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Giorgio BIAGETTI

Pubblicazioni

Giorgio BIAGETTI

 

75 pubblicazioni classificate nel seguente modo:

Nr. doc. Classificazioni
34 4 Contributo in Atti di Convegno (Proceeding)
20 1 Contributo su Rivista
19 2 Contributo in Volume
1 5 Altro
1 6 Brevetti
Anno Risorsa
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 A driving technique for AC-AC direct matrix converters based on sigma-delta modulation
ENERGIES
Autore/i: Orcioni, Simone; Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Direct conversion of AC power between three-phase systems operating at different frequencies can be achieved using solid-state circuits known as matrix converters. These converters do not need energy storage elements, but they require sophisticated control algorithms to operate the switches. In this work we propose and evaluate the use of a sigma-delta modulation approach to control the operation of a direct matrix converter, together with a revised line filter topology suited to better handle the peculiarities of the switching noise produced by the sigma-delta modulation. Simulation results show the feasibility of such an approach, which is able to generate arbitrary output waveforms and adjust its input reactive power. Comparison with a space vector modulation implementation shows also better performance about total harmonic distortion, i.e., less harmonics in the input and output.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266196 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

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 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 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 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 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 SystemC-WMS: Wave Mixed Signal Simulator for Nonlinear Heterogeneous Systems
INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS
Autore/i: Giorgio Biagetti; Marco Giammarini; Mauro Ballicchia; Massimo Conti; Simone Orcioni
Classificazione: 1 Contributo su Rivista
Abstract: The present paper proposes a methodology for extending SystemC to mixed signal heterogeneous systems. To that end, a method for modelling analog modules using wave quantities is proposed, and a new kind of port and channel were defined. This class library is plugged directly on top of the standard SystemC kernel, so as to allow a seamless integration with the pre-existing simulation environment, and is designed to permit total interconnection freedom between analog modules to ease the development of reusable analog libraries. Moreover, this allows for a uniform treatment of heterogeneous domains. To highlight all these aspects a buck-converter with digital control and an induction motor were simulated.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/128689 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 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 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 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 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

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

2008 System Level Modelling of RF IC in SystemC-WMS
EURASIP JOURNAL ON EMBEDDED SYSTEMS
Autore/i: S. Orcioni; M. Ballicchia; G. Biagetti; R. D. d'Aparo; M. Conti
Classificazione: 1 Contributo su Rivista
Abstract: This paper proposes a methodology for modelling and simulation of RF systems in SystemC-WMS. Analog RF modules have been described at system level only by using their specifications. A complete Bluetooth transceiver, consisting of digital and analog blocks, has been modelled and simulated using the proposed design methodology. The developed transceiver modules have been connected to the higher levels of the Bluetooth stack described in SystemC, allowing the analysis of the performance of the Bluetooth protocol at all the different layers of the protocol stack.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/58325 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 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

2007 Mixed Signal SystemC modelling of a SoC architecture with Dynamic Voltage Scaling
Proc. of SPIE’07, Int. Conference VLSI Circuits and Systems 2007
Autore/i: Leoce, G.; Daparo, R.; Conti, Massimo; Vece, G.; Biagetti, Giorgio; Orcioni, Simone
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45626 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 SystemC-WMS: Mixed Signal Simulation based on Wave exchanges
Applications of Specification and Design Languages for SoCs
Autore/i: Orcioni, Simone; Biagetti, Giorgio; Conti, Massimo
Editore: Springer
Luogo di pubblicazione: Dordrecht
Classificazione: 2 Contributo in Volume
Abstract: Selected papers from FDL 2005.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51679 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 SystemC-WMS: A wave mixed signal simulator
Proceedings of the 8th ECSI Forum on specification & Design Languages (FDL '05)
Autore/i: S. ORCIONI; G. BIAGETTI; M. CONTI
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51454 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

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 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 A mixed signal fuzzy controller using current mode circuits
ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING
Autore/i: Orcioni, Simone; Biagetti, Giorgio; Conti, Massimo
Classificazione: 1 Contributo su Rivista
Abstract: A mixed analog-digital fuzzy logic inference processor chip, designed in a 0.35-μmCMOS technology, is presented. The analog fuzzy engine is based on a novel current-mode CMOS circuit used for the implementation of fuzzy partition membership functions. The architecture consists of a 3 inputs-1 output analog fuzzy engine, internal digital registers to store the parameters of the fuzzy controller, and a digital subsystem that allows the programmability of the fuzzy controller via an I2C interface. The architecture, circuits, and some Cadence Spectre simulations are presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51883 Collegamento a IRIS

2004 Multistable circuits for analog memories implementation
ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING
Autore/i: Biagetti, Giorgio; Conti, Massimo; Orcioni, Simone
Classificazione: 1 Contributo su Rivista
Abstract: In this work two multistable circuits suitable for analog memories implementation will be presented. These circuits have been used to implement completely asynchronous analog memories, one type based on a flash converter and thus having a linear complexity in resolution; the other based on an asynchronous successive approximation converter and thus having a logarithmic complexity in resolution. Experimental results from measurements over prototype chips will be shown and analyzed, and connections with design choices highlighted.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51136 Collegamento a IRIS

2004 Extending SystemC to Analog Modeling and Simulation
Languages for System Specification
Autore/i: Biagetti, Giorgio; M., Caldari; Conti, Massimo; Orcioni, Simone
Editore: Kluwer Academic Publishers
Luogo di pubblicazione: BOSTON, MA
Classificazione: 2 Contributo in Volume
Abstract: Selected Contributions on UML, SystemC, SystemVerilog, Mixed-Signal Systems, and Property Specification from FDL'03.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/41416 Collegamento a IRIS

2003 Analog Circuit Modeling in SystemC
Proceedings of the 6th ECSI Forum on specification & Design Languages (FDL '03)
Autore/i: M. CONTI; M. CALDARI; S. ORCIONI; G. BIAGETTI
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53068 Collegamento a IRIS

2002 SiSMA: A statistical simulator for mismatch analysis of MOS ICs
IEEE/ACM Digest of Technical Papers of International Conference on Computer Aided Design (ICCAD 2002)
Autore/i: G. BIAGETTI; S. ORCIONI; L. SIGNORACCI; C. TURCHETTI; P. CRIPPA; M. ALESSANDRINI
Editore: IEEE
Luogo di pubblicazione: Piscataway
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a simulator for the statistical analysis of MOS integrated circuits affected by mismatch effect. The tool is based on a rigorous formulation of circuit equations including random current sources to take into account technological tolerances. The simulator requires a simulation time of several orders of magnitude lower than that required by Montecarlo analysis, while ensuring a good accuracy.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53064 Collegamento a IRIS

1999 A Current-Mode Multistable Memory using Asynchronous Successive Approximation A/D Converters
Proceedings of the 6th IEEE International Conference on Electronics, Circuits and Systems (ICECS '99)
Autore/i: M. CONTI; S. ORCIONI; C. TURCHETTI; G. BIAGETTI
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/49601 Collegamento a IRIS


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