Paolo Crippa

Multicomponent AM–FM Representations: An Asymptotically Exact Approach

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

Parametric yield formulation of MOS IC's affected by mismatch effect

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.

On the approximation of stochastic processes by approximate identity neural networks

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.

A statistical methodology for the design of high-performance CMOS current-steering digital-to-analog converters

With the shrinking of device sizes, random device variations become a key factor limiting the performances of high-resolution complementary metal-oxide-semiconductor (CMOS) current-steering digital-to-analog converters (DACs). In this paper, we present a novel design methodology based on statistical modeling of MOS transistor drain current that allows designers to explore different DAC architectures and to study the effects of technological variations on system performance without using time-consuming Monte Carlo simulations. This technique requires as a first step the estimation of the mean value and the autocorrelation function of a single stochastic process. This stochastic process models the device drain current and summarizes all the random sources associated with the process/device variations since the current represents the effect of all of them. Subsequently, on the basis of such an approach, a behavioral model of current-steering DACs has been developed. Finally, the statistical simulation of static performances such as differential nonlinearity and integral nonlinearity has been carried out for different DAC architectures based on the behavioral model previously derived.

System-level power analysis methodology applied to the AMBA AHB bus [SoC applications]

The specification on power consumption of a digital system is extremely important due to the growing relevance of the market of portable devices and must be taken into account since the early phases of a complex system-on-chip design. In this paper, some guidelines are provided for the integration of the information on power consumption in the executable model of parameterized cores, with particular attention to the AMBA AHB bus. This gives important information for the analysis and choice between different design architectures driven by functional, timing and power constraints of the system-on-chip.

Instruction based power consumption estimation methodology

The paper presents a new model of the dynamic power dissipated by a circuit described at gate or behavioural level. A procedure is presented for an accurate estimate of the power dissipated during the execution of each instruction by using gate level or behavioural level digital simulations. The information on power consumption stored in a look-up table can be used in a system level simulation. The methodology has been applied to the design of an I/sup 2/C bus driver.

Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM–FM Decomposition

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.

Nonlinear System Identification: An Effective Framework Based on the Karhunen–LoÈve Transform

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-Loeve 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.

A novel KLT algorithm optimized for small signal sets [speech processing applications]

The Karhunen-Loeve 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.

A non-probabilistic recognizer of stochastic signals based on KLT

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