Publications
Dynamic S-Box and PWLCM-Based Robust Watermarking Scheme
Feb 25, 2022Journal Springer Nature Link
Publisher 25 February 2022
DOI https://doi.org/10.1007/s11277-022-09562-9
Issue July 2022
Volume 125
Due to the increased number of cyberattacks, numerous researchers are motivated towards the design of such schemes that can hide digital information in a signal. Watermarking is one of the promising technologies that can protect digital information. However, traditional watermarking schemes are either slow or less secure. In this paper, a dynamic S-Box based efficient watermarking scheme is presented. The original image was extracted at the receiver’s end without any loss of sensitive information. Firstly, the Secure Hash Algorithm is applied to the original image for the generation of the initial condition. Piece Wise Linear Chaotic Map is then used to generate 16 16 dynamic Substitution Box (S-Box). As an additional security feature, the watermark is substituted through dynamic S-Box. Hence, it is hard for the eavesdroppers to attack the proposed scheme due to the dynamic nature of S-Box. Lastly, lifting wavelet transform is applied to the host image and the High Low and High High blocks of host image are replaced with least significant bits and most significant bits of the substituted watermark, respectively. Robustness, efficiency and security of the proposed scheme is verified using Structure Similarity Index, Structure Dissimilarity Index, Structure Content, Mutual Information, energy, entropy, correlation tests and classical attacks analysis.
Design and multichannel electromyography system-based neural network control of a low-cost myoelectric prosthesis hand
Feb 4, 2021Journal Mechanical Sciences
Publisher 04 Feb 2021
DOI https://doi.org/10.5194/ms-12-69-2021, 2021.
Issue 1
Volume 12
This study introduces a new control method for electromyography (EMG) in a prosthetic hand application with a practical design of the whole system. The hand is controlled by a motor (which regulates a significant part of the hand movement) and a microcontroller board, which is responsible for receiving and analyzing signals acquired by a Myoware muscle device. The Myoware device accepts muscle signals and sends them to the controller. The controller interprets the received signals based on the designed artificial neural network. In this design, the muscle signals are read and saved in a MATLAB system file. After neural network program processing by MATLAB, they are then applied online to the prosthetic hand. The obtained signal, i.e., electromyogram, is programmed to control the motion of the prosthetic hand with similar behavior to a real human hand. The designed system is tested on seven individuals at Gaziantep University. Due to the sufficient signal of the Mayo armband compared to Myoware sensors, Mayo armband muscle is applied in the proposed system. The discussed results have been shown to be satisfactory in the final proposed system. This system was a feasible, useful, and cost-effective solution for the handless or amputated individuals. They have used the system in their day-to-day activities that allowed them to move freely, easily, and comfortably.
Robust Controller Electromyogram Prosthetic Hand with Artificial Neural Network Control and Position.
Apr 1, 2020Journal Indian Journal of Forensic Medicine & Toxicology
Publisher Academic Journal
DOI 10.37506/ijfmt.v14i2.2854
Issue 2
Volume 14
In this study, we proposed and designed a new control method for an electromyographically (EMG) controlled prosthetic hand. The objective is to increase the control efficiency of the human–machine interface and afford greater control of the prosthetic hand. The process works as follows: EMG biomedical signals acquired from Myoware sensors positioned on the relevant muscles are sent to the robot that consist of hand, Arduino and MATLAB program, which computes and controls the hand position in free space along with hand grasping operations. The Myoware device acquires muscle signals and sends them to the Arduino. The Arduino analyzes the received signals, based on which it controls the motor movement. In this design, the muscle signals are read and saved in a MATLAB system file. After program processing on the industrial hand which is applied by MATLAB simulation, the corresponding movement is transferred to the hand, enabling movements, such as, hand opening and closing according to the signal stored in the MATLAB system. In this study, hand and fingerprints were designed using a three-dimensional printer by separate recording finger and thumb signals. The muscle signals were then analyzed in order to obtain peak signal points and convert them into data. These results indicate the effectiveness of the proposed method and demonstrate the superiority of the method for amputees because of the improved controllability and perceptibility afforded by the design.
DNA key based visual chaotic image encryption
Sep 9, 2019Journal Journal of Intelligent & Fuzzy Systems
Publisher 09 September 2019
DOI 10.3233/JIFS-182778
Volume 37
With the exponential growth of Internet technologies, digital information exchanged over the Internet is also significantly increased. In order to ensure the security of multimedia contents over the open natured Internet, data should be encrypted. In this paper, the quantum chaotic map is utilized for random vectors generation. Initial conditions for the chaos map are computed from a DNA (Deoxyribonucleic acid) sequence along with plaintext image through Secure Hash Algorithm-512 (SHA-512). The first two random vectors break the correlation among pixels of the original plaintext image via row and column permutation, respectively. For the diffusion characteristics, the permuted image is bitwise XORed with a random matrix generated through the third random vectors. The diffused image is divided into Least Significant Bit (LSB) and Most Significant Bits (MSBs) and Discrete Wavelet Transform (DWT) is applied to the carrier image. The HL and HH blocks of the carrier image are replaced with LSBs and MSBs of the diffused image for the generation of a visually encrypted image. The detailed theoretical analysis and experimental simulation of the designed scheme show that the proposed encryption algorithm is highly secured. Efficiency and robustness of the proposed visually image encryption scheme is also verified via a number of attack analyses, i.e., sensitivity attack analysis (> 99%), differential attack analysis (NPCR > 99, UACI > 33), brute force attack (almost 7.9892), statistical attack (correlation coefficient values are almost 0 or less than zero), noise tolerance, and cropping attack. Further security analyses such as encryption quality (ID ≅ 1564, DH = 3.000), homogeneity (0.3798), contrast (10.4820) and energy (0.0144) of the scheme are also evaluated.