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scheduler</title> <meta name="description" content="Search results for: scheduler"> <meta name="keywords" content="scheduler"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="scheduler"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 23</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: scheduler</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">23</span> Core Number Optimization Based Scheduler to Order/Mapp Simulink Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asma%20Rebaya">Asma Rebaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Imen%20Amari"> Imen Amari</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaouther%20Gasmi"> Kaouther Gasmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Salem%20Hasnaoui"> Salem Hasnaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over these last years, the number of cores witnessed a spectacular increase in digital signal and general use processors. Concurrently, significant researches are done to get benefit from the high degree of parallelism. Indeed, these researches are focused to provide an efficient scheduling from hardware/software systems to multicores architecture. The scheduling process consists on statically choose one core to execute one task and to specify an execution order for the application tasks. In this paper, we describe an efficient scheduler that calculates the optimal number of cores required to schedule an application, gives a heuristic scheduling solution and evaluates its cost. Our proposal results are evaluated and compared with Preesm scheduler results and we prove that ours allows better scheduling in terms of latency, computation time and number of cores. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computation%20time" title="computation time">computation time</a>, <a href="https://publications.waset.org/abstracts/search?q=hardware%2Fsoftware%20system" title=" hardware/software system"> hardware/software system</a>, <a href="https://publications.waset.org/abstracts/search?q=latency" title=" latency"> latency</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-cores%20platform" title=" multi-cores platform"> multi-cores platform</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a> </p> <a href="https://publications.waset.org/abstracts/67853/core-number-optimization-based-scheduler-to-ordermapp-simulink-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67853.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">283</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22</span> Incorporating Priority Round-Robin Scheduler to Sustain Indefinite Blocking Issue and Prioritized Processes in Operating System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Heng%20Chia%20Ying">Heng Chia Ying</a>, <a href="https://publications.waset.org/abstracts/search?q=Charmaine%20Tan%20Chai%20Nie"> Charmaine Tan Chai Nie</a>, <a href="https://publications.waset.org/abstracts/search?q=Burra%20Venkata%20Durga%20Kumar"> Burra Venkata Durga Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process scheduling is the method of process management that determines which process the CPU will proceed with for the next task and how long it takes. Some issues were found in process management, particularly for Priority Scheduling (PS) and Round Robin Scheduling (RR). The proposed recommendations made for IPRRS are to combine the strengths of both into a combining algorithm while they draw on others to compensate for each weakness. A significant improvement on the combining technique of scheduler, Incorporating Priority Round-Robin Scheduler (IPRRS) address an algorithm for both high and low priority task to sustain the indefinite blocking issue faced in the priority scheduling algorithm and minimize the average turnaround time (ATT) and average waiting time (AWT) in RR scheduling algorithm. This paper will delve into the simple rules introduced by IPRRS and enhancements that both PS and RR bring to the execution of processes in the operating system. Furthermore, it incorporates the best aspects of each algorithm to build the optimum algorithm for a certain case in terms of prioritized processes, ATT, and AWT. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=round%20Robin%20scheduling" title="round Robin scheduling">round Robin scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=priority%20scheduling" title=" priority scheduling"> priority scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=indefinite%20blocking" title=" indefinite blocking"> indefinite blocking</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20management" title=" process management"> process management</a>, <a href="https://publications.waset.org/abstracts/search?q=sustain" title=" sustain"> sustain</a>, <a href="https://publications.waset.org/abstracts/search?q=turnaround%20time" title=" turnaround time"> turnaround time</a> </p> <a href="https://publications.waset.org/abstracts/160706/incorporating-priority-round-robin-scheduler-to-sustain-indefinite-blocking-issue-and-prioritized-processes-in-operating-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160706.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21</span> Wait-Optimized Scheduler Algorithm for Efficient Process Scheduling in Computer Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md%20Habibur%20Rahman">Md Habibur Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaeho%20Kim"> Jaeho Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Efficient process scheduling is a crucial factor in ensuring optimal system performance and resource utilization in computer systems. While various algorithms have been proposed over the years, there are still limitations to their effectiveness. This paper introduces a new Wait-Optimized Scheduler (WOS) algorithm that aims to minimize process waiting time by dividing them into two layers and considering both process time and waiting time. The WOS algorithm is non-preemptive and prioritizes processes with the shortest WOS. In the first layer, each process runs for a predetermined duration, and any unfinished process is subsequently moved to the second layer, resulting in a decrease in response time. Whenever the first layer is free or the number of processes in the second layer is twice that of the first layer, the algorithm sorts all the processes in the second layer based on their remaining time minus waiting time and sends one process to the first layer to run. This ensures that all processes eventually run, optimizing waiting time. To evaluate the performance of the WOS algorithm, we conducted experiments comparing its performance with traditional scheduling algorithms such as First-Come-First-Serve (FCFS) and Shortest-Job-First (SJF). The results showed that the WOS algorithm outperformed the traditional algorithms in reducing the waiting time of processes, particularly in scenarios with a large number of short tasks with long wait times. Our study highlights the effectiveness of the WOS algorithm in improving process scheduling efficiency in computer systems. By reducing process waiting time, the WOS algorithm can improve system performance and resource utilization. The findings of this study provide valuable insights for researchers and practitioners in developing and implementing efficient process scheduling algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20scheduling" title="process scheduling">process scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=wait-optimized%20scheduler" title=" wait-optimized scheduler"> wait-optimized scheduler</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20time" title=" response time"> response time</a>, <a href="https://publications.waset.org/abstracts/search?q=non-preemptive" title=" non-preemptive"> non-preemptive</a>, <a href="https://publications.waset.org/abstracts/search?q=waiting%20time" title=" waiting time"> waiting time</a>, <a href="https://publications.waset.org/abstracts/search?q=traditional%20scheduling%20algorithms" title=" traditional scheduling algorithms"> traditional scheduling algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=first-come-first-serve" title=" first-come-first-serve"> first-come-first-serve</a>, <a href="https://publications.waset.org/abstracts/search?q=shortest-job-first" title=" shortest-job-first"> shortest-job-first</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20performance" title=" system performance"> system performance</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20utilization" title=" resource utilization"> resource utilization</a> </p> <a href="https://publications.waset.org/abstracts/165734/wait-optimized-scheduler-algorithm-for-efficient-process-scheduling-in-computer-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165734.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20</span> CPU Architecture Based on Static Hardware Scheduler Engine and Multiple Pipeline Registers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ionel%20Zagan">Ionel Zagan</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasile%20Gheorghita%20Gaitan"> Vasile Gheorghita Gaitan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of CPUs and of real-time systems based on them made it possible to use time at increasingly low resolutions. Together with the scheduling methods and algorithms, time organizing has been improved so as to respond positively to the need for optimization and to the way in which the CPU is used. This presentation contains both a detailed theoretical description and the results obtained from research on improving the performances of the nMPRA (Multi Pipeline Register Architecture) processor by implementing specific functions in hardware. The proposed CPU architecture has been developed, simulated and validated by using the FPGA Virtex-7 circuit, via a SoC project. Although the nMPRA processor hardware structure with five pipeline stages is very complex, the present paper presents and analyzes the tests dedicated to the implementation of the CPU and of the memory on-chip for instructions and data. In order to practically implement and test the entire SoC project, various tests have been performed. These tests have been performed in order to verify the drivers for peripherals and the boot module named Bootloader. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hardware%20scheduler" title="hardware scheduler">hardware scheduler</a>, <a href="https://publications.waset.org/abstracts/search?q=nMPRA%20processor" title=" nMPRA processor"> nMPRA processor</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20systems" title=" real-time systems"> real-time systems</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling%20methods" title=" scheduling methods"> scheduling methods</a> </p> <a href="https://publications.waset.org/abstracts/58047/cpu-architecture-based-on-static-hardware-scheduler-engine-and-multiple-pipeline-registers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58047.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">267</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">19</span> Improving the Performances of the nMPRA Architecture by Implementing Specific Functions in Hardware</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ionel%20Zagan">Ionel Zagan</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasile%20Gheorghita%20Gaitan"> Vasile Gheorghita Gaitan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Minimizing the response time to asynchronous events in a real-time system is an important factor in increasing the speed of response and an interesting concept in designing equipment fast enough for the most demanding applications. The present article will present the results regarding the validation of the nMPRA (Multi Pipeline Register Architecture) architecture using the FPGA Virtex-7 circuit. The nMPRA concept is a hardware processor with the scheduler implemented at the processor level; this is done without affecting a possible bus communication, as is the case with the other CPU solutions. The implementation of static or dynamic scheduling operations in hardware and the improvement of handling interrupts and events by the real-time executive described in the present article represent a key solution for eliminating the overhead of the operating system functions. The nMPRA processor is capable of executing a preemptive scheduling, using various algorithms without a software scheduler. Therefore, we have also presented various scheduling methods and algorithms used in scheduling the real-time tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nMPRA%20architecture" title="nMPRA architecture">nMPRA architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=pipeline%20processor" title=" pipeline processor"> pipeline processor</a>, <a href="https://publications.waset.org/abstracts/search?q=preemptive%20scheduling" title=" preemptive scheduling"> preemptive scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20system" title=" real-time system"> real-time system</a> </p> <a href="https://publications.waset.org/abstracts/57899/improving-the-performances-of-the-nmpra-architecture-by-implementing-specific-functions-in-hardware" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57899.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">368</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18</span> Multi-Agent System for Irrigation Using Fuzzy Logic Algorithm and Open Platform Communication Data Access</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Wanyama">T. Wanyama</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Far"> B. Far</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic irrigation systems usually conveniently protect landscape investment. While conventional irrigation systems are known to be inefficient, automated ones have the potential to optimize water usage. In fact, there is a new generation of irrigation systems that are smart in the sense that they monitor the weather, soil conditions, evaporation and plant water use, and automatically adjust the irrigation schedule. In this paper, we present an agent based smart irrigation system. The agents are built using a mix of commercial off the shelf software, including MATLAB, Microsoft Excel and KEPServer Ex5 OPC server, and custom written code. The Irrigation Scheduler Agent uses fuzzy logic to integrate the information that affect the irrigation schedule. In addition, the Multi-Agent system uses Open Platform Connectivity (OPC) technology to share data. OPC technology enables the Irrigation Scheduler Agent to communicate over the Internet, making the system scalable to a municipal or regional agent based water monitoring, management, and optimization system. Finally, this paper presents simulation and pilot installation test result that show the operational effectiveness of our system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=community%20water%20usage" title="community water usage">community water usage</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=irrigation" title=" irrigation"> irrigation</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20system" title=" multi-agent system"> multi-agent system</a> </p> <a href="https://publications.waset.org/abstracts/60277/multi-agent-system-for-irrigation-using-fuzzy-logic-algorithm-and-open-platform-communication-data-access" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60277.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">298</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17</span> Deployment of Beyond 4G Wireless Communication Networks with Carrier Aggregation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bahram%20Khan">Bahram Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Anderson%20Rocha%20Ramos"> Anderson Rocha Ramos</a>, <a href="https://publications.waset.org/abstracts/search?q=Rui%20R.%20Paulo"> Rui R. Paulo</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20J.%20Velez"> Fernando J. Velez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the growing demand for a new blend of applications, the users dependency on the internet is increasing day by day. Mobile internet users are giving more attention to their own experiences, especially in terms of communication reliability, high data rates and service stability on move. This increase in the demand is causing saturation of existing radio frequency bands. To address these challenges, researchers are investigating the best approaches, Carrier Aggregation (CA) is one of the newest innovations, which seems to fulfill the demands of the future spectrum, also CA is one the most important feature for Long Term Evolution - Advanced (LTE-Advanced). For this purpose to get the upcoming International Mobile Telecommunication Advanced (IMT-Advanced) mobile requirements (1 Gb/s peak data rate), the CA scheme is presented by 3GPP, which would sustain a high data rate using widespread frequency bandwidth up to 100 MHz. Technical issues such as aggregation structure, its implementations, deployment scenarios, control signal techniques, and challenges for CA technique in LTE-Advanced, with consideration of backward compatibility, are highlighted in this paper. Also, performance evaluation in macro-cellular scenarios through a simulation approach is presented, which shows the benefits of applying CA, low-complexity multi-band schedulers in service quality, system capacity enhancement and concluded that enhanced multi-band scheduler is less complex than the general multi-band scheduler, which performs better for a cell radius longer than 1800 m (and a PLR threshold of 2%). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=component%20carrier" title="component carrier">component carrier</a>, <a href="https://publications.waset.org/abstracts/search?q=carrier%20aggregation" title=" carrier aggregation"> carrier aggregation</a>, <a href="https://publications.waset.org/abstracts/search?q=LTE-advanced" title=" LTE-advanced"> LTE-advanced</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a> </p> <a href="https://publications.waset.org/abstracts/125299/deployment-of-beyond-4g-wireless-communication-networks-with-carrier-aggregation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125299.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16</span> Power Energy Management For A Grid-Connected PV System Using Rule-Base Fuzzy Logic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nousheen%20Hashmi">Nousheen Hashmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shoab%20Ahmad%20Khan"> Shoab Ahmad Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Active collaboration among the green energy sources and the load demand leads to serious issues related to power quality and stability. The growing number of green energy resources and Distributed-Generators need newer strategies to be incorporated for their operations to keep the power energy stability among green energy resources and micro-grid/Utility Grid. This paper presents a novel technique for energy power management in Grid-Connected Photovoltaic with energy storage system under set of constraints including weather conditions, Load Shedding Hours, Peak pricing Hours by using rule-based fuzzy smart grid controller to schedule power coming from multiple Power sources (photovoltaic, grid, battery) under the above set of constraints. The technique fuzzifies all the inputs and establishes fuzzify rule set from fuzzy outputs before defuzzification. Simulations are run for 24 hours period and rule base power scheduler is developed. The proposed fuzzy controller control strategy is able to sense the continuous fluctuations in Photovoltaic power generation, Load Demands, Grid (load Shedding patterns) and Battery State of Charge in order to make correct and quick decisions.The suggested Fuzzy Rule-based scheduler can operate well with vague inputs thus doesn’t not require any exact numerical model and can handle nonlinearity. This technique provides a framework for the extension to handle multiple special cases for optimized working of the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=photovoltaic" title="photovoltaic">photovoltaic</a>, <a href="https://publications.waset.org/abstracts/search?q=power" title=" power"> power</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20generators" title=" distributed generators"> distributed generators</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20of%20charge" title=" state of charge"> state of charge</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20shedding" title=" load shedding"> load shedding</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20functions" title=" membership functions"> membership functions</a> </p> <a href="https://publications.waset.org/abstracts/36815/power-energy-management-for-a-grid-connected-pv-system-using-rule-base-fuzzy-logic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36815.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">480</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15</span> An Efficient Hardware/Software Workflow for Multi-Cores Simulink Applications </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asma%20Rebaya">Asma Rebaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaouther%20Gasmi"> Kaouther Gasmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Imen%20Amari"> Imen Amari</a>, <a href="https://publications.waset.org/abstracts/search?q=Salem%20Hasnaoui"> Salem Hasnaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over these last years, applications such as telecommunications, signal processing, digital communication with advanced features (Multi-antenna, equalization..) witness a rapid evaluation accompanied with an increase of user exigencies in terms of latency, the power of computation… To satisfy these requirements, the use of hardware/software systems is a common solution; where hardware is composed of multi-cores and software is represented by models of computation, synchronous data flow (SDF) graph for instance. Otherwise, the most of the embedded system designers utilize Simulink for modeling. The issue is how to simplify the c code generation, for a multi-cores platform, of an application modeled by Simulink. To overcome this problem, we propose a workflow allowing an automatic transformation from the Simulink model to the SDF graph and providing an efficient schedule permitting to optimize the number of cores and to minimize latency. This workflow goes from a Simulink application and a hardware architecture described by IP.XACT language. Based on the synchronous and hierarchical behavior of both models, the Simulink block diagram is automatically transformed into an SDF graph. Once this process is successfully achieved, the scheduler calculates the optimal cores’ number needful by minimizing the maximum density of the whole application. Then, a core is chosen to execute a specific graph task in a specific order and, subsequently, a compatible C code is generated. In order to perform this proposal, we extend Preesm, a rapid prototyping tool, to take the Simulink model as entry input and to support the optimal schedule. Afterward, we compared our results to this tool results, using a simple illustrative application. The comparison shows that our results strictly dominate the Preesm results in terms of number of cores and latency. In fact, if Preesm needs m processors and latency L, our workflow need processors and latency L'< L. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hardware%2Fsoftware%20system" title="hardware/software system">hardware/software system</a>, <a href="https://publications.waset.org/abstracts/search?q=latency" title=" latency"> latency</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-cores%20platform" title=" multi-cores platform"> multi-cores platform</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduler" title=" scheduler"> scheduler</a>, <a href="https://publications.waset.org/abstracts/search?q=SDF%20graph" title=" SDF graph"> SDF graph</a>, <a href="https://publications.waset.org/abstracts/search?q=Simulink%20model" title=" Simulink model"> Simulink model</a>, <a href="https://publications.waset.org/abstracts/search?q=workflow" title=" workflow"> workflow</a> </p> <a href="https://publications.waset.org/abstracts/66459/an-efficient-hardwaresoftware-workflow-for-multi-cores-simulink-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66459.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">269</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">14</span> Analytical Study of CPU Scheduling Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keshav%20Rathi">Keshav Rathi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aakriti%20Sharma"> Aakriti Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinayak%20R.%20Dinesh"> Vinayak R. Dinesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Irfan%20Ramzan%20Parray"> Irfan Ramzan Parray</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Scheduling is a basic operating system function since practically all computer resources are scheduled before use. The CPU is one of the most important computer resources. Central Processing Unit (CPU) scheduling is vital because it allows the CPU to transition between processes. A processor is the most significant resource in a computer; the operating system can increase the computer's productivity. The objective of the operating system is to allow as many processes as possible to operate at the same time in order to maximize CPU utilization. The highly efficient CPU scheduler is based on the invention of high-quality scheduling algorithms that meet the scheduling objectives. In this paper, we reviewed various fundamental CPU scheduling algorithms for a single CPU and showed which algorithm is best for the particular situation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20science" title="computer science">computer science</a>, <a href="https://publications.waset.org/abstracts/search?q=Operating%20system" title=" Operating system"> Operating system</a>, <a href="https://publications.waset.org/abstracts/search?q=CPU%20scheduling" title=" CPU scheduling"> CPU scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=cpu%20algorithms" title=" cpu algorithms"> cpu algorithms</a> </p> <a href="https://publications.waset.org/abstracts/194885/analytical-study-of-cpu-scheduling-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194885.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">8</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13</span> Designing AI-Enabled Smart Maintenance Scheduler: Enhancing Object Reliability through Automated Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arun%20Prasad%20Jaganathan">Arun Prasad Jaganathan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today's rapidly evolving technological landscape, the need for efficient and proactive maintenance management solutions has become increasingly evident across various industries. Traditional approaches often suffer from drawbacks such as reactive strategies, leading to potential downtime, increased costs, and decreased operational efficiency. In response to these challenges, this paper proposes an AI-enabled approach to object-based maintenance management aimed at enhancing reliability and efficiency. The paper contributes to the growing body of research on AI-driven maintenance management systems, highlighting the transformative impact of intelligent technologies on enhancing object reliability and operational efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AI" title="AI">AI</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20maintenance" title=" predictive maintenance"> predictive maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=object-based%20maintenance" title=" object-based maintenance"> object-based maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20team%20scheduling" title=" expert team scheduling"> expert team scheduling</a> </p> <a href="https://publications.waset.org/abstracts/185812/designing-ai-enabled-smart-maintenance-scheduler-enhancing-object-reliability-through-automated-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185812.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">58</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12</span> Centralized Peak Consumption Smoothing Revisited for Habitat Energy Scheduling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Benbouzid">M. Benbouzid</a>, <a href="https://publications.waset.org/abstracts/search?q=Q.%20Bresson"> Q. Bresson</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Duclos"> A. Duclos</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Longo"> K. Longo</a>, <a href="https://publications.waset.org/abstracts/search?q=Q.%20Morel"> Q. Morel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, electricity suppliers must predict the consumption of their customers in order to deduce the power they need to produce. It is, then, important in a first step to optimize household consumption to obtain more constant curves by limiting peaks in energy consumption. Here centralized real time scheduling is proposed to manage the equipment's starting in parallel. The aim is not to exceed a certain limit while optimizing the power consumption across a habitat. The Raspberry Pi is used as a box; this scheduler interacts with the various sensors in 6LoWPAN. At the scale of a single dwelling, household consumption decreases, particularly at times corresponding to the peaks. However, it would be wiser to consider the use of a residential complex so that the result would be more significant. So, the ceiling would no longer be fixed. The scheduling would be done on two scales, firstly, per dwelling, and secondly, at the level of a residential complex. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=smart%20grid" title="smart grid">smart grid</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20box" title=" energy box"> energy box</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=Gang%20Model" title=" Gang Model"> Gang Model</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20consumption" title=" energy consumption"> energy consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20management%20system" title=" energy management system"> energy management system</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20network" title=" wireless sensor network"> wireless sensor network</a> </p> <a href="https://publications.waset.org/abstracts/1855/centralized-peak-consumption-smoothing-revisited-for-habitat-energy-scheduling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1855.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">313</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11</span> Automatic Queuing Model Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Suleiman">Fahad Suleiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Queuing, in medical system is the process of moving patients in a specific sequence to a specific service according to the patients’ nature of illness. The term scheduling stands for the process of computing a schedule. This may be done by a queuing based scheduler. This paper focuses on the medical consultancy system, the different queuing algorithms that are used in healthcare system to serve the patients, and the average waiting time. The aim of this paper is to build automatic queuing system for organizing the medical queuing system that can analyses the queue status and take decision which patient to serve. The new queuing architecture model can switch between different scheduling algorithms according to the testing results and the factor of the average waiting time. The main innovation of this work concerns the modeling of the average waiting time is taken into processing, in addition with the process of switching to the scheduling algorithm that gives the best average waiting time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=queuing%20systems" title="queuing systems">queuing systems</a>, <a href="https://publications.waset.org/abstracts/search?q=queuing%20system%20models" title=" queuing system models"> queuing system models</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling%20algorithms" title=" scheduling algorithms"> scheduling algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=patients" title=" patients"> patients</a> </p> <a href="https://publications.waset.org/abstracts/38535/automatic-queuing-model-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38535.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10</span> Utilizing AI Green Grader Scope to Promote Environmental Responsibility Among University Students</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Taha%20Kandil">Tarek Taha Kandil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In higher education, the use of automated grading systems is on the rise, automating the assessment of students' work and providing practical feedback. Sustainable Grader Scope addresses the environmental impact of these computational tasks. This system uses an AI-powered algorithm and is designed to minimize grading process emissions. It reduces carbon emissions through energy-efficient computing and carbon-conscious scheduling. Students submit their computational workloads to the system, which evaluates submissions using containers and a distributed infrastructure. A carbon-conscious scheduler manages workloads across global campuses, optimizing emissions using real-time carbon intensity data. This ensures the university stays within government-set emission limits while tracking and reducing its carbon footprint. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sustainability" title="sustainability">sustainability</a>, <a href="https://publications.waset.org/abstracts/search?q=green%20graders" title=" green graders"> green graders</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20sustainable%20grader%20scope" title=" digital sustainable grader scope"> digital sustainable grader scope</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20responsibility%3B%20higher%20education." title=" environmental responsibility; higher education."> environmental responsibility; higher education.</a> </p> <a href="https://publications.waset.org/abstracts/194789/utilizing-ai-green-grader-scope-to-promote-environmental-responsibility-among-university-students" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194789.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9</span> Research on Load Balancing Technology for Web Service Mobile Host</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yao%20Lu">Yao Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiuguo%20Zhang"> Xiuguo Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiying%20Cao"> Zhiying Cao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, Load Balancing idea is used in the Web service mobile host. The main idea of Load Balancing is to establish a one-to-many mapping mechanism: An entrance-mapping request to plurality of processing node in order to realize the dividing and assignment processing. Because the mobile host is a resource constrained environment, there are some Web services which cannot be completed on the mobile host. When the mobile host resource is not enough to complete the request, Load Balancing scheduler will divide the request into a plurality of sub-requests and transfer them to different auxiliary mobile hosts. Auxiliary mobile host executes sub-requests, and then, the results will be returned to the mobile host. Service request integrator receives results of sub-requests from the auxiliary mobile host, and integrates the sub-requests. In the end, the complete request is returned to the client. Experimental results show that this technology adopted in this paper can complete requests and have a higher efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dinic" title="Dinic">Dinic</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20balancing" title=" load balancing"> load balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20host" title=" mobile host"> mobile host</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20service" title=" web service"> web service</a> </p> <a href="https://publications.waset.org/abstracts/63208/research-on-load-balancing-technology-for-web-service-mobile-host" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63208.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">328</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> An Intelligent Thermal-Aware Task Scheduler in Multiprocessor System on a Chip</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sina%20Saadati">Sina Saadati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiprocessors Systems-On-Chips (MPSOCs) are used widely on modern computers to execute sophisticated software and applications. These systems include different processors for distinct aims. Most of the proposed task schedulers attempt to improve energy consumption. In some schedulers, the processor's temperature is considered to increase the system's reliability and performance. In this research, we have proposed a new method for thermal-aware task scheduling which is based on an artificial neural network (ANN). This method enables us to consider a variety of factors in the scheduling process. Some factors like ambient temperature, season (which is important for some embedded systems), speed of the processor, computing type of tasks and have a complex relationship with the final temperature of the system. This Issue can be solved using a machine learning algorithm. Another point is that our solution makes the system intelligent So that It can be adaptive. We have also shown that the computational complexity of the proposed method is cheap. As a consequence, It is also suitable for battery-powered systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=task%20scheduling" title="task scheduling">task scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=MOSOC" title=" MOSOC"> MOSOC</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=architecture%20of%20computers" title=" architecture of computers"> architecture of computers</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/162971/an-intelligent-thermal-aware-task-scheduler-in-multiprocessor-system-on-a-chip" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162971.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> Survival Analysis Based Delivery Time Estimates for Display FAB</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paul%20Han">Paul Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun-Geol%20Baek"> Jun-Geol Baek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the flat panel display industry, the scheduler and dispatching system to meet production target quantities and the deadline of production are the major production management system which controls each facility production order and distribution of WIP (Work in Process). In dispatching system, delivery time is a key factor for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors and a forecasting model of delivery time. Of survival analysis techniques to select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the Accelerated Failure Time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the Mean Square Error (MSE) criteria, the AFT model decreased by 33.8% compared to the existing prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing a delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=delivery%20time" title="delivery time">delivery time</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20analysis" title=" survival analysis"> survival analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Cox%20PH%20model" title=" Cox PH model"> Cox PH model</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerated%20failure%20time%20model" title=" accelerated failure time model"> accelerated failure time model</a> </p> <a href="https://publications.waset.org/abstracts/4881/survival-analysis-based-delivery-time-estimates-for-display-fab" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4881.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">543</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Strategic Mine Planning: A SWOT Analysis Applied to KOV Open Pit Mine in the Democratic Republic of Congo</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Patrick%20May%20Mukonki">Patrick May Mukonki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> KOV pit (Kamoto Oliveira Virgule) is located 10 km from Kolwezi town, one of the mineral rich town in the Lualaba province of the Democratic Republic of Congo. The KOV pit is currently operating under the Katanga Mining Limited (KML), a Glencore-Gecamines (a State Owned Company) join venture. Recently, the mine optimization process provided a life of mine of approximately 10 years withnice pushbacks using the Datamine NPV Scheduler software. In previous KOV pit studies, we recently outlined the impact of the accuracy of the geological information on a long-term mine plan for a big copper mine such as KOV pit. The approach taken, discussed three main scenarios and outlined some weaknesses on the geological information side, and now, in this paper that we are going to develop here, we are going to highlight, as an overview, those weaknesses, strengths and opportunities, in a global SWOT analysis. The approach we are taking here is essentially descriptive in terms of steps taken to optimize KOV pit and, at every step, we categorized the challenges we faced to have a better tradeoff between what we called strengths and what we called weaknesses. The same logic is applied in terms of the opportunities and threats. The SWOT analysis conducted in this paper demonstrates that, despite a general poor ore body definition, and very rude ground water conditions, there is room for improvement for such high grade ore body. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mine%20planning" title="mine planning">mine planning</a>, <a href="https://publications.waset.org/abstracts/search?q=mine%20optimization" title=" mine optimization"> mine optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=mine%20scheduling" title=" mine scheduling"> mine scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=SWOT%20analysis" title=" SWOT analysis"> SWOT analysis</a> </p> <a href="https://publications.waset.org/abstracts/72126/strategic-mine-planning-a-swot-analysis-applied-to-kov-open-pit-mine-in-the-democratic-republic-of-congo" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72126.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">225</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> An Efficient Subcarrier Scheduling Algorithm for Downlink OFDMA-Based Wireless Broadband Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassen%20Hamouda">Hassen Hamouda</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ouwais%20Kabaou"> Mohamed Ouwais Kabaou</a>, <a href="https://publications.waset.org/abstracts/search?q=Med%20Salim%20Bouhlel"> Med Salim Bouhlel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The growth of wireless technology made opportunistic scheduling a widespread theme in recent research. Providing high system throughput without reducing fairness allocation is becoming a very challenging task. A suitable policy for resource allocation among users is of crucial importance. This study focuses on scheduling multiple streaming flows on the downlink of a WiMAX system based on orthogonal frequency division multiple access (OFDMA). In this paper, we take the first step in formulating and analyzing this problem scrupulously. As a result, we proposed a new scheduling scheme based on Round Robin (RR) Algorithm. Because of its non-opportunistic process, RR does not take in account radio conditions and consequently it affect both system throughput and multi-users diversity. Our contribution called MORRA (Modified Round Robin Opportunistic Algorithm) consists to propose a solution to this issue. MORRA not only exploits the concept of opportunistic scheduler but also takes into account other parameters in the allocation process. The first parameter is called courtesy coefficient (CC) and the second is called Buffer Occupancy (BO). Performance evaluation shows that this well-balanced scheme outperforms both RR and MaxSNR schedulers and demonstrate that choosing between system throughput and fairness is not required. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OFDMA" title="OFDMA">OFDMA</a>, <a href="https://publications.waset.org/abstracts/search?q=opportunistic%20scheduling" title=" opportunistic scheduling"> opportunistic scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=fairness%20hierarchy" title=" fairness hierarchy"> fairness hierarchy</a>, <a href="https://publications.waset.org/abstracts/search?q=courtesy%20coefficient" title=" courtesy coefficient"> courtesy coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=buffer%20occupancy" title=" buffer occupancy"> buffer occupancy</a> </p> <a href="https://publications.waset.org/abstracts/59645/an-efficient-subcarrier-scheduling-algorithm-for-downlink-ofdma-based-wireless-broadband-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59645.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">300</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> General Architecture for Automation of Machine Learning Practices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20Borasi">U. Borasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amit%20Kr.%20Jain"> Amit Kr. Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Rakesh"> Rakesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Piyush%20Jain"> Piyush Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data collection, data preparation, model training, model evaluation, and deployment are all processes in a typical machine learning workflow. Training data needs to be gathered and organised. This often entails collecting a sizable dataset and cleaning it to remove or correct any inaccurate or missing information. Preparing the data for use in the machine learning model requires pre-processing it after it has been acquired. This often entails actions like scaling or normalising the data, handling outliers, selecting appropriate features, reducing dimensionality, etc. This pre-processed data is then used to train a model on some machine learning algorithm. After the model has been trained, it needs to be assessed by determining metrics like accuracy, precision, and recall, utilising a test dataset. Every time a new model is built, both data pre-processing and model training—two crucial processes in the Machine learning (ML) workflow—must be carried out. Thus, there are various Machine Learning algorithms that can be employed for every single approach to data pre-processing, generating a large set of combinations to choose from. Example: for every method to handle missing values (dropping records, replacing with mean, etc.), for every scaling technique, and for every combination of features selected, a different algorithm can be used. As a result, in order to get the optimum outcomes, these tasks are frequently repeated in different combinations. This paper suggests a simple architecture for organizing this largely produced “combination set of pre-processing steps and algorithms” into an automated workflow which simplifies the task of carrying out all possibilities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=automation" title=" automation"> automation</a>, <a href="https://publications.waset.org/abstracts/search?q=AUTOML" title=" AUTOML"> AUTOML</a>, <a href="https://publications.waset.org/abstracts/search?q=architecture" title=" architecture"> architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=operator%20pool" title=" operator pool"> operator pool</a>, <a href="https://publications.waset.org/abstracts/search?q=configuration" title=" configuration"> configuration</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduler" title=" scheduler"> scheduler</a> </p> <a href="https://publications.waset.org/abstracts/182057/general-architecture-for-automation-of-machine-learning-practices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182057.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">58</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Using the SMT Solver to Minimize the Latency and to Optimize the Number of Cores in an NoC-DSP Architectures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imen%20Amari">Imen Amari</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaouther%20%20Gasmi"> Kaouther Gasmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Asma%20Rebaya"> Asma Rebaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Salem%20Hasnaoui"> Salem Hasnaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The problem of scheduling and mapping data flow applications on multi-core architectures is notoriously difficult. This difficulty is related to the rapid evaluation of Telecommunication and multimedia systems accompanied by a rapid increase of user requirements in terms of latency, execution time, consumption, energy, etc. Having an optimal scheduling on multi-cores DSP (Digital signal Processors) platforms is a challenging task. In this context, we present a novel technic and algorithm in order to find a valid schedule that optimizes the key performance metrics particularly the Latency. Our contribution is based on Satisfiability Modulo Theories (SMT) solving technologies which is strongly driven by the industrial applications and needs. This paper, describe a scheduling module integrated in our proposed Workflow which is advised to be a successful approach for programming the applications based on NoC-DSP platforms. This workflow transform automatically a Simulink model to a synchronous dataflow (SDF) model. The automatic transformation followed by SMT solver scheduling aim to minimize the final latency and other software/hardware metrics in terms of an optimal schedule. Also, finding the optimal numbers of cores to be used. In fact, our proposed workflow taking as entry point a Simulink file (.mdl or .slx) derived from embedded Matlab functions. We use an approach which is based on the synchronous and hierarchical behavior of both Simulink and SDF. Whence, results of running the scheduler which exist in the Workflow mentioned above using our proposed SMT solver algorithm refinements produce the best possible scheduling in terms of latency and numbers of cores. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-cores%20DSP" title="multi-cores DSP">multi-cores DSP</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=SMT%20solver" title=" SMT solver"> SMT solver</a>, <a href="https://publications.waset.org/abstracts/search?q=workflow" title=" workflow"> workflow</a> </p> <a href="https://publications.waset.org/abstracts/67632/using-the-smt-solver-to-minimize-the-latency-and-to-optimize-the-number-of-cores-in-an-noc-dsp-architectures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67632.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">286</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> ACO-TS: an ACO-based Algorithm for Optimizing Cloud Task Scheduling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Y.%20Al-dawish">Fahad Y. Al-dawish</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The current trend by a large number of organizations and individuals to use cloud computing. Many consider it a significant shift in the field of computing. Cloud computing are distributed and parallel systems consisting of a collection of interconnected physical and virtual machines. With increasing request and profit of cloud computing infrastructure, diverse computing processes can be executed on cloud environment. Many organizations and individuals around the world depend on the cloud computing environments infrastructure to carry their applications, platform, and infrastructure. One of the major and essential issues in this environment related to allocating incoming tasks to suitable virtual machine (cloud task scheduling). Cloud task scheduling is classified as optimization problem, and there are several meta-heuristic algorithms have been anticipated to solve and optimize this problem. Good task scheduler should execute its scheduling technique on altering environment and the types of incoming task set. In this research project a cloud task scheduling methodology based on ant colony optimization ACO algorithm, we call it ACO-TS Ant Colony Optimization for Task Scheduling has been proposed and compared with different scheduling algorithms (Random, First Come First Serve FCFS, and Fastest Processor to the Largest Task First FPLTF). Ant Colony Optimization (ACO) is random optimization search method that will be used for assigning incoming tasks to available virtual machines VMs. The main role of proposed algorithm is to minimizing the makespan of certain tasks set and maximizing resource utilization by balance the load among virtual machines. The proposed scheduling algorithm was evaluated by using Cloudsim toolkit framework. Finally after analyzing and evaluating the performance of experimental results we find that the proposed algorithm ACO-TS perform better than Random, FCFS, and FPLTF algorithms in each of the makespaan and resource utilization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20Task%20scheduling" title="cloud Task scheduling">cloud Task scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20%28ACO%29" title=" ant colony optimization (ACO)"> ant colony optimization (ACO)</a>, <a href="https://publications.waset.org/abstracts/search?q=cloudsim" title=" cloudsim"> cloudsim</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a> </p> <a href="https://publications.waset.org/abstracts/32867/aco-ts-an-aco-based-algorithm-for-optimizing-cloud-task-scheduling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32867.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Predicting Provider Service Time in Outpatient Clinics Using Artificial Intelligence-Based Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haya%20Salah">Haya Salah</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20Sharan"> Srinivas Sharan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clinical%20decision%20support%20system" title="clinical decision support system">clinical decision support system</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20algorithms" title=" machine learning algorithms"> machine learning algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=patient%20scheduling" title=" patient scheduling"> patient scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20models" title=" prediction models"> prediction models</a>, <a href="https://publications.waset.org/abstracts/search?q=provider%20service%20time" title=" provider service time"> provider service time</a> </p> <a href="https://publications.waset.org/abstracts/120944/predicting-provider-service-time-in-outpatient-clinics-using-artificial-intelligence-based-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120944.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">121</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> 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