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Coded BPSK OFDM-FSO over strong turbulence channel 2

Coded BPSK OFDM-FSO over strong turbulence channel

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2nd international conference on Advances in Big data, Computing and data communication Systems, icABCD 2019

作者： Ajewole, Bukola D. Odeyemi, Kehinde O. Owolawi, Pius A. Srivastava, Viranjay M. Department of Electronics Engineering Howard College University of KwaZulu-Natal KwaZulu-Natal Durban South Africa Department of Electrical and Electronics Engineering University of Ibaadan Nigeria Department of Computer System Engineering Tshwane University of Technology Pretoria South Africa

ISBN: (纸本)9781538692363

Free space optical (FSO) communication has become a research area of interest in the domain of optical communication in recent years. However, atmospheric turbulence plays a significant role in degrading its performance. Orthogonal frequency division multiplexing (OFDM) has been considered to enhance the performance of the FSO system. The aim of this paper is to investigate the performance of Binary Phase Shift Keying (BPSK) OFDM-FSO communication system by employing a convolutional coding scheme over the atmospheric (Gamma-Gamma) turbulence channel. Results presented show that the uncoded BPSK modulation scheme offers the system poor error rate but enhanced with convolutional coding technique. © 2019 IEEE.

关键词： Atmospheric turbulence

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Energy-Efficient Virtual Machines Dynamic Integration for Robotics 2nd

Energy-Efficient Virtual Machines Dynamic Integration for Ro...

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2nd EAI international conference on Robotic Sensor Networks, ROSENET 2018

作者： Wen, Haoyu Zhou, Sheng Wang, Zie Wang, Ranran Lu, Jianmin School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China

ISBN: (纸本)9783030177621

The rapid development of cloud computing technology has brought a lot of energy consumption. However, the utilization rate of resources such as data center CPUs is often less than half. Therefore, if the virtual machines in operation are centrally integrated into some servers, and idle servers are switched to low-power modes, the power consumption of data centers can be greatly reduced. The consumption. The traditional research on the integration of virtual machines is mainly based on the current load of the host to set a high-load threshold or periodically perform the migration. At present, research based on time-series prediction faces the problem of low prediction accuracy. In order to solve these problems, this paper synthetically considers the influence of multi-order Markov model and CPU state at different times, and proposes a new K-order mixed Markov model for CPU load prediction of the host for a period of time in the future. By conducting large-scale data experiments on the CloudSim simulation platform, the host load forecasting method proposed in this paper is compared with traditional load detection methods, and the proposed model is greatly reduced in the number of virtual machine migrations and data center energy consumption. And the violation of the SLA is also at an acceptable level. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： Cloud computing

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Effect of Dropout and Batch Normalization in Siamese Network for Face Recognition 2nd

Effect of Dropout and Batch Normalization in Siamese Network...

引用

2nd international conference on Innovative Computing and communication, ICICC 2019

作者： Chakraborty, Nilagnik Dan, Anirban Chakraborty, Amit Neogy, Sarmistha Department of Computer Science & Engineering Government College of Engineering & Ceramic Technology Kolkata India Department of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (纸本)9789811503238

The paper focuses on maximizing feature extraction and classification using one-shot learning (meta-learning). The present work discusses how to maximize the performance of the Siamese Neural Network using various regularization and normalization techniques for very low epochs. In this paper we perform multi-class Face Recognition. A unique pairing of face images helps us to understand the generalization capacity of our network which is scrutinized on AT&T-ORL face databases. We performed experiments to see how learning can be made to converge within a few epochs, and the approach has also made a telling performance on unseen test data which is about 96.01%. Besides, we discuss the ways to speed up learning particularly for a Siamese network and achieve convergence within 5 epochs. We found one of the better regularization techniques for fast reduction of the loss function. It is apparent from our findings that only normalization is the effective approach while working within less epochs. Also, Dropout After Batch Normalization configuration results in smooth loss reduction. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Underwater acoustic target classification based on LOFAR spectrum and convolutional neural network 2020

Underwater acoustic target classification based on LOFAR spe...

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2nd international conference on Artificial Intelligence and Advanced Manufacture, AIAM 2020

作者： Yin, Xiaohan Sun, Xiaodong Liu, Peishun Wang, Liang Tang, Ruichun College of Information Science and Engineering Ocean University of China Qingdao China

ISBN: (纸本)9781450372022

The underwater acoustic target classification task has always been an important research direction of acoustic recognition and classification. The acoustic classification models include traditional models such as Gaussian Mixture Model (GMM), and deep learning models such as Convolutional Neural Network (CNN) and Long and Short Time Memory Network (LSTM). This paper proposes a deep sound feature extraction network based on VGGNet. An underwater acoustic target classification framework based on LOFAR spectrum and CNN is proposed. Although ordinary CNN can also extract underwater acoustic features, too few or too many network layers will cause problems such as insufficient features or increased calculations. Therefore, we draw on the excellent structure of VGGNet in feature extraction and delete several layers for feature extraction and classification of underwater acoustic targets. The accuracy are 94%, 98% and 96% respectively in three real data sets of civil ships, and the accuracy were improved com-pared with the traditional methods. © 2020 ACM.

关键词： Underwater acoustics

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Big data techniques: Today and tomorrow 2nd

Big data techniques: Today and tomorrow

引用

2nd international conference on communication and Computing systems, ICCCS 2018

作者： Khatana, Priyanka Soni, Yashvardhan Department of Information Technology Dronacharya College of Engineering GurugramHaryana India Department of Computer Science Engineering Dronacharya College of Engineering. GurugramHaryana India

ISBN: (纸本)9780367001476

This paper is written before doing any practical work on the above topic. It is based on the research done on the topic-"Big data Techniques: Today and Tomorrow". Big data refers to a large dataset which is not easy to handle or understand. We have given a brief description about the present scenario and the future scenario of big data. We are going to explain what big data is, how it came into existence, a brief about the technologies involved in data extraction. By the end of the paper we will get a basic knowledge about big data and its benefits and its use in our daily life also. This paper aims to analyze some of the different tools and techniques which can be applied to big data. © 2019 Taylor & Francis Group, London.

关键词： Large dataset

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Discussion on problems and solutions in hardware implementation of algorithms for a car-type autonomous vehicle 2nd

Discussion on problems and solutions in hardware implementat...

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2nd international conference on data engineering and communication technology, icdect 2017

作者： Walambe, Rahee Nikte, Shreyas Joshi, Vrunda Ambike, Abhishek Pitke, Nimish Ghole, Mukund Symbiosis Institute of Technology Pune India PVG’s College of Engineering and Technology Pune India

ISBN: (纸本)9789811316098

The self-driving cars will bring fundamental change in the transportation industry. Many hazardous situations like land-mine detection, war zones, nuclear decommissioning highlights the need of autonavigation in open terrain. In the last few decades, due to the increasing interest in mobile robots, a number of autonavigation algorithms have been developed. The autonomous vehicles are used extensively in different domains from passenger car to the hazardous applications. These autonomous cars extensively use mathematical calculations and machine intelligence. In order for the car-type vehicle to manoeuver smoothly in a given workspace, accurate planning (motion and path) algorithms are essential. As part of the research, our group has developed the nonholonomic motion planning algorithms for the car-type vehicle based on differential flatness approach. In the previous work, the hardware realization of these algorithms is presented. This paper discusses the hardware implementation issues that we have faced during this work. Hardware implementation comes with various inherent challenges, such as manufacturing error in the car hardware components, physical limitation of the component, limited processing power of low-power onboard computer, accuracy of data from sensors in diverse conditions. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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Research on Performance Optimization of Web Application System based on JAVA EE 2

Research on Performance Optimization of Web Application Syst...

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2019 2nd international Symposium on Big data and Applied Statistics, ISBDAS 2019

作者： Cai, Zengyu Li, Jingxiao Zhang, Jianwei School of Computer and Communication Engineering Zhengzhou University of Light Industry Henan Key Laboratory of Food Safety Data Intelligence Zhengzhou China Software Engineering College Zhengzhou University of Light Industry Henan Key Laboratory of Food Safety Data Intelligence Zhengzhou China

With the development of informatization, the Web application system is being used by more and more enterprises and institutions. With the increase of the user group, the concurrent visits are increasing, which leads to the poor service quality of Web system. Several high-concurrency solutions existed, but they address only one aspect of the performance problem, and small and medium enterprises need to consider the cost. This paper will analyze the performance factors affecting the Web application system through multiple levels, aiming to build a Web application system with high availability, high concurrency, high expansion and less cost. Based on the factors affecting the performance of the Web application system, the project of the online mall system was optimized, and the service support of the Web application system was adjusted to make it more suitable for the system requirements. Besides, related SQL statements were optimized, Java code layer was optimized, user experience layer was optimized, and performance testing and analysis were completed. © Published under licence by IOP Publishing Ltd.

关键词： Informatization

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1D CNN based network intrusion detection with normalization on imbalanced data 2

1D CNN based network intrusion detection with normalization ...

引用

2nd international conference on Artificial Intelligence in Information and communication, ICAIIC 2020

作者： Azizjon, Meliboev Jumabek, Alikhanov Kim, Wooseong Gachon University Dept. of Computer Engineering SeongNam Korea Republic of Inha University Dept. of Computer and Information Engineering Incheon Korea Republic of

ISBN: (纸本)9781728149851

Intrusion detection system (IDS) plays an essential role in computer networks protecting computing resources and data from outside attacks. Recent IDS faces challenges improving flexibility and efficiency of the IDS for unexpected and unpredictable attacks. Deep neural network (DNN) is considered popularly for complex systems to abstract features and learn as a machine learning technique. In this paper, we propose a deep learning approach for developing the efficient and flexible IDS using one-dimensional Convolutional Neural Network (1D-CNN). Two-dimensional CNN methods have shown remarkable performance in detecting objects of images in computer vision area. Meanwhile, the 1D-CNN can be used for supervised learning on time-series data. We establish a machine learning model based on the 1D-CNN by serializing Transmission Control Protocol/Internet Protocol (TCP/IP) packets in a predetermined time range as an invasion Internet traffic model for the IDS, where normal and abnormal network traffics are categorized and labeled for supervised learning in the 1D-CNN. We evaluated our model on UNSWNB15 IDS dataset to show the effectiveness of our method. For comparison study in performance, machine learning-based Random Forest (RF) and Support Vector Machine (SVM) models in addition to the 1D-CNN with various network parameters and architecture are exploited. In each experiment, the models are run up to 200 epochs with a learning rate in 0.0001 on imbalanced and balanced data. 1D-CNN and its variant architectures have outperformed compared to the classical machine learning classifiers. This is mainly due to the reason that CNN has the capability to extract high-level feature representations that represent the abstract form of low-level feature sets of network traffic connections. © 2020 IEEE.

关键词： Network architecture

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引用

2nd international conference on data engineering and communication technology, icdect 2017

作者： Abujar, Sheikh Hasan, Mahmudul Hossain, Syed Akhter Department of Computer Science and Engineering Daffodil International University Dhanomondi Dhaka1205 Bangladesh Department of Computer Science and Engineering Comilla University Comilla3506 Bangladesh

ISBN: (纸本)9789811316098

One of the key challenges of natural language processing (NLP) is to identify the meaning of any text. Text summarization is one of the most challenging applications in the field of NLP where appropriate analysis is needed of given input text. Identifying the degree of relationship among input sentences will help to reduce the inclusion of insignificant sentences in summarized text. Result of summarized text always may not identify by optimal functions, rather a better summarized result could be found by measuring sentence similarities. The current sentence similarity measuring methods only find out the similarity between words and sentences. These methods state only syntactic information of every sentence. There are two major problems to identify similarities between sentences. These problems were never addressed by previous strategies provided the ultimate meaning of the sentence and added the word order, approximately. In this paper, the main objective was tried to measure sentence similarities, which will help to summarize text of any language, but we considered English and Bengali here. Our proposed methods were extensively tested by using several English and Bengali texts, collected from several online news portals, blogs, etc. In all cases, the proposed sentence similarity measures mentioned here was proven effective and satisfactory. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Big data and hadoop: A review 2nd

Big data and hadoop: A review

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2nd international conference on communication and Computing systems, ICCCS 2018

作者： Kaur, Maninder Goel, Megha Department of Information and Technology Dronacharya College of Engineering Gurgaon India Department of Computer Science and Engineering Dronacharya College of Engineering GurgaonHaryana India

ISBN: (纸本)9780367001476

This Now days information is increasing day by day thanks to the employment of social networking sites, advancement within the technology. The growing desires of knowledge is resolved by victimization massive information. massive information manage structured, unstructured, semi structured information terribly with efficiency. massive information is assortment of huge datasets that aren’t managed by area unit previous computing techniques. Massive information makes the business quicker. several corporations like Google, Amazon, Facebook etc the employment massive information. In aid, sector massive information additionally play vital role. Hadoop is that the framework utilized by massive information. Hadoop is open supply software system. it’s written in java. Hadoop additionally permits distributed process. © 2019 Taylor & Francis Group, London.

关键词： Big data

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