Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems. Most RED algorithms are iterative batch procedures, which limits their applicability to very large datasets. In this pape...
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ISBN:
(数字)9781728150239
ISBN:
(纸本)9781728150246
Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems. Most RED algorithms are iterative batch procedures, which limits their applicability to very large datasets. In this paper, we address this limitation by introducing a novel online RED (On-RED) algorithm, which processes a small subset of the data at a time. We establish the theoretical convergence of On-RED in convex settings and empirically discuss its effectiveness in non-convex ones by illustrating its applicability to phase retrieval. Our results suggest that On-RED is an effective alternative to the traditional RED algorithms when dealing with large datasets.
We address an energy-efficient scheduling problem for practical multiple-input single-output (MISO) systems with stringent execution-time requirements. Optimal user-group scheduling is adopted to enable timely and ene...
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ISBN:
(纸本)9781538631805
We address an energy-efficient scheduling problem for practical multiple-input single-output (MISO) systems with stringent execution-time requirements. Optimal user-group scheduling is adopted to enable timely and energy-efficient data transmission, such that all the users' demand can be delivered within a limited time. The high computational complexity in optimal iterative algorithms limits their applications in real-time network operations. In this paper, we rethink the conventional optimizationalgorithms, and embed machine-learning based predictions in the optimization process, aiming at improving the computational efficiency and meeting the stringent execution-time limits in practice, while retaining competitive energy-saving performance for the MISO system. Numerical results demonstrate that the proposed method, i.e., optimization with machine-learning predictions (OMLP), is able to provide a time-efficient and high-quality solution for the considered scheduling problem. Towards online scheduling in real-time communications, OMLP is of high computational efficiency compared to conventional optimal iterative algorithms. OMLP guarantees the optimality as long as the machine-learning based predictions are accurate.
This book highlights the latest advances in engineering mathematics with a main focus on the mathematical models, structures, concepts, problems and computational methods and algorithms most relevant for applications ...
ISBN:
(纸本)9783319824994
This book highlights the latest advances in engineering mathematics with a main focus on the mathematical models, structures, concepts, problems and computational methods and algorithms most relevant for applications in modern technologies and engineering. It addresses mathematical methods of algebra, applied matrix analysis, operator analysis, probability theory and stochastic processes, geometry and computational methods in network analysis, data classification, ranking and optimisation. The individual chapters cover both theory and applications, and include a wealth of figures, schemes, algorithms, tables and results of data analysis and simulation. Presenting new methods and results, reviews of cutting-edge research, and open problems for future research, they equip readers to develop new mathematical methods and concepts of their own, and to further compare and analyse the methods and results discussed. The book consists of contributed chapters covering research developed as a result of a focused international seminar series on mathematics and applied mathematics and a series of three focused international research workshops on engineering mathematics organised by the Research Environment in Mathematics and Applied Mathematics at Mlardalen University from autumn 2014 to autumn 2015: the internationalworkshop on Engineering Mathematics for Electromagnetics and Health Technology; the internationalworkshop on Engineering Mathematics, Algebra, Analysis and Electromagnetics; and the 1st Swedish-Estonian internationalworkshop on Engineering Mathematics, Algebra, Analysis and applications. It serves as a source of inspiration for a broad spectrum of researchers and research students in applied mathematics, as well as in the areas of applications of mathematics considered in the book.
Nowadays, road traffic management is becoming a major challenge for realistic society. As a result, reliable communication between vehicles is the key point to this challenge. Currently, IEEE802.11p which is considere...
Nowadays, road traffic management is becoming a major challenge for realistic society. As a result, reliable communication between vehicles is the key point to this challenge. Currently, IEEE802.11p which is considered as de facto standard for road communication is designed to solve this challenge. However, the communication channel medium is still expected to get congested when a large number of vehicles exist. Target to solve this, European Telecommunication Standards Institute (ETSI) has standardized a set of Decentralized Congestion Control (DCC) mechanisms to control channel load. One of the main topics is achieving channel load control to guarantee reliable communication for platooning systems. In this paper, we focus on investigating on DCC reactive control approaches, aiming to provide comprehensive insights of how DCC framework transmission parameters, i.e. message generation rate, transmission power and data rate, will impact the stability of platooning systems. Besides, for each instance of the transmission parameter, we target to optimize the parameter and propose more stable control algorithms by running repetitive simulations.
We present recent developments on flexible a-IGZO TFT technology scaling in terms of TFT channel length and operating voltage, as well as manufacturing cost optimization with a focus on R2R processing compatibility. W...
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In order to solve combinatorial optimization problem are used mainly hybrid heuristics. Inspired from nature, both genetic and ant colony algorithms could be used in a hybrid model by using their benefits. The paper i...
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ISBN:
(纸本)9783319625249
In order to solve combinatorial optimization problem are used mainly hybrid heuristics. Inspired from nature, both genetic and ant colony algorithms could be used in a hybrid model by using their benefits. The paper introduces a new model of Ant Colony optimization using multiple colonies with different level of sensitivity to the ant's pheromone. The colonies react different to the changing environment, based on their level of sensitivity and thus the exploration of the solution space is extended. Several discussion follows about the fuzziness degree of sensitivity and its influence on the solution of a complex problem.
The partial shading condition (PSC) often occur in large photovoltaic (PV) generation system (PGS), it causes system losses and many problems in reliability of power system. The power voltage ( p-v) curve under the PS...
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The paper is devoted to the problem of motion optimization of a ship on a given long trajectory with respect to weather forecast and additional constraints. This problem has practical meaning for long distances, where...
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The paper is devoted to the problem of motion optimization of a ship on a given long trajectory with respect to weather forecast and additional constraints. This problem has practical meaning for long distances, where saving of fuel consumption and reducing travel time is of significant importance. A finite-dimensional formulation of the optimization problem is considered taking into account the mathematical model of ship motion, weather forecast, constraints and given cost functionals. The computational algorithms for calculation of travel time and fuel consumption are proposed. The main result is presented by the algorithm for optimizing the velocity distribution on a given trajectory. This algorithm is based on the representation of the route of motion on a fixed trajectory in two-dimensional space. The obtained results are demonstrated by illustrative examples. (C) 2018, IFAC (international Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Scheduling a set of jobs over a collection of machines is a fundamental problem that needs to be solved millions of times a day in various computing platforms: In operating systems, in large data clusters, and in data...
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In the Image Processing (IP) domain, optimizationalgorithms have to be applied in many cases. Nature-inspired heuristics allow obtaining near optimal solutions using lower computing resources. In this paper the Firew...
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ISBN:
(纸本)9783319625218
In the Image Processing (IP) domain, optimizationalgorithms have to be applied in many cases. Nature-inspired heuristics allow obtaining near optimal solutions using lower computing resources. In this paper the Fireworks Algorithm (FWA) behavior is studied for Image Registration (IR) problems. The IR results accuracy is analyzed for different types of images, mainly in case of pixel based registration using the Normalized Mutual Information. FWA is compared to Particle Swarming (PSO), Cuckoo Search (CSA) and Genetic algorithms (GA) in terms of results accuracy and number of objective function evaluations required to obtain the optimal geometric transform parameters. Because the pixel based IR may fail in case of images containing graphic drawings, a features based IR approach is proposed for this class of images. Comparing to other nature inspired algorithms, FWA performances are close to those of PSO and CSA in terms of accuracy. Considering the required computing time, that is determined by the number of cost function evaluations, FWA is little slower than PSO and much faster than CSA and GA.
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