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31st European Conference on Modelling and Simulation, ECMS 2017

作者： Ershov, Egor I. Shvets, Evgeny A. Khanipov, Timur M. Nikolaev, Dmitry P. Moscow Inst Phys & Technol 9 Inst Per Dolgoprudnyi 141700 Moscow Region Russia RAS Inst Informat Transmiss Problems 19 Bolshoy Karetny Per Moscow 127994 Russia

ISBN: (纸本)9780993244049

In this paper we investigate the problem of finding the minimal operations number for the generalized Hough transform computation (GHT). We demonstrate that this problem is equivalent to the addition chain problem and is therefore NP-complete. Three greedy methods for generating GHT computation algorithms are proposed and their performance is compared against the fast Hough transform (FHT) for different discrete straight line pattern types. The additional result of this work is the experimental proof of the FHT non-optimality.

关键词： Hough transform fast Hough transform generalized Hough transform greedy algorithm graph matching

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Model and algorithms the Quay Cranes Scheduling Based Yard Trucks Route Optimization

Model and Algorithms the Quay Cranes Scheduling Based Yard T...

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第37届中国控制会议

作者： Xiao-jun Li Ran Zhou Le-qun Zhu Policy Research Center Tianjin Research Institute for Water Transport Engineering M.O.T.

Minimizing turnaround time of ship and the free travel distance of yard trucks(YT) are the important objectives for most port operation schedules. The turnaround time of ship is also the working time of quay cranes(QC). This paper focuses on QC scheduling problem based on YT routing optimization. In order to solve this problem, the YT route optimization model is proposed as a mixed integer programming model and QC scheduling model is developed based on YT route optimization model. Two heuristic approaches are proposed to solve these models. Approaches are respectively considered task's the integral and separation with the greedy algorithm. Finally, certain data in cases are used to evaluate the differences of two approaches, and computer experiments shows the greedy algorithm for separation task have better performance in solving the QC scheduling.

关键词： Quay cranes scheduling Route optimization greedy algorithm Yard trucks

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Task Allocation in Agricultural Remote Sensing Applications using Submodular Maximization algorithm

Task Allocation in Agricultural Remote Sensing Applications ...

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第37届中国控制会议

作者： Min-Guk Seo Hyo-Sang Shin Antonios Tsourdos School of Aerospace Transport and Manufacturing Cranfield University

This paper addresses the application of task allocation algorithm for agricultural remote sensing using a group of UAVs. A task allocation problem is formulated as a form of reward function maximization problem. The reward function is defined by considering the following three aspects. The first aspect is to assign tasks to UAVs with proper sensors to detect the most severe hazards for the crop raised in each field at the observation season. The minimization of the total operation time of UAVs is another aspect for short observation time and small fuel consumption. The last aspect is to assign tasks uniformly to the UAVs to prevent overloads on certain UAVs. In order to apply submodular maximization-based algorithm, the subexponential submodularity tester is applied to verify the submodularity of the defined reward function. A greedy algorithm-based task allocation method is applied to the designed task allocation problem as an example of submodular maximization-based *** are conducted to show the characteristics of the designed reward function and to compare the results of the task allocation method with the optimal solutions.

关键词： Task Allocation Precision Agriculture Submodular Maximization greedy algorithm

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Matching Projection Decoding Method for Ambisonics System

Matching Projection Decoding Method for Ambisonics System

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Tianshu Qu Zhichao Huang Yue Qiao Xihong Wu Key Laboratory on Machine Perception (Ministry of Education) Speech and Hearing Research Center Peking University Beijing China

ISBN: (纸本)9781538646595

The basic Ambisonics decoding method will break down when the playback loudspeakers distribute unevenly. This paper proposes a modified Ambisonics method, the matching projection decoding method, for solving this problem. The matching projection decoding method is a kind of the greedy algorithm. It firstly calculates the projection value of the object Ambisonics signal over each Ambisonics signal of loudspeakers, then the maximum projection value is assigned to the corresponding loudspeaker. This process is repeated until all the loudspeakers have been assigned a gain value. The objective and subjective experiments were performed to evaluate the proposed system and the basic system. Objective evaluation results show that the accuracy of the sound field generated by the matching projection decoding method is better than that of the basic method;and the subjective evaluation results show a more correct directional perception of the matching projection decoding method than the basic one.

关键词： HOA Ambisonics encoding Ambisonics decoding Sound field reproduction greedy algorithm greedy algorithm Matrilin-3 ambisonics acoustic field Loudspeakers DECODING PROJECTION Projection

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Distributed Submodular Maximization for Large Vocabulary Continuous Speech Recognition

Distributed Submodular Maximization for Large Vocabulary Con...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Jun Qi Xu Liu Shunshuke Kamijo Javier Tejedor Electrical Engineering University of Washington Seattle USA Institute of Industrial Science The University of Tokyo Japan Escuela Politecnica Superior Universidad San Pablo-CEU CEU Universities Madrid Spain

ISBN: (纸本)9781538646595

Huge training datasets for automatic speech recognition (ASR) typically contain redundant information so that a subset of data is generally enough to obtain similar ASR performance to that obtained when the entire dataset is employed for training. Although the centralized submodular-based data selection methods have been successfully applied to obtain a representable subset involving the most significant information of the whole dataset, the submodular data selection conveys problems in adapting to an extremely massive dataset. This paper proposes to use distributed submodular maximization (DSM) for efficiently selecting a data subset that maintains the ASR performance, while reducing tremendously the computational overhead. There are two approaches for the distributed submodular maximization problem: one is based on an homogeneous submodular function, and the other relies on decomposable submodular functions in which heterogeneous submodular functions are applied. Our experiments show that the data subset output by the DSM algorithms can maintain the ASR performance, while significantly reducing the computational overhead.

关键词： distributed submodular maximization automatic speech recognition decomposable submodular function greedy algorithm Speech recognition greedy algorithm aquifer storage and recovery Subset Dataset selection methods information redundancy Vocabulary LARGE gene

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Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation

Sampling and Reconstruction of Graph Signals via Weak Submod...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Abolfazl Hashemi Rasoul Shafipour Haris Vikalo Gonzalo Mateos Dept. of Electrical and Computer Engineering University of Texas at Austin Austin TX USA Dept. of Electrical and Computer Engineering University of Rochester Rochester NY USA

ISBN: (纸本)9781538646595

We study the problem of sampling a bandlimited graph signal in the presence of noise, where the objective is to select a node subset of prescribed cardinality that minimizes the signal reconstruction mean squared error (MSE). To that end, we formulate the task at hand as the minimization of MSE subject to binary constraints, and approximate the resulting NP-hard problem via semidefinite programming (SDP) relaxation. Moreover, we provide an alternative formulation based on maximizing a monotone weak submodular function and propose a randomized-greedy algorithm to find a sub-optimal subset. We then derive a worst-case performance guarantee on the MSE returned by the randomized greedy algorithm for general non-stationary graph signals. The efficacy of the proposed methods is illustrated through numerical simulations on synthetic and realworld graphs. Notably, the randomized greedy algorithm yields an order-of-magnitude speedup over state-of-the-art greedy sampling schemes, while incurring only a marginal MSE performance loss.

关键词： graph signal processing sampling weak sub-modularity semidefinite programming randomized algorithms randomized algorithms Mean square error greedy algorithm Sampling signals Reconstructions

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A Fast Iterative greedy algorithm for MEG Source Localization

A Fast Iterative Greedy Algorithm for MEG Source Localizatio...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： G. Obregon-Henao B. Babadi C. Lamus E.N. Brown P.L. Purdon 149 13th Street Room 4.013 Charlestown MA 02129 USA Department of Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital USA

ISBN: (纸本)9781457717871

Recent dynamic source localization algorithms for the Magnetoencephalographic inverse problem use cortical spatio-temporal dynamics to enhance the quality of the estimation. However, these methods suffer from high computational complexity due to the large number of sources that must be estimated. In this work, we introduce a fast iterative greedy algorithm incorporating the class of subspace pursuit algorithms for sparse source localization. The algorithm employs a reduced order state-space model resulting in significant computational savings. Simulation studies on MEG source localization reveal substantial gains provided by the proposed method over the widely used minimumnorm estimate, in terms of localization accuracy, with a negligible increase in computational complexity.

关键词： A Fast Iterative greedy algorithm MEG Source Localization

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TDOA-based Localization via Stochastic Gradient Descent Variants

TDOA-based Localization via Stochastic Gradient Descent Vari...

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IEEE Vehicular Technology Conference

作者： Luis F. Abanto-Leon Arie Koppelaar Sonia Heemstra de Groot Eindhoven University of Technology NXP Semiconductors

ISBN: (纸本)9781538663592;9781538663585

Source localization is of pivotal importance in several areas such as wireless sensor networks and Internet of Things (IoT), where the location information can be used for a variety of purposes, e.g. surveillance, monitoring, tracking, etc. Time Difference of Arrival (TDOA) is one of the well-known localization approaches where the source broadcasts a signal and a number of receivers record the arriving time of the transmitted signal. By means of computing the time difference from various receivers, the source location can be estimated. On the other hand, in the recent few years novel optimization algorithms have appeared in the literature for (i) processing big data and for (ii) training deep neural networks. Most of these techniques are enhanced variants of the classical stochastic gradient descent (SGD) but with additional features that promote faster convergence. In this paper, we compare the performance of the classical SGD with the novel techniques mentioned above. In addition, we propose an optimization procedure called RM-SProp+AF, which is based on RMSProp algorithm but with the advantage of incorporating adaptation of the decaying factor. We show through simulations that all of these techniques--which are commonly used in the machine learning domain--can also be successfully applied to signal processing problems and are capable of attaining improved convergence and stability. Finally, it is also shown through simulations that the proposed method can outperform other competing approaches as both its convergence and stability are superior.

关键词： gradient methods greedy algorithms Internet of Things learning (artificial intelligence) neural nets optimisation signal processing stochastic processes time-of-arrival estimation wireless sensor networks time-of-arrival estimation Internet of things Neural network greedy algorithm gradient methods Wireless sensor networks Markov chain signal processing Position measurement POSITION INFORMATION Optimization Procedure Stochastic Processes Time difference of arrival

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Lensless 3D Imaging Using Mask-Based Cameras

Lensless 3D Imaging Using Mask-Based Cameras

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： M. Salman Asif Department of Electrical and Computer Engineering University of California Riverside CA 92521

ISBN: (纸本)9781538646595

Recently, coded masks have been used to demonstrate a thin form-factor lensless camera, FlatCam, in which a mask is placed immediately on top of a bare image sensor. In this paper, we present an imaging model and algorithm to jointly estimate depth and intensity information in the scene from a single or multiple FlatCams. We use a light field representation to model the mapping of 3D scene onto the sensor in which light rays from different depths yield different modulation patterns. We present a greedy depth pursuit algorithm to search the 3D volume and estimate the depth and intensity of each pixel within the camera field-of-view. We present simulation results to analyze the performance of our proposed model and algorithm with different FlatCam settings.

关键词： computational photography depth estimation coded aperture greedy algorithms greedy algorithm Cameras Masks Three dimensional imaging Light Sensors (image) Pursuit algorithms

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Hybrid greedy algorithms for fuzzy tardiness/earliness minimisation in a special single machine scheduling problem: case study and generalisation

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2016年第8期29卷 870-888页

作者： Niroomand, Sadegh Hadi-Vencheh, Abdollah Mirzaei, Nima Molla-Alizadeh-Zavardehi, Saber Firouzabad Inst Higher Educ Dept Ind Engn Firouzabad Fars Iran Islamic Azad Univ Khorasgan Isfahan Branch Dept Math Esfahan Iran Istanbul Aydin Univ Dept Ind Engn Istanbul Turkey Islamic Azad Univ Masjed Soleiman Branch Dept Ind Engn Masjed Soleiman Iran Eastern Mediterranean Univ Dept Ind Engn Northern Cyprus Turkey

This paper focuses on earliness and tardiness minimisation of a special case of single machine scheduling problem with common fuzzy due-date. The problem arises from a cable manufacturing system where cables are produced in different sizes and colours. The problem is generalised by considering two attributes for each product (job) and different levels for each attribute. Setup time between a pair of jobs is different when the level of one attribute or both attributes is changed, as is the case in this study. Three hybrid greedy algorithms and a genetic algorithm are introduced to solve the test problems generated for the generalised problem while Taguchi experimental design method is used to find the best level of parameters for each algorithm. Finally, the comparisons are employed to select the best method.

关键词： scheduling problem meta-heuristic greedy algorithm Taguchi method fuzzy number

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