GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A non-random and periodic training sequence is proposed to be added arithmetically on the information sequence for energy efficient channel estimation within the future generation of wireless receivers. This eliminates the need of separate overhead time/frequency slots for training sequence. The results of the proposed technique are compared with the techniques in the existing literature -the notable first order statistics based channel estimation technique with ST. The normalized channel mean-square error (NCMSE) and bit-error-rate (BER) are chosen as performance measures for the simulation based analysis. It is established that the proposed technique performs better in terms of the accuracy of estimated channel; subsequently the quality of service (QoS), while retrieving information sequence at the receiver. With respect to its comparable counterpart, the proposed GA based scheme delivers an improvement of about 1dB in NCMSE at 12 dB SNR and a gain of about 2 dB in SNR at 10-1 BER, for the population size set at twice the length of channel. It is also demonstrated that, this achievement in performance improvement can further be enhanced at the cost of computational power by increasing the population size

Human Gait Recognition Subject to Different Covariate Factors in a Multi-View Environment

Human gait recognition system identifies individuals based on their biometric traits. A human’s biometric features can be grouped into physiologic or behavioral traits. Biometric traits, such as the face [1], ears [2], iris [3], finger prints, passwords, and tokens, require highly accurate recognition and a well-controlled human interaction to be effective. In contrast, behavioral traits such as voice, signature, and gait do not require any human interaction and can be collected in a hidden and non-invasive mode with a camera system at a low resolution. In comparison with other physiological traits, one of the main advantages of gait analysis is the collection of data from a certain distance. However, gait is less powerful than physiological traits, yet it still has widespread application in surveillance for unfavorable situations. From traditional algorithms to deep learning models, a gait survey provides a detailed history of gait recognition

Artificial Immune System–Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals

Artificial immune systems (AIS) are intelligent algorithms derived from the principles inspired by the human immune system. In this study, electroencephalography (EEG) signals for four distinct motor movements of human limbs are detected and classified using a negative selection classification algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV–Graz dataset 2a, comprising nine subjects) has been used. Mel frequency cepstral coefficients (MFCCs) are extracted as selected features from recorded EEG signals. Dimensionality reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic algorithm (GA) optimized detectors (artificial lymphocytes) are trained using negative selection algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with the optimized radius of the detector as a nonself detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over nine subjects with a maximum individual subject classification accuracy of 97.5% for subject number eight

A review of tactile sensing technologies with applications in biomedical engineering. Sens. Actuator A 2012

a b s t r a c t Any device which senses information such as shape, texture, softness, temperature, vibration or shear and normal forces, by physical contact or touch, can be termed a tactile sensor. The importance of tactile sensor technology was recognized in the 1980s, along with a realization of the importance of computers and robotics. Despite this awareness, tactile sensors failed to be strongly adopted in industrial or consumer markets. In this paper, previous expectations of tactile sensors have been reviewed and the reasons for their failure to meet these expectations are discussed. The evolution of different tactile transduction principles, state of art designs and fabrication methods, and their pros and cons, are analyzed. From current development trends, new application areas for tactile sensors have been proposed. Literature from the last few decades has been revisited, and areas which are not appropriate for the use of tactile sensors have been identified. Similarly, the challenges that this technology needs to overcome in order to find its place in the market have been highlighted

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%