Hybrid Feature Selection for Myoelectric Signal Classification Using MICA
This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100% accuracy.
Steganography is the foremost influential approach to hide data. Images serve as the most appropriate cover media for steganography. This paper intends to do a performance evaluation of color images and its comparison with the recently proposed approaches, using the modified technique already proposed for grayscale images, by the authors. This approach hides large data in color image using the blocking concept. The blocking process is applied on approximation coefficients of secret image and detail coefficients of red, green and blue components of cover image. The blocks of detail coefficients are replaced with approximation coefficients of secret image using root mean square error method. The key is used to store the position of best matching blocks. It is being predicated that the work will be able to hide large data in a single image. The stego image (ST) has better visual quality based on the peak signal to noise ratio values.
The main problem of classical clustering technique is that it is easily trapped in the local optima. An attempt has been made to solve this problem by proposing the grey wolf algorithm (GWA)-based clustering technique, called GWA clustering (GWAC), through this paper. The search capability of GWA is used to search the optimal cluster centers in the given feature space. The agent representation is used to encode the centers of clusters. The proposed GWAC technique is tested on both artificial and real-life data sets and compared to six well-known metaheuristic-based clustering techniques. The computational results are encouraging and demonstrate that GWAC provides better values in terms of precision, recall, G-measure, and intracluster distances. GWAC is further applied for gene expression data set and its performance is compared to other techniques. Experimental results reveal the efficiency of the GWAC over other techniques.
Finding the optimal number of clusters and the appropriate partitioning of the given dataset are the two major challenges while dealing with clustering. For both of these, cluster validity indices are used. In this paper, seven widely used cluster validity indices, namely DB index, PS index, I index, XB index, FS index, K index, and SV index, have been developed based on line symmetry distance measures. These indices provide the measure of line symmetry present in the partitioning of the dataset. These are able to detect clusters of any shape or size in a given dataset, as long as they possess the property of line symmetry. The performance of these indices is evaluated on three clustering algorithms: K-means, fuzzy-C means, and modified harmony search-based clustering (MHSC). The efficacy of symmetry-based validity indices on clustering algorithms is demonstrated on artificial and real-life datasets, six each, with the number of clusters varying from 2 to where n is the total number of data points existing in the dataset. The experimental results reveal that the incorporation of line symmetry-based distance improves the capabilities of these existing validity indices in finding the appropriate number of clusters. Comparisons of these indices are done with the point symmetric and original versions of these seven validity indices. The results also demonstrate that the MHSC technique performs better as compared to other well-known clustering techniques. For real-life datasets, analysis of variance statistical analysis is also performed.
In this paper, the problem of automatic data clustering is treated as the searching of optimal number of clusters so that the obtained partitions should be optimized. The automatic data clustering technique utilizes a recently developed parameter adaptive harmony search (PAHS) as an underlying optimization strategy. It uses real-coded variable length harmony vector, which is able to detect the number of clusters automatically. The newly developed concepts regarding “threshold setting” and “cutoff” are used to refine the optimization strategy. The assignment of data points to different cluster centers is done based on the newly developed weighted Euclidean distance instead of Euclidean distance. The developed approach is able to detect any type of cluster irrespective of their geometric shape. It is compared with four well-established clustering techniques. It is further applied for automatic segmentation of grayscale and color images, and its performance is compared with other existing techniques. For real-life datasets, statistical analysis is done. The technique shows its effectiveness and the usefulness.
Densities, ρ and ultrasonic speeds, u of L-histidine (0.02–0.12 mol·kg−1) in water and 0.1 mol·kg−1 aqueous citric acid solutions were measured over the temperature range (298.15–313.15) K with interval of 5 K at atmospheric pressure. From these experimental data apparent molar volume ΦV, limiting apparent molar volume ΦVO and the slope SV, partial molar expansibilities ΦEO, Hepler’s constant, adiabatic compressibility β, transfer volume ΦV, trO, intermolecular free length (Lf), specific acoustic impedance (Z) and molar compressibility (W) were calculated. The results are interpreted in terms of solute–solute and solute–solvent interactions in these systems. It has also been observed that L-histidine act as structure maker in water and aqueous citric acid.
Myoelectric signal classification is one of the most difficult pattern recognition problems because large variations in surface electromyogram features usually exist. In the literature, attempts have been made to apply various pattern recognition methods to classify surface electromyography into components corresponding to the activities of different muscles, but this has not been very successful, as some muscles are bigger and more active than others. This results in dataset discrepancy during classification. Multicategory classification problems are usually solved by solving many, one-versus-rest binary classification tasks. These subtasks unsurprisingly involve unbalanced datasets. Consequently, we need a learning methodology that can take into account unbalanced datasets in addition to large variations in the distributions of patterns corresponding to different classes. Here, we attempt to address the above issues using hybrid features extracted from independent component analysis and twin support vector machine techniques.
Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques
The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when the level of muscle contraction is very low. Due to this the current applications of surface electromyogram (sEMG) are infeasible and unreliable in pattern classification. This research reports a new technique of sEMG using Independent Component Analysis (ICA). The technique uses blind source separation (BSS) methods to classify the patterns of Myo-electrical signals during different Maximum Voluntary Contraction (MVCs) at different low level finger movements. The results of the experiments indicate that patterns using ICA of sEMG is a reliable (p<0.001) measure of strength of muscle contraction even when muscle activity is only 20% MVC. The authors propose that ICA is a useful indicator of muscle properties and is a useful indicator of the level of muscle activity.
In this article, new metal-mediated cyclization and reductive coupling reactions of bicyclic olefins with alkynes are described. Oxabicyclic alkenes undergo cyclization with alkyl propiolates at 80 C catalyzed by nickel complexes to give benzocoumarin derivatives in high yields. The reaction of bicyclic alkenes (oxa- and azacyclic alkenes) with alkyl propiolates at room temperature in the presence of the same nickel complex gave 1,2-dihydro-napthelene derivatives in good-to-excellent yields. This reductive coupling reaction proceeds under very mild conditions in complete regio- and stereoselective fashion. A mechanism to account for the coumarin formation and the reductive coupling is proposed.