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Parkinson's Disease (PD) is a neurodegenerative disease that predominantly alter patients' motor performance and compromises the speed, the automaticity and fluidity of natural movements. The patients fluctuate between periods in which they can move almost normally for some hours (ON state) and periods with motor disorders (OFF state). Gait properties are affected by the motor state of a patient: reduced stride length, reduced gait speed, increased stride width etc. The ability to assess the motor states (ON/OFF) on a continuous basis for long time without disturbing the patients' daily life activities is an important component of PD management. An accurate report of motor states could allow clinics to adjust the medication regimen to avoid OFF periods. The real-time monitoring will also allow an online treatment by combining, for instance, with automatic drug-administration pump doses. Many studies have attempted to extract gait properties through a belt-worn single tri-axial accelerometer. In this paper, a user friendly position is proposed to place the accelerometer and three step detection methods and three step length estimators are compared considering the proposed sensor placement in signals obtained from healthy volunteers and PD patients. Adaptation methods to these step length estimators are also proposed and compared. The comparison shows that the adapted estimators improve the performance with the new proposed step detection method and reduce errors in respect of the original methods.

Parkinson's Disease (PD) is a neurodegenerative disease that predominantly alter patients' motor performance and compromises the speed, the automaticity and fluidity of natural movements. The patients fluctuate between periods in which they can move almost normally for some hours (ON state) and periods with motor disorders (OFF state). Gait properties are affected by the motor state of a patient: reduced stride length, reduced gait speed, increased stride width etc. The ability to assess the motor states (ON/OFF) on a continuous basis for long time without disturbing the patients' daily life activities is an important component of PD management. An accurate report of motor states could allow clinics to adjust the medication regimen to avoid OFF periods. The real-time monitoring will also allow an online treatment by combining, for instance, with automatic drug-administration pump doses. Many studies have attempted to extract gait properties through a belt-worn single tri-axial accelerometer. In this paper, a user friendly position is proposed to place the accelerometer and six step length estimators are compared considering the proposed sensor placement in a preliminary database of healthy volunteers. Adaptation methods to some of these estimators are also proposed and compared. The comparison shows that the adapted estimators improve the performance and reduce errors in respect of the original methods applied in the new sensor location.

This paper presents an algorithm for the automatic estimation of spatio temporal gait properties from signals provided by inertial body sensors. The approach is based on time series analysis. Here, a minimum number of body sensor devices is used, which imposes limitations for the automatic extraction of relevant properties of the gait like step length and velocity. The human gait is represented as a dynamical system (DS), which internal states are hidden. Sensor information is interpreted as an observation of a particular trajectory of the DS, from wich a reconstruction space can be obtained. The reconstruction space is then transformed using standard principal components analysis (PCA). From the transformed space, reliable models to estimate step length and velocities are successfully constructed.

The purpose of this paper is to describe a dynamic fault tolerance scaling technique that is supported by the self-adaptive features of a hardware architecture developed within the framework of the AETHER project. The architecture is composed of an array of cells that support dynamic and distributed self-routing and self-placement of components in the system. The combination of a large array of cells together with component-level routing ultimately constitutes a SANE (Self-Adaptive Networked Entity). The dynamic fault tolerance scaling technique proposed in this paper permits a given subsystem to modify autonomously its structure in order to achieve fault detection and fault recovery. The decision to modify or not its organization is based on the actual power consumption of the system.

This article focuses at studying the implementation of the self-configuration concept on a novel unconventional hardware architecture. This proposed programmable architecture implements self-placement and self-routing which, due to its intrinsic design, enable the development of systems with self-configuration, self-repair and/or fault tolerance capabilities [1],[2]. For scalability issues, the architecture has been defined as a regular array of homogeneous elements. It is able to analyze and modify its own circuits. This means that the functionality can be modified and improved over time, leading to more versatile designs. The system can check for faulty cells or regions and simply work around them. Those cells can be marked as bad regions so the system can keep working. This permits to deal with imperfectly-manufactured cell arrays, which is a key issue in the extremely complex systems envisioned for the future micro and nano technologies.

An information-theoretic approach oriented to develop on-line algorithms for electrochemical array data processing is presented. We deal with the multichannel processing of the signals acquired by an array of silicon-based chemical sensors. The objective is to extract multiple desired signals by the cancellation of multiple interferences in high noisy environments. The nonlinear mixture separation is achieved in the presence of interference and strong cross nonlinearities, by minimizing the output mutual information in multi-input, multi-output learning machines, considering mutually independent sources. Numerical results demonstrate the viability of the proposed approach in the context of array signal extraction.

A novel approach to design low cost/high performance on-line physiological data and water monitoring systems is presented and discussed. The approach is based on a set of solid state chemical sensors (i.e. ISFETs/CHEMFETs), and a post-processing stage which performs the estimation of the ionic activities presented in the solution. This estimation is performed by a non-linear blind separation algorithm that uses some prior knowledge about how sensors simultaneously respond to several ionic activities. Additionally, the presented approach addresses some important problems of these sensors like temperature effects, cross sensitivity and drift; it also takes into account some hardware implementation constraints. Experimental results confirm the effectiveness of the proposed architecture.

In this work we present a new on-line water pollution monitoring system. The system includes a smart array of ion-selective field effect transistors (ISFETs) as a front-end and also at post-processing stages in order to transmit the stored measures of ion concentrations. The intelligence in the smart sensors is provided by a blind source separation (BSS) algorithm which continuously learns from measures of how to detect the ion concentrations available in the mixed signal observed in the array's output. The computational simplicity of the BSS algorithm and its capability of continuous learning from the environment, allow the design of a low-power, cheap and small system that monitors water in real-time, and is a contrast to the classical off-line approach based on a water analysis of the extracted measures in the laboratory. The work is in progress, as part of the SEWING project (IST-2000-28084).

In this paper an attempt to design decision part of the iris identification system, which will be able to identify persons just by a look at the camera, is discussed. Proposed system will be cheap in comparison with present ones. Modified Haralick's co-occurrence method with multilayer perceptron is used for extraction and classification of the irises. Some preliminary research results are also presented.

Associative memories can be modeled theoretically from neural networks using the feature extractor and a MAXNET. However, from the practical standpoint, the feature extractor has the inconvenience of needing complex operations and/or analog weight values, while the MAXNET is difficult to implement due to the high interconnectivity and analog weight values. The proposed solution is to use an encoding scheme which performs the Hamming distance calculation on encoded vectors, equivalent to a real-valued distance calculation. A memory compression technique is then applied to reduce the number of bits. For the MAXNET, synchronous digital circuit techniques are used. The resulting associative memory processors are simple, modular, and not too costly in terms of logic. The proposed content addressable memory matches data according to that which produces a minimum distance between the searched key and each of the stored record's fields. Each field's relevance in the search operation is controlled using a mask. >