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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing II"

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Image Download and Rate Allocation of Internet-of-Things Analytics at Gateways in Smart Cities

Image Download and Rate Allocation of Internet-of-Things Ana...

引用

IEEE Global Communications conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Wang, Yu-Jung Dubey, Vijay Hsu, Cheng-Hsin Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781728182988

Internet-of-Things (IoT) devices are connected to the Internet through a gateway, which can host IoT analytics encapsulated in containers to convert raw sensor data into more condensed processed data. In this paper, we study two research problems to maximize the overall Quality-of-Service (QoS) level of all IoT analytics that run on both data center servers and gateways. The first problem is to select additional IoT analytics to deploy on a gateway to save upload bandwidth due to transmitting raw sensor data. The second problem is to allocate the residue upload bandwidth among all IoT analytics to maximize the overall QoS level. We propose several algorithms to solve these two research problems. We have implemented real testbeds to evaluate our proposed system and algorithms. Our experiment results reveal that the proposed algorithms: (i) capitalize the download bandwidth and storage space of the gateway for saving the upload bandwidth consumption and (ii) achieve high QoS levels without overloading the network and gateway.

关键词： IoT gateway edge computing optimization experimental virtualization analytics machine learning

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Hierarchical Classification of Android Malware Traffic 21

Hierarchical Classification of Android Malware Traffic

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21st IEEE International conference on Trust, Security and Privacy in computing and Communications (IEEE TrustCom)

作者： Bovenzi, Giampaolo Persico, Valerio Pescape, Antonio Piscitelli, Anna Spadari, Vincenzo Univ Napoli Federico II Naples Italy

ISBN: (纸本)9781665494250

In the last few years, Android mobile devices have encountered a large spread and nowadays a huge part of the traffic traversing the Internet is related to them. In parallel, the number of possible threats and attacks has also increased, thus emphasizing the need for accurate automatic malware detection systems. In this paper, we design and evaluate a system to detect whether a traffic object (biflow) is benign or malicious, possibly understanding its specific nature in the latter case. The proposal leverages machine learning in a hierarchical fashion, in order to capitalize on the structure of the traffic data and reap both design and performance benefits. The comparative evaluation-performed considering the public CICAndMal2017 dataset-assesses the performance of several machine-learning algorithms and witnesses that the hierarchical approach leads to improved performance w.r.t. the flat approach (up to +0.18 F1-score, depending on the granularity of the analysis and the machine learning algorithm considered). In addition, we evaluate the impact of a reject-option mechanism, showing the trade-off between classification accuracy and ratio of classified biflows.

关键词： Android traffic classification malware detection hierarchical approach LCPN

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Implementation of crow search algorithm for optimal allocation of DG and DSTATCOM in practical distribution system 2

Implementation of crow search algorithm for optimal allocati...

引用

2nd IEEE International conference on Power, Instrumentation, Control and computing, PICC 2018

作者： Sannigrahi, Surajit Acharjee, Parimal Electrical Engineering Department National Institute of Technology Durgapur Durgapur India

ISBN: (纸本)9781538624623

In this paper, a modified crow search algorithm (MCSA) is proposed for obtaining the optimal size and site of different types of distributed generations (DG) and distributed static compensator (DSTATCOM) in a 51-bus practical distribution system (PDS) with an aim to improve system voltage, reduce line losses, maximize economic benefit, and decrease the pollution level. Logical and innovative formulas are developed to measure the technical, economic, and environmental impact of these devices. In the modified version of CSA (MCSA), the control parameters are logically tuned to make them adaptive with the iteration. The optimal allocation for each type of device is conducted at different load levels and their techno-economic and environmental performances are accomplished to identify the most suitable device for PDS. The proposed algorithm is compared with the other algorithms to show its effectiveness. © 2018 IEEE.

关键词： Iterative methods

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Reducing Handover Metrics For Access Network Selection In Heterogeneous Wireless Networks

Reducing Handover Metrics For Access Network Selection In He...

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2nd International conference on Innovative computing Technology (INTECH)

作者： Lahby, Mohamed Cherkaoui, Leghris Adib, Abdellah Hassan II Univ Casablanca Fac Sci & Technol Mohammedia Dept Comp Sci LIM Lab BP 146 Mohammadia Morocco

ISBN: (纸本)9781467326797

Nowadays, with the evolution of the mobile devices towards a multimode architecture, the users have the opportunity to utilize a wealth of services across a multitude of radio access technologies (RAT's). Variety of vertical handoff algorithms (VHA) have been proposed to help the user to select dynamically the best access network (BAN) in terms of quality of service. However, due to nature of VHA which is NP-Hard problem, the objective of this paper is to provide an optimal network selection algorithm that allow mobile users to choose the BAN with seamless manner. The optimized algorithm exploits a minimum of criteria and combines two multi attribute decision making (MADM) methods such as analytic network process (ANP) method to find the weights of the available networks, and the the grey relational analysis (GRA) method to rank the alternatives. Simulation results are presented to evaluate our optimized network selection decision.

关键词： Heterogeneous Wireless Networks Network Selection IEEE 802.21 Multi Attribute Decision Making Criticality Analysis

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Robust Detection of Heart Beats in Multimodal Data: The PhysioNet/computing in Cardiology Challenge 2014 41

Robust Detection of Heart Beats in Multimodal Data: The Phys...

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41st computing in Cardiology conference (CinC)

作者： Moody, George Moody, Benjamin Silva, Ikaro MIT Room E25-505A Cambridge MA 02139 USA

ISBN: (纸本)9781479943463

The 15th annual PhysioNet/CinC Challenge aims to encourage the exploration of robust methods for locating heart beats in continuous long-term data from bedside monitors and similar devices that record not only ECG but usually other physiologic signals as well, including pulsatile signals that directly reflect cardiac activity, and other signals that may have few or no observable markers of heart beats. Our goal is to accelerate development of open-source research tools that can reliably, efficiently, and automatically analyze data such as that contained in the MIMIC 11 Waveform Database, making use of all relevant information. Data for this Challenge are 10-minute (or occasionally shorter) excerpts ("records") of longer multi-parameter recordings of human adults, including patients with a wide range of problems as well as healthy volunteers. Each record contains four to eight signals;the first is an ECG signal in each case, but the others are a variety of simultaneously recorded physiologic signals that may be useful for robust beat detection. We prepared and posted 100 training records, and retained 300 hidden test records for evaluation of Challenge entries. A total of 1,332 entries from 60 teams were processed during the challenge period.

关键词： electrocardiography medical signal detection ECG MIMIC ii Waveform Database PhysioNet computing PhysioNet/CinC Challenge cardiac activity cardiology challenge 2014 heart beats detection human adults multiparameter recordings pulsatile signals Abstracts Heart Java Robustness Software Software algorithms Subspace constraints

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Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision 19

Source Compression with Bounded DNN Perception Loss for IoT ...

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25th Annual International conference on Mobile computing and Networking (MobiCom)

作者： Xie, Xiufeng Kim, Kyu-Han Hewlett Packard Labs Palo Alto CA 94304 USA

ISBN: (纸本)9781450361699

IoT and deep learning based computer vision together create an immense market opportunity, but running deep neural networks (DNNs) on resource-constrained IoT devices remains challenging. Offloading DNN inference to an edge server is a promising solution, but limited wireless bandwidth bottlenecks its end-to-end performance and scalability. While IoT devices can adopt source compression to cope with the limited bandwidth, existing compression algorithms (or codecs) are not designed for DNN (but for human eyes), and thus, suffer from either low compression rates or high DNN inference errors. This paper presents GRACE, a DNN-aware compression algorithm that facilitates the edge inference by significantly saving the network bandwidth consumption without disturbing the inference performance. Given a target DNN, GRACE (i) analyzes DNN's perception model w.r.t both spatial frequencies and colors and (ii) generates an optimized compression strategy for the model - one-time offline process. Next, GRACE deploys thus-generated compression strategy at IoT devices (or source) to perform online source compression within the existing codec framework, adding no extra overhead. We prototype GRACE on JPEG (the most popular image codec framework), and our evaluation results show that GRACE indeed achieves the superior compression performance over existing strategies for key DNN applications. For semantic segmentation tasks, GRACE reduces a source size by 23% compared to JPEG with similar interference accuracy (0.38% lower than GRACE). Further, GRACE even achieves 7.5% higher inference accuracy than JPEG with a commonly used quality level of 75 does. For classification tasks, GRACE reduces the bandwidth consumption by 90% over JPEG with the same inference accuracy.

关键词： mobile edge computing neural networks image compression

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Object Recognition Through Smartphone Using Deep Learning Techniques 1

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2nd International conference on Soft computing Systems (ICSCS)

作者： Kamble, Kiran Kulkarni, Hrishikesh Patil, Jaydeep Sukhatankar, Saurabh Walchand Coll Engn Dept Comp Sci & Engn Sangli Sangli India

ISBN: (数字)9789811319365

ISBN: (纸本)9789811319365;9789811319358

Object recognition technology has matured to a point at which exciting applications have become possible. Indeed, industry has created a variety of computer vision products and services from the traditional area of machine inspection to more recent applications such as object detection, video surveillance, or face recognition. This paper is about achieving the goal of object recognition through advanced techniques like deep learning on handy devices like smartphones and tablets. Deep learning algorithms (Convolutional Neural Networks (CNN)) are used for the primary aim of object recognition. Images are clicked through the camera of the smartphone during experimentation and are fed to the CNN network. The top four results predicted by the network are depicted on the smartphone screen in the audio and the visual form i. e. predicted object name and the probability of predicted object being the one actually clicked in the decreasing order of probabilities. The accuracy obtained in object recognition is about 93% through the application.

关键词： Modern object recognition technique Deep learning application CNN Mobile application

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A parallel independent component implement based on field-programmable- gate-array

A parallel independent component implement based on field-pr...

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5th International conference on Natural Computation, ICNC 2009

作者： Wang, Jinghui Zhao, Yuanchao Tianjin Key Laboratory of Intelligence Computing Novel Software Technology Tianjin University of Technology 300191 Tianjin China

ISBN: (纸本)9780769537368

The Independent component analysis (ICA) algorithm has been extensively investigated in its theory, implementation and applications. Up to now, there have many algorithms about ICA. The ICA algorithms can be roughly categorized as gradient-based, the second order gradientbased and diagonalization-based, etc. ICA algorithm requires a huge number of calculations. Our work wants to solve these limitations by increasing the computational speed. One way of increasing the computational speed is using multiple IP cores work together for a single problem. In the paper,we designed parallel ICA IP core and completed the parallel ICA algorithm modules on Altera Stratix ii GX devices. © 2009 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Design of photonic devices using bio-inspired algorithms

Design of photonic devices using bio-inspired algorithms

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SBMO/IEEE MTT-S International conference on Microwave and Optoelectronics (IMOC)

作者： Carlos H. Silva-Santosa Kleucio Claudiob Hugo E. Hernández-Figueroac Marcos Sérgio Gonçalves Department of Microwave and Optics/FEEC University of Campinas Brasil School of Technology University of Campinas Limeira Brasil

ISBN: (纸本)9781424453566;9781424453573

The design of photonic devices in computer simulators is a complex task and, in some cases, there are not analytical solutions to support. An alternative is the application of some optimizations techniques. For this reason, this work presents three different bio-inspired algorithms (GA, ES and AIS) to optimize three different photonic devices. The main objectives are providing a flexible and efficient computational optimization tool to design different photonic devices and benchmarking the efficiency of each one. In addition, the efficiency results compare the optimization convergence and computational efforts necessary for each optimization.

关键词： Algorithm design and analysis Optical computing Biology computing Stochastic processes Genetic algorithms Evolution (biology) Genetic mutations Optical design Computational modeling Filters

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Condar: Context-Aware Distributed Dynamic Object Detection on Radar Data

Condar: Context-Aware Distributed Dynamic Object Detection o...

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2024 Military Communications conference

作者： Harshbarger, Ian Zejda, Colin Levorato, Marco Univ Calif Irvine Irvine CA 92697 USA

ISBN: (纸本)9798350374247;9798350374230

Deep neural networks are an established class of algorithms widely used for real-time data analysis in computer vision-based mobile applications. However, on one hand the constrained resources - computing power, energy reservoir and memory - available to mobile devices clash with the complexity of high-performance neural models. On the other hand, offloading the execution of the computing task to a wirelessly connected edge/cloud server requires the transfer of information rich signals over volatile and capacity constrained channels. To address this issue, the Split computing (SC) paradigm has been proposed. In SC frameworks, a baseline DNN model is used to create two models, namely head and tail models, that are executed at a mobile device and edge/cloud server, respectively. In this work, a SC paradigm - Condar - is presented that introduces several core design and conceptual innovations: (i) we target object detection on radar data rather than 2D RGB images;(ii) we design encoders/decoder structures that are specialized to the specific operating environment;(iii) we develop a multi-branched model that can dynamically select the best performing encoder while executing the main model. Our design is based on the CenterNet model with a split ResNet50 backbone trained on the RADIATE dataset's radar images of resolution 1152x1152. Our results show that Condar has performance analogous to that of a Generalized CenterNet Model with a difference of 4.41%, 1.72%, and 2.18% on mAP@50, mAP@75, and mAP@COCO respectively, while simultaneously performing compression on the features of the data to 15.5KB. This datasize is smaller than lowest possible compression data size of JPEG compression at 21KB, which results in a considerable performance degradation.

关键词： Dynamic Deep Neural Networks Edge computing Split computing Radar Object Detection

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