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检索条件"主题词=Optimization Algorithms"

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FHSS SIGNAL RECONSTRUCTION BASED ON THE COMPRESSIVE SAMPLING PRINCIPLE

FHSS SIGNAL RECONSTRUCTION BASED ON THE COMPRESSIVE SAMPLING...

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20th Telecommunications Forum

作者： Draganic, Andjela Orovic, Irena Stankovic, Srdjan Amin, Moeness G. Univ Crne Gore Elektrotehn Fak Podgorica 81000 Montenegro

ISBN: (纸本)9781467329842;9781467329835

The method for the reconstruction of Frequency Hopping Spread Spectrum (FHSS) signals is described in the paper. FHSS is a modulation technique employed in spread spectrum communications. Reconstruction procedure is based on the compressive sampling principle, which allows signal reconstruction using a significantly smaller number of samples than required by the Nyquist-Shannon sampling theorem. The method is applied on signals having a sparse representation in transform domain, which often appears in real applications. Reconstruction is based on the optimization algorithms using l(1)-norm minimization in the domain in which the signal is sparse. In addition, the joint time-frequency signal analysis is combined with a compressive sampling method, in order to compare parameters of the original and the reconstructed signal.

关键词： Bluetooth FHSS kompresivno odabiranje l1-norma optimizacioni algoritmi vremensko-frekvencijska analiza Bluetooth Signal reconstruction frequency hopping spread spectrum signals optimization algorithms Reconstructive Surgical Procedures Signal analysis sparse representation Spread spectrum communication

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SCHEDULING PROBLEMS WITH PRECEDENCE CONSTRAINTS: MODELS AND algorithms

SCHEDULING PROBLEMS WITH PRECEDENCE CONSTRAINTS: MODELS AND ...

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European Simulation and Modelling Conference (ESM 2012)

作者： Montemanni, Roberto Univ Appl Sci Southern Switzerland SUPSI Dalle Molle Inst Artificial Intelligence IDSIA CH-6928 Manno Switzerland

ISBN: (纸本)9789077381731

The sequential ordering problem is a basic scheduling problem with precedence constraints. It can be used to model many real world applications arising in different fields in terms of combinatorial optimization. In this paper we review two approaches to the problem recently appeared in the literature. Both the methods are based on a mixed integer linear programming, which is in turn introduced and discussed. Experimental results are presented to analyze the performance of the algorithms.

关键词： Sequential ordering problem scheduling problems precedence constraints mixed integer linear programming optimization algorithms

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Orchestrating Constrained Programming and Local Search to Solve a Large Scale Energy Management Problem

Orchestrating Constrained Programming and Local Search to So...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Buljubasic, Mirsad Gavranovic, Haris Univ Sarajevo Fac Nat Sci Sarajevo 71000 Bosnia & Herceg Int Univ Sarajevo & Strategeme Lab Bosnia & Herzegovina Sarajevo Bosnia & Herceg

ISBN: (纸本)9788360810484

This paper presents a heuristic approach combining constraint satisfaction, local search and a constructive optimization algorithm for a large-scale energy management and maintenance scheduling problem. The methodology shows how to successfully combine and orchestrate different types of algorithms and produce competitive results. The local search for production assignment is a simple yet optimal solution for the relaxed initial problem. We also propose an efficient way to scale the method for huge instances. A large part of the presented work is done to compete in the ROADEF/EURO Challenge 2010, organized jointly by the ROADEF, EURO and the Electricite de France. The numerical results obtained for the official competition instances testify about the quality of the approach. The method achieves 3 out of 15 possible best results.

关键词： Energy management constraint satisfaction problems optimization algorithms scaling method Scale formation Programming

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On Objective Feature Selection for Affective Sounds Discrimination

On Objective Feature Selection for Affective Sounds Discrimi...

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54th ELMAR International Symposium

作者： Chmulik, Michal Jarina, Roman Kuba, Michal Univ Zilina Audiolab Dept Telecommun & Multimedia Univ 2 Zilina 01026 Slovakia

ISBN: (纸本)9789537044138

We present an objective acoustic feature selection for automatic affective sounds detection based on stochastic evolutionary optimization algorithms. Particle Swarm optimization (PSO) as well as Genetic algorithms (GA) are exploit to select the most appropriate audio features from a large set of available features. We performed experiments on a dataset containing about two hours of affective sounds - cry, laughter and applause, and supplemented with several hours of recordings of other sounds (speech, music and various types of noise). Applying the feature selection methods, the classification performance is increased about 4-9 % with final accuracy 92-98 % while feature space dimension is reduced about 50-90 %.

关键词： Genetic algorithms optimization algorithms Particle Swarm optimization Affective Sound discrimination

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In-Network Local Distributed Estimation for Power-constrained Wireless Sensor Networks

In-Network Local Distributed Estimation for Power-constraine...

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IEEE 75th Vehicular Technology Conference (VTC Spring)

作者： Shah, Santosh Beferull-Lozano, Baltasar Univ Valencia Grp Informat & Commun Syst Inst Robot & Tecnol Informac & Comunicac Paterna 46980 Valencia Spain

ISBN: (纸本)9781467309905;9781467309882

In this paper, we consider the problem of power-efficient distributed estimation of a localized event in the large-scale Wireless Sensor Networks (WSNs). In order to increase the power efficiency in these networks, we develop a joint optimization problem that involves both selecting a subset of active sensors and the routing structure so that the quality of estimation at a given querying node is the best possible subject to a total imposed communication cost. We first formulate our problem as an optimization problem and show that it is NP-Hard. Then, we propose a local distributed optimization algorithm that is based on an Estimate-and-Forward (EF) strategy, which allows to perform sequentially this joint optimization in an efficient way. We also provide a lower bound for our optimization problem and show that our local distributed optimization algorithm provides a performance that is close to this bound. Although there is no guarantee that the gap between this lower bound and the optimal solution of the main problem is always small, our numerical experiments support that this gap is actually very small in many cases. An important result from our work is that because of the interplay between the communication cost over the links and the gains in estimation accuracy obtained by choosing certain sensors, the traditional Shortest Path Tree (SPT) routing structure, widely used in practice, is no longer optimal, that is, our routing structures provide a better trade-off between the overall power efficiency and the final estimation accuracy obtained at the querying node. Our experimental results show that our algorithms yield a significant energy saving.

关键词： dynamic efficiency optimization algorithms Distribute Network routing

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A Learning Adaptive Bollinger Band System

A Learning Adaptive Bollinger Band System

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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Butler, Matthew Kazakov, Dimitar Univ York Dept Comp Sci Artificial Intelligence Grp York YO10 5DD N Yorkshire England

ISBN: (纸本)9781467318037

This paper introduces a novel forecasting algorithm that is a blend of micro and macro modelling perspectives when using Artificial Intelligence (AI) techniques. The micro component concerns the fine-tuning of technical indicators with population based optimization algorithms. This entails learning a set of parameters that optimize some economically desirable fitness function as to create a dynamic signal processor which adapts to changing market environments. The macro component concerns combining the heterogeneous set of signals produced from a population of optimized technical indicators. The combined signal is derived from a Learning Classifier System (LCS) framework that combines population based optimization and reinforcement learning (RL). This research is motivated by two factors, that of non-stationarity and cyclical profitability (as implied by the adaptive market hypothesis [10]). These two properties are not necessarily in contradiction but they do highlight the need for adaptation and creation of new models, while synchronously being able to consult others which were previously effective. The results demonstrate that the proposed system is effective at combining the signals into a coherent profitable trading system but that the performance of the system is bounded by the quality of the solutions in the population.

关键词： signal processor signals optimization algorithms micro Learning Artificial Intelligence Self tuning System performance given signal

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Neurosphere fate prediction: an analysis-synthesis approach for feature extraction

Neurosphere fate prediction: an analysis-synthesis approach ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rigaud, Stephane U. Lomenie, Nicolas Sankaran, Shvetha Ahmed, Sohail Lim, Joo-Hwee Racoceanu, Daniel Image & Pervasive Access Lab Singapore Singapore Inst Med Biol Singapore Singapore Univ Paris 06 Paris France

ISBN: (纸本)9781467314909

The study of stem cells is one of the current most important biomedical research field. Understanding their development could allow multiple applications in regenerative medicine. For this purpose, we need automated methods for the segmentation and the modeling of neural stem cell development process into a neurosphere colony from phase contrast microscopy. We use such methods to extract relevant structural and textural features like cell division dynamism and cell behavior patterns for biological interpretation. The combination of phase contrast imaging, high fragility and complex evolution of neural stem cells pose many challenges in image processing and image analysis. This study introduces an on-line analysis method for the modeling of neurosphere evolution during the first three days of their development. From the corresponding time-lapse sequences, we extract information from the neurosphere using a combination of fast level set and curve detection for segmenting the cells. Then, based on prior biological knowledge, we generate possible and optimal 3-dimensional configuration using registration and evolutionary optimisation algorithm.

关键词： phase contrast Stem Cells optimization algorithms Feature extraction Neural Stem Cells analysis of flowline Phase-Contrast Microscopy Automated methods Modeling Level set Regenerative Medicine Fate Image processing image analysis

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A Comparison of Interpolation Techniques for RR Interval Fitting in AR Spectrum Estimation

A Comparison of Interpolation Techniques for RR Interval Fit...

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IEEE Biomedical Circuits and Systems Conference (BioCAS)

作者： Jang, Dae-Geun Hahn, Minsoo Jang, Jae-Keun Farooq, Umar Park, Seung-Hun Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea Kyung Hee Univ Dept Biomed Engn Suwon South Korea

ISBN: (纸本)9781467322935

In this paper, we have compared basic interpolation techniques (linear interpolation, Lagrange interpolation, Hermite interpolation, and cubic spline interpolation) to find the optimum method for RR interval fitting in heart rate variability (HRV) analysis. It is required that a sequence of RR intervals have to be resampled to make it as if it is a regularly sampled signal since the input signal has to be satisfied a steady state assumption for frequency domain analysis. Several interpolation techniques have been applied to cope with this problem. To find the optimum algorithm among them, we have compared the algorithms in terms of processing times and error rates of HRV parameters (normalized low frequency (LFnorm), normalized high frequency (HFnorm), LF/HF ratio). We have also employed EUROBAVAR datasets which include 10-12 min recorded RR interval data for the experiment. From the experiment, we can notice that the Lagrange interpolation technique with order of 3 is the most appropriate algorithm for the RR interval fitting in the autoregressive spectrum estimation since it requires low processing time (0.028 seconds in the Intel Core 2 Quad @ 2.40 GHz desktop computer) and shows the lowest error rates in HRV parameter calculation.

关键词： RR interval Error analysis Spectrum Analysis optimization algorithms Interpolation input signal SPLINE INTERPOLATION Frequency-domain analysis MACHINE SHOP PRACTICE Lagrange interpolation processing time

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Measurement-Based Optimizing Algorithm for Modulation and Coding Scheme Selection in Downlink LTE Self-Organizing Networks

Measurement-Based Optimizing Algorithm for Modulation and Co...

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IEEE International Conference on Wireless Information Technology and Systems (ICWITS)

作者： Jiang, Jiamo Peng, Mugen Li, Yuan Wei, Yao Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Wireless Signal Proc & Network Lab Beijing 100876 Peoples R China

ISBN: (纸本)9781467309479;9781467309462

The traditional wireless network management becomes no longer efficient because of the new features and technologies in the next generation radio access network. The main purpose of self-organizing network (SON) is to meet new technical requirements, simplify wireless network design and operation, and implement the self-configuration, self-optimization and self-healing of the network. Adaptive modulation and coding (AMC) is one of the key technologies of link adaptation in TD-LTE system to achieve high spectral efficiency on the fading channels. A key factor to determine the performance of AMC is the threshold values of MCS applied at eNode B, which decides the suitable MCS selection in the next transmission time interval (TTl). Unsuitable MCS selection will impair the system performance seriously. An improved threshold adjustment to precisely guarantee the predetermined frame error rate for AMC was proposed. A novel signal-to-noise ratio (SNR) estimation algorithm which reduces computational complexity in obtaining channel quality indictor (CQI) and corresponding MCS was investigated. None of these took fully into account of the feedback and periodic performance measurement simultaneously, and the interference was not considered. Meanwhile, eNode B can't obtain the value of signal to interference plus noise ratio (SINR) directly. In this paper, a measurementbased optimizing algorithm (MBOA) for modulation and coding schemes (MCS) selection to determine an appropriate MCS for initial transmission is proposed. Differing from the traditional algorithm which selects the MCS only by channel quality indicator (CQI) feedback, the proposed algorithm can adjust the threshold values of MCS according to the performance measurements and CQI feedback, including fast and independent adjustment (FIA) and slow and centralized adjustment (SCA). FIA optimizes the threshold values by the initial transmission success ratio (ITSR). Then SCA changes threshold values by the measurement-

关键词： MCS gene optimization algorithms Wireless networks AMCN gene antecedent moisture condition (amc) Performance metrics labeling scheme Self-organizing networks system level Bit error rate Threshold threshold adjustment Network design block error probability signal to noise ratios

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Model Predictive Current Control of a Quasi Six-phase Voltage Source Inverter

Model Predictive Current Control of a Quasi Six-phase Voltag...

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21st IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ahmed, S. K. Moin Texas A&M Univ Qatar Elect & Comp Engn Program Doha Qatar

ISBN: (纸本)9781467301589

The paper presents model based current control of a six-phase voltage source inverter considering a finite set of control actions. The space vector model of a six-phase voltage source inverter (VSI) yields 64 vectors, with 60 active and four zero vectors. The presented technique utilizes reduced number of switching actions to track the reference current. Only 13 space vectors are utilized for the current control and hence the name finite control set is given. Optimal algorithm is employed to find the vector which minimizes the chosen cost function. The effect of selecting the cost function, the number of space vectors and the sampling time is investigated and reported. The developed technique is tested for RLE load using simulation approach.

关键词： voltage source inverter Current control space vector Space vector models optimization algorithms control set reference current six-phase sampling time Cloning Vectors Cost functions control action

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