Search

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.compeleceng.2004.11.005 Byline: Ali Shatnawi (a), Jehad Ghanim (a), M.O. Ahmad (b) Abstract: A technique for scheduling and processor allocation leading to the synthesis of integrated heterogeneous pipelined processing elements, implementing digital signal processing applications, is proposed. The proposed technique achieves efficient hardware implementations at the logic-level by minimizing the number of processing units used, without compromising the rate and delay optimality criteria. The proposed algorithm is found to outperform algorithms resulting in homogeneous implementations, as it gives schedules with lower iteration periods, requires less hardware resources, and has lower time complexity at design time. In comparison with the already existing heterogeneous algorithms, the proposed algorithm produces schedules of lower time complexity and lower iteration period for some applications. The optimal performance of the proposed algorithm has been verified on several benchmarks. Author Affiliation: (a) Department of Computer Engineering, Jordan University of Science and Technology, Box 3030, Irbid 22110, Jordan (b) Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada H3G 1M8

Deep Learning (DL) is one of the hottest fields. To foster the growth of DL, several open source frameworks appeared providing implementations of the most common DL algorithms. These frameworks vary in the algorithms they support and in the quality of their implementations. The purpose of this work is to provide a qualitative and quantitative comparison among three such frameworks: TensorFlow, Theano and CNTK. To ensure that our study is as comprehensive as possible, we consider multiple benchmark datasets from different fields (image processing, NLP, etc.) and measure the performance of the frameworks’ implementations of different DL algorithms. For most of our experiments, we find out that CNTK’s implementations are superior to the other ones under consideration. [ABSTRACT FROM AUTHOR]

The cache memory has a direct effect on the performance of a computer system. Instructions and data are fetched from a fast cache instead of a slow memory to save hundreds of cycles. Reducing the cache miss ratio will definitely improve the execution time of an application. In this work, we propose cache memory designs that reduce the number of conflict misses significantly. The proposed designs reduce the conflict misses in the last level multi-way set associative cache. Each set is divided into a group of subsets: the first is referred to as the exclusive subset, and the rest are the shared subsets. The exclusive is configured as a traditional cache where each block is mapped to the set whose index matches the block index. In addition to their standard cache indexing role, the shared subsets are configured to host blocks with different indices. A memory block can be mapped to one subset from the exclusive type or one of multiple subsets from the shared type. Since the proposed technique is based on combining multiple sets of the shared part to form a larger set, that is shared between memory blocks with different indices, we have chosen the name “set folding.” The decision as to where to map a memory block depends on the number of misses encountered at each of the potential target sets. To evaluate the proposed design based on the overall hit rate, twenty-three benchmarks from SPEC CPU 2006 were simulated using the SuperESCalar simulator. The proposed designs require a few extra storage bits which adds a small overhead on the hardware complexity in comparison with the conventional cache. However, the proposed designs achieve lower miss rates for most of the benchmarks. [ABSTRACT FROM AUTHOR]

Vehicular traffic is continuously increasing around the world, especially in large urban areas. The resulting congestion has become a major concern to transportation specialists and decision makers. The existing methods for traffic management, surveillance and control are not adequately efficient in terms of performance, cost, maintenance, and support. In this paper, the design of a system that utilizes and efficiently manages traffic light controllers is presented. In particular, we present an adaptive traffic control system based on a new traffic infrastructure using Wireless Sensor Network (WSN) and using new techniques for controlling the traffic flow sequences. These techniques are dynamically adaptive to traffic conditions on both single and multiple intersections. A WSN is used as a tool to instrument and control traffic signals roadways, while an intelligent traffic controller is developed to control the operation of the traffic infrastructure supported by the WSN. The controller embodies traffic system communication algorithm (TSCA) and the traffic signals time manipulation algorithm (TSTMA). Both algorithms are able to provide the system with adaptive and efficient traffic estimation represented by the dynamic change in the traffic signals' flow sequence and traffic variation. Simulation results show the efficiency of the proposed scheme in solving traffic congestion in terms of the average waiting time and average queue length on the isolated (single) intersection and efficient global traffic flow control on multiple intersections. A test bed was also developed and deployed for real measurements. The paper concludes with some future highlights and useful remarks. [ABSTRACT FROM AUTHOR]

This paper introduces a novel technique to obtain a schedule for a cyclic data-flow graph (DFG) onto a multiprocessor system. The optimality criteria considered in this scheduling technique are the maximum throughput, minimum input–output (I/O) delay, and minimum hardware resources. In this technique, an all-pair longest path algorithm is used to evaluate the relative firing times of the nodes of the given DFG. The proposed technique for finding these times is quite simple to implement and it has lower time complexity than all the previously proposed techniques. The technique is tested on various benchmark problems to demonstrate its optimal performance. All the optimality criteria are achieved on all the tested benchmarks. However, finding a minimum hardware resource schedule is an NP complete problem, and thus cannot be theoretically ensured. A formal proof of achieving both the throughput and the I/O delay optimality simultaneously is given, and an efficient technique to ensure this is also presented. This technique is quite simple and can be used to ensure delay optimality in any scheduling technique. [ABSTRACT FROM PUBLISHER]

Over the last few decades, many improved Evolutionary Algorithms (EAs) have been proposed to tackle different types of optimization problems. Genetic Algorithm (GA) among other canonical algorithms have not shown consistent performance over a range of different optimization problems with complex characteristics. In this paper, an improved class of real-coded Genetic Algorithm is introduced to solve complex optimization problems. The first algorithm, Genetic Algorithm embedded with a new Differential Evolution crossover, GA–DEx, proposes a new variant of Differential Evolution mutation which is used as a new multi-parent crossover in Genetic Algorithms. The main purpose of this algorithm is to enhance the search ability of the GA algorithm by combining a new Differential Evolution crossover with a GA algorithm to avoid premature convergence and stagnation scenarios by exploring more solutions in the problem search space. The second amalgam algorithm, GA–DEx SPS , uses an effective and efficient successful parent selection strategy to provide a successful alternative for the selection of parents during the Differential Evolution crossover process. This strategy improves the performance of first introduced algorithm by selecting more promising parents to guide the evolutionary search. The third algorithm, GA– a DEx SPS , introduces an aging mechanism and a success-history-based adaptive Genetic Algorithm. This algorithm adapts the alpha parameter used by Differential Evolution crossover in a history-based adaptive manner. This adaptation helps the search to discover more promising regions and to prevent stagnation and premature convergence scenarios. To verify the performance of our class, a challenging test suite of 30 benchmark functions from the IEEE CEC2014 real parameter single objective competition is used. The results affirm the effectiveness and robustness of the proposed algorithms compared to other state-of-the-art well-known crossovers and recent Genetic Algorithms variants. [ABSTRACT FROM AUTHOR]

Background Understanding parental knowledge and attitudes is crucial for effective public health interventions. This study aimed to explore knowledge and attitudes reagrding the COVID-19 crisis among parents who visited King Abdullah University Hospital (KAUH) in Irbid governorate, Jordan, during the second wave of the pandemic. Methods This study employed a cross-sectional design for the parents who frequently visit KAUH in the period December 2020-January 2021. A quantitative study design was used via an online survey questionnaire via WhatsApp. The data from the responses were analyzed to understand the participants' knowledge and attitudes toward COVID-19. Results A total of 600 parents sample participated in this study. The findings showed that around 57% of the parents had high awareness and knowledge of the different symptoms of COVID-19. Additionally, around 60% of the parents who visited the KAUH during the second wave of this crisis in December 2020 and aged from 40 to 50 years old had more awareness of the different symptoms of this virus. Knowledge and attitudes towards SARS-CoV-2 were assessed, showing that high-education respondents (n = 425) identified sneezing (80%) and touching surfaces (90%) as common transmission routes, while low-education respondents (n = 153) emphasized sneezing (100%). Preventive actions such as wearing masks (high education: 80%, low education: 100%) and avoiding crowds (high education: 80%, low education: 83.3%) were widely recognized. Both groups believed in the benefits of vitamin C-rich foods (high education: 50%, low education: 25%), but not in alcohol, for treatment. They also rejected the idea that the virus could be transmitted from mothers to fetuses or through breastfeeding. Both education levels agreed that elderly individuals are at higher risk of severe complications, with high-education respondents (90%) and low-education respondents (100%) citing severe lung infection as a major risk. Conclusions Generally, the sample of different age groups was more concerned with the risks and threats that could happen because of this pandemic. New studies investigating similar issues in this crisis are necessary to expand the perceptions of managing and controlling this situation., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Jordan University of Science and Technology issued approval 2022.1.12. The Institutional Review Board (IRB) at Jordan University of Science and Technology approved the study. The study was performed in accordance with the principles of the Declaration of Helsinki (1975). Informed consent was waived by the IRB committee due to the retrospective nature of the study, as the research could not be carried out practically without the waiver. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Negresh et al.)