Ehsan Asadi

Subash Gautam, Alejandro Vargas-Uscategui, Peter King et al.

Additive manufacturing (AM) is an emerging digital manufacturing technology to produce complex and freeform objects through a layer-wise deposition. High deposition rate robotic AM (HDRRAM) processes, such as cold spray additive manufacturing (CSAM), offer significantly increased build speeds by delivering large volumes of material per unit time. However, maintaining shape accuracy remains a critical challenge, particularly due to process instabilities in current open-loop systems. Detecting these deviations as they occur is essential to prevent error propagation, ensure part quality, and minimize post-processing requirements. This study presents a real-time monitoring system to acquire and reconstruct the growing part and directly compares it with a near-net reference model to detect the shape deviation during the manufacturing process. The early identification of shape inconsistencies, followed by segmenting and tracking each deviation region, paves the way for timely intervention and compensation to achieve consistent part quality.

Muhammad Shoaib, Ehsan Asadi, Joono Cheong et al.

Significant attention has been paid to robotic rehabilitation using various types of actuator and power transmission. Amongst those, cable-driven rehabilitation robots (CDRRs) are relatively newer and their control strategies have been evolving in recent years. CDRRs offer several promising features, such as low inertia, lightweight, high payload-to-weight ratio, large work-space and configurability. In this paper, we categorize and review the cable-driven rehabilitation robots in three main groups concerning their applications for upper limb, lower limb, and waist rehabilitation. For each group, target movements are identified, and promising designs of CDRRs are analyzed in terms of types of actuators, controllers and their interactions with humans. Particular attention has been given to robots with verified clinical performance in actual rehabilitation settings. A large part of this paper is dedicated to comparing the control strategies and techniques of CDRRs under five main categories of: Impedance-based, PID-based, Admittance-based, Assist-as-needed (AAN) and Adaptive controllers. We have carefully contrasted the advantages and disadvantages of those methods with the aim of assisting the design of future CDRRs

Sahar Sadrizadeh, Nematollah Zarmehi, Ehsan Asadi et al.

In this paper, we propose a new method to reconstruct a signal corrupted by noise where both signal and noise are sparse but in different domains. The problem investigated in this paper arises in different applications such as impulsive noise removal from images, audios and videos, decomposition of low-rank and sparse components of matrices, and separation of texts from images. First, we provide a cost function for our problem and then present an iterative method to find its local minimum. The analysis of the algorithm is also provided. As an application of this problem, we apply our algorithm for impulsive noise Salt-and-Pepper noise (SPN) and Random-Valued Impulsive Noise (RVIN)) removal from images and compare our results with other notable algorithms in the literature. Furthermore, we apply our algorithm for removing clicks from audio signals. Simulation results show that our algorithms is simple and fast, and it outperforms other state-of-the-art methods in terms of reconstruction quality and/or complexity.