps (SOMs) and further develops their best statistical model under the fixed-rank condition. The resulting blind identification of the statistical system model is based on marginal probability density functions (PDFs), whose convergence to the statistical system model based on Monte Carlo simulations has also been demonstrated by asymptotically vanishing the Kullback-Leibler divergences. A large number of simulations on both synthetic and real systems demonstrated the validity and the excellent performances of this technique that is irrespective of the system order, the stochastic surface topology, the true marginal PDFs, and the knowledge of the statistics of the noise superimposed to the output signals. Finally, this approach could also represent a suitable and promising technique for the noninvasive diagnosis of a large class of medical pathologies originated by unknown physiological factors (nonlinear compositions of unknown input signals) and/or when they are difficult or unpractical to measure.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/46367 Collegamento a IRIS

2009 2.4 GHz wireless electromyograph system with statistically optimal automatic gain control: Design and performance analysis
Proceedings of the 2009 International Conference on Bio-inspired Systems and Signal Processing
Autore/i: A. MORICI; G. BIAGETTI; C. TURCHETTI
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/49833 Collegamento a IRIS

2009 Computational intelligence for the collaborative identification of distributed systems
Computational Intelligence: Collaboration, Fusion and Emergence (Series: Intelligent Systems Reference Library, Vol. 1)
Autore/i: BIAGETTI G; P. CRIPPA; GIANFELICI F; TURCHETTI C
Editore: Springer-Verlag
Luogo di pubblicazione: Berlin/Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: In this chapter, on the basis of a rigorous mathematical formulation, a new algorithm for the identification of distributed systems by large scale collaborative sensor networks is suggested. The algorithm extends a KLT-based identification approach to a decentralized setting, using the distributed Karhunen-Loéve transform (DKLT) recently proposed by Gastpar et al.. The proposed approach permits an arbitrarily accurate identification since it exploits both the asymptotic properties of convergence of DKLT and the universal approximation capabilities of radial basis functions neural networks. The effectiveness of the proposed approach is directly related to the reduction of total distortion in the compression performed by the single nodes of the sensor network, to the identification accuracy, as well as to the low computational complexity of the fusion algorithm performed by the fusion center to regulate the intelligent cooperation of the nodes. Some identification experiments, that have been carried out on systems whose behavior is described by partial differential equations in 2-D domains with random excitations, confirm the validity of this approach. It is worth noting the generality of the algorithm that can be applied in a wide range of applications without limitations on the type of physical phenomena, boundary conditions, sensor network used, and number of its nodes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50578 Collegamento a IRIS

2009 Nonlinear system identification: An effective framework based on the Karhunen-Loève transform
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Autore/i: Turchetti, Claudio; Giorgio, Biagetti; Francesco, Gianfelici; Crippa, Paolo
Classificazione: 1 Contributo su Rivista
Abstract: This paper proposes, on the basis of a rigorous mathematical formulation, a general framework that is able to define a large class of nonlinear system identifiers. This framework exploits all those relationships that intrinsically characterize a limited set of realizations, obtained by an ensemble of output signals and their parameterized inputs, by means of the separation property of the Karhunen-Loève transform. The generality and the flexibility of the approximating mappings (ranging from traditional approximation techniques to multiresolution decompositions and neural networks) allow the design of a large number of distinct identifiers each displaying a number of properties such as linearity with respect to the parameters, noise rejection, low computational complexity of the approximation procedure. Exhaustive experimentation on specific case studies reports high identification performance for four distinct identifiers based on polynomials, splines, wavelets and radial basis functions. Several comparisons show how these identifiers almost always have higher performance than that obtained by current best practices, as well as very good accuracy, optimal noise rejection, and fast algorithmic elaboration. As an example of a real application, the identification of a voice communication channel, comprising a digital enhanced cordless telecommunications (DECT) cordless phone for wireless communications and a telephone line, is reported and discussed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50178 Collegamento a IRIS

2009 "Editorial" of the International Journal of Computational Intelligence Studies (IJCIStudies)
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE STUDIES
Autore/i: Turchetti C.; Crippa P.
Editore: Inderscience Enterprises Ltd.
Luogo di pubblicazione: Geneva
Classificazione: 7 Curatele
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/58314 Collegamento a IRIS

2009 "Editorial" of the International Journal of Computational Intelligence Studies (IJCIStudies)
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE STUDIES
Autore/i: TURCHETTI C.; CRIPPA P.
Editore: Inderscience Enterprises Ltd.
Luogo di pubblicazione: Genève
Classificazione: 7 Curatele
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50248 Collegamento a IRIS

2009 A non-probabilistic recognizer of stochastic signals based on KLT
SIGNAL PROCESSING
Autore/i: GIANFELICI, Francesco; TURCHETTI, Claudio; CRIPPA, Paolo
Classificazione: 1 Contributo su Rivista
Abstract: This paper presents an efficient algorithm which is able to accurately recognize non-deterministic signals generated by synthetic non-chaotic and chaotic stochastic processes (SPs), as well as by natural phenomena (that are inherently stochastic) such as speech, image, and electroencephalographic signals. This recognition algorithm exploits a Karhunen–Loève transform (KLT)-based model able to characterize signals in terms of non-deterministic trajectories and consists of the concatenation of (i) a training stage, which iteratively extracts suitable parameter collections by means of the KLT and (ii) a recognition procedure based on ad hoc metric that measures the trajectory-proximities, in order to associate the unknown signal to the SP which this signal can be considered a realization of. The proposed methodology is able to recognize SPs without estimating their probability density function (pdf), thus requiring a low computational complexity to be implemented. Exhaustive experimentation on specific case-studies was performed and some experimental results were compared to other existing techniques such as hidden Markov model (HMM), vector quantization (VQ), and dynamic time warping (DTW). Recognition performance is similar to current best practices for non-chaotic signals and higher for chaotic ones. A better noise rejection was also achieved, and a reduction of two orders of magnitude in training-times compared with HMM was obtained, thus making the proposed methodology one of the current best practices in this field. Finally, the experimental results obtained by three different applications of the recognizer (an automatic speech recognition system, an automatic facial recognition system, and an automatic diagnosis system of the ictal and interictal epilepsy) clearly show excellent classification performance, and it is worth noting as complex filters are not needed unlike other current best practices.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52545 Collegamento a IRIS

2008 A computational intelligence technique for the identification of non-linear non-stationary systems
IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence)
Autore/i: C. TURCHETTI; F. GIANFELICI; G. BIAGETTI; P. CRIPPA
Editore: IEEE
Luogo di pubblicazione: Piscataway
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper addresses nonlinear nonstationary system identification from stimulus-response data, a problem concerning a large variety of applications, in dynamic control as well as in signal processing, communications, physiological system modelling and so on. Among the different methods suggested in the vast literature for nonlinear system modelling, the ones based on the Volterra series and the Neural Networks are the most commonly used. However, a strong limitation for the applicability of these methods lies in the necessary property of stationarity, an assumption that cannot be considered as valid in general and strongly affecting the validity of results. Another weakness of the approaches currently used is that they refer to differential systems, thus being unsuitable to model systems described by integral equations. A computational intelligence technique that exploits the potentialities of both the Karhunen-Loève Transform (KLT) and Neural Networks (NNs) representation and without any of the limitations of the previous approaches is suggested in this paper. The technique is suitable for modelling the wide class of systems described by nonlinear nonstationary functional, thus including both differential and integral systems. It takes advantage of the KLT separable kernel representation that is able to separate the dynamic and static behaviours of the system as two distinct components, and the approximation property of NNs for the identification of the nonlinear no-memory component. To validate the suggested technique comparisons with experimental results on both nonlinear nonstationary differential and integral systems are reported.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52939 Collegamento a IRIS

2008 A novel approach to statistical simulation of ICs affected by non-linear variabilities
Proceedings of 2008 IEEE International Symposium on Circuits and Systems
Autore/i: Giorgio, Biagetti; Paolo, Crippa; Alessandro, Curzi; Simone, Orcioni; Claudio, Turchetti
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a methodology for statistical simulation of non-linear integrated circuits affected by device mismatch. This simulation technique is aimed at helping designers maximize yield, since it can be orders of magnitude faster than other readily available methods, e.g. Monte Carlo. DC, AC, and transient analyses are performed by means of the stochastic modified nodal analysis, using a piecewise linearization technique with respect to the stochastic sources, around a few automatically selected points.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52330 Collegamento a IRIS

2008 Representation of nonlinear random transformations by non-Gaussian stochastic neural networks
IEEE TRANSACTIONS ON NEURAL NETWORKS
Autore/i: Claudio, Turchetti; Paolo, Crippa; Massimiliano, Pirani; Giorgio, Biagetti
Classificazione: 1 Contributo su Rivista
Abstract: The learning capability of neural networks is equivalent to modeling physical events that occur in the real environment. Several early works have demonstrated that neural networks belonging to some classes are universal approximators of input-output deterministic functions. Recent works extend the ability of neural networks in approximating random functions using a class of networks named stochastic neural networks (SNN). In the language of system theory, the approximation of both deterministic and stochastic functions falls within the identification of nonlinear no-memory systems. However, all the results presented so far are restricted to the case of Gaussian stochastic processes (SPs) only, or to linear transformations that guarantee this property. This paper aims at investigating the ability of stochastic neural networks to approximate nonlinear input-output random transformations, thus widening the range of applicability of these networks to nonlinear systems with memory. In particular, this study shows that networks belonging to a class named non-Gaussian stochastic approximate identity neural networks (SAINNs) are capable of approximating the solutions of large classes of nonlinear random ordinary differential transformations. The effectiveness of this approach is demonstrated and discussed by some application examples.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51047 Collegamento a IRIS

2008 Sensor network-based nonlinear system identification
12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2008) - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Autore/i: BIAGETTI G; P. CRIPPA; GIANFELICI F; TURCHETTI C
Editore: Springer-Verlag
Luogo di pubblicazione: Berlin/Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: In this paper, a new algorithm for the identification of distributed systems by large scale collaborative sensor networks is suggested. The algorithm, that uses the distributed Karhunen-Loève transform, extends in a decentralized setting the KLT-based identification approach that have recently been proposed for a centralized setting. The effectiveness of the proposed methodology is directly related to the reduction of total distortion in the compression performed by the single nodes of the sensor network, to the identification accuracy as well as to the low computational complexity of the fusion algorithm performed by the fusion center to regulate the intelligent cooperation of the nodes. The results in the identification of a system whose behavior is described by a partial differential equation in a 2-D domain with random excitation confirms the effectiveness of this technique.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/43088 Collegamento a IRIS

2007 Efficient classification of chaotic signals with application to secure communications
Proceedings of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2007)
Autore/i: GIANFELICI, Francesco; TURCHETTI, Claudio; CRIPPA Paolo
Editore: IEEE
Luogo di pubblicazione: PISCATAWAY
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents an exhaustive study on the classification capabilities of an efficient algorithm, which is able to accurately classify non-deterministic signals generated by chaotic dynamical systems, without estimating their probability density function (pdf). Experimental results were compared to other existing techniques such as hidden Markov model (HMM), vector quantization (VQ), and dynamic time warping (DTW). Classification performance is higher than current best practices for chaotic signals. A better noise rejection was also achieved, and a reduction of two orders of magnitude in training-times compared with HMM was obtained, thus making the proposed methodology one of the current best practices in this field. As an application example, the recognition of encrypted chaotic-signals in a secure-communication context, is reported and discussed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53819 Collegamento a IRIS

2007 Efficient synthesis of piano tones with damped Bessel functions
Proceedings of the 2007 15th International Conference on Digital Signal Processing (DSP 2007)
Autore/i: BIAGETTI G; P. CRIPPA; TURCHETTI C; MORICI A
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper a novel technique for efficient synthesis of waveforms generated by musical instruments is presented. This methodology represents single tones produced by musical instruments as a series of orthogonal Bessel functions, similarly to an additive synthesis that, instead, uses sinusoidal partials. Bessel functions possess a pitch that slowly varies with time, and are thus suited to model musical tones that usually exhibit similar characteristics. A comparative listening test has been performed, and the synthetically created piano sounds have been compared to those generated by traditional additive synthesis. Bessel-based synthesis generally achieved a higher score than the sinusoidal-based approach. The limited amount of memory resources used makes this technique suitable to be implemented on a digital signal processor.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52896 Collegamento a IRIS

2007 Generalization of a recognition algorithm based on Karhunen-Loève transform
11th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems in conjunction with XVII Italian Workshop on Neural Network (KES 2007/WIRN 2007) - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Part I
Autore/i: GIANFELICI F; TURCHETTI C; P. CRIPPA; BATTISTELLI V
Editore: Springer-Verlag
Luogo di pubblicazione: Berlin/Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: This paper presents a generalization of a recognition algorithm that is able to classify non-deterministic signals generated by a set of Stochastic Processes (SPs), the number of which may be arbitrarily chosen. This generalized recognizer exploits the nondeterministic trajectories generated by the Karhunen-Loève Transform (KLT) with no additional constraints or explicit limitations, and without the probability density function (pdf) estimation. Several experimentations were performed on SPs generated as solutions of non-linear differential equations with parameters and initial conditions being random variables. The results show a recognition rate which is close to 100%, thus demonstrating the validity of the generalized algorithm.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/42322 Collegamento a IRIS

2007 Multicomponent AM-FM demodulation: The state of the art after the development of the iterated Hilbert transform
Proceedings of 2007 IEEE International Conference on Signal Processing and Communications (ICSPC 2007)
Autore/i: GIANFELICI, Francesco; TURCHETTI, Claudio; CRIPPA, Paolo
Editore: IEEE
Luogo di pubblicazione: Piscataway
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents the state of the art of the multicomponent AM-FM demodulation techniques. On the basis of exhaustive comparisons between the current best practices: the impact of the iterated Hilbert transform on the milestones and the pioneering results of several decades of the advanced research done between MIT, Harvard, Bell-Labs, and NASA, has been analyzed. Finally, past performance, open problems, and future trends of AM-FM models have been considered and discussed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/46846 Collegamento a IRIS

2007 Multicomponent AM-FM representations: An asymptotically exact approach
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING
Autore/i: Gianfelici, Francesco; Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: This paper presents, on the basis of a rigorous mathematical formulation, a multicomponent sinusoidal model that allows an asymptotically exact reconstruction of nonstationary speech signals, regardless of their duration and without any limitation in the modeling of voiced, unvoiced, and transitional segments. The proposed approach is based on the application of the Hilbert transform to obtain an amplitude signal from which an AM component is extracted by filtering, so that the residue can then be iteratively processed in the same way. This technique permits a multicomponent AM-FM model to be derived in which the number of components (iterations) may be arbitrarily chosen. Additionally, the instantaneous frequencies of these components can be calculated with a given accuracy by segmentation of the phase signals. The validity of the proposed approach has been proven by some applications to both synthetic signals and natural speech. Several comparisons show how this approach almost always has a higher performance than that obtained by current best practices, and does not need the complex filter optimizations required by other techniques.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53078 Collegamento a IRIS

2007 SiSMA: Simulator for Statistical Mismatch Analysis
presented at the University Booth at the 10th Design, Automation and Test in Europe (DATE 07)
Autore/i: BIAGETTI G.; ORCIONI S.; CURZI A.; CRIPPA P.; TURCHETTI C.
Classificazione: 5 Altro
Abstract: Presentato a "The University Booth" della conferenza internazionale "10th Design, Automation and Test in Europe (DATE 07)", 16-20 Aprile, 2007, Nizza, Francia.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50212 Collegamento a IRIS

2006 Modeling of speech signals based on Bessel-like orthogonal transform
Proceedings of the 9th International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP)
Autore/i: BIAGETTI, Giorgio; CRIPPA, Paolo; TURCHETTI, Claudio
Editore: ISCA
Luogo di pubblicazione: BONN
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper a novel modeling technique for speech signals, based on the source-filter model of speech production and on orthogonal transform theory, is presented. The proposed approach models the impulse response of such filter, by projection onto a basis of damped Bessel functions, which have been chosen for their similarity to the signal to be modeled. In such a way an orthogonal transform pair is defined which provides a simple and effective methodology for the extraction of model parameters, and its effectiveness in the case of voiced speech has been demonstrated by synthesizing natural sounding speech signals with the aid of only a few extracted parameters.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53742 Collegamento a IRIS

2006 A non probabilistic algorithm based on Karhunen-Loève transform for the recognition of stochastic signals
Proceedings of 6th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2006)
Autore/i: GIANFELICI, Francesco; TURCHETTI, Claudio; CRIPPA, Paolo
Editore: IEEE
Luogo di pubblicazione: PISCATAWAY
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper proposes an efficient methodology that is able to accurately recognize nondeterministic signals generated by stochastic processes (SPs). This technique is based on (i) a training algorithm, which iteratively extracts suitable parameter collections; (ii) a recognition procedure that measures the trajectory-proximities by means of an ad-hoc metric, in order to associate the unknown signal to an SP by using a representation based on Karhunen-Loeve transform (KLT). The recognition algorithm exploits a modelling of several signal classes based on KLT, inasmuch this representation effectively characterizes projections of every SP signal in terms of nondeterministic trajectories defined on associated spaces. The methodology is able to recognize SPs without probability density function (pdf) estimation, and with low-computational complexity: exhaustive experimentations on specific case-studies have shown high recognition performance. As application examples, SPs generated by stochastic nonlinear-differential-equations (SNDEs), with different initial conditions and coefficients being random variables (RVs), have been considered.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53713 Collegamento a IRIS

2005 A novel KLT algorithm optimized for small signal sets
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05)
Autore/i: GIANFELICI F; BIAGETTI G; P. CRIPPA; TURCHETTI C
Editore: IEEE
Luogo di pubblicazione: Piscataway
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Karhunen-Loève Transform, being able to represent stochastic processes under appropriate conditions, is a powerful signal processing tool. But the high computational cost incurred in the modeling of long signals has limited its use in the recognition of speech segmented at the word level. In this paper we present a novel algorithm that significantly reduces the computational cost when the number of signals to be treated is small in comparison to their samples.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52861 Collegamento a IRIS

2005 AM-FM decomposition of speech signals: An asymptotically exact approach based on the iterated Hilbert transform
2005 IEEE/SP 13th Workshop on Statistical Signal Processing (SSP), Vols 1 and 2
Autore/i: G. GIANFELICI; G. BIAGETTI; P. CRIPPA; C. TURCHETTI
Editore: IEEE
Luogo di pubblicazione: PISCATAWAY
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a multicomponent sinusoidal model of speech signals, obtained through a rigorous mathematical formulation that ensures an asymptotically exact reconstruction of these nonstationary signals, despite the presence of transients, voiced segments, or unvoiced segments. This result has been obtained by means of the iterated use of the Hilbert transform, and the convergence properties of the proposed method have been both analytically investigated and empirically tested. Finally, an adaptive segmentation algorithm used to accurately compute instantaneous frequencies from unwrapped phases, suited to complete the proposed AM-FM model, is presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52869 Collegamento a IRIS

2005 Advances in Lee-Schetzen method for Volterra filter identification
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
Autore/i: Orcioni, Simone; Pirani, M.; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: This paper concerns the identification of nonlinear discrete causal systems that can be approximated with the Wiener–Volterra series. Some advances in the efficient use of Lee–Schetzen (L–S) method are presented, which make practical the estimate of long memory and high order models. Major problems in L–S method occur in the identification of diagonal kernel elements. Two approaches have been considered: approximation of gridded data, with interpolation or smoothing, and improved techniques for diagonal elements estimation. A comparison of diagonal elements esti- mated, with different methods has been shown with extended tests on fifth order Volterra systems.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53147 Collegamento a IRIS

2005 Power Analysis Methodology and Library in SystemC
SPIE Int. Conference VLSI Circuits and Systems II 2005, Siviglia
Autore/i: Conti, Massimo; Pieralisi, L; Caldari, M; VECE G., B; Orcioni, Simone; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53097 Collegamento a IRIS

2005 Modeling of power control schemes in induction cooking devices
Proc. of SPIE’05 Int. Conference VLSI Circuits and Systems II 2005
Autore/i: Beato, A; Conti, Massimo; Orcioni, Simone; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53646 Collegamento a IRIS

2005 Asymptotically exact AM-FM decomposition based on iterated Hilbert transform
Proceedings of 6th INTERSPEECH 2005 and 9th European Conference on Speech Communication and Technology (EUROSPEECH)
Autore/i: Gianfelici, F; Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio
Editore: International Speech Communication Association (ISCA)
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a multicomponent sinusoidal model of speech signals, obtained through a rigorous mathematical formulation that ensures an asymptotically exact reconstruction of these nonstationary signals, despite the presence of transients, voiced segments, or unvoiced segments. This result has been obtained by means of the iterated use of the Hilbert transform, and the convergence properties of the proposed method have been both analytically investigated and empirically tested. Finally, an adaptive segmentation algorithm used to accurately compute instantaneous frequencies from unwrapped phases, suited to complete the proposed AM-FM model, is presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53747 Collegamento a IRIS

2005 Development Languages and Environments for Induction Cooking System Design and Simulation
Proc. of ICECS ’05 Int. Conf. on Electronics, Circuits and Systems
Autore/i: Beato, A.; Conti, M.; Turchetti, C.; Vece, G. B.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45420 Collegamento a IRIS

2004 “Stochastic models of Neural Networks”
Autore/i: C. TURCHETTI
Editore: IOS Press
Classificazione: 3 Libro
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/40117 Collegamento a IRIS

2004 Tecnica per la predizione degli effetti delle variazioni costruttive sui circuiti integrati non lineari
Autore/i: C. TURCHETTI; S. ORCIONI; G. BIAGETTI; M. ALESSANDRINI; P. CRIPPA
Classificazione: 6 Brevetti
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53626 Collegamento a IRIS

2004 A DC-5 GHz NMOSFET SPDT T/R switch in 0.25-μm SiGe BiCMOS technology
APPLIED SURFACE SCIENCE
Autore/i: Crippa, Paolo; Orcioni, Simone; Ricciardi, Francesco; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, the design of a fully integrated DC-5 GHz NMOS single-pole double throw (SPDT) transmit/receive (T/R) switch for radio-frequency (RF) applications in a 0.25-μm SiGe BiCMOS/RFCMOS technology, is presented. The switch insertion loss is <1.4dB, the isolation is >30.1dB, all over the 0-5 GHz band, and the return loss is >19.9dB in the 0.8-1 GHz band and is >10.2dB in the 0-0.8 GHz and 1-5 GHz bands.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51057 Collegamento a IRIS

2004 A 4.4 to 5 GHz SiGe low noise amplifier
APPLIED SURFACE SCIENCE
Autore/i: Crippa, Paolo; Orcioni, Simone; Ricciardi, Francesco; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: This paper describes a low noise amplifier (LNA), designed for a 0.25μm SiGe process, operating in the 4.4-5GHz band with a noise figure of 2.2dB. A power gain of 12.8dB at 5GHz has been achieved with a power consumption of 23.77mW using a 2V power supply. The input power at 1dB output power compression is -6.2dBm.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50132 Collegamento a IRIS

2004 SiSMA - A tool for efficient analysis of analog CMOS integrated circuits affected by device mismatch
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
Autore/i: Giorgio, Biagetti; Simone, Orcioni; Claudio, Turchetti; Paolo, Crippa; Michele, Alessandrini
Classificazione: 1 Contributo su Rivista
Abstract: In this paper a simulator for the statistical analysis of analog CMOS integrated circuits affected by technological tolerance effects, including device mismatch, is presented. The tool, able to perform dc, ac, and transient analyses, is based on a rigorous formulation of circuit equations starting from the modified nodal analysis and including random current sources to take into account technological tolerances. Statistical simulation of specific circuits shows that the simulator requires a simulation time several orders of magnitude lower than that required by Monte Carlo analysis, while ensuring a good accuracy.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52193 Collegamento a IRIS

2004 Diagonal kernel point estimation of n-th order discrete Volterra-Wiener systems
EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING
Autore/i: Pirani, M.; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: The estimation of diagonal elements of a Wiener model kernel is a well-known problem. The new operators and notations proposed here aim at the implementation of efficient and accurate nonparametric algorithms for the identification of diagonal points. The formulas presented here allow a direct implementation of Wiener kernel identification up to the th order. Their efficiency is demonstrated by simulations conducted on discrete Volterra systems up to fifth order.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/34949 Collegamento a IRIS

2004 A stochastic model of neural computing
Knowledge-Based Intelligent Information and Engineering Systems - 8th International Conference (KES 2004) - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Part II
Autore/i: P. CRIPPA; TURCHETTI C; PIRANI M
Editore: Springer-Verlag
Luogo di pubblicazione: Berlin/Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: This paper addresses the problem of neural computing by a fundamentally different approach to the one currently adopted in digital computers. The approach is based on the experience, rather than on the specification of operators as it is done in the conventional mathematical approach and it is well suited for implementation by neural networks.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/42843 Collegamento a IRIS

2004 Performance Analysis of different arbitration algorithms of the AMBA AHB Bus
Proc. of the Design Automation Conference DAC ’04
Autore/i: Conti, Massimo; Marco, Caldari; GIOVANNI B., Vece; Orcioni, Simone; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: paper 35.5, http://www.dac.com/41st/41acceptedpapers.nsf/browse
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50351 Collegamento a IRIS

2004 Elementi di Elettronica
Autore/i: C. TURCHETTI; M. CONTI
Luogo di pubblicazione: Bologna
Classificazione: 5 Altro
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/41162 Collegamento a IRIS

2003 Diagonal Kernel Point Estimation of n-th Order Discrete Volterra-Wiener Systems
Proceedings of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing
Autore/i: M. Pirani; S. Orcioni; C. Turchetti
Luogo di pubblicazione: Grado
Classificazione: 5 Altro
Abstract: Proceedings of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing. Grado, Italy. 2003
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/54376 Collegamento a IRIS

2003 Design and power analysis in SystemC of an I2C bus driver
Proceedings of Forum on Specifications & Design Languages (FDL'03)
Autore/i: Caldari, Marco; Conti, Massimo; Crippa, Paolo; Orcioni, Simone; Turchetti, Claudio
Editore: ECSI
Luogo di pubblicazione: GIÈRES
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The paper presents a methodology to integrate information on power consumption in a high level functional description of a System-on-chip. The power dissipated during the execution of each system level instruction, stored in a Look-up Table, is used in a System level simulation. The methodology has been applied to the design of an I2C bus driver.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53825 Collegamento a IRIS

2003 A modular test structure for CMOS mismatch characterization
IEEE Proceedings of 2003 International Symposium on Circuits and Systems (ISCAS '03)
Autore/i: CONTI M; P. CRIPPA; FEDECOSTANTE F; ORCIONI S; RICCIARDI F; TURCHETTI C; VENDRAME L
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this work a new test structure for mismatch characterization of CMOS technologies is presented. The test structure is modular, with a reduced area and it can be inserted in the space between the dies (scribe lines) on the wafers. The test structure has been implemented in a standard 0.18-μm digital CMOS technology.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/46851 Collegamento a IRIS

2003 SystemC modeling of a Bluetooth transceiver: Dynamic management of packet type in a noisy channel
Proceedings of the Design, Automation and Test in Europe Conference and Exhibition, DATE 2003
Autore/i: Caldari, Marco; Conti, Massimo; Crippa, Paolo; Marozzi, Giuliano; Di Gennaro, Fabio; Orcioni, Simone; Turchetti, Claudio
Editore: IEEE
Luogo di pubblicazione: PISCATAWAY
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: High level design methodologies are needed to overcome the complexity of system on chip design. In this paper, the SystemC environment has been used to design a Bluetooth transceiver. The high simulation speed allowed a high level performance analysis of the IP developed and the definition of an algorithm for selecting the best packet type in the presence of channel noise.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53733 Collegamento a IRIS

2003 An integrated CAD methodology for yield enhancement of VLSI CMOS circuits including statistical device variations
ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING
Autore/i: Conti, Massimo; Crippa, Paolo; Orcioni, Simone; Pesare, Marcello; Turchetti, Claudio; Vendrame, Loris; Lucherini, Silvia
Classificazione: 1 Contributo su Rivista
Abstract: In this paper a novel CAD methodology for yield enhancement of VLSI CMOS circuits including random device variations is presented. The methodology is based on a preliminary characterization of the technological process by means of specific test chips for accurate mismatch modeling. To this purpose, a very accurate position-dependent parameter mismatch model has been formulated and extracted. Finally a CAD tool implementing this model has been developed. The tool is fully integrated in an environment of existing commercial tools and it has been experimented in the STMicroelectronics Flash Memory CAD Group. As an example of application, a bandgap reference circuit has been considered and the results obtained from simulations have been compared with experimental data. Furthermore, the methodology has been applied to the read path of a complex Flash Memory produced by STMicroelectronics, consisting of about 16,000 MOSFETs. Measurements of electrical performances have confirmed the validity of the methodology, and the accuracy of both the mismatch model and the simulation flow.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52937 Collegamento a IRIS

2003 Learning of SAINNs from covariance function: Historical learning
Knowledge-Based Intelligent Information and Engineering Systems - 7th International Conference (KES 2003) - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Part I
Autore/i: P. CRIPPA; TURCHETTI C
Editore: Springer-Verlag
Luogo di pubblicazione: Berlin/Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: In this paper the learning capabilities of a class of neural networks named Stochastic Approximate Identity Neural Networks (SAINNs) have been analyzed. In particular these networks are able to approximate a large class of stochastic processes from the knowledge of their covariance function.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/42844 Collegamento a IRIS

2003 Design of a 4.4 to 5 GHz LNA in 0.25-um SiGe BiCMOS technology
IEEE Proceedings of the 2003 International Symposium on Circuits and Systems (ISCAS '03)
Autore/i: Crippa, Paolo; Orcioni, Simone; Ricciardi, F; Turchetti, Claudio
Editore: IEEE
Luogo di pubblicazione: Piscataway
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper describes a Low-Noise Amplifier (LNA), designed using a 0.25-μm SiGe process, operating in the 4.4-5 GHz band. A power gain of 12.8 dB at 5 GHz has been achieved with a power consumption of 23.77 mW using a 2 V power supply. The noise figure is 2.2 dB while the input referred 1dB compression point is -6.2 dBm.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/46895 Collegamento a IRIS

2003 Transaction-Level Models for AMBA Bus Architecture Using SystemC 2.0
Designers’ Forum of the Conf Design Automation and Test in Europe DATE 2003
Autore/i: Caldari, M.; Conti, Massimo; Coppola, M.; Curaba, S.; Pieralisi, L.; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45674 Collegamento a IRIS


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