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Optimizing Web Server Performance: A Comparative Analysis of Central Manager and Round Robin Load Balancing Algorithms 4

Optimizing Web Server Performance: A Comparative Analysis of...

引用

4th International Conference on Electrical engineering and computer science, ICECOS 2024

作者： Permana Arrasyid, Fahmi Stiawan, Deris Afifah, Nurul Heryanto, Ahmad Azhar, Iman Saladin B. Bardadi, Ali Yudho Suprapto, Bhakti Pahendra Anto Saputra, Iwan Sriwijaya University COMNETS Rerearch Group Faculty of Camp. Science Palembang Indonesia Sriwijaya University Faculty of Comp. Science Computer Engineering Dept. Palembang Indonesia Sriwijaya University Faculty of Comp. Science Information System Dept. Palembang Indonesia Sriwijaya University Faculty of Engineering Electrical Engineering Dept. Palembang Indonesia

ISBN: (纸本)9798350368253

Central manager algorithm has a lower average throughput compared to the round robin algorithm. This is evidenced by a significant decrease in throughput values during measurements. Central manager algorithm provides a faster average response time than round robin algorithm, despite experiencing an increase in response time values over time. Central manager algorithm shows a higher request loss rate and many requests are not served. Under normal performance conditions, both algorithms yield the same average request loss rate, achieving stable values without failed requests. The central manager algorithm ensures relatively balanced resource availability, including CPU and memory usage, across each slave web server compared to the round robin algorithm. The results show that increasing the number of clients and client requests during system testing to better observe server resource availability and load distribution performance. For future research, it is recommended to increase the number of clients in the system testing to enhance server load and to introduce client request actions on the web server to further highlight resource availability on each web server. © 2024 IEEE.

关键词： Response time (computer systems)

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Brain Controlled Communication system using Machine Learning techniques of computer Vision 5

Brain Controlled Communication system using Machine Learning...

引用

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Sabharwal, Munish Sultania, Shantam Jindal, Harsh Galgotias University Greater Noida India Wissen Technology Banglore India Chandigarh University Dept. Of Computer Science And Engineering Mohali India

ISBN: (纸本)9798350330861

Nowadays computers have become a necessity for all computers have made a great leap for us and with the help of that we are able to move to a golden age of Artificial Intelligence. Artificial Intelligence or A.I has huge advantages and potentials for making the lives easy and more accessible. However, we as a society have to keep on innovating and developing new innovations and products to help different aspects of the community. The proposed system uses a brain-controlled interface, eye tracking along with a camera and screen for communication and training. The system requires input in the form of an image captured with the help of a camera. After the image is taken as an input, it will be used to perform eye-tracking and eye-blink detection. Both of these actions will be performed by employing a machine learning algorithm. The algorithm will process a morse code entered by the user, and then will enable the speech functionality using built-in speakers. Thus, rendering the system speak out whatever the user wanted to convey. The proposed system follows a coordinated approach for eye pattern detection and mapping. It detects the gazing spot of the eyes, and calculates the midpoint of the eye gaze. As a result, it predicts the eye blink pattern. This eye pattern recognition system is proposed to help out people with disabilities who find it difficult to communicate. © 2023 IEEE.

关键词： Cameras

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A Fully Connected Neural Network For Polar Channel Decoding 20

A Fully Connected Neural Network For Polar Channel Decoding

引用

20th International Conference on Electrical engineering, Computing science and Automatic Control, CCE 2023

作者： Sánchez-Rodríguez, Jesús Ángel Martinez-Enriquez, Ana M. Lara, Mauricio CINVESTAV-IPN Communications Section Dept. Electrical Engineering Mexico City Mexico CINVESTAV-IPN Dept. Computer Science Mexico City Mexico

ISBN: (纸本)9798350306767

One of the most essential components of a communications system is channel coding, which provides protection for the original source message. Polar codes have gained great importance due to the fact that they reach the channel capacity for the Binary Discrete Memoryless Channel. Both encoding and decoding, for this type of codes, are serial processes, which increases latency. In this research, we propose a fully connected neural network architecture that tackles this problem in decoding thanks to the inherent parallelization of this type of structure. Our experimental results demonstrate that a fully connected neural network reduces decoding delay, although further study is still needed to reach the accuracy of the probabilistic decoding methods. © 2023 IEEE.

关键词： Decoding

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About Measurement System in Stations and Substations using Original Data Acquisition Systems

About Measurement System in Stations and Substations using O...

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2023 Power Quality and Electromagnetic Compatibility at Low-Frequency, PQEMC-LF 2023

作者： Nicolae, Ileana-Diana Nicolae, Petre n Marian Nicolae, Marian-Stefan University of Craiova Computer Science and Information Technology Dept. Craiova Romania University of Craiova Electrical Engineering Energetic and Aeronautics Dept. Craiova Romania

ISBN: (纸本)9798350341331

The paper deals with a method and measurement system designed for the simultaneous monitoring and recording of time variations of electric quantities with no inter-correlation requirements. The method and measurement system were obtained by realizing a multi-unit data acquisition system (MUDAS), relying on at most 3 independent data acquisition systems which operate jointly, in a synchronous way, by means of a synchronizer. Because each of the individual data acquisition system can acquire data for at most 8 electrical quantities (4 voltages and 4 currents), the maximum total number of electrical quantities to be monitored, acquired, and transferred simultaneously to a central control unit is equal to 24. The sampling rate allows for the determination of power quality parameters according to existing standards. MUDAS can also be used in a structure with only 2 individual data acquisition system, in order to simultaneously acquire and display at most 16 electrical quantities. Two examples of utilization of MUDAS are presented. © 2023 IEEE.

关键词： Data acquisition

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Evaluation of FinFET in Ultra Low Power ALU 4

Evaluation of FinFET in Ultra Low Power ALU

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4th IEEE International Conference on Design and Test of Integrated Micro and Nano-Systems, DTS 2022

作者： Yadhav, Avinash Rizkalla, Maher Ytterdal, Trond Lee, John J. Balasubramanian, Linknath Surya Gopinath, Anoop Indiana University Purdue University at Indianapolis Dept. of Electrical and Computer Engineering Indianapolis United States Norwegian University of Science and Technology Dept. of Electrical and Computer Engineering Trondheim Norway

ISBN: (纸本)9781665497831

The data path and memory elements are integral hardware components to evaluate the performance of a complex ASIC low power architecture. In this study, a FinFET model was evaluated via 7 nm FinFET process using ASAP PDK and applied to 6T SRAM cells and 32-bit ALU architecture. Devices of 0.207 V threshold were biased at 0.7V supply voltage. The clock router was properly modeled with accurate values of resistances and capacitances for the metal layers. The noise glitch and cross talk within the parallel running interconnects and the driving loads were incorporated in the model. A command set clock uncertainty is also integrated into the model to incorporate the clock jitter and clock skew effects. The architecture was optimized for worst case scenario with typical RC corners. The Non-Linear Delay Model (NLDM) was incorporated for the cell synthesis where a delay that is based on the input transition and output load capacitance was incorporated for better quality of results (QoR). The low power architecture was evaluated for worst case scenario considering typical-typical (TT) corners. A maximum effective device current of 18.26μA with subthreshold slope of 60mV/decade were estimated and considered in the system evaluation. The output ports were loaded with a typical 2fF capacitance. At 1 GHz operation, the internal power, switching power, and leakage power are reported to be 0.2705 mW, 0.7599 mW, and 0.00743 mW, respectively. © 2022 IEEE.

关键词： FinFET

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Natural Language Processing and Machine Learning Approaches to Detect Bangla Hate Speech on Social Media 26

Natural Language Processing and Machine Learning Approaches ...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Islam, Md. Shamimul Saha, Subrata Alam, Md. Mahbub Kumar Datta, Nayan Ali, Md. Haidar Mahbub Alam, Md. Hossain, Md. Dulal Golam Moazzam, Md. Bangladesh Atomic Energy Commission Bangladesh Bangladesh Atomic Energy Commission Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350359015

In this modern era, with the popularity of the Unicode system, many people use Bangla text to express their opinions, comments, emotions on Facebook, Twitter, and YouTube, etc. However, this social connectivity always does not create positive impacts. Trolling, cyberbullying, online harassment, sexual and political abuse, and cybercrime often create a huge negative physiological effect on victims. In most cases, women, children, and celebrities are the victims which may often lead them to depression and suicidal activity. Therefore, at the early stage, cyberbullying and hate speech detection can reduce these cyber crimes.A few researches have been carried out in the Bangla language to detect cyberbullying and hate speech due to annotated corpora, lexical and morphological analyzers. This research aims to design and develop an effective model to detect Bangla cyberbullying and hate speech text with the combination of Natural Language Processing (NLP) and Machine Learning Classifier algorithms. In this study, we have collected 6244 Bangla text data from various online social platforms consisting of 6 categories of hate speeches. Then, we labeled the data into two main classes-bullied and neutral that were used to train our classifiers after preprocessing and features extracted by the Bangla NLP tool and TF-IDF(Term Frequency -Inverse Document Frequency). Our comparative study outlines that, with an accuracy rate of 89% and considering parameters Recall, Precision, Specificity, F-Score, and ROC, the Random Forest Classifier achieves the best results compared to others. © 2023 IEEE.

关键词： Bangla text classification Detection Hate Speech machine learning NLP

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Optimization of the Modified Gaussian Filter for Mobile GPU Usage in Game Workloads

Optimization of the Modified Gaussian Filter for Mobile GPU ...

引用

2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023

作者： Kang, Jieui Sim, Jaehyeong Bahn, Hyokyung Ewha Womans University Dept of Artificial Intelligence Convergence Seoul Korea Republic of Ewha Womans University Dept of Computer Science & Engineering Seoul Korea Republic of

ISBN: (纸本)9798350302561

The harnessing of mobile Graphics Processing Units (mGPUs) has become increasingly significant in modern computing technology, especially for mobile devices such as smartphones and tablets. In recent years, however, the focus of research has shifted from graphics-specific studies to those involving General-Purpose computing on Graphics Processing Units (GPGPU), with an emphasis on AI workloads. As a result, there has been a lack of progress in GPU research tailored to the needs of mobile GPU-intensive workloads, such as 3D, VR, and AR gaming. To address this issue, we analyze gaming workloads, which demand significant GPU usage compared to other applications, to identify the specific constraints they impose on mobile GPUs. Subsequently, we investigate optimization strategies that can be applied to address these constraints while ensuring efficient performance for mobile gaming workloads. We provide a detailed breakdown of the differences in shader usage and filter values between game and general workloads. Our observations show that game workloads mostly make use of Linear Filter instead of Nearest Filter, which is widely used in common mobile apps. Although Linear Filters are more accurate than other filters, the Peak Signal-to-Noise Ratio (PSNR) evaluation metric reveals that they are not the most optimal solution for efficiency in game apps. Based on this observation, we present a new filter, called Gaussian+, optimized for game workloads in mobile GPU systems. We show that Gaussian+ achieves up to 38% improvement in performance with minimal accuracy loss compared to the any other alternate filters. © 2023 IEEE.

关键词： Graphics processing unit

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Selinet: A Lightweight Model for Single Channel Speech Separation 48

Selinet: A Lightweight Model for Single Channel Speech Separ...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tan, Ha Minh Vu, Duc-Quang Wang, Jia-Ching National Central University Dept. of Computer Science and Information Engineering Taiwan Thai Nguyen University of Education Thai Nguyen Viet Nam

ISBN: (纸本)9781728163277

The time-domain speech separation methods adopting deep learning have obtained impressive performance. However, the computational complexity, model size, and performance are still the challenges for the implementation on real-time low-resource devices. In this paper, we introduce a lightweight yet effective network for speech separation, namely SeliNet. The SeliNet is the one-dimensional convolutional architecture that employs bottleneck modules, and atrous temporal pyramid pooling. In bottleneck modules, the depth-wise separable convolution significantly decreases the model size and computational cost meanwhile the squeeze excitation uses a context vector to interact with the entire hidden state vector. Specifically, the atrous temporal pyramid pooling recognizes long-time sequences of various lengths and extracts context at different field-of-views. This helps SeliNet to obtain impressive performance while still maintaining the small computational cost and model size. © 2023 IEEE.

关键词： Convolution

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Enhancing Lighter Neural Network Performance with Layer-wise Knowledge Distillation and Selective Pixel Attention 13

Enhancing Lighter Neural Network Performance with Layer-wise...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Zaravashan, Siavash Torabi, Sajjad Zaravashan, Hesam Iran University of Science and Technology Dept. Computer Engineering Tehran Iran Data Scientist Part Research Center of Ai Tehran Iran

ISBN: (纸本)9798350330151

Deep neural networks have made a revolution in different areas of Artificial Intelligence such as image classifi-cation and object detection. However, most of these models are heavy and time-consuming at the inference stage. If we desire to use real-time applications, it's necessary to lighten them up. However, this could negatively affect the performance of models. One suggested approach for enhancing the capabilities of smaller models involves a technique called knowledge distillation. In this method, a less advanced model, referred to as the student model, learns from and emulates the more advanced teacher model to enhance its performance. This method has been proven as an effective technique to compress the model and increase its accuracy. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. In this paper, we investigate the effect of connections between different layers of teacher and student networks and reveal their great importance. We have employed three different loss functions - attention-driven distillation, hierarchical context loss, and non-local loss - to enhance the classifier's overall performance. Extensive experiments on the CIFAR-100 dataset show that the proposed method has a great performance improvement. For the benefit of the research community, we will make the code for this study available on GitHub. © 2023 IEEE.

关键词： Image classification

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Details Extraction of Non-Standard License Plates Including Arabic and Indian Digits 24

Details Extraction of Non-Standard License Plates Including ...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Fares, Charbel Abi Assaf, Maroun Rachini, Ali Hamam, Yskandar Holy Spirit University of Kaslik Dept. Computer Science and It Jounieh Lebanon Tshwane University of Technology Dept. of Electrical Engineering Pretoria South Africa

ISBN: (纸本)9798350384307

This article details the development of a new solution specifically tailored for the extraction of information from non-standard license plates, encompassing the recognition of Arabic and Indian numerical characters. The system operates in three distinct stages: cleansing, segmentation, and recognition. the system is capable of identifying irregularities in license plates such as blurred or missing numbers. © 2023 IEEE.

关键词： Image Processing License Plate Recognition Neural Network Watershed Segmentation

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