Create low power, higher performance circuits with shorter design times using this practical guide to asynchronous design. This practical alternative to conventional synchronous design enables performance close to full-custom designs with design times that approach commercially available ASIC standard cell flows. It includes design trade-offs, specific design examples, and end-of-chapter exercises. Emphasis throughout is placed on practical techniques and real-world applications, making this ideal for circuit design students interested in alternative design styles and system-on-chip circuits, as well as circuit designers in industry who need new solutions to old problems.

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The dynamic deployment of sensors in wireless networks significantly affects the performance of the network. However, the efficient application of dynamic deployments which determines the positions of the sensors within the network increases the coverage area of the network. As a result of this, dynamic deployment increases the efficiency of the wireless sensor networks (WSNs). In this paper, dynamic deployment was applied to WSNs which consist of mobile sensors by aiming at increasing the coverage area of the network with electromagnetism-like (EM) algorithm which is a population-based optimization algorithm. A new approach has been improved in calculating the coverage rate of the sensors by using binary detection model so as to carry out the dynamic deployments of sensors and it has been thought to reach realistic results efficiently. Simulation results have shown that the EM algorithm can be preferred in the dynamic deployment of mobile sensors within the wireless networks.

This paper introduces two new high-speed quasi delay insensitive (QDI) asynchronous pipeline templates. These new high throughput templates support complex non-linear pipeline structures and are well suited for fine-grain pipelining. Timing analysis and HSPICE simulations show that these templates are 20% and 40% faster than known QDI counterparts.

Abstract

Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans and artificial intelligence techniques. Classification of patient CT scans results in a serious loss of radiology professionals' valuable time. Considering the rapid increase in COVID-19 infections, in order to automate the analysis of CT scans and minimize this loss of time, in this paper a new method is proposed using BO (BO)-based MobilNetv2, ResNet-50 models, SVM and kNN machine learning algorithms. In this method, an accuracy of 99.37% was achieved with an average precision of 99.38%, 99.36% recall and 99.37% F-score on datasets containing COVID and non-COVID classes. When we examine the performance results of the proposed method, it is predicted that it can be used as a decision support mechanism with high classification success for the diagnosis of COVID-19 with CT scans.

In the recent times, Quality of Service (QoS) terminology has been frequently used for Wireless Sensor Networks (WSNs) because of the fact that the targets which are covered by a different number of sensor nodes have different detection quality. QoS of the WSNs is determined depending upon the number of sensors that cover each target, sampling fraction etc. factors. Therefore, the optimization of the target Qcoverage problem is an important issue as an effective way that increases the lifetime of the network in continuous monitoring of the remote physical targets with different QoS restrictions. In this study, targets will be ensured to be covered by different detection quality by optimizing the Q-coverage problem which significantly plays a role in determining the coverage quality requirement of each target point in the related area. For this purpose, a new approach has been proposed based on Electromagnetism-Like (EM) algorithm, which is meta-heuristic. As a result of the comparison of the proposed algorithm with Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms, it was determined by the simulations made that the approach proposed in providing the Q-coverage requirements which were defined for all targets was an effective solution in increasing the lifetime of the network by decreasing the energy consumptions of the sensor nodes.

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