FEATURES ANALYSIS AND SYSTEM IDENTIFICATION OF MECHANOMYOGRAPHY AND ELECTROMYOGRAPHY UNDER TRANSCRANIAL MAGNETIC STIMULATION

Transcranial magnetic stimulation (TMS) is an electrophysiological technique that uses alternating magnetic fields to deliver electric current and stimulate the cerebral cortex. When TMS is used for the evaluation of brain diseases, it is necessary to detect the contraction of the corresponding muscles in the cerebral cortex stimulated by TMS, and the muscle activity referred to as motor evoked potential (MEP). This study simultaneously recorded the mechanomyography (MMG) and electromyography (EMG) from the right abductor pollicis brevis muscle during TMS with different intensities in order to observe whether the MEP parameters from MMG signals showed similar trait of EMG recordings. Moreover, the subspace method (N4SID) and transfer function were used to identify the TMS–MMG system. In this system, the input was a pulse signal of TMS, and the output was the MMG signal detected from the target muscle. The TMS–MMG system was identified as a fourth-order model. This study also analyzed the internal features of the system and demonstrated that the poles of healthy subjects were distributed in a range, and the gain increased with the increase of the TMS intensity. It was found that MMG signals can be used as diagnostic indicators of TMS, and the TMS–MMG model can be used to further explore the details of how TMS generates responses measured with MMG.

Lateral Motion Control Invariance Helicopter on the Roll Angle. Analytical Synthesis

For the linearized fourth-order model of the isolated lateral motion of a single-rotor helicopter as a MIMO system containing two inputs, the control is analytically synthesized, which ensures the invariance of the roll angle motion in the presence of disturbances in the control channels, as well as the required placement of the poles of the closed-loop system, given any specific values from the area of their stability. The approach to the synthesis of invariant control consists in finding a matrix of feedback coefficients of a linear system that satisfies the invariance conditions, which are a system of power matrix equations of a certain design. The synthesis is based on the application of theor ems based on the use of the regularization condition of the matrix equation and the invariance conditions under disturbances in the control channels, as well as theorems that make it possible to place the poles of the MIMO system using the original decomposition of the control object. Regularization of a matrix equation is understood as a solution to the problem of providing a given set of singular values for an inverted symmetric square matrix. The invariance of the MIMO system is considered with respect to unmeasured disturbances inthe control channels. The use of such an approach to the synthesis of invariant control made it possible to obtain an analytical solution that is versatile and can be applied in various flight modes of single-rotor helicopters with different dynamic properties. The results of the numerical synthesis of the lateral motion of a singlerotor helicopter using the obtained laws of invariant control, which confirm the reliability of the analytical expressions, areshown.

Sliding Mode Control of Chaos in a Single Machine Connected to an Infinite Bus Power System

This paper deals with the control of chaos in a power system. A fourth-order model is adopted for the power system. Three controllers are proposed to suppress the chaos and avoid voltage collapse. The controllers are a feedback linearization controller, a conventional sliding mode controller, and a second-order super-twisting sliding mode controller. It is shown that the proposed controllers guarantee the convergence of the states of the system to their desired values. Simulations studies are presented to show the effectiveness of the proposed control schemes.

Image denoising using anisotropic second and fourth order diffusions based on gradient vector convolution

In this paper, novel second order and fourth order diffusion models are proposed for image denoising. Both models are based on the gradient vector convolution (GVC) model. The second model is coined by incorporating the GVC model into the anisotropic diffusion model and the fourth order one is by introducing the GVC to the You-Kaveh fourth order model. Since the GVC model can be implemented in real time using the FFT and possesses high robustness to noise, both proposed models have many advantages over traditional ones, such as low computational cost, high numerical stability and remarkable denoising effect. Moreover, the proposed fourth order model is an anisotropic filter, so it can obviously improve the ability of edge and texture preserving except for further improvement of denoising. Some experiments are presented to demonstrate the effectiveness of the proposed models.