Karan Veer

Background: Over recent years, the security of the publicly available audio, video and text data has become a big challenge, i.e., social media communication; hence the author presents an exceedingly secure communication system to hide the data from an attentive adversary. Objective: The study aims to design and analyze a novel algorithm to be implemented for message transmission using multi-image LSB steganography and crucial variable-length cryptography. Encoder, decoder and social media as a channel are the main parts of the study. Methods: Various computer techniques like cryptography and steganography with video handling are used to perform the proposed work. The main objective of this work is to implement the raw video as the cover on the QR code image. This image is to be transmitted as information by enshrouding it in the raw footage. Additionally, encryption of QR code image using VLMKG (Variable Length Mixed key Generation) cryptography enhances the reliability of work. The variablelength Key is generated separately to implement the cryptography. Multi-image LSB steganography is imposed with one-bit replacement in the spatial domain. Wavelet Transform (Daubechies family) is applied for denoising, which enhances the accuracy of the received data. Results: The proposed work provides a secure way to transmit any information in the form of QR code image successfully. It is verified with the help of five QR Code images and three raw video groupings. Conclusion: Implementation of LSB steganography, variable-length key cryptography and MATLAB usage had superbly extracted the image features; calculations and the consequences were observed to be palatable.

Background: Since surface electromyogram (SEMG) signal plays a vital role in prosthetic designs, hence the evaluation of these signals for identifying the upper arm motions leading to myoelectric control based design of artificial devices is presented. Methods: A total of two upper limb locations were chosen for recording of data, thereafter the evaluation and interpretation of signal was done for the estimation of extracted parameters using simulated algorithm (four arm activities were performed). A statistical algorithm of two way ANOVA for estimating the effectiveness of recorded signal followed by a discriminant classifier for pattern recognition task was investigated. Results: Outcome of the proposed study after analyzing the effectiveness of recorded data supports the formularize reparability of the classification approach for signal accuracies (97.50%). Conclusion: Finally, the simulation study based on muscle modeling allowed us to explore the parameters that affect the muscle-force relationship prior to prostheses design. Further, the proposed study will also help researchers in understanding the nature of these complex signals particularly in biomedical applications.

Accurate muscular force estimation (from upper arm muscles) based on surface electromyogram forms an important issue in upper limb prosthetic design applications. The whole system consists of surface electrodes, signal acquisition protocols, and signal conditioning at different levels. Labview soft scope was used to acquire the surface electromyogram signal from the designed hardware. The study is concerned with the estimation of characteristics of recorded signals, and for that, statistical techniques of PCA were exercised for verifying the effectiveness of the processed signal against different upper arm motions before its classification. Thereafter, artificial neural network classifier was implemented for the classification surface electromyogram signals with best classification rate of 89.30%. Finally, the processing technique was used to significantly (p < .05) improve classification rate, without much loss of information.

This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.

Dual-channel evaluation of surface electromyogram (SEMG) signals acquired from amputee subjects using computational techniques for classification of arm motions is presented in this study. SEMG signals were classified by the neural network (NN) and interpretation was done using statistical techniques to extract the effectiveness of the recorded signals. From the results, it was observed that there exists a calculative difference in amplitude gain across different motions and that SEMG signals have great potential to classify arm motions. The outcomes indicated that the NN algorithm performs significantly better than other algorithms, with a classification rate (CR) of 96.40%. Analysis of variance (ANOVA) presents the results to validate the effectiveness of the recorded data to discriminate SEMG signals. The results are of significant thrust in identifying the operations that can be implemented for classifying upper-limb movements suitable for prostheses’ design.

Here, the wavelet analysis has been investigated to improve the quality of myoelectric signal before use in prosthetic design. Effective Surface Electromyogram (SEMG) signals were estimated by first decomposing the obtained signal using wavelet transform and then analysing the decomposed coefficients by threshold methods. With the appropriate choice of wavelet, it is possible to reduce interference noise effectively in the SEMG signal. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square value and signal power values. The combined results of root mean square value and signal power shows that wavelet db4 performs the best denoising among the wavelets. Furthermore, time domain and frequency domain methods were applied for SEMG signal analysis to investigate the effect of muscle-force contraction on the signal. It was found that, during sustained contractions, the mean frequency (MNF) and median frequency (MDF) increase as muscle force levels increase.