In this paper, an adaptive polynomial compression technique is introduced of hard and soft thresholding of transformed residual image that efficiently exploited both the spatial and frequency domains, where the technique starts by applying the polynomial coding in the spatial domain and then followed by the frequency domain of discrete wavelet transform (DWT) that utilized to decompose the residual image of hard and soft thresholding base. The results showed the improvement of adaptive techniques compared to the traditional polynomial coding technique.

The Sliding Mode Control (SMC) has been among powerful control techniques increasingly. Much attention is paid to both theoretical and practical aspects of disciplines due to their distinctive characteristics such as insensitivity to bounded matched uncertainties, reduction of the order of sliding equations of motion, decoupling mechanical systems design. In the current study, two-link robot performance in the Classical SMC is enhanced via Adaptive Sliding Mode Controller (ASMC) despite uncertainty, external disturbance, and coulomb friction. The key idea is abstracted as follows: switching gains are depressed to the low allowable values, resulting in decreased chattering motion and control's efforts of the two-link robo

Abstract-Servo motors are important parts of industry automation due to their several advantages such as cost and energy efficiency, simple design, and flexibility. However, the position control of the servo motor is a difficult task because of different factors of external disturbances, nonlinearities, and uncertainties. To tackle these challenges, an adaptive integral sliding mode control (AISMC) is proposed, in which a novel bidirectional adaptive law is constructed to reduce the control chattering. The proposed control has three steps to be designed. Firstly, a full-order integral sliding manifold is designed to improve the servo motor position tracking performance, in which the reaching phase is eliminated to achieve the invariance of

A new de-blurring technique was proposed in order to reduced or remove the blur in the images. The proposed filter was designed from the Lagrange interpolation calculation with adjusted by fuzzy rules and supported by wavelet decomposing technique. The proposed Wavelet Lagrange Fuzzy filter gives good results for fully and partially blurring region in images.
 

More than 95% of the industrial controllers in use today are PID or modified PID controllers. However, the PID is manually tuning to be responsive so that the Process Variable is rapidly and steady moved to track the set point with minimize overshoot and stable output. The paper presents generic teal-time PID controller architecture. The developed architecture is based on the adaption of each of the three controller parameters (PID) to be self- learning using individual least mean square algorithm (LMS). The adaptive PID is verified and compared with the classical PID. The rapid realization of the adaptive PID architecture allows the readily fabrication into a hardware version either ASIC or reconfigurable.