The dynamical behaviors of the proposed autonomous jerk oscillator with sine nonlinearity (AJOSN) and its application to secure s-EMG (Surface ElectroMyoGraphic) data are discussed in this paper. The AJOSN has two or no-equilibrium points and the stability of the two equilibrium points shows that one of the equilibrium points is stable while the other is unstable. The AJOSN reveals fast-spiking and periodic bursting, relaxing and periodic oscillations, hidden chaotic attractors and coexisting attractors. The numerical analysis results are validated by the Field Programmable Gate Array (FPGA) implementation. Finally, the AJOSN's chaotic behavior coupled with the logistic map is exploited to encrypt the s-EMG signals. To improve security, the proposed encryption and decryption algorithm encrypts the s-EMG signal twice at the same time. After being converted to 2D in the form of a grayscale image, the s-EMG signal is encrypted for the first time using the chaotic signal generated by the AJOSN. The resulting encrypted signal is called to as "encrypted signal jerk." The chaotic logistics map is used to encrypt the final signal. The resulting encrypted signal is therefore the end product of the proposed encryption technique. The encryption and decryption results obtained are quite encouraging and offer a great prospect.

In this article, we make a comparative study for a new approach compression between discrete cosine transform (DCT) and discrete wavelet transform (DWT). We seek the transform proper to vector quantization to compress the EMG signals. To do this, we initially associated vector quantization and DCT, then vector quantization and DWT. The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding. The method is demonstrated and evaluated on actual EMG data. Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio. The results show that method based on the DWT is more efficient than the method based on the DCT.

The field programmable gate array (FPGA) implementation of the nonlinear resistor-capacitor-inductor shunted Josephson junction (NRCISJJ) model and its application to sEMG (Surface ElectroMyoGraphic) signal encryption through image encrypted technique are reported in this study. Thanks to the numerical simulations and FPGA implementation of the NRCISJJ model, different shapes of chaotic attractors are revealed by varying the parameters. The chaotic behaviour found in the NRCISJJ model is used to encrypt the sEMG signal through image encryption technique. The results obtained are interesting and open up many perspectives.

A new Modified Discrete Wavelets Packets Transform (MDWPT) based method for the compression of Surface EMG signal (s-EMG) data is presented. A Modified Discrete Wavelets Packets Transform (MDWPT) is applied to the digitized s-EMG signal. A Discrete Cosine Transforms (DCT) is applied to the MDWPT coefficients (only on detail coefficients). The MDWPT+ DCT coefficients are quantized with a Uniform Scalar Dead-Zone Quantizer (USDZQ). An arithmetic coder is employed for the entropy coding of symbol streams. The proposed approach was tested on more than 35 actuals S-EMG signals divided into three categories. The proposed approach was evaluated by the following parameters: Compression Factor (CF), Signal to Noise Ratio (SNR), Percent Root mean square Difference (PRD), Mean Frequency Distortion (MFD) and the Mean Square Error (MSE). Simulation results show that the proposed coding algorithm outperforms some recently developed s-EMG compression algorithms.

In this paper, we present the study, modelling and simulation of the duty cycle modulation (DCM) based on SVPWM control technique using Matlab/Simulink software. It is one of the most advanced control techniques of space vector modulation (SVM), which can be used for controlling static converters or for controlling electrical machines to achieve better dynamic performance. DCSVM is a control technique that generates control signals for the two-level voltage converter as well as for the intermediate times. The main advantage of this control technique is the reduction of the number of calculations, especially for the trigonometric functions and the generation of the reference voltage. In order to reduce the computational effort, we have designed a DCSVM controller that is able to faithfully reproduce the same vectors and output quantities as a classical SVM. In order to test the functionality and validity of the DCSVM control, we have developed different simulations that result in a total harmonic distortion (THD) of the voltage and current of 41.19% and 15.19% respectively with fundamental values of 61.51 V for the voltage and 2.80 A for the current; in contrast to the SVM which gives 47.27 V for the voltage and 2.01 A for the current with THDs of 77.16% for the voltage and 16.00% for the current. This results in an improvement in the distortion rate of around 25.5%. The results obtained are very satisfactory. The DCSVM is a real competitor to the SVM and its various variants.