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10th International conference on Simulated Evolution and Learning, SEAL 2014

10th International Conference on Simulated Evolution and Lea...

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10th International conference on Simulated Evolution and Learning, SEAL 2014

ISBN: (纸本)9783319135625

The proceedings contain 71 papers. The special focus in this conference is on Simulated Evolution and Learning. The topics include: Solving dynamic optimisation problem with variable dimensions;a probabilistic evolutionary optimization approach to compute quasiparticle braids;adaptive system design by a simultaneous evolution of morphology and information processing;generating software test data by particle swarm optimization;a steady-state genetic algorithm for the dominating tree problem;evolution of developmental timing for solving hierarchically dependent deceptive problems;the introduction of asymmetry on traditional 2-parent crossover operators for crowding and its effects;the performance effects of interaction frequency in parallel cooperative coevolution;customized selection in estimation of distribution algorithms;a hybrid GP-tabu approach to QoS-aware data intensive web service composition;a modified screening estimation of distribution algorithm for large-scale continuous optimization;clustering problems for more useful benchmarking of optimization algorithms;fuzzy clustering with fitness predator optimizer for multivariate data problems;effects of mutation and crossover operators in the optimization of traffic signal parameters;a GP approach to QoS -aware web service composition and selection;user preferences for approximation-guided multi-objective evolution;multi-objective optimisation, software effort estimation and linear models;adaptive update range of solutions in MOEA/D for multi and many-objective optimization;classification of lumbar ultrasound images with machine learning;schemata bandits for binary encoded combinatorial optimisation problems;anomaly detection using replicator neural networks trained on examples of one class and genetic programming for multiclass texture classification using a small number of instances.

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A high performance hardware based RNS-to-binary converter

A high performance hardware based RNS-to-binary converter

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IEEE International conference on Consumer Electronics (ICCE)

作者： K. M. Karthik C. H. Vun Nanyang Technological University Singapore

Due to its small size and parallel arithmetic operations, Residue Number System (RNS) based data is well suitable for implementing various digital signal processing functions that are commonly used in many consumer electronic devices nowadays. This paper presents the design of the RNS to Binary reverse converter which is typically the bottleneck in an RNS based signal processing system. Specifically, it describes a pipelined parallel prefix based modular adders which is used to implement the reverse converter for the {2 k -1, 2 k , 2 k +1} moduli set based RNS system.

关键词： Adders Hardware Logic gates Consumer electronics Digital signal processing Computer architecture Delays

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small cell switch policy: A reinforcement learning approach

Small cell switch policy: A reinforcement learning approach

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International conference on Wireless Communications and signal processing (WCSP)

作者： Luyang Wang Xinxin Feng Xiaoying Gan Jing Liu Hui Yu Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781479973408

small cell is a flexible solution to satisfy the continuously increasing wireless traffic demand. In this paper, we focus on on-off switch operation on small cell base stations (SBS) in heterogeneous networks. In our scenario, the users can either choose SBS when it is active or macro cell base station (MBS) to transmit data. Start-up energy cost is considered when SBS switches on. The whole network acts as a queueing system, and network latency is also under consideration. The network traffic is modeled by a Markov Modulated Poisson Process (MMPP) whose parameters are unknown to the network control center. To maximize the system reward, we introduce a reinforcement learning approach to obtain the optimal on-off switch policy. The learning procedure is defined as a Markov Decision Process (MDP). An estimation method is proposed to measure the load of the network. A single-agent Q-learning algorithm is proposed afterwards. The convergence of this algorithm is proved. Simulation results are given to evaluate the performance of the proposed algorithm.

关键词： Switches Scattering Computer architecture Microprocessors Energy consumption Convergence Markov processes

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VOICE CONVERSION IN TIME-INVARIANT SPEAKER-INDEPENDENT SPACE

VOICE CONVERSION IN TIME-INVARIANT SPEAKER-INDEPENDENT SPACE

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IEEE International conference on Acoustics, Speech and signal processing

作者： Toru Nakashika Tetsuya Takiguchi Yasuo Ariki Graduate School of System Informatics Kobe University 1-1 Rokkodai Nada-ku Kobe 657-8501 Japan

ISBN: (纸本)9781479928941

In this paper, we present a voice conversion (VC) method that utilizes conditional restricted Boltzmann machines (CRBMs) for each speaker to obtain time-invariant speaker-independent spaces where voice features are converted more easily than those in an original acoustic feature space. First, we train two CRBMs for a source and target speaker independently using speaker-dependent training data (without the need to parallelize the training data). Then, a small number of parallel data are fed into each CRBM and the high-order features produced by the CRBMs are used to train a concatenating neural network (NN) between the two CRBMs. Finally, the entire network (the two CRBMs and the NN) is fine-tuned using the acoustic parallel data. Through voice-conversion experiments, we confirmed the high performance of our method in terms of objective and subjective evaluations, comparing it with conventional GMM, NN, and speaker-dependent DBN approaches.

关键词： Voice Loudspeakers speaker-dependent Eigenvector HIGH-PERFORMANCE Training data subjective evaluation TRAIN

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ACOUSTIC MODEL SELECTION USING LIMITED data FOR ACCENT ROBUST SPEECH RECOGNITION

ACOUSTIC MODEL SELECTION USING LIMITED DATA FOR ACCENT ROBUS...

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European signal processing conference

作者： Maryam Najafian Saeid Safavi Abualsoud Hanani Martin Russell School of EECE University of Birmingham Faculty of Information Technology Birzeit University

ISBN: (纸本)9781479946037

This paper investigates techniques to compensate for the effects of regional accents of British English on automatic speech recognition (ASR) performance. Given a small amount of speech from a new speaker, is it better to apply speaker adaptation, or to use accent identification (AID) to identify the speaker's accent followed by accent-dependent ASR? Three approaches to accent-dependent modelling are investigated: using the 'correct' accent model, choosing a model using supervised (ACCDIST-based) accent identification (AID), and building a model using data from neighbouring speakers in 'AID space'. All of the methods outperform the accent-independent model, with relative reductions in ASR error rate of up to 44%. Using on average 43s of speech to identify an appropriate accent-dependent model outperforms using it for supervised speaker-adaptation, by 7%.

关键词： Speech recognition Acoustic data selection Accent identification

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Low signal-to-noise ratio radar target detection using Linear Support Vector Machines (L-SVM)

Low signal-to-noise ratio radar target detection using Linea...

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IEEE National conference on Radar

作者： John E. Ball Department of Electrical and Computer Engineering Mississippi State University Starkville MS USA

ISBN: (纸本)9781479920365

This paper examines target detection using a Linear Support Vector Machine (L-SVM). Traditional radars typically use a Constant False Alarm Rate (CFAR) processor to adaptively adjust the detection threshold based on the fast-time return signal. The SVM formulation uses the same block-diagram structure as the CFAR approach; however, data from the leading and lagging windows is directly used to classify each cell under test. The L-SVM method is compared to a Cell-Averaging CFAR (CA-CFAR) on simulated radar return signals with and without Swerling I targets. The results show that the L-SVM is able to detect very small SNR signals, while the CA-CFAR is unable to detect these signals below -10 dB SNR. In addition, the probability of detection and probability of false alarm for the L-SVM degrade much more gracefully than for the CA-CFAR detector for low-SNR targets.

关键词： signal to noise ratio Support vector machines Radar Computer architecture Microprocessors Detectors Clutter

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Compression for the feature points with binary descriptors

Compression for the feature points with binary descriptors

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International conference on Digital signal processing (DSP)

作者： Jian-Jiun Ding Szu-Wei Fu Ching-Wen Hsiao Pin-Xuan Lee Yen-Chun Chen Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

Feature points, such as SIFT, BRISK, ORB, and FREAK, are effective for template matching, pattern recognition, and object alignment. However, since an image usually has 200-4000 feature points and the size of each descriptor is 512 or 256, an efficient way for encoding the descriptors and locations of feature points is required. In this paper, we propose an algorithm to encode the descriptors, locations, and angles of BRISK, ORB, and FREAK points efficiently. We apply both the global and local statistical characteristics and apply different reference points for the cases where the previous bit is 1 or 0. Moreover, the facts that feature points do not uniformly distribute and that two feature points with a short distance always have a small angle difference are also applied for compression. Simulations show that the proposed algorithm can much reduce the data sizes required for encoding feature points.

关键词： Encoding Algorithm design and analysis signal processing algorithms Image coding Digital signal processing Context Airplanes

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An effective doa estimation by exploring the spatial sparse representation of the inter-sensor data ratio model

An effective doa estimation by exploring the spatial sparse ...

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IEEE China Summit & International conference on signal and Information processing (ChinaSIP)

作者： Yuexian Zou Yifan Guo Weiqiao Zheng C. H. Ritz Jiangtao Xi ADSPLAB Peking University Shenzhen China School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia

This paper investigates speaker direction of arrival (DOA) estimation using a single acoustic vector sensor (AVS). With the definition of the inter-sensor data ratio (ISDR) in the time-frequency (TF) domain and the use of the high local signal-to-noise ratio (HLSNR) TF points, an effective ISDR data model is derived, which determines the relationship between the ISDR and the AVS manifold vector. With the spatial sparse representation of the ISDR data, the DOA estimation is formulated by recovering the sparse matrix and locating the peak of the power spectrum of the reconstructed sparse matrix. Preliminary experimental results using simulations and real AVS recordings show that the proposed DOA estimation method is able to achieve high elevation and azimuth estimation accuracy for all angles when the SNR is above 10dB, avoiding the spatial aliasing problem and suppressing the adverse impact of the room reverberation. It is expected that the proposed DOA estimation method may find wide applications in portable devices due to its small compact physical size and superior performance.

关键词： Direction-of-arrival estimation Estimation Speech Vectors data models Reverberation

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Optimization of OTT small data services: Network capacity and cost analysis

Optimization of OTT small data services: Network capacity an...

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International conference on Wireless Communications and signal processing (WCSP)

作者： Yuxi Han Ming Zhao Wuyang Zhou Dept.of Electronic Engineering and Information Science University of Science and Technology of China Hefei Anhui P.R. China

ISBN: (纸本)9781479973408

The popularity of smartphones and mobile applications has boosted over the top (OTT) services during the recent years, and this trend is expected to continue in the future. However, frequent connection reestablishments caused by OTT services which send or receive small amounts of data often lead to a heavy signalling load within the mobile networks. That has brought about signalling storm, which is a specific problem of mobile networks. It is mainly limited by the scarcity of wireless spectrum resources. Heartbeat mechanism is one of OTT services. Smartphones communicate with the mobile networks by the heartbeat mechanism to report whether they are online. Although heartbeat message belongs to small data services, it lead that users update their status frequently. The payload of update message can not be ignored. Thus, for OTT services such as Wechat, mobile QQ, the optimization of heartbeat mechanism has become one of the best methods to reduce the network payload. In this paper, we proposed a new concept visual user equipment (VUE) which can imitate some periodic information of user equipment (UE) and replace the UE to communicate with applications in the cable end. We also proposed a scheme to reduce the transmission of heartbeat signallings according to the state of OTT services. Under the same end-to-end payload condition our scheme has greater network capacity and higher throughput at the expense of a little rise in packet loss rate only when the arrival rate of service packets is high. On the other hand, our scheme can relieve the signalling storm of OTT services to a great extent.

关键词： Heart beat Servers Mobile communication Payloads Smart phones Throughput Packet loss

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The recovery of data flow based on weighted L1/2-regularization

The recovery of data flow based on weighted L1/2-regularizat...

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Chinese Control conference (CCC)

作者： Yuanyuan Wang Chenglin Wen Institute of Systems Science and Control Engineering Hangzhou Dianzi University Hangzhou Zhejiang CN

With the popularization of computer and network in recent years, the information degree of daily life is increasing, the demand for information transmission and processing is increasing too. data flow in the network transmission is very large, it is necessary to propose effective data stream processing method. Compressed sensing can reconstruct the entire signal with cost a small amount of observed data, this significant savings hardware resources and the cost of processing data. Compressed sensing ideas brought great improvements in data stream processing problems. In this paper, we use the latest ideas of compressed sensing to solve the optimization problem of the reconstruction of data streams, and provide adaptive weighted regularization method. The simulation examples show that the proposed method can reconstruct data stream well, and have some superiority on the reconstruction compare with other reconstruction algorithms.

关键词： Compressed sensing Algorithm design and analysis Image reconstruction Optimization Sparse matrices Equations Reconstruction algorithms

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