Joan Cabestany

Editorial: Computational intelligence and bioinspired systems

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.

Bio-inspired systems. Computational and ambient intelligence

In the present issue of Neurocomputing, it is apleasure to present you a collection of 12 extended versions of selected papers from the 10 the dition of the International Work Conference on Artificial Neural Networks (IWANN2009). This is a conference held every two year in Spain, and focus in gon the foundations, theory, models and applications of systems, which are inspired by nature (e.g.neural networks, fuzzy logic and evolutionary systems).

Otolith shape contour analysis using affine transformation invariant wavelet transforms and curvature scale space representation

Fish otolith morphology has been closely related to landmark selection in order to establish the most discriminating points that can help to differentiate or find common characteristics in sets of otolith images. Fourier analysis has traditionally been used to represent otolith images, since it can reconstruct a version of the contour that is close to the original by choosing a reduced set of harmonic terms. However, it is difficult to locate the contours singularities from this spectrum. As an alternative, wavelet transform and curvature scale space representation allow us to quantify the irregularities of the contour and determine its precise position. These properties make these techniques suitable for pattern recognition purposes, ageing, stock determination and species identification studies. In the present study both techniques are applied and used in an otolith classification system that shows robustness against affine image transformations, shears and the presence of noise. The results are interpreted and discussed in relation to traditional morphology studies.

Multielectrode arrays with elastomeric microstructured overlays for extracellular recordings from patterned neurons.

Multielectrode array technology constitutes a promising approach for the characterization of the activity-dependent neuronal plasticity underlying information processing in the nervous system. For this purpose, long-term monitoring and stimulation of cultured neuronal networks with one-to-one neuron-sensor interfacing is advantageous. Existing neurochips that meet these specifications have made use of custom 3D structures requiring clean-room intensive microfabrication techniques. Low-cost fabrication procedures with potential for mass production would facilitate progress in the area. To this end, we have developed a sandwich structure comprising an elastomeric film, microstructured by replica moulding and microhole punching, for neuronal patterning, and a standard planar microelectrode array (MEA), for stimulation and recording. The elastomeric film includes microwells for cell body confinement, and microchannels capable of guiding neurites for network topology specification. The device is formed by overlaying the elastomeric structures on planar arrays. The combination of replica moulding, rapid prototyping and planar MEAs results in low-cost neurochips accessible to most neurophysiology labs. Single neuron patterning and recordings of extracellular potentials are demonstrated.

A wearable inertial measurement unit for long-term monitoring in the dependency care area

Human movement analysis is a field of wide interest since it enables the assessment of a large variety of variables related to quality of life. Human movement can be accurately evaluated through Inertial Measurement Units (IMU), which are wearable and comfortable devices with long battery life. The IMUs movement signals might be, on the one hand, stored in a digital support, in which an analysis is performed a posteriori. On the other hand, the signal analysis might take place in the same IMU at the same time as the signal acquisition through online classifiers. The new sensor system presented in this paper is designed for both collecting movement signals and analyzing them in real-time. This system is a flexible platform useful for collecting data via a triaxial accelerometer, a gyroscope and a magnetometer, with the possibility to incorporate other information sources in real-time. A μSD card can store all inertial data and a Bluetooth module is able to send information to other external devices and receive data from other sources. The system presented is being used in the real-time detection and analysis of Parkinsons disease symptoms, in gait analysis, and in a fall detection system.

Multisite Recording of Extracellular Potentials Produced by Microchannel-Confined Neurons In-Vitro

Towards establishing electrical interfaces with patterned in vitro neurons, we have previously described the fabrication of hybrid elastomer-glass devices polymer-on-multielectrode array technology and obtained single-electrode recordings of extracellular potentials from confined neurons (Claverol-Tintureacute , 2005). Here, we demonstrate the feasibility of spatially localized multisite recordings from individual microchannel-guided neurites extending from microwell-confined somas with good signal-to-noise ratios (20 dB) and spike magnitudes of up to 300 muV. Single-cell current source density (scCSD) analysis of the spatio-temporal patterns of membrane currents along individual processes is illustrated

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

Otolith shape feature extraction oriented to automatic classification with open distributed data

The present study reviewed some of the critical pre-processing steps required for otolith shape char- acterisation for automatic classification with heterogeneous distributed data. A common procedure for optimising automatic classification is to apply data pre-processing in order to reduce the dimension of vector inputs. One of the key aspects of these pre-processing methods is the type of codification method used for describing the otolith contour. Two types of codification methods (Cartesian and Polar) were evaluated, and the limitations (loss of infor- mation) and the benefits (invariance to affine transformations) associated with each method were pointed out. The comparative study was developed using four types of shape descriptors (morphological, statistical, spectral and multiscale), and focused on data codification techniques and their effects on extracting shape features for automatic classification. A new method derived from the Karhunen-Loeve transformation was proposed as the main procedure for standardising the codification of the otolith contours.

An in-system routing strategy for evolvable hardware programmable platforms

On of the major limiting factors for the development of hardware platforms able to support evolvable hardware principles is the lack of simple and compact in-system dynamic routing strategies. In this paper we shall present a programmable hardware architecture whose internal organization permits to perform dynamic routing processes. The architecture is based on a regular bi-dimensional array of functional cells. A hierarchical layered organization has been provided for these cells. Specific routing resources have been included in one of these layers, so that they permit to construct in an incremental way routing paths among the functional cells. The dynamic routing strategy is based on a replication process that is able to connect a source cell with various target cells. One of the major advantages of the proposed routing strategy lies in the fact that its complexity grows only linearly with the array size. Furthermore it is scalable, accommodating without performance degradation to any array size. Behavioral hardware descriptions haven been created for the functional cells that constitute the array. As the simulation and synthesis results will show, the proposed routing strategy will permit the implementation of actual evolvable hardware principles.

Evaluation of solar cell parameters by nonlinear algorithms

A numerical procedure is described to calculate the parameters of the equivalent circuit of a solar cell, based on the application of algorithms to optimise nonlinear functions defining the difference between experimental characteristics and the theoretical model. The authors have obtained empirical data from dark I(V) characteristics. A study of the method to obtain the initial solution is also included. Results obtained for space solar cells and commercial cells (terrestrial use) are given. Modifications of the commonly used equivalent circuit model of solar cells are suggested.