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Joint Power and Spectrum optimization in Wireless Localization Networks

Joint Power and Spectrum Optimization in Wireless Localizati...

引用

international Conference on Communication workshop

作者： Chuan Qin Liyuan Song Tingting Zhang Yuan Shen Andreas F. Molisch Qinyu Zhang Communication Engineering Research Center Harbin Institute of Technology Shenzhen P. R. China Department of Electronic Engineering Tsinghua University Beijing P. R. China Department of Electronic Engineering University of Southern California Los Angeles USA

Network localization is a key feature in many wireless services and applications. In typical rang-based localization techniques, "agents" try to perform position estimation through ranging with respect to "anchors" with known positions. Based on the definition of squared positional error bound (SPEB), the localization accuracy can be determined by the transmit power, carrier frequency and signal bandwidth, etc. This paper analyzes the joint power and spectrum allocation (JPSA) optimization problems in resource restricted wireless localization systems. We first formulate both interference-free and interference-limited JPSA problems. Since both problems are non-convex, we then develop two approximation algorithms based on single condensation and Taylor linearization. Both algorithms are able to find solutions close the global optimum. Numeric results validate our analysis, and show that, we are able to find optimal resource deployment in wireless localization networks based on the proposed frameworks.

关键词： Joint Power Spectrum optimization Wireless Localization Networks

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A new distributed modified extremal optimization for optimizing protein structure alignment

A new distributed modified extremal optimization for optimiz...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Keiichi Tamura Hajime Kitakami Tatsuhiro Sakai Yoshifumi Takahashi Hiroshima Shiritsu Daigaku Hiroshima JP Graduate School of Information Sciences Hiroshima City University Hiroshima Japan

Identifying similar structures in proteins has emerged as one of the most attractive research topics in the post-genome era. Protein structure alignment, which is similar to sequence alignment, identifies the structural homology between two protein structures according to their three-dimensional conformation. One of the simplest yet most robust techniques for optimizing protein structure alignment is the contact map overlap maximization problem (the CMO problem). In this paper, we focus on heuristics for the CMO problem. In our previous work, we proposed a bio-inspired heuristic using distributed modified extremal optimization (DMEO) for the CMO problem. DMEO is a hybrid of population-based modified extremal optimization (PMEO) and the island model. DMEO enhances population diversity; however, individual evolution is extremely monotonous because evolutions of it is based on the greedy moving approach. To address this issue, we propose a novel bio-inspired heuristic, i.e., DMEO with different evolutionary strategy (DMEODES). DMEODES is also based on the island model; however, some of the islands, called hot-spot islands, have a different evolutionary strategy. To evaluate DMEODES, we used actual protein structures. Experimental results showed that DMEODES outperforms DMEO.

关键词： Proteins optimization Sociology Statistics Biological system modeling Linear programming Genetic algorithms

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Rapid antenna prototyping method by evolutionary computation and inkjet printing

Rapid antenna prototyping method by evolutionary computation...

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IEEE MTT-S international Microwave workshop Series on Advanced Materials and Processes for RF and THz applications (IMWS-AMP)

作者： Xinyang Shi Yoshiaki Narusue Yoshihiro Kawahara Tohru Asami Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Japan Society for the Promotion of Science Kojimachi Business Center Building Tokyo Japan JST PRESTO Research Division Gobancho Building Tokyo Japan

Antenna designs using evolutionary computation methods have been widely used because of the rapid development of wireless technologies. However, a large number of antennas must be analyzed when evolutionary computation methods are adopted. Running a simulation is the main method of obtaining information about an antenna, such as bandwidth and input impedance, but it is very time-consuming because complicated calculations are needed. In this paper, we proposed a method that quickly realizes optimization of structures of inkjet-printed antennas. We propose the use of inkjet technology to implement candidates generated by evolutionary computation methods and use the measurement results instead of complex calculations. This method also solves the problem in which the fitness of candidates is sometimes difficult to directly realize from simulations. This paper presents the optimization of an inkjet-printed antenna for energy harvesting from TV broadcast waves using the proposed technique and a genetic algorithm. An antenna in which the output voltage is 1.4 times that of a rectangular dipole was designed.

关键词： Dipole antennas Antenna measurements optimization Voltage measurement TV broadcasting Genetic algorithms

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Identification of hybrid systems using stable spline kernels

Identification of hybrid systems using stable spline kernels

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IEEE workshop on Machine Learning for Signal Processing

作者： Gianluigi Pillonetto Dept. of Information Engineering University of Padova

All the approaches for identification of hybrid systems appeared in the literature assume known the model complexity. Widely used models are e.g. piecewise ARX with a priori fixed orders. In addition, the developed algorithms are typically tested only on quite simple systems, e.g. with ARX subsystems of order 1 or at most 2. This is a significant limitation for real applications. Here, we propose a new regularized technique for identification of piecewise affine systems, which we dub the hybrid stable spline algorithm (HSS). HSS exploits the recently introduced stable spline kernel to model the submodels impulse responses as zero-mean Gaussian processes, embedding information on submodels predictor stability. Using the Bayesian interpretation of regularization, the problem of classifying and distributing the data to the subsystems is cast as marginal likelihood optimization. An approximated optimization is performed by a Markov chain Monte Carlo scheme. Then, the stable spline algorithm is used to reconstruct each subsystem. Numerical experiments show that HSS can identify high-order piecewise affine systems, without having exact information on ARX subsystems order.

关键词： Splines (mathematics) optimization Kernel Random variables Indexes Bayes methods Markov processes

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Large-Scale Linear Support Vector Ordinal Regression Solver

Large-Scale Linear Support Vector Ordinal Regression Solver

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IEEE international Conference on Data Mining workshops (ICDM workshops)

作者： Yong Shi Huadong Wang Lingfeng Niu Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing China University Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467384940

In multiple classification, there is a type of commonproblems where each instance is associated with an ordinal label, which arises in various settings such as text mining, visual recognition and other information retrieval tasks. The support vectorordinal regression (SVOR) is a good model widely used for ordinalregression. In some applications such as document classification, data usually appears in a high dimensional feature space andlinear SVOR becomes a good choice. In this work, we developan efficient solver for training large-scale linear SVOR basedon alternating direction method of multipliers(ADMM). Whencompared empirically on benchmark data sets, the proposedsolver enjoys advantages in terms of both training speed andgeneralization performance over the method based on SMO, which invalidate the effectiveness and efficiency of our algorithm.

关键词： Support vector machines Training Prediction algorithms Machine learning algorithms Big data Measurement optimization

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A peer to peer traffic identification method based on support vector machine and artificial bee colony algorithm

A peer to peer traffic identification method based on suppor...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Chunzhi Wang Huili Zhang Zhiwei Ye Yuanli Du Hubei University of Technology School of Computer Science Wuhan China

ISBN: (纸本)9781467383622

Today, Peer-to-peer (P2P) traffic is the most important network flow on the Internet; meanwhile it gives rise to many security problems for the network management. Therefore P2P traffic identification is the hottest topic of P2P traffic management. Support vector machine (SVM) has advantages with resolving small samples for P2P classification problems. However, the performance of SVM is primarily dependent on its parameters. At present, genetic algorithm and particle swarm optimization algorithm are commonly used to learn best parameters for SVM. However, these methods are easy to run into local optimal solution. Thus, artificial bee colony algorithm is proposed to optimize the parameters of SVM and has been applied to P2P traffic identification. And the real campus traffic data is used as the research object to do simulating experiments. The experimental results show that the parameters of support vector machine optimized by artificial bee colony have high P2P classification accuracy.

关键词： Support vector machines Classification algorithms optimization Kernel Algorithm design and analysis Training Genetic algorithms

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QPSO-ELM: An evolutionary extreme learning machine based on quantum-behaved particle swarm optimization

QPSO-ELM: An evolutionary extreme learning machine based on ...

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international workshop on Advanced Computational Intelligence (IWACI)

作者： Zeping Yang Xinxiu Wen Zhanquan Wang School of Information Science and Engineering East China University of science and Technology Shanghai China Department of Computer Science and Engineering University of Minnestoa USA

Extreme learning machine (ELM), as an emergent technology, has attracted tremendous attention from various fields for its fast learning speed. Different from traditional gradient-based learning algorithms for feed-forward neural networks, ELM need not be neuron alike and learns with good generalization performance. However, ELM may require more hidden neurons than traditional tuning-based learning algorithms in some applications due to the random assignment of the input weights and hidden biases. In this paper, a novel evolutionary ELM is proposed named QPSO-ELM which uses the quantum-behaved particle swarm optimization (QPSO) to select the input weights and hidden layer biases and reduces both the structural and empirical risks. The experimental results demonstrate the effectiveness of the proposed method with more compact networks.

关键词： Training Testing Neural networks Nickel Accuracy Glass Liver

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An image threholding approach based on cuckoo search algorithm and 2D maximum entropy

An image threholding approach based on cuckoo search algorit...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Wei Zhao Zhiwei Ye Mingwei Wang Lie Ma Wei Liu School of Computer Science Hubei University of Technology Engineering China Beijing Key Laboratory of Urban Spatial Information Beijing China

ISBN: (纸本)9781467383622

The image thresholding approach based on the basis of 2-D maximum entropy has better segmentation performance by the use of local space information of pixels, but it is unpractical for heavy computation required by this method. In the paper, an image segmentation technology based on cuckoo search and 2-D maximum entropy is presented, which views the seeking of 2-D maximum entropy of the image as a function optimization problem and uses the behavior of the obligate brood parasitism of some cuckoo species to simulate the process of searching optimal threshold. Furthermore, a local search strategy is employed to improve the results in the cuckoo search algorithm. The experimental results proves that compared with 2-D maximum entropy thresholding optimized with genetic algorithm, differential evolution algorithm and particle swarm optimization algorithm, the proposed method is able to get the optimal thresholds quickly and with a higher probability to get optimal threshold, which is a fast and robust image segmentation method.

关键词： Entropy Image segmentation Histograms optimization Algorithm design and analysis Search problems Genetic algorithms

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Improving Performance of Structured-Memory, Data-Intensive applications on Multi-core Platforms via a Space-Filling Curve Memory Layout

Improving Performance of Structured-Memory, Data-Intensive A...

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IEEE international Symposium on Parallel and Distributed Processing workshops and Phd Forum (IPDPSW)

作者： E. Wes Bethel David Camp David Donofrio Mark Howison Lawrence Berkeley National Laboratory Berkeley CA USA Lawrence Berkeley National Laboratory and Brown University Providence RI USA

ISBN: (纸本)9781467376853

Many data-intensive algorithms -- particularly in visualization, image processing, and data analysis -- operate on structured data, that is, data organized in multidimensional arrays. While many of these algorithms are quite numerically intensive, by and large, their performance is limited by the cost of memory accesses. As we move towards the exascale regime of computing, one central research challenge is finding ways to minimize data movement through the memory hierarchy, particularly within a node in a shared-memory parallel setting. We study the effects that an alternative in-memory data layout format has in terms of runtime performance gains resulting from reducing the amount of data moved through the memory hierarchy. We focus the study on shared-memory parallel implementations of two algorithms common in visualization and analysis: a stencil-based convolution kernel, which uses a structured memory access pattern, and ray casting volume rendering, which uses a semi-structured memory access pattern. The question we study is to better understand to what degree an alternative memory layout, when used by these key algorithms, will result in improved runtime performance and memory system utilization. Our approach uses a layout based on a Z-order (Morton-order) space-filling curve data organization, and we measure and report runtime and various metrics and counters associated with memory system utilization. Our results show nearly uniform improved runtime performance and improved utilization of the memory hierarchy across varying levels of concurrency the applications we tested. This approach is complementary to other memory optimization strategies like cache blocking, but may also be more general and widely applicable to a diverse set of applications.

关键词： Layout Runtime Arrays Three-dimensional displays Algorithm design and analysis Indexes Data visualization

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Fusion of Inertial and Visual Measurements for RGB-D SLAM on Mobile Devices

Fusion of Inertial and Visual Measurements for RGB-D SLAM on...

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international Conference on Computer Vision workshops (ICCV workshops)

作者： Nicholas Brunetto Samuele Salti Nicola Fioraio Tommaso Cavallari Luigi Di Stefano Luigi Di Stefano Department of Computer Science and Engineering University of Bologna Bologna Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste Trieste Friuli-Venezia Giulia IT

Simultaneous Localization and Mapping (SLAM) algorithms have been recently deployed on mobile devices, where they can enable a broad range of novel applications. Nevertheless, pure visual SLAM is inherently weak at operating in environments with a reduced number of visual features. Indeed, even many recent proposals based on RGB-D sensors cannot handle properly such scenarios, as several steps of the algorithms are based on matching visual features. In this work we propose a framework suitable for mobile platforms to fuse pose estimations attained from visual and inertial measurements, with the aim of extending the range of scenarios addressable by mobile visual SLAM. The framework deploys an array of Kalman filters where the careful selection of the state variables and the preprocessing of the inertial sensor measurements result in a simple and effective data fusion process. We present qualitative and quantitative experiments to show the improved SLAM performance delivered by the proposed approach.

关键词： Visualization Simultaneous localization and mapping Cameras Kalman filters Mobile communication optimization Mobile handsets

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