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A virtual preemption paradigm for using priority rules to solve job shop scheduling problems

A virtual preemption paradigm for using priority rules to so...

引用

2005 IEEE International Conference on Robotics and Automation

作者： Chiang, Tsung-Che Fu, Li-Chen Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)078038914X

To solve job shop scheduling problems, the priority rule is one of the most popular approach. It has the appeal because of simplicity, efficiency and effectiveness. However, the paradigm conventionally used to apply priority rules has a certain flaw. In this paper, we first point out this flaw and then propose a paradigm to remove it. A rule is also developed to exploit the potential of the new paradigm. The performance of the proposed approach is verified by several simulation experiments. The experimental results are quite satisfactory. © 2005 IEEE.

关键词： Resource allocation

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View synthesis for robust distributed video compression in wireless camera networks

View synthesis for robust distributed video compression in w...

引用

14th IEEE International Conference on Image Processing, ICIP 2007

作者： Chuohao, Yeo Jiajun, Wang Ramchandran, Kannan Dept. of Electrical Engineering and Computer Science University of California Berkeley United States

ISBN: (纸本)1424414377

We propose a method for delivering error-resilient video from wireless camera networks in a distributed fashion over lossy channels. Our scheme is based on distributed source coding that exploits inter-view correlation, among cameras with overlapping views. The main focus in this work is on robustness which is imminently needed in a wireless setting. The proposed approach has low encoding complexity, is robust while satisfying tight latency constraints, and requires no inter-camera communication. Our system is built on and is a multi-camera extension of PRISM[1], an earlier proposed single-camera distributed video compression system. Decoder motion search, a key attribute of single-camera PRISM, is extended to the multi-view setting by using estimated scene dept. information when it is available. In particular, dense stereo correspondence and view synthesis are utilized to generate side-information. When combined with decoder motion search, our proposed method can be made insensitive to small errors in camera calibration, disparity estimation and view synthesis. In experiments over a simulated wireless channel, the proposed approach achieves up to 2.1 dB gain in PSNR over a system using H.263+ with forward error correction. © 2007 IEEE.

关键词： Sensor networks

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Dynamic Fencing for Interference Mitigation in Integrated Satellite-Terrestrial Networks

Dynamic Fencing for Interference Mitigation in Integrated Sa...

引用

2022 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2022

作者： Khan, Aman Kherani, Nomaan Alam Baswade, Anand M. Indian Institute of Technology Dept. of Electrical Engineering and Computer Science Bhilai India

ISBN: (纸本)9781665473408

Integrated Satellite-Terrestrial Network (ISTN) is one of the promising approaches for global wireless connection in 5th Generation (5G) and beyond networks. The integration of terrestrial and non-terrestrial networks open a wide range of opportunities in the networking domain. Though the integrated system has numerous benefits, it also has various challenges including the interference caused by the different networks, (satellite and terrestrial) sharing the same frequency band. The interference between both the networks degrades the performance of integrated network communication systems. This paper presents a scenario for integrated satellite and terrestrial networks (ISTN) and proposes a novel solution for interference management by coordinating satellite and terrestrial networks in order to minimize interference and enhance the overall throughput of the integrated network. To address this issue, a dynamic fencing mechanism is applied where a fence is created for a time slot to provide fairness to the victim users getting affected by the severe interference. © 2022 IEEE.

关键词： 5G mobile communication systems

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Prediction on Intent Classification of Java and C# Web queries using Semi-supervision 24

Prediction on Intent Classification of Java and C# Web queri...

引用

24th International Conference on computer and Information Technology, ICCIT 2021

作者： Haque, Md. Ashfaqul Dristy, Israt Jahan Tuhin, Mohammad Tariqul Islam Sagar, Ali Hossain Barek, Jayed Mohammad Brac University Dept. of Computer Science and Engineering Mohakhali Dhaka1212 Bangladesh

ISBN: (纸本)9781665494359

Proper intent classification of web queries is significant in providing users with accurate search results. For STEM-related searches, the generalized search engine provides some discrete results, and it becomes challenging to find the desired ones. Here, in our work, we have used a semi-supervised process and compared it with supervising approaches. This process has been done on Java and C# Bing web queries. From the performance comparison, we have found that our semi-supervised model has performed better than others according to accuracy and f1-score. We have also analyzed the performance by changing training data size, doing error analysis on all models, and finished by presenting how this prediction can be used on a search data fetching process. © 2021 IEEE.

关键词： Search engines

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Identity-based integrity verification (IBIV) protocol for cloud data storage 1

Identity-based integrity verification (IBIV) protocol for cl...

引用

1st IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021

作者： Sahu, Indra Kumar Nene, Manisha J. Dept. of Computer Science &Engineering Defence Institute of Advanced Technology Pune India

ISBN: (纸本)9781728157900

With meteoric advancement in quantum computing, the traditional data integrity verifying schemes are no longer safe for cloud data storage. A large number of the current techniques are dependent on expensive Public Key Infrastructure (PKI). They cost computationally and communicationally heavy for verification which do not stand with the advantages when quantum computing techniques are applied. Hence, a quantum safe and efficient integrity verification protocol is a research hotspot. Lattice-based signature constructions involve matrix-matrix or matrix vector multiplications making computation competent, simple and resistant to quantum computer attacks. Study in this paper uses Bloom Filter which offers high efficiency in query and search operations. Further, we propose an Identity-Based Integrity Verification (IBIV) protocol for cloud storage from Lattice and Bloom filter. We focus on security against attacks from Cloud Service Provider (CSP), data privacy attacks against Third Party Auditor (TPA) and improvement in efficiency. © 2021 IEEE

关键词： Cloud storage

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Multi-Class Medical Image Classification Based on Feature Ensembling using DeepNets 9

Multi-Class Medical Image Classification Based on Feature En...

引用

9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Asharani Kagolanu, Venkata Srilakshmi Thimmareddy, Lakshmi Kanala, Kavitha Lakshmi Sirisha, B. Maturi Venkata Subba Rao Engineering College Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9789380544441

Medical image classification plays a key role in computer-aided detection and diagnosis systems. Conventional machine learning, neural network-based approaches strongly depends on the type of features i.e. texture, edge, shape, and/or blob along with their amalgamations, extracted from the medical image. Since most of the feature extraction algorithms are specific to the modalities and problem. The machine learning or neural network-based model built using these features lack- of generalization and high-level problem representation ability. Deep Learning (DL) methods based on deep features deliver an active way to build an edge-to-edge model which has the ability to reckon the classification labels with the input raw pixels of medical domain images. Due to the unavailability of massive medical data, high noise rate, and resolution of medical images, DL approaches grieve in terms of classification rate. To improve the classification rate, we propose to fuse the contemporary features (high-level features) extracted from a deep convolution neural network (DCNN) and conventional texture features. The building of the planned model comprises the subsequent steps. Firstly, DCNNs (MobileNet, GoogelNet, and Resnet.) are trained as feature extractors, and the feature vectors are extracted. Second, we extract the traditional texture features of medical images. Lastly, we fuse the deep features and texture features which represent robust high-level features for medical image classification. We assess the proposed method on standard medical image benchmark datasets: MEDMNIST We attain classification accuracy of 99.1%, experimental results confirm that fusing texture features and deep learning features results in the increase of accuracy of medical image classification. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Textures

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BERN-NN: Tight Bound Propagation For Neural Networks Using Bernstein Polynomial Interval Arithmetic 26

BERN-NN: Tight Bound Propagation For Neural Networks Using B...

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26th ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2023, Part of CPS-IoT Week 2023

作者： Fatnassi, Wael Khedr, Haitham Yamamoto, Valen Shoukry, Yasser University of California Irvine United States Dept. of Electrical Engineering and Computer Science

ISBN: (纸本)9798400700330

In this paper, we present BERN-NN as an efficient tool to perform bound propagation of Neural Networks (NNs). Bound propagation is a critical step in wide range of NN model checkers and reachability analysis tools. Given a bounded input set, bound propagation algorithms aim to compute tight bounds on the output of the NN. So far, linear and convex optimizations have been used to perform bound propagation. Since neural networks are highly non-convex, state-of-the-art bound propagation techniques suffer from introducing large errors. To circumvent such drawback, BERN-NN approximates the bounds of each neuron using a class of polynomials called Bernstein polynomials. Bernstein polynomials enjoy several interesting properties that allow BERN-NN to obtain tighter bounds compared to those relying on linear and convex approximations. BERN-NN is efficiently parallelized on graphic processing units (GPUs). Extensive numerical results show that bounds obtained by BERN-NN are orders of magnitude tighter than those obtained by state-of-the-art verifiers such as linear programming and linear interval arithmetic. Moreoveer, BERN-NN is both faster and produces tighter outputs compared to convex programming approaches like alpha-CROWN. © 2023 Copyright held by the owner/author(s).

关键词： Convex optimization

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The effectiveness of virtual reality technology applying in vocational training on occupational competency among the students with intellectual disability in senior high school

The effectiveness of virtual reality technology applying in ...

引用

2021 IEEE International Conference on Social sciences and Intelligent Management, SSIM 2021

作者： Yang, Hsuan-Yu Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Hong, William National Central University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9781728168586

Under the atmosphere of social inclusion, it becomes the essential goal of trans-disciplines professionals to promote people with intellectual disability (ID) work in community independently. Nevertheless, limitation of cognitive function of people with ID results in their difficulties in learning and insufficient vocational ability. So our team will develop the Virtual reality technology (VRT) system which will allow ID students to learn and adapt to the complex workplace effectively. © 2021 IEEE.

关键词： Students

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DeepWeight: A Semi-Autonomous Food Weight Billing System using Deep Learning 5

DeepWeight: A Semi-Autonomous Food Weight Billing System usi...

引用

5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Titus, Ashley Mahesh, U.S. Nandu Krishnan, G. Francis, Mercelin Marian Engineering College Dept. of Computer Science & Engg. Trivandrum India

ISBN: (纸本)9798350334128

The manual billing process in grocery store food sections often results in long lines and inefficiency. This not only wastes time but can also discourage customers from purchasing food items. DeepWeight offers a solution to this problem by using deep learning to train a CNN model that can detect the type of food, collect weight data using a load cell, capture an image of the food using a camera, predict the type of food in the image using the model, and print a label with relevant information using a printer. This system is powered by technologies such as Python3, ***, a USB camera, a load cell module, a printer, and a Raspberry Pi 3. Our model is built on the ResNet-152 architecture and achieves an accuracy of 99.22% after 5 epochs of training. Additionally, it employs a unique approach of using weight cell readings to trigger the system, rather than the conventional method of image sensing. This approach helps to reduce billing time, avoid false triggers, and improve the efficiency of grocery stores. © 2023 IEEE.

关键词： Convolutional neural networks

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Localization for Autonomous Vehicle: Analysis of Importance of IoT Network Localization for Autonomous Vehicle Applications

Localization for Autonomous Vehicle: Analysis of Importance ...

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2018 International Conference on Applied Electromagnetics, Signal Processing and Communication, AESPC 2018

作者： Shit, Rathin Chandra Sharma, Suraj International Institute of Information Technology Dept. of Computer Science Engineering Bhubaneswar India

ISBN: (纸本)9781538683330

The safe operation of the autonomous vehicle relies on an exact localization system. A notable amount of localization approaches suggested in the existing literature but a little of them shows its applicability towards autonomous vehicle scenarios. This paper presents investigation of the localization techniques in the light of applicability in autonomous vehicle scenarios. In this paper analysis of the role of sensor nodes and infrastructure for localization in autonomous vehicular application is carried out. The variation of localization error in different environmental condition like darkness or snow and driving scenario is analyzed. The analysis of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) platforms are examined with sensor data of vehicular network. Adding network information along with sensor data for localization can make robust and exact localization system for autonomous vehicles. However, the performance depends upon network infrastructure and quality of network services. © 2018 IEEE.

关键词： Autonomous vehicles

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