Test-bed of a real time detection system for L/H and H/L transitions implemented with the ITMS platform

A basic requirement of the data acquisition systems used in long pulse fusion experiments is to detect events of interest in the acquired signals in real time. Developing such applications is usually a complex task, so it is necessary to develop a set of hardware and software tools that simplify their implementation. An example of these tools is the Intelligent Test and Measurement System (ITMS), which offers distributed data acquisition, distribution and real time processing capabilities with advanced, but easy to use, software tools that simplify application development and system setup. This paper presents the application of the ITMS platform to solve the problem of detecting L/H and H/L transitions in real time based on the use of efficient pattern recognition algorithms

Intelligent Reconfigurable Integrated Satellite Processor

We present our Intelligent Reconfigurable Integrated Satellite (IRIS) Processor. At the heart of the system are our reconfigurable vision chips which are capable of massively parallel analog processing. The smart vision chips are capable of not only centroiding and pattern recognition but also tracking and controlling devices including MEMs devices and active pixel arrays. In addition to discussing the active optic and active electronic devices, several small satellite system applications are presented along with experimental and simulation results

Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.Comment: 16 pages, to appear in Springer's Lecture Notes in Computer Science volume "Pattern Recognition Applications and Methods 2013", part of series on Advances in Intelligent and Soft Computin

Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations

Design of Neural Network System to Communicate a Blind Person with a Computer Using a Braille

Artificial intelligence systems are widely used nowadays in solving different problems facing mankind. Artificial intelligence systems were developed based on how human brains can think and function; this distinguished character gives these systems the wide range of applications as compared to conventional systems. Among the artificial intelligent systems involves artificial neural intelligent, fuzzy systems and neural fuzzy systems. Blind person interface to computer is among the problem which the world is facing. In this paper the Artificial Neural Network (ANN) is used in designing the Braille system which is capable of enabling the interface between a blind person and the computer. The Multilayer perceptron (MLP) with four layers and nineteen neural is used for the implementation of pattern recognition of the Braille. The pattern of the Braille is used as the inputs to the MLP whereby through MATLAB developed program the patterns training are achieved. The developed system is capable of enabling the blind person to typing letters, numbers and to enter different commands to the computer.