The paper proposes a novel Electrochemical Impedance Spectroscopy methodology that takes into account the nonlinearties present in the battery. For this purpose, the current-voltage relationship of the cell is modeled in discrete domain as a Volterra filter and the linear part of the nonlinear model is efficiently estimated using Orthogonal Periodic Sequences. Preliminary experimental results involving six lithium-ion cells show the soundness of the proposed approach also in comparison with the classical method.

This manuscript describes a methodology for measuring the first-order Volterra kernel of a discrete-time nonlinear system, that is, the impulse response for small signal amplitudes of the nonlinear system. In the proposed approach, multiple linear identifications are performed using the same excitation signal multiplied by different gains, and the first-order Volterra kernel is obtained from a polynomial interpolation of the measured values. Any linear identification method proposed in the literature can be used with this approach. The proposed approach is a multiple variance (MV) method that, in contrast to all other MV methods, aims to estimate one of the kernels of the Volterra model, the first-order kernel, with high precision. The manuscript discusses the proposed methodology, determines the mean square deviation (MSD) due to noise of the measured coefficients, and the value of the optimal gains that minimize the MSD. Remarkably, it is shown in the manuscript that the optimal gains assume only a reduced set of values that depends on the order of nonlinearity. The optimal number of measurements is also determined. The conditions under which the proposed methodology is more convenient than the classical linear methods are discussed. The experimental results demonstrate the proposed methodology and its strengths.

This paper reports the methodology and results of the test campaign performed in the context of the development of the Low Complexity Enhancement Video Coding (LCEVC) ISO standard. LCEVC is a video coding technology that can be applied in conjunction to any other single-layer video coding technology to re-encode the residuals between the original video and its compressed representation. The first part of the paper describes the methodology for the verification tests: Requirements, Test Conditions, Test Sequences, Test Methods (Metrics); the second one reports the results of the objective and subjective tests carried out applying such methodology. The bitrate savings achieved with LCEVC when used in conjunction with four video codecs (AVC, HEVC, EVC, VVC) are reported.

Healthy sleep is required for sufficient restoration of the human body and brain. Therefore, in the case of sleep disorders, appropriate therapy should be applied timely, which requires a prompt diagnosis. Traditionally, a sleep diary is a part of diagnosis and therapy monitoring for some sleep disorders, such as cognitive behaviour therapy for insomnia. To automatise sleep monitoring and make it more comfortable for users, substituting a sleep diary with a smartwatch measurement could be considered. With the aim of providing accurate results, a study with a total of 30 night recordings was conducted. Objective sleep measurement with a Samsung Galaxy Watch 4 was compared with a subjective approach (sleep diary), evaluating the four relevant sleep characteristics: time of getting asleep, wake up time, sleep efficiency (SE), and total sleep time (TST). The performed analysis has demonstrated that the median difference between both measurement approaches was equal to 7 and 3 minutes for a time of getting asleep and wake up time correspondingly, which allows substituting a subjective measurement with a smartwatch. The SE was determined with a median difference between the two measurement methods of 5.22%. This result also implicates a possibility of substitution. Some single recordings have indicated a higher variance between the two approaches. Therefore, the conclusion can be made that a substitution provides reliable results primarily in the case of long-term monitoring. The results of the evaluation of the TST measurement do not allow to recommend substitution of the measurement method.

The Low Complexity Enhancement Video Coding (LCEVC) specification is a recent standard approved by the ISO/IEC JTC 1/SC 29/WG04 (MPEG) Video Coding. The main goal of LCEVC is to provide a standalone toolset for the enhancement of any other existing codec. It works on top of other coding schemes, resulting in a multi-layer video coding technology, but unlike existing scalable video codecs, adds enhancement layers completely independent from the base video. The LCEVC technology takes as input the decoded video at lower resolution and adds up to two enhancement sub-layers of residuals encoded with specialized low-complexity coding tools, such as simple temporal prediction, frequency transform, quantization, and entropy encoding. This paper provides an overview of the main features of the LCEVC standard: high compression efficiency, low complexity, minimized requirements of memory and processing power.

Normal breathing during sleep is essential for people’s health and well-being. Therefore, it is crucial to diagnose apnoea events at an early stage and apply appropriate therapy. Detection of sleep apnoea is a central goal of the system design described in this article. To develop a correctly functioning system, it is first necessary to define the requirements outlined in this manuscript clearly. Furthermore, the selection of appropriate technology for the measurement of respiration is of great importance. Therefore, after performing initial literature research, we have analysed in detail three different methods and made a selection of a proper one according to determined requirements. After considering all the advantages and disadvantages of the three approaches, we decided to use the impedance measurement-based one. As a next step, an initial conceptual design of the algorithm for detecting apnoea events was created. As a result, we developed an activity diagram on which the main system components and data flows are visually represented.

Homomorphic deconvolution is applied to sEMG signals recorded during walking. Gastrocnemius lateralis and tibialis anterior signals were acquired according to SENIAM recommendation. MUAP parameters like amplitude and scale were estimated, whilst the MUAP shape parameter was fixed. This features a useful time-frequency representation of sEMG signal. Estimation of scale MUAP parameter was verified extracting the mean frequency of filtered EMG signal, extracted from the scale parameter estimated with two different MUAP shape values.

An open problem in room impulse response (RIR) measurement is the effect of nonlinearities, especially those with memory, present in the measurement system, specifically in the power amplifier and in the loudspeaker. The nonlinearities can corrupt the measurement introducing artifacts. The paper discusses a RIR measurement method that is robust towards these nonlinearities. The proposed methodology allows measuring the RIR using the cross-correlation method, i.e., computing the cross-correlation between the output signal and an appropriate sequence. In contrast to other cross-correlation based methods, the proposed approach directly estimates the first-order kernel of the Volterra filter modeling the measurement systems, i.e., the system impulse response for small signals. The proposed approach exploits the concepts of orthogonal periodic sequences, recently proposed in the literature. The input signal can be any periodic persistently exciting sequence and can also be a quantized sequence. Measurements performed both on an emulated scenario and in real environments illustrate the validity of the approach and compare it with other competing RIR measurement methods.

Lithium-ion battery pack performance and longevity can be severely affected by cell-to-cell variations. Statistical modeling is an important tool for optimization of performance and safety of battery packs. This work presents uncertainty quantification of Lithium-ion battery performance, in particular sensitivity analysis. This analysis is based on calculation of Sobol' indices from a surrogate model, based on Polynomial Chaos Expansion. Performance is compared with Monte Carlo simulations.

The paper introduces a novel family of deterministic signals, the orthogonal periodic sequences (OPSs), for the identification of functional link polynomial (FLiP) filters. The novel sequences share many of the characteristics of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of a FLiP filter on a finite time interval with the cross-correlation method. In contrast to the PPSs, OPSs can identify also non-orthogonal FLiP filters, as the Volterra filters. With OPSs, the input sequence can have any persistently exciting distribution and can also be a quantized sequence. OPSs can often identify FLiP filters with a sequence period and a computational complexity much smaller than that of PPSs. Several results are reported to show the effectiveness of the proposed sequences identifying a real nonlinear audio system.

The absence of the queen in a beehive is a very strong indicator of the need for beekeeper intervention. Manually searching for the queen is an arduous recurrent task for beekeepers that disrupts the normal life cycle of the beehive and can be a source of stress for bees. Sound is an indicator for signalling different states of the beehive, including the absence of the queen bee. In this work, we apply machine learning methods to automatically recognise different states in a beehive using audio as input. We investigate both support vector machines and convolutional neural networks for beehive state recognition, using audio data of beehives collected from the NU-Hive project. Results indicate the potential of machine learning methods as well as the challenges of generalizing the system to new hives.

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.

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.

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.

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.

The increasing complexity of VLSI digital systems has dramatically supported system-level representations in modeling and design activities. This evolution makes often necessary a compliant rearrangement of the modalities followed in validation and analysis tasks, as in the case of power performances estimation. Nowadays, transaction-level paradigms are having a wider and wider consideration in the research on electronic system-level design techniques. With regard to the available modeling resources, the most relevant framework is probably the transaction-level extension of the SystemC language (SystemC/TLM), which therefore represents the best platform for defining transaction-level design techniques. In this paper we present a macro-modeling power estimation methodology valid for SystemC/TLM prototypes and of general applicability. The present discussion illustrates the implementation modalities of the proposed approach, verifying its effectiveness through a comparison with RTL estimation techniques.

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 toward 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.

The common algorithm used to control DC-AC switching power converters is Pulse Width Modulation. This work presents the use of Dynamic Oversamplig Ratio Sigma- Delta modulators for the control of the switches of DC-AC switching power converters. The proposed control algorithm presents the advantage of a reduction of conducted electromagnetic interferences with respect to Pulse Width Modulation. A prototype of a DC-AC switching power converter has been implemented.

This paper proposes a methodology, based on the regularization theory, to find the planar spiral inductors with optimum quality factor in the whole space of geometric parameters. Moreover a new technique, based on the Debye approximation, has been applied to extract an equivalent circuit model of these inductors. This model provides an accurate wideband frequency characterization up to 30 GHz.

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.

Bus performances are extremely important in a platform-based design. System Level analysis of bus performances gives important information for the analysis and choice between different architectures driven by functional, timing and power constraints of the System-on-Chip. This paper presents the effect of different arbitration algorithms and bus usage methodologies on the bus AMBA AHB performances in terms of effective throughput and power dissipation. SystemC and VHDL models have been developed and simulations have been performed.

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.

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.

This paper presents a simulator for the statistical analysis of MOS integrated circuits a ected by mismatch e ect. 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.

A novel current-mode CMOS circuit is used to implement fuzzy partition membership functions. The architecture, circuit and cadence spectra simulations are presented.

A rigorous formulation of the parametric yield for very large scale integration (VLSI) designs including the mismatch effect is proposed. The theory has been carried out starting from a general statistical model relating random variations of device parameters to the stochastic behavior of process parameters. The model predicts a dependence of correlation, between devices fabricated in the same die, on their dimensions and mutual distances so that mismatch between equally designed devices can be considered as a particular case of such a model. As an application example, a new model for the autocorrelation function is proposed from which the covariance matrix of the parameters is derived. By assuming a linear approximation, a suitable formulation of the parametric yield for VLSI circuit design is obtained in terms of the covariance matrix of parameters.

The ability of a neural network to learn from experience can be viewed as closely related to its approximating properties. By assuming that environment is essentially stochastic it follows that neural networks should be able to approximate stochastic processes. The aim of this paper is to show that some classes of artificial neural networks exist such that they are capable of providing the approximation, in the mean square sense, of prescribed stochastic processes with arbitrary accuracy. The networks so defined constitute a new model for neural processing and extend previous results concerning approximating capabilities of artificial neural networks.

Adequate analytical device models for circuit simulators are required in the design of IC's, especially in analog design, because the reliability of simulation results is closely related to model accuracy. A semiempirical approach to device modeling offers many advantages over physics-based modeling, in which complex equations have to be solved, and seems to be the most appropriate for circuit simulation. However as this approach is not founded on a rigorous mathematical theory no general methodologies are available for its implementation. Furthermore, device models currently used in circuit simulators are derived by neglecting randomness in experimental data. This assumption is not realistic for mass-produced IC's where technological tolerances cause variations in the measured data, even if they are obtained using the same experimental conditions. The aim of this work is to develop a new scheme for semiempirical device modeling founded on the rigorous mathematical framework of "regularization theory" and to suggest a parameter extraction method based on Bayesian parameter estimation, which takes into account randomness in data. The application of the method to the development of an accurate model for MOS transistors shows the validity of the proposed theoretical approach which is further confirmed by extensive analysis of experimental data.