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international Conference on Wireless Communications, Signal Processing and Networking

作者： Dipti Dash Debarshi Kumar Sanyal Arup Sarkar Dibyajyoti Guha School of Computer Engineering KIIT University Bhubaneswar 751024 India

ISBN: (纸本)9781467393393

The aim of topology-transparent scheduling algorithms for multi-hop wireless networks is to find a schedule for the nodes that does not need re-computation when the network topology changes. It caters to highly dynamic scenarios where topology changes occur faster than the speed at which schedule updates can be orchestrated. These algorithms normally take as input only global network parameters like the maximum number of nodes and the maximum degree of a node in the network, rather than the detailed topology of the network. In this paper, we study two classical topology-transparent scheduling algorithms: the algorithm due to Chlamtac and Farag6 and the optimal algorithm due to Ju and Li. We provide qualitative comparison of the algorithms followed by numerical simulations to study their throughput characteristics which are also compared with those of multi-hop slotted-ALOHA and TDMA.

关键词： Wireless network Media access control Topology Scheduling Mobility TDMA

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Battery-Aware Routing in Wireless Ad Hoc Networks - Part I: Energy Model

Battery-Aware Routing in Wireless Ad Hoc Networks - Part I: ...

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The 19th international Teletraffic congress-ITC19

作者： Yuanyuan Yang and Chi Ma Department of Electrical and Computer engineering State University of New York at Stony Brook, Stony Brook, NY 11790 Department of Computer Science State University of New York at Stony Brook, Stony Brook, NY 11790

A key challenge in wireless ad hoc networks is to achieve maximum lifetime for battery-powered mobile devices with dynamic energy efficient algorithms. Recent study in battery technology reveals that the behavior of battery discharging is more complex than we used to think. Battery powered devices might waste a huge amount of energy if we do not carefully schedule and budget their discharging. In this paper, we introduce a novel energy model for batteries and study the effect of battery behavior on routing in wireless ad hoc networks. Based on this model, we also propose a battery-aware routing protocol. The paper consists of two parts. In Part I of the paper, we propose an on-line computable discrete time analytical model to mathematically model battery discharging behavior. The model has low computational complexity and does not require large look-up tables. It is suitable for on-line battery capacity computation in ad hoc network routing. We use the data collected from actual nickel-cadmium battery to evaluate the performance of our model and the results show that it can accurately capture the behavior of battery discharging. In Part II of the paper [1], a battery-aware routing protocol (BAR) is proposed based on the new battery model. By dynamically choosing the nodes with well recovered batteries as routers, and leaving the "fatigue" nodes for recovery, the BAR protocol can effectively recover the node's battery capacity and achieve higher energy efficiency. Our simulation results show that the BAR protocol can increase network lifetime and total data throughput by up to 28% and 24%, respectively, compared with previous routing protocols. As far as we know, this is the first work considering battery-awareness with an accurate on-line computable battery model in ad hoc network routing. We believe our battery model can be used to explore other energy efficient schemes for wireless networks as well.

关键词： Wireless ad hoc networks, energy efficiency, battery models, battery-awareness, routing protocols.

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Fast Nonnegative Tensor Factorization based on Graph-Preserving for 3D Facial Expression Recognition

Fast Nonnegative Tensor Factorization based on Graph-Preserv...

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2016 IEEE 13th international Conference on Signal Processing（ICSP2016）

作者： Yunfang Fu Qiuqi Ruan Gaoyun An Yi Jin Institute of Information Science Beijing Jiaotong University School of Computer Science & Engineering Shijiazhuang University Beijing Key Laboratory of Advanced Information Science and Network Technology

In this paper, we utilize 4D tensor structure to explore efficient structure information for 3D facial expression recognition. As a powerful tool to analyze multidimensional nonnegative tensor data, nonnegative tensor factorization(NTF) aims to obtain a partly localized representation of highdimensional tensors. However, the NTF algorithms often suffer from both high computational complexity and memory requirements for high-order tensor data. To overcome these disadvantages, we present a low-rank approximation method to decrease the computation complexity. Meanwhile, we extract local geometric and discriminant information to improve facial recognition. Therefore, we propose fast nonnegative tensor factorization based on graph-preserving(FNTFGP) algorithm for 3D facial expression recognition(FER). It is a trial to explore and apply the proposed method into 3D FER. Experiments are conducted on the BU-3DFE database. The result of experiments shows the validity and efficiency of the proposed approaches. The proposed algorithm opens a promising direction for the higher performance of 3D facial expression recognition.

关键词： 3D facial expression recognition nonnegative tensor factorization low-rank approximation graph preserving neighborhood preserving constraint

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A comparative account of modelling seizure detection system using wavelet techniques

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international Journal of Systems Science: Operations and Logistics 2017年第1期4卷 41-52页

作者： Swami, Piyush Gandhi, Tapan K. Panigrahi, Bijaya K. Bhatia, Manvir Santhosh, Jayasree Anand, Sneh Centre for Biomedical Engineering Indian Institute of Technology Delhi and Department of Biomedical Engineering All India Institute of Medical Sciences New Delhi India Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Department of Neurology Saket City Hospital New Delhi India Department of Biomedical Engineering University of Malaya Kuala Lumpur Malaysia

Over the last decade, application of wavelet transform (WT) has been realised for extracting features during epileptic seizure detection. Although noteworthy developments have been made in WT algorithms, most of the seizure detection works have been confined to using discrete wavelet transform (DWT). In this present effort, comparisons are made between DWT, wavelet packet transform and dual-tree complex wavelet transform (DT-CWT) for detection of epileptiform patterns in electroencephalography. For the study, combinations of energy, root-mean-square values and standard deviations are used as extracted feature inputs to the general regression neural network classifier. The paper describes unique methodology of using minimal training during K-folds cross-validation to highlight the robustness of the expert model. Classification rates, statistical parameters and computation timings are finally calculated and one-way analysis of variance is applied to validate the results. The results demonstrate statistically significant and ceiling level of classification performances (>98%) using DT-CWT extracted coefficients. The present study provides a comparative account for selection of the best model for faithful diagnosis of epilepsy with speed. © 2016, © 2016 Informa UK Limited, trading as Taylor & Francis Group.

关键词： discrete wavelet transform (DWT) dual-tree complex wavelet transform (DT-CWT) Electroencephalography (EEG) seizure wavelet packet transform (WPT)

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复杂齿轮传动过程多性态建模及齿面摩擦因数反求研究

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中国工程机械学报 2016年第4期14卷 295-299页

作者：符双学周长江广州民航职业技术学院广东广州510403 湖南大学汽车车身先进设计制造国家重点实验室湖南长沙410082

基于齿轮传动动测试验,引入有限单元法多场耦合分析和反求技术,提出了面向工程的复杂齿轮传动摩擦多性态概念,建立了复杂齿轮传动的"计算-试验综合模型".在典型传动工况下,通过试验提取轮齿敏感区域的特征参数(齿根应变),并... 详细信息

基于齿轮传动动测试验,引入有限单元法多场耦合分析和反求技术,提出了面向工程的复杂齿轮传动摩擦多性态概念,建立了复杂齿轮传动的"计算-试验综合模型".在典型传动工况下,通过试验提取轮齿敏感区域的特征参数(齿根应变),并以此作为齿面摩擦因数反求的输入;借助有限单元法多场耦合分析技术真实模拟相应传动工况下的齿轮传动过程,并提取对应位置的齿根应变;由此建立以摩擦因数为自变量的目标函数,利用隔代映射遗传算法(international projection Genentic Algorithm,IP-GA)的全局快速的搜索能力进行优化,输出在此传动工况下的齿面摩擦因数.相对于传统求解方法而言,该方法将系统建模、理论研究、数值模拟与试验测试相结合,实现了复杂齿轮传动系统齿面摩擦因数的量化计算,求解更趋合理.齿面摩擦的深入研究,对完整构建齿面摩擦特性参数的计算理论体系、探索齿轮摩擦机理、促进减振降噪技术的开发均具有重要的意义.

关键词：齿轮传动多性态建模摩擦因数遗传算法反求

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Preparations of deproteinized and pale-colored natural rubber films

Preparations of deproteinized and pale-colored natural rubbe...

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The 3rd international congress on Advanced Materials（AM2016）

作者： Wiwat Pichayakorn Wirach Taweepreda Jirapornchai Suksaeree Chanin Bumroongrat Nantanat Polraksa Department of Pharmaceutical Technology Faculty of Pharmaceutical Sciences Prince of Songkla University Department of Materials Science and Technology Faculty of Science Prince of Songkla University Department of Pharmaceutical Chemistry Faculty of Pharmacy Rangsit University

One problem of dried rubber sheet from natural rubber latex is non-rubber protein contamination and dark color of its films after aging. This study aimed to prepare the dried rubber films with pale-color and low protein contamination. Some additives and leaching process were chosen to prove this problem. Protein contamination was measured by Kjeldahl method. Color change of CIE L*a*b* scale technique was determined by color spectrophotometer, and E color difference algorithms were calculated for pale-color evaluation. The mechanical properties of thin film were measured by texture analyzer. It was found that both Texapon-N70(70% sodium lauryl ether sulfate) surfactant and 1% Uniphen P-23 preservative were the effective additives to stabilize latex. After appropriate incubation time, these mixtures were leached and centrifuged to remove the soluble serum, and then reprocessed as needed. Increase cycles of re-centrifuged processing could increase the efficacy of deproteinization and color-reducing. However, this reprocess caused the loss of rubber mass from latex, and increased the production cost. Increase amounts of Texapon-N70 also increased the efficacy of deproteinization and color-reducing. The combination with potassium hydroxide(KOH) could enhance the efficacy of deproteinization and color-reducing that could reduce the amount of Texapon-N70 used, but their tensile strength and elongation values slightly decreased. The appropriate condition for this study was 0.5% Texapon-N70, 0.5% KOH, 1% Uniphen P-23, 60 minutes incubation time, and 1 cycle leaching with water that could reduce protein contamination for 91.05% and color change for E=482.22. This condition could be reproducible and scaled-up in pilot scale.

关键词： Deproteinization Pale-color Natural rubber Additives Leaching

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BGFormer: Deep Bayersian Gabor Filtering Guided Transformer for Medical Image Denoising

BGFormer: Deep Bayersian Gabor Filtering Guided Transformer ...

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IEEE international Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Yue He Rui Zhang Zhichao Jiang Hu Zhu School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

In recent years, modern diagnostic medical technology has developed rapidly, and computer tomography (CT) has become an important tool due to its fast detection speed and low cost. It is frequently used to assist in diagnosing complex conditions such as fractures and tumors. However, low-dose CT can lead to a decrease in image quality. For de-noising low-dose CT images, the enhanced performance not only relies on local features but also depends on the overall global characteristics due to the specific nature of medical images, where the shape and location of lesions vary for each patient. Nowadays, deep learning methods have gradually replaced traditional methods as the main-stream direction in low-dose CT image denoising, owing to their powerful feature representation capabilities. However, most algorithms still suffer from issues such as unclear local details, blurred line contours, and high computational complexity. In this paper, we propose a medical image denoising method based on gabor filtering neural network, combining the advantages of traditional filters with the unique strengths of deep learning. First, since gabor filtering is particularly effective for medical image processing, we use gabor filter obtained through variational inference instead of traditional convolutions, which enhances performance while reducing computational complexity. Second, we incorporate the use of Transformers in feature extraction to address the limitations of upsampling and downsampling operations, which cannot effectively model long-term context interactions and maintain the overall texture features of the images. Finally, we validate our method on the 2016 AAPM-Mayo Clinic Low-Dose CT Grand Challenge dataset and a fluorescence microscopy denoising dataset. The results show that our approach outperforms existing denoising methods in terms of PSNR, SSIM, and RMSE metrics, demonstrating its superiority.

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9627卷 v页

作者： Petreschi, Rossella Kaykobad, Mohammad Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Computer Science Sapienza University of Rome Rome Italy

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基于新闻的国际天然气价格趋势预测方法

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计算机科学 2021年第S1期48卷 235-239页

作者：裴莹李天祥王鏖清付加胜韩霄松长春财经学院信息工程学院长春130122 中国科学院大学北京100049 中国科学院新疆理化技术研究所乌鲁木齐830011 吉林大学计算机科学与技术学院符号计算与知识工程教育部重点实验室长春130012 中国石油集团工程技术研究院有限公司北京102206

天然气作为新型清洁能源,不仅有着重要的能源意义,作为期货交易的大宗商品之一,也有着重要的经济意义,是国家经济和国际贸易的重要组成。但是由于天然气价格受经济因素、政治因素、自然因素甚至人为因素等多种因素的影响,准确预测其价... 详细信息

天然气作为新型清洁能源,不仅有着重要的能源意义,作为期货交易的大宗商品之一,也有着重要的经济意义,是国家经济和国际贸易的重要组成。但是由于天然气价格受经济因素、政治因素、自然因素甚至人为因素等多种因素的影响,准确预测其价格十分困难。因此,文中设计了一种基于新闻的天然气价格趋势预测方法,该方法首先利用爬虫获取大量天然气相关新闻,并针对新闻进行嵌入表示和情感分析,运用格兰杰因果检验方法证明了天然气价格与相关新闻的情感倾向具有因果关系,并将新闻情感作为新闻向量的权值,将其相乘作为模型输入,然后构建了一个CNN-LSTM融合模型,CNN用于提取新闻特征,LSTM用于捕捉新闻和天然气价格时间序列信息,从而得到了62%的准确率,优于绝大多数机器学习算法。

关键词：自然语言处理天然气价格深度学习因果检验新闻

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环形综合学习粒子群算法研究

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湘南学院学报 2021年第5期42卷 36-42,50页

作者：林安平李翔于盈湘南学院物理与电子电气工程学院湖南郴州423000 交通运输部北海航海保障中心大连航标处辽宁大连116000

综合学习粒子群算法(CLPSO)具有较强的勘探能力,在多峰函数中表现出色.然而由于CLPSO取消了向全局最优学习环节,引起开采能力不足.为了进一步提高CLPSO的性能和适应性,本研究将环形粒子群算法(RPSO)和CLPSO相结合,提出了环形综合学习粒... 详细信息

综合学习粒子群算法(CLPSO)具有较强的勘探能力,在多峰函数中表现出色.然而由于CLPSO取消了向全局最优学习环节,引起开采能力不足.为了进一步提高CLPSO的性能和适应性,本研究将环形粒子群算法(RPSO)和CLPSO相结合,提出了环形综合学习粒子群算法(RCLPSO).RCLPSO采用环形结构生成学习样本,并增加了向全局最优学习环节以增强开采能力.为验证RCLPSO的性能,采用国际进化计算大会单目标优化测试集(CEC2017)对RCLPSO进行测试,将测试结果与PSO-cf, CLPSO,HCLPSO,EPSO,ABC和CS六种算法进行比较.测试结果表明,RCLPSO具有较高的性能和较强的适应性,其综合表现优于六种对比算法.在拓谱雷达优化中,RCLPSO展示出较高的精度和较高的稳定性.

关键词：综合学习粒子群算法勘探开采

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