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Practice of Applied Edge Analytics in intelligent Learning Framework

Practice of Applied Edge Analytics in Intelligent Learning F...

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Information technology (ACIT)

作者： Kayal Padmanandam Lakshmi Lingutla Information Technology BVRITH College of Engineering for Women India Computer Science & Engineering BVRITH College of Engineering for Women India

ISBN: (数字)9781728188553

ISBN: (纸本)9781728188560

The advent of IoT has brought in a huge amount of data that is exponentially growing every second. This seemingly growing data has paved the way to rethink on various technologies to capture the data and analyze it properly. Such huge data is the fuel for various analytics and intelligent systems like machine learning and deep learning applications. The deployment of machine learning and deep learning intelligence across the analytical network takes place in the central data system (cloud servers) which is a very expensive challenge in terms of time, money, data privacy. But the application of such intelligence at the edge computing, which is a new paradigm of the cloud-enabled network, has solved the problem by offering high security and reliability. Unlike Cloud computing, edge computing is a decentralized, distributed architecture where analytics and insight happens near or at the data source itself that solves the expensive challenges mentioned above. This paper describes the network of edge computing and its variance from cloud computing, edge architecture, and diverse applications of machine learning algorithms and deep learning framework deployed at the edge network for intelligent analytics.

关键词： Cloud computing Edge computing Machine learning algorithms Machine learning Servers Logistics Clouds

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Multi-objective optimization on multistage half-band FIR filter design using simulated annealing algorithm 21

Multi-objective optimization on multistage half-band FIR fil...

引用

21st IEEE international conference on High Performance computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhang, Jin Hu, Wei Wang, Yonghao Zhu, Xiangyu College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System Wuhan China Digital Media Technology Lab Birmingham City University Birmingham United Kingdom

ISBN: (纸本)9781728120584

The design of a finite impulse response (FIR) digital filter is essentially a multi-parameter and multi-objective optimization problem, different optimization techniques can be utilized to minimize the latency in the live audio processing chain by minimization or maximization of the objective function. Using cascaded FIR filters as anti-aliasing and anti-image filters for high-resolution audio conversion are common. We can optimize the parameters of each stage to achieve the overall performance requirements. the method of modified Simulated Annealing (SA) Algorithm is proposed to resize the best distribution of passband ripple, transition band and stopband attenuation for each level of Multi-stage filter. It shows the proposed a new multi-stage filter design method improves the efficiency of the designed filter in terms of filter cost and group delay comparing with traditional design. © 2019 IEEE.

关键词： FIR filters

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Accelerating distributed training in heterogeneous clusters via a straggler-aware parameter server 21

Accelerating distributed training in heterogeneous clusters ...

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作者： Yu, Huihuang Zhu, Zongwei Chen, Xianglan Cheng, Yuming Hu, Yahui Li, Xi University of Science and Technology of China Hefei China

ISBN: (纸本)9781728120584

Different from homogeneous clusters, when distributed training is performed in heterogeneous clusters, there will be great performance degradation due to the effect of stragglers. Instead of the synchronous stochastic optimization commonly used in homogeneous clusters, we choose an asynchronous approach, which does not require waiting for stragglers but has the problem of using stale parameters. To solve this problem, we design a straggler-aware parameter server (SaPS), which can detect stragglers through the version of parameters and mitigate their effect by a coordinator which can limit the staleness of parameters without waiting for stragglers. Experimental results show that SaPS can converge faster than fully synchronous, fully asynchronous and some SGD variants. © 2019 IEEE.

关键词： Optimization

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Assessment of Modernization of Electric Multiple Units - Case study

Assessment of Modernization of Electric Multiple Units - Cas...

引用

international Scientific conference Transport of the 21st Century, ISCT21 2019

作者： Wojciechowski, Jerzy Lukasik, Zbigniew Jakubowski, Czeslaw Faculty of Transport and Electrical Kazimierz Pulaski University of Technology and Humanities in Radom Malczewskiego 29 Radom26-600 Poland

ISBN: (纸本)9783030276867

The development of passenger rail transport is an element of the development of our civilization. The requirements set by passengers as to the quality and comfort of driving are very high. This mainly applies to travel conditions, i.e. the means of transport itself. The second important factor is the cost of travel, resulting, inter alia, from the energy consumption of the means of transport used. The purchase of new railway units is expensive. A competitive solution is the modernization of units already in use. The authors presented in the article an analysis of the modernization of the group of electric multiple unit vehicles (EMU). This modernization involved, among other things, the reconstruction of almost the entire drive unit, the reconstructed driver’s cab, the change of the external appearance and the reconstruction of the interior. The article presents technical solutions used in the modernization process. The authors focused on the energy issues of vehicles before and after modernization, presenting the results of their own research. Measurements are carried out by the authors at EMU functioning on railway lines connecting Lódź with: Warsaw, Tomaszów Mazowiecki and Częstochowa (Poland). Data regarding electricity consumption by EMU before and after modernization were presented. The data on energy recuperation by the analyzed EMU group were presented. Comparative results of electric energy consumption by a selected group of drivers are presented. In the final part, the authors referred to the economic issues of the modernization. © 2020, Springer Nature Switzerland AG.

关键词： Energy utilization

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Real-Time Uber Data Analysis of Popular Uber Locations in Kubernetes Environment 5

Real-Time Uber Data Analysis of Popular Uber Locations in Ku...

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5th international conference on Information technology Research, ICITR 2020

作者： Gunawardena, T.M Jayasena, K.P.N Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Sri Lanka

ISBN: (纸本)9781665414753

Data is crucial in today's business and technology environment. There is a growing demand for Big Data applications to extract and evaluate information, which will provide the necessary knowledge that will help us make important rational decisions. These ideas emerged at the beginning of the 21st century, and every technological giant is now exploiting Big Data technologies. Big Data refers to huge and broad data collections that can be organized or unstructured. Big Data analytics is the method of analyzing massive data sets to highlight trends and patterns. Uber is using real-time Big Data to perfect its processes, from calculating Uber's pricing to finding the optimal positioning of taxis to maximize profits. Real-time data analysis is very challenging for the implementation because we need to process data in real-time, if we use Big Data, it is more complex than before. Implementation of real-time data analysis by Uber to identify their popular pickups would be advantageous in various ways. It will require high-performance platform to run their application. So far no research has been done on real-time analysis for identifying popular Uber locations within Big Data in a distributed environment, particularly on the Kubernetes environment. To address these issues, we have created a machine learning model with a Spark framework to identify the popular Uber locations and use this model to analyze real-time streaming Uber data and deploy this system on Google Dataproc with the different number of worker nodes with enabling Kubernetes and without Kubernetes environment. With the proposed Kubernetes environment and by increasing the worker nodes of Dataproc clusters, the performance can be significantly improved. The future development will consist of visualizing the real-time popular Uber locations on Google map. © 2020 IEEE.

关键词： Big data

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User Identification and Authentication Through Voice Samples 1st

User Identification and Authentication Through Voice Samples

引用

1st international conference on Computational Intelligence in Pattern Recognition, CIPR 2019

作者： Rakshit, Soubhik Pucho Technology and Department of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India

ISBN: (纸本)9789811390418

Voice authentication is a fundamental topic of research in today’s technology. Reliable speech recognition is hard to achieve, but many approaches have been proposed in recent years to achieve such with an improved degree of accuracy. The following paper presents a novel approach through which users can be authenticated with reasonable accuracy using a small voice sample. The proposed method uses MFCCs, a well-known methodology for extracting features from the voice sample and finally uses Gaussian Mixture Models (GMM) for classification. An advantage of using MFCCs as the speech features is that the model is language independent. A model trained in one language can work equally well for a model trained in a different language. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Speech recognition

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Wireless Access Support for Distribution Management System (DMS) to Microgrid Communication in Power Network 1st

Wireless Access Support for Distribution Management System (...

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1st international conference on Electronic Systems and intelligent computing, ESIC 2020

作者： Himabindu, N. Mandi, Rajashekar P. Hampannavar, Santoshkumar Manasani, Swapna School of Electrical and Electronics Engineering REVA University Bengaluru India National Institute of Technology Silchar Silchar India

ISBN: (纸本)9789811570308

With indispensable and rapid ingress of renewable energy sources in power systems, the communication between the distributed energy sources has become very crucial to have a reliable distribution management system (DMS). In this paper, wireless access support is proposed for Distribution Management System (DMS) and microgrid control center (MGCC) using WiMAX protocol. MATLAB/Simulink was used to model the WiMAX physical layer. The bit error rate (BER) v/s signal-to-noise ratio (SNR) curves were plotted and promising results were obtained. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Renewable energy resources

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K-means parallel acceleration for sparse data dimensions on flink 21

K-means parallel acceleration for sparse data dimensions on ...

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作者： Zeng, Zihao Li, Kenli Duan, Mingxing Liu, Chubo Liao, Xiangke Dept. College of Computer Science and Electronic Engineering Hunan University Changsha China Dept. College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781728120584

The K-means algorithm is a clustering algorithm which widely used in various applications, and it's running time is dramatically increased as the data size expanded. When the volume of data exceeds the range that can be carried by a single machine, the parallel operation of the algorithm must be implemented by using a distributed computing framework. Generally, during the parallel operation of the task, there are differences among the running time of each task due to the data skew, and the running progress of the entire job is determined by the task with the longest running time. In this paper, we propose an optimal data partitioning method for the application of the k-means algorithm on the sparsely dimensioned dataset to eliminate the data skew problem and further accelerate the parallel execution of the algorithm. Experimental evaluation on large-scale text datasets demonstrate the effectiveness of our partitioning approach on Flink. © 2019 IEEE.

关键词： K-means clustering

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Fundamental structure of Orthogonal Variable Commutative Quaternion Neurons

Fundamental Structure of Orthogonal Variable Commutative Qua...

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international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Tohru Nitta Hui Hu Gan Graduate School of Artificial Intelligence and Science Rikkyo University Tokyo Japan Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

In this paper, we formulate a commutative quaternion neuron model with parameters represented by an orthogonal coordinate, and makes clear the differences among the commutative quaternion neuron, a real-valued neuron,... 详细信息

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

关键词： Neurons Quaternions Biological neural networks Mathematical model Context modeling Synthetic aperture radar Signal processing algorithms

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A budget constraint incentive mechanism in spatial-temporal mobile crowdsensing 21

A budget constraint incentive mechanism in spatial-temporal ...

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作者： Kong, Xujiao Li, Peng Zhang, Tao Tu, Meimei Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

Mobile crowdsensing is an effective way to collect sensing data about regions of interest. Effective incentive mechanisms are invaluable in mobile crowd sensing, for recruiting a number of mobile users to fulfill coverage requirement in the interested regions. In this paper, we exploit the potential of mobile users by designing a spatial-temporal crowd sensing incentive mechanism for smartphone users. We first use the logistic regression technique to mine user preferences from the user's historical data. Then, we propose a sub-module approximation algorithm to solve the participant recruitment problem, which is proved to be an NP-hard problem. Moreover, we design the winner auction algorithm (WAA) based on the reverse auction mechanism to solve the critical payment allocation problem. Finally, we demonstrate that our mechanism achieves truthfulness, individual rationality, and high computation efficiency through both theoretical analysis and experimental simulation. © 2019 IEEE.

关键词： Approximation algorithms

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