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An Improved cs algorithm Based on the Curved Trajectory in Geosynchronous SAR

IEEE JOURNAL OF SELECTED TOPIcs IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2012年第3期5卷 795-808页

作者： Hu, Cheng Liu, Zhipeng Long, Teng Beijing Inst Technol Dept Elect Engn Beijing 100081 Peoples R China China Acad Elect & Informat Technol Beijing 100041 Peoples R China

In Geosynchronous (GEO) Synthetic Aperture Radar (SAR), the linear trajectory model fits the actual slant range history in a poor approximation. For example, when the satellite passes the apogee, the velocity and squint angle based on the linear trajectory model will be imaginary numbers;when the satellite passes the perigee and equator, the linear trajectory model can cause large phase error such as hundreds of radians. Therefore, the classical chirp scaling (cs) algorithm under the linear trajectory model is not useable in GEO SAR. In this paper, an improved cs algorithm is proposed to solve the imaging in GEO SAR. Firstly, the true slant histories (curved trajectory) are accurately modeled based on vector expression. Then, an accurate Two-Dimension spectrum in GEO SAR is obtained for the first time based on the high order Taylor expansion and series reversion method. The new cs factor, range migration factor, chirp frequency rate, range and azimuth compression functions are all analytically derived over again in sequence. Finally, the simulation results show that the improved cs algorithm can realize the focusing of large scene and long synthetic aperture time (hundreds of seconds) in GEO SAR.

关键词： cs algorithm curved trajectory GEO SAR linear trajectory

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Energy-delay aware request scheduling in hybrid Cloud and Fog computing using improved multi-objective cs algorithm

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SOFT COMPUTING 2024年第5期28卷 4037-4050页

作者： BahraniPour, Fatemeh Mood, Sepehr Ebrahimi Farshi, Mohammad Yazd Univ Dept Comp Sci Daneshgah Blvd Yazd 89158741 Iran

The growth of the Internet of Things (IoT) and its application in various fields has resulted in the generation of significant amounts of data for processing. The Hybrid Cloud-Fog architecture makes it possible to send latency-sensitive tasks to Fog resources because of its proximity to IoT devices. More complex tasks should be sent to the Cloud data center because of its computational power and storage capacity. The scheduling of requests to optimize latency and energy consumption is a major challenge and an NP-hard problem in Cloud-Fog computing. In this study, first, we define a new multi-objective function to make a good trade-off between latency and energy. Then, to optimize this multi-objective function, we propose a Multi Objectives Cuckoo Search algorithm (MOcs) algorithm, improved by Boltzmann function, to reduce the latency and energy consumption. The simulation result proves that the improved MOcs algorithm is more effective in optimizing latency and energy consumption compared to the state-of-the-art algorithms. Using Evaluation Criteria indicates that the improved MOcs, based on the Boltzmann function, enhances its exploration and exploitation capabilities.

关键词： Task scheduling cs algorithm Multi-objective optimization Cloud computing Fog computing Boltzmann function

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Comparison of MF and cs algorithm in 3-D Near-Field SAR Imaging 7

Comparison of MF and CS Algorithm in 3-D Near-Field SAR Imag...

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7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Zhou, Zichen Wei, Shunjun Wang, Mou Liu, Xinyuan Wei, Jinshan Shi, Jun Zhang, Xiaoling Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781728173337

The combination of multi-input multi-output (MIMO) millimeter-wave radar sensor and synthetic aperture radar (SAR) makes the implementation of 3-D near-field millimeter-wave imaging cheaper and more practical. In this paper, a 3-D SAR imaging system based on the millimeter-wave sensor is constructed, through experiment contrast and analyzed based on the theory of matched filtering (MF) imaging algorithm and based on the theory of compressed sensing (cs) in the near-field millimeter-wave imaging algorithm of 3-D imaging results of SAR system, the pros and cons of two kinds of algorithms are verified in theory, the imaging algorithm based on cs theory imaging effect is better than conventional MF imaging algorithm of imaging effect is concluded, along with various real imaging results.

关键词： millimeter-wave radar 3-D SAR imaging MF algorithm cs algorithm

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Advanced cs algorithm for image processing of spaceborne SAR

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Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology 2004年第SUPPL.期26卷 116-120页

作者： Zhang, Jie Hu, Dong-Hui Lab. of Microwave Imaging Technol. Inst. of Electron. Chinese Academy of Sciences Beijing 100080 China

This paper introduces an advanced cs algorithm on the basis of equivalent squint range model. Furthermore, the displacement of point target is studied due to this algorithm. The simulate test results prove that this a... 详细信息

关键词： cs algorithm SAR image processing

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A New Design Method for Stable IIR Filters With Nearly Linear-Phase Response Based on Fractional Derivative and Swarm Intelligence

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IEEE TRANSACTIONS ON EMERGING TOPIcs IN COMPUTATIONAL INTELLIGENCE 2017年第6期1卷 464-477页

作者： Agrawal, Nikhil Kumar, Anil Bajaj, Varun Pandit Dwarka Prasad Mishra Indian Inst Informat Jabalpur 482005 India Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea

In this paper, a new design method based on fractional derivative (FD) is proposed for designing digital stable infinite impulse response (IIR) filters with nearly linear-phase response. In this method, the design problem is formulated as a phase error optimization of an all-pass filter connected in parallel with a pure delay function. FD is employed to improve the frequency response of the filter at some reference point (omega(0)) in the passband. Optimal values of FD constraints and reference points in passband are determined by minimizing the sum of error in passband (E-p) and stopband (E-s) of an IIR filter, using different evolutionary techniques such as particle swarm optimization (PSO), constraint factor inertia PSO (CFI-PSO), quantum PSO, artificial bee colony algorithm, and cuckoo search technique. Comparative study provides evidence that the proposed method, based on CFI-PSO, gives the best performance amongst the employed swarm-based techniques. Experimental results show the impact of the proposed method as compared to earlier reported techniques in terms of improved response in passband and sharper transition width. However, small reduction in stopband attenuation (A(s)) is observed within the allowable limit when compared to nonfractional design approaches.

关键词： ABC algorithm cs algorithm evolutionary techniques (ETs) fractional derivative constraints (FDcs) PSO QPSO

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Webpage Recommendation System Using Interesting Subgraphs and Laplace Based k-Nearest Neighbor

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第3期34卷

作者： Jayalakshmi, N. Padmaja, P. Suma, G. Jaya VITAM Coll Engn Visakhapatnam Andhra Pradesh India ANITS Coll Engn Dept IT Visakhapatnam Andhra Pradesh India JNTUK UCEV Univ Coll Engn Dept IT Vizianagaram Andhra Pradesh India

An interesting research area that permits the user to mine the significant information, called frequent subgraph, is Graph-Based Data Mining (GBDM). One of the well-known algorithms developed to extract frequent patterns is GASTON algorithm. Retrieving the interesting webpages from the log files contributes heavily to various applications. In this work, a webpage recommendation system has been proposed by introducing Chronological Cuckoo Search (Chronological-cs) algorithm and the Laplace correction based k-Nearest Neighbor (LKNN) to retrieve the useful webpage from the interesting webpage. Initially, W-Gaston algorithm extracts the interesting subgraph from the log files and provides it to the proposed webpage recommendation system. The interesting subgraphs subjected to clustering with the proposed Chronological-cs algorithm, which is developed by integrating the chronological concept into Cuckoo Search (cs) algorithm, provide various cluster groups. Then, the proposed LKNN algorithm recommends the webpage from the clusters. Simulation of the proposed webpage recommendation algorithm is done by utilizing the data from MSNBC and weblog database. The results are compared with various existing webpage recommendation models and analyzed based on precision, recall, and F-measure. The proposed webpage recommendation model achieved better performance than the existing models with the values of 0.9194, 0.8947, and 0.86736, respectively, for the precision, recall, and F-measure.

关键词： Webpage recommendation interesting subgraph cs algorithm k-Nearest neighbor Laplace correction

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A novel single phase grid connected solar photovoltaic system for state of charge estimation using recurrent neural networks

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2023年第1期45卷 841-859页

作者： Kannan, Elango Avudaiappan, Maheswari Kaliyaperumal, Saravanan Muthusamy, Suresh Pandiyan, Santhiya Panchal, Hitesh Manickam, Kannan Shanmugam, Chandrasekar SRM Valliammai Engn Coll Autonomous Dept Elect & Elect Engn Chennai India SRM Inst Sci & Technol Dept Computat Intelligence Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Elect & Elect Engn Chennai India Kongu Engn Coll Autonomous Dept Elect & Commun Engn Erode India Kongu Engn Coll Autonomous Dept Comp Sci & Engn Erode India Govt Engn Coll Dept Mech Engn Patan India Kongu Engn Coll Autonomous Dept Elect & Elect Engn Erode India Kongu Engn Coll Autonomous Dept Elect & Commun Engn Erode 638052 Tamil Nadu India

Renewable energy (RE) resources have reached the top-notch state in satisfying the energy demands of recent days. Solar photovoltaic (PV) is considered as a clean form of energy among the various RE resources. A novel single-phase grid-integrated solar PV system with Re-lift Luo converter with aid of a chicken swarm (cs) optimization algorithm is presented. The Re-lift Luo converter is implemented as it has maximum power density, extreme efficacy, high-voltage-gain ratio, and reduced switching losses. Since the PV output is intermittent, the output of the converter also gets varied, which will be maintained constant by implementing a proportional-integral (PI)-based closed loop control through the optimized cs algorithm. The normalized output is further applied to the grid via single-phase voltage source inverter. A battery is utilized to store the daytime's solar energy and discharges to the grid during the night time. The monitoring of the battery's state of charge (SoC) is performed by recurrent neural network (RNN). A PI controller achieves the real and reactive grid side power control. The novelty of the proposed work is the utilization of three control strategies such as PI, cs, and RNN in improving the overall performance of a grid connected system in improving the power quality. Proper control and analysis of the converters and battery through intelligent controllers are the major objectives of the paper. The outcome of the proposed concept reigns in the improvement of power quality and the proper estimation of SoC, which was supported by the detailed simulation and experimental studies. The proposed configuration is verified by simulation in MATLAB/Simulink and also tested in a real-time experimental setup, and the obtained results are as per the IEEE standards.

关键词： cs algorithm grid synchronization PV panel Re-lift Luo converter RNN SoC

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Cuckoo Search approach enhanced with genetic replacement of abandoned nests applied to optimal allocation of distributed generation units

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第13期12卷 3353-3362页

作者： de Oliveira, Victoria Y. M. de Oliveira, Rodrigo M. S. Affonso, Carolina M. Fed Univ Para UFPA Postgrad Program Elect Engn PPGEE Rua Augusto Correa 01 Belem Para Brazil Fed Inst Para IFPA Dept Educ Ind Proc Informat & Commun DEPIC Av Alm Barroso 1155 Belem Para Brazil

Here, it is presented a novel Cuckoo Search (cs) algorithm called Cuckoo-GRN (Cuckoo Search with Genetically Replaced Nests), which combines the benefits of genetic algorithm (GA) into the cs algorithm. The proposed method handles the abandoned nests from cs more efficiently by genetically replacing them, significantly improving the performance of the algorithm by establishing optimal balance between diversification and intensification. The algorithm is used for the optimal location and size of distributed generation units in a distribution system, in order to minimise active power losses while improving system voltage stability and voltage profile. The allocation of single and multiple distribution generation units is considered. The proposed algorithm is extensively tested in mathematical benchmark functions as well as in the 33-bus and 119-bus distribution systems. Simulation results show that Cuckoo-GRN can lead to a substantial performance improvement over the original cs algorithm and others techniques currently known in literature, regarding not only the convergence but also the solution accuracy.

关键词： search problems distributed power generation minimisation power system stability genetic algorithms abandoned nests distributed generation unit optimal allocation cs algorithm cuckoo search with genetically replaced nests genetic algorithm distribution system active power losses minimization system voltage stability voltage profile multiple distribution generation unit allocation single distribution generation unit allocation mathematical benchmark functions 119-bus distribution systems 33-bus distribution systems Cuckoo-GRN

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Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms

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IET SIGNAL PROCESSING 2013年第8期7卷 720-730页

作者： Soni, Vivek Bhandari, Ashish Kumar Kumar, Anil Singh, Girish Kumar Indian Inst Informat Technol Design & Mfg Jabalpur 482011 MP India Indian Inst Technol Roorkee Dept Elect Engn Roorkee 247667 Uttrakhand India

In this study, an improved method based on evolutionary algorithms for denoising of satellite images is proposed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (cs) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the cs algorithm and ABC algorithm-based denoising approach give better performance in terms of edge preservation index or edge keeping index (EPI or EKI) peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) as compared to PSO-based denoising approach. The proposed technique has been tested on satellite images. The quantitative (EPI, PSNR and SNR) and visual (denoised images) results show superiority of the proposed technique over conventional and state-of-the-art image denoising techniques.

关键词： evolutionary computation image denoising particle swarm optimisation remote sensing improved subband adaptive thresholding function satellite image denoising evolutionary algorithm stochastic global optimisation technique Cuckoo search algorithm artificial bee colony technique particle swarm optimisation cs algorithm ABC technique PSO technique edge preservation index edge keeping index peak signal-to-noise ratio signal-to-noise ratio

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An imaging algorithm for high-resolution imaging sonar system

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 31957-31973页

作者： Yang, Peixuan Acoust Signal & Elect Sci & Technol Corp Xijin Rd Lanzhou 730050 Peoples R China

Multireceiver synthetic aperture sonar (SAS) can produce high-resolution images by coherently superposing successive echoed signal. However, multireceiver SAS imaging is a difficult issue since the system can be divided into many bistatic SASs. Traditional imaging algorithms cannot be directly used to process the multireceiver SAS echoed signal, as they are just applied to monostatic sonar system. In order to solve this issue, this paper proposes an imaging algorithm based on Loffeld's bistatic formula (LBF) for multireceiver SAS system. With the LBF method, the point target reference spectrum (PTRS) of multireceiver SAS system can be split into quasi-monostatic term and the bistatic deformation term. The quasi-monostatic term is similar to the spectrum of monostatic SAS while the bistatic deformation term is caused by the displacement of transmitter and receiver. In this paper, the data of multireceiver SAS is firstly coerced into monostatic one by compensating the bistatic deformation term. After this step, the chirp scaling (cs) algorithm based on monostatic SAS can be directly applied. At last, simulations are exploited to demonstrate the presented method. Compared to traditional multireceiver SAS cs algorithms, the presented method significantly improves the imaging performance. Both ghosts target and higher sidelobes that traditional multireceiver SAS cs algorithms suffer from can be successfully suppressed. Furthermore, the presented method can obtain high-resolution results which are nearly identical to the benchmark of BP algorithm.

关键词： Multireceiver Synthetic aperture sonar Loffeld's bistatic formula Imaging cs algorithm

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