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Cerebral images include several artifacts, such as partial volume effect which limit the diagnostic potential of brain imaging. So, the main objective of this paper is to reduce the effect of partial volume averaging on the boundaries of the ventricles. We thus proposed a fuzzy-genetic brain segmentation scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes from brain...
The effect of partial volume related to anatomical MRI and functional images limit the diagnostic potential of brain imaging. To remedy for this problem, we propose a fuzzy-genetic brain segmentation scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes, from brain images of Alzheimer patients from a real database. This clustering process based on Possibilistic C-Means...
This work contributes to the virtualization of the physical layer of wireless communication protocols, with focus on LTE. It describes the implementation of the uplink receiver side symbol processing functions of LTE at the base station physical layer, using a general-purpose computer equipped with a GPU. We describe the system components and the functions parallelization needed to make the virtualization...
Electromyography (EMG) is commonly used for the diagnosis of neuromuscular and motor control disorders. This work presents an accurate system for the classification of healthy and neuropathic EMG signals. The proposed system employs an auto-regressive moving average (ARMA) model to capture the evolution in time of the EMG signal. Then, the obtained ARMA coefficients serve as inputs to a linear discriminant...
In this paper, we report experimental results of hybrid system using Hidden Markov Models/Multi-Layer Perceptron (HMM/MLP) model as acoustic model and based on the Fuzzy C-Means (FCM) clustering with optimization with Genetic Algorithm (GA). In this context, we use the result of FCM clustering as initial population of GA, this allows training the GA with a population of empirically generated chromosomes...
Mobility over wireless sensor networks (WSNs) has been largely studied, with many protocol enhancements investigated to support mobile nodes and dynamic topological changes in primarily static architectures. Many research efforts have also been devoted to optimize energy consumption in WSN by developing energy aware protocols and algorithms. On the other hand, there have been fewer studies devoted...
We present a novel approach for large speech databases quantization. It uses an unsupervised iterative process to regulate a similarity measure to set the number of clusters and their boundaries, thus overcoming the shortcomings of conventional clustering algorithms such as k-Means and Fuzzy C-Means, which require a priori knowledge of the number of clusters and a similarity measure that follows the...
Partial differential équation (PDE)-based denoising methods are suitable to preserve image edges and describe intrinsic geometry. Besides, spatial filter based image denoising methods are suitable to describe texture while removing noise. In this work, we compare four hybrid filters that combine two spatial based denoising methods, namely the Wiener filter or the first order local statistics (FOLS),...
Recent findings about using memristor devices to mimic biological synapses in neuromorphic systems open a new vision in neuroscience. Ultra-dense learning architectures can be implemented through the Spike-Timing-Dependent-Plasticity (STDP) mechanism by exploiting these nanoscale nonvolatile devices. In this paper, a Spiking Neural Network (SNN) that uses biologically plausible mechanisms is implemented...
The use of renewable energy at roadside units (RSUs) in vehicular ad hoc networks is a great alternative to the electric grid, since it lowers the carbon footprint, and the cost of deployment and maintenance. This paper describes a scheduler for serving vehicles by RSUs that use energy harvesting, with the aim to maximize the number of served vehicles. We start by defining an integer linear programming...
We present a four-channel, high-sensitivity and linearity electrochemical biosensor for neurotransmitter (NT) detection and measurement. Using a multi-channel microfluidic platform makes this biosensor capable of detecting NT-related currents going from nanoamperes to milliamperes, with a sensitivity of the order of picoamperes. Moreover, by using a fully differential potentiostat architecture, the...
Passing multiple light wavelengths through a blood sample makes it possible to investigate the presence and composition of cells, metabolytes and analytes such as blood cells, glucose, lactate and oxygen, providing valuable indications for diagnostic and health monitoring. In this paper, we present a test prototype of a multi-wavelength blood spectroscopy platform integrated with a microfluidic substrate...
This work presents the implementation of operant conditioning (OC) and classical conditioning (CC) with a single spiking neural network (SNN) architecture, thus suggesting that the two types of leaning may relate to the same cognitive process. Both are achieved by using a modified version of spike-timing-dependent plasticity (STDP), where the connection weight between a cue neuron and an action neuron...
Assuming a point-to-point communication between wireless nodes that harvest ambient energy for operation, this work presents a method that uses a Kalman filter to predict the receiver state of charge. This prediction is performed at the transmitter in order to dynamically adjust the number of bits to be sent so as to avoid data loss due to potential receiver battery depletion. Our simulations, using...
A robot is presented whose behavior is based on two fundamental types of learning in the animal world: Classical Conditioning (CC) and Operant Conditioning (OC). It is shown how both share Spike-Timing-Dependent-Plasticity (STDP) as learning process for a Spiking Neural Network (SNN). STDP was implemented on a Field-Programmable Gate Array (FPGA) with very low-demanding resources, using an adaptation...
In this paper, we present a low-power, low-noise and wide dynamic range biosensor for neurotransmitter detection that exhibits high sensitivity and linearity. The design uses a fully differential difference amplifier (FDDA) to decrease the number of amplifiers in the control part, with reduced overall power consumption as a result. Moreover, this amplifier provides an output voltage swing that is...
We present a novel biomedical image denoising system that combines Wiener and partial differential equation (PDE) filtering to form a sequential hybrid filter. The local Wiener filter is employed to remove additive Gaussian noise in a first step; then, the PDE filter is applied to the resulting filtered image for further noise removal while preserving image edges. Experimental results showed the superiority...
SysML/UML activity diagrams are widely used for the modeling and analysis of complex systems and they have become a de-facto standard for software and embedded systems. Previously in our group, we formalized SysML activity diagrams by developing a calculus called New Activity Calculus (NuAC). In this work, we redefine NuAC terms to support code generation for ARM Cortex-M processors and we present...
Proper acquisition of the photoplethysmography signals is essential in a pulse oximetry system and sensor placement plays an important role in this respect. Due to the complex structure of the finger tissue, inadequate sensor placement will have an adverse effect on the light path and high signal quality may become impossible to achieve [1]. In this paper, we present a ring shaped oximeter that uses...
We show how a minimal components requirement and very low resource demanding field-programmable gate array (FPGA) implementation of an adapted version of the synapto-dendritic Kernel Adapting Neuron (SKAN) model can be used to underlie two of the most basic learning processes: classical conditioning (CC) and operant conditioning (OC). In the CC architecture, this adapted SKAN model is used in a spiking...
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