Ada Fort

A Class of Maximum-Period Nonlinear Congruential Generators Derived From the Rényi Chaotic Map

In this paper, a family of nonlinear congruential generators (NLCGs) based on the digitized Reacutenyi map is considered for the definition of hardware-efficient pseudorandom number generators (PRNGs), and a theoretical framework for their study is presented. The authors investigate how the nonlinear structure of these systems eliminates some of the statistical regularities spoiling the randomness of sequences generated with linear techniques. In detail, in this paper, a necessary condition that the considered NLCGs must satisfy to have maximum period length is given, and a list of such maximum period PRNGs for period lengths up to 231-1 is provided. Referring to the NIST800-22 statistical test suite, two PRNG examples are presented and compared to well-known PRNGs based on linear recurrencies requiring a similar amount of resources for their implementation

SBT soft fault diagnosis in analog electronic circuits: a sensitivity-based approach by randomized algorithms

This paper addresses the fault diagnosis issue based on a simulation before test philosophy in analog electronic circuits. Diagnosis, obtained by comparing signatures measured at the test nodes with those contained in a fault dictionary, allows for sub-systems testing and fault isolation within the circuit. A novel method for constructing the fault dictionary under the single faulty component/unit hypothesis is proposed. The method, based on a harmonic analysis, allows for selecting the most suitable test input stimuli and nodes by means of a global sensitivity approach efficiently carried out by randomized algorithms. Applicability of the method to a wide class of circuits and its integration in diagnosis tools are granted since randomized algorithms assure that the selection problem can be effectively carried out with a poly-time algorithm independently from the fault space, structure, and complexity of the circuit.

Fault diagnosis of electronic analog circuits using a radial basis function network classifier

Abstract In this paper a fault diagnosis technique, which employs neural networks to analyze signatures of analog circuits, is proposed. Radial basis functions networks (RBFN) are used to process circuit input–output measurements, and to perform soft fault location. Both noise and effect of parameter variations in the tolerance ranges of non-faulty components are taken into account. The network is trained with circuit signatures, obtained by measuring and coding both circuit input and output signals, which are contained in a ‘fault dictionary’. In this context RBFN architecture is selected, because it is able to cope with ‘new fault’ conditions not well represented in the fault dictionary used for network training. The RBFN classifier was applied to linear and non-linear sample circuits, considering faults both at sub-system level and at component level. Simulations and experimental results show that the developed nets succeeded in classifying faults. The nets trained with single faults has in many cases detected also multiple faults.

Selectivity enhancement of SnO2 sensors by means of operating temperature modulation

Abstract In this paper results concerning the classification of water solutions with ethanol and other volatile organic compounds by means of SnO2 sensors are presented. This study aims at the selection of the most appropriate measurement technique for this application, in terms of both selectivity and repeatability. The final aim of this research is the application of electronic noses based on metal oxide sensors to wine classification. In particular, a comparison is performed between two measurement techniques: chemical transient analysis and sensor temperature modulation.

Model and Experimental Characterization of the Dynamic Behavior of Low-Power Carbon Monoxide MOX Sensors Operated With Pulsed Temperature Profiles

Wireless sensor networks for home automation or environment monitoring require low-cost low-power sensors. Carbon monoxide (CO) metal-oxide (MOX) sensors could be suitable in terms of device cost, but they show some severe limits, such as the need to be heated, which means large power consumption and the need for complex and frequent calibration procedures, which increases the overall cost. This paper investigates the possibility to partially overcome these limits by a low-cost detection system based on a suitable commercial sensor (TGS 2442, Figaro, Inc.) and an ad hoc measurement technique exploiting specifically tailored temperature profiles. To this aim, the authors study the dynamic behavior of low-power CO MOX sensors operated with pulsed temperature profiles by means of two approaches: 1) sensor modeling and 2) experimental evaluation. To analyze how the sensor dynamic response changes as a function of the CO concentration, the authors individuate a temperature profile, which ensures satisfactory sensitivity to the target gas and very low power consumption. Moreover, some parameters describing the sensor response shape are selected, which prove to be significant in terms of both robustness to environmental conditions and calibration simplicity.

Parametric and non-parametric estimation of speech formants: application to infant cry

The present paper addresses the issue of correctly estimating the peaks in the speech envelope (formants) occurring in newborn infant cry. Clinical studies have shown that the analysis of such spectral characteristics is a helpful noninvasive diagnostic tool. In fact it can be applied to explore brain function at very early stage of child development, for a timely diagnosis of neonatal disease and malformation. The paper focuses on the performance comparison between some classical parametric and non-parametric estimation techniques particularly well suited for the present application, specifically the LP, ARX and cepstrum approaches. It is shown that, if the model order is correctly chosen, parametric methods are in general more reliable and robust against noise, but exhibit a less uniform behaviour than cepstrum. The methods are compared also in terms of tracking capability, since the signals under study are nonstationary. Both simulated and real signals are used in order to outline the relevant features of the proposed approaches.

A real-time two-dimensional pulsed-wave Doppler system

An experimental system was developed to acquire and visualise in real-time two-dimensional (2-D) velocity maps. Data acquisition is performed by using a modified commercial echograph based on a 5-MHz, 128-element linear-array transducer with electronic focussing and beam steering. Additional electronics were integrated into the echograph to implement a 2-D Doppler system capable of measuring the velocity component on the scanning plane. Suitable axial and lateral scanning methods were studied to obtain Doppler measurements over a scanning area. A colour image of the estimated velocity field is presented in real time on a personal computer using different visualisation techniques. The system performance was tested experimentally both in vitro and in vivo on a human carotid artery.

Tin oxide gas sensing: comparison among different measurement techniques for gas mixture classification

In this paper, a study is presented aimed at the selection of the most appropriate measurement technique for wine classification. In particular, the problem of detecting typical wine aroma components in mixtures where ethanol is present is taken into account. The literature proposes different solutions in order to enhance metal-oxide sensor selectivity. An interesting approach concerns the application of different measurement techniques. In this work, three methods based on chemical transient, AC measurements, and temperature modulation have been investigated.

A fuzzy approach for soft fault detection in analog circuits

Abstract An automatic fault diagnosis technique based on a fuzzy approach is presented and its performance is analyzed by means of two examples. The proposed technique is applied to the detection and the isolation of single soft faults in analog electronic circuits. Both faults at component and sub-system level are considered. A ‘simulation before test’ approach is followed: a fault dictionary is a priori generated by collecting signatures of different fault conditions simulated in the frequency domain. A fuzzy classifier is trained by data contained in the fault dictionary and used to process the circuit under test response during test phase. The effects of noise and of non-faulty parameter tolerance are taken into account.

A measurement system for odor classification based on the dynamic response of QCM sensors

In this paper, an innovative measurement system for odor classification, based on quartz crystal microbalances (QCMs), is presented. The application proposed in this paper is the detection of typical wine aroma compounds in mixtures containing ethanol. In QCM sensors, the sensitive layer is, e.g., a polymeric layer deposited on a quartz surface. Chemical mixtures are sorbed in the sensitive layer, inducing a change in the polymer mass and, therefore, in the quartz resonance frequency. In this paper, the frequency shift is measured by a dedicated, fully digital front-end hardware implementing a technique that allows reducing the measurement time while maintaining a high-frequency resolution . The developed system allows, therefore, measuring variations of the QCM resonance frequency shifts during chemical transients obtained with abrupt changes in odor concentration. Hence, the reaction kinetics can be exploited to enhance the sensor selectivity. In this paper, some measurements obtained with an array of four sensors with different polymeric sensitive layers are presented. An exponential fitting of the transient responses is used for feature extraction. Finally, to reduce data dimensionality, principal component analysis is used.