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Silicon on Insulator C-VTFET Based Design of low Complexity Sparse Quadrature Mirror Filter Using Differential Search Algorithm

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SILICON 2022年第17期14卷 11545-11560页

作者： Singh, Hitendra Dwivedi, Atul Kumar Nagaria, Deepak Bundelkhand Inst Engn & Technol Dept Elect & Commun Engn Jhansi 284128 Uttar Pradesh India

In this paper, a 60 nm Complementary-Vertical TFET (C-VTFET) is designed using silicon on insulation technology is implemented for low power quadrature mirror filter design for different search algorithm using silvaco TCAD simulation. Various advantages of SOI have been incorporated for the realization of low-voltage with low power (LVLP) for VLSI design digital circuits. To prevent the losses of the Lattice mismatch structure, gate staking of high k -dielectric (HfO2) material with SiO2 was employed using equivalent oxide thickness method. Various engineering method incorporated to optimized the Drain-Voltage characteristics. N-Type and P-Type VTFET is considered using the mixed mode technique. The SiGe layer is employed to enhance the tunneling method of by reducing the bandgap firm 1.1 eV to 0.7 eV. The highest I-ON current and minimum I(OFF )current is reported for the proposed device is I (3.62 x 10(-4) A/mu m) and (1.58 x 10(-18) A/mu m) respectively. The ON/OFF current ratio out recorded as similar to 10(13) respectively. The reconstruction quadrature mirror filter architecture in this manuscript is computationally efficient and virtually flawless. Sparsity among the coefficients is introduced to reduce the number of multipliers and adders required to construct prototype the filter H-0(Z), resulting in a reduction in computational complexity. A well-known population-based evolutionary optimization differential search algorithm is used to optimized the objective function. However, Levy's differential search method is also advocated due to sluggish exploitation of traditional differential search algorithm. The suggested algorithm's effectiveness is evaluated by comparing it to other evolutionary optimization techniques. The results demonstrate that the proposed technique outperforms previously published evolutionary optimization algorithms. The above analysis validated the proposed device are well incorporated for the execution to design low power filter design

关键词： Silicon-on-insulator complementary VTFET Heterojunction Differential search algorithm evolutionary optimization algorithms Quadrature mirror filter

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optimization of wireless body area network performance with simplified electromagnetic model of the body

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2018年第Sup1期57卷 S143-S154页

作者： Januszkiewicz, Lukasz Di Barba, Paolo Hausman, Slawomir Lodz Univ Technol Inst Elect Wolczanska 211-215 St Lodz Poland Univ Pavia Dept Elect Comp & Biomed Engn I-27100 Pavia Italy

In this paper the optimization method and example for a wireless body area network is presented. The optimization goal is to improve the performance of the body area sensor network with respect to the on-body transmission channel and the off-body link performance jointly. The position of the two nodes of the network on the user's trunk are the design variables. The objective function used in the design process includes two components: one which controls the on-body data transmission between the 3 sensor nodes and another controlling the off-body transmission between the nodes and the remote receiver. The optimization procedure utilizes a low-cost EStra which is an evolutionary strategy optimization algorithm linked with finite-difference time-domain method based XFdtd electromagnetic simulator. Considering the two on-body and off-body components, the objective-alternance approach applied in this research yields a solution of the problem that compromise between on-body and off-body optimal solution.

关键词： WBAN wearable antenna FDTD evolutionary optimization algorithms computational electromagnetics

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Optimal design of the Own Ship maneuver in the bearing-only target motion analysis problem using a heuristically supervised Extended Kalman Filter

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OCEAN ENGINEERING 2016年 123卷 146-153页

作者： Sabet, M. T. Fathi, A. R. Daniali, H. R. Mohammadi Nooshirvani Univ Technol Dept Mech Engn Babol Sar Iran

The state estimation algorithm and the Own Ship maneuvering specifications have great effects on the accuracy of the bearing-only tracking (BUT) method. In the BOT problem, the EKF algorithm is widely used as the nonlinear state estimation algorithm while it suffers from its sensitivity to the initial values of covariance matrixes. This paper aims at improving the accuracy of the BOT problem by using the metaheuristic evolutionary optimization algorithms as supervising algorithms. Three different optimization algorithms, Particle Swarm optimization (PSO), Genetic Algorithm (GA) and Cuckoo Search (CS) are used in finding the optimal initial values of the dynamic and the measurement process noise covariance matrixes of the Extended Kalman Filter (EKF). Moreover, the optimal path (leg) planning of the Own Ship maneuver is also done by minimizing the Fisher Information Matrix (FIM). The Monte Carlo analysis of the simulation results demonstrates the effectiveness of the evolutionary algorithms in improving the performance of the EKF in a BOT problem. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Bearing only tracking Extended Kalman Filter evolutionary optimization algorithms

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SOCCA: SOcial-based Colonial Competitive Algorithm 22

SOCCA: SOcial-based Colonial Competitive Algorithm

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22nd Iranian Conference on Electrical Engineering (ICEE)

作者： Basiri, Javad Taghiyareh, Fattaneh Univ Tehran Dept Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781479944095

evolutionary algorithms have been successfully applied as optimization tools in various applications. CCA-Colonial Competitive Algorithm- is a recently-developed socio-politically inspired evolutionary optimization algorithm. Initial population of CCA are divided in to some collections called empires and all the individuals in each empire move toward the best one to identify increasingly better area of the search space. This paper presents SOCCA (SOcial-based Colonial Competitive Algorithm) as an improved version of CCA. In each empire of our algorithm, a social network is generated, assigning neighbours for each individual to interact with and individuals move toward their best neighbours. To assess the SOCCA capability, it was applied to four benchmark optimization functions and results show that the SOCCA algorithm has the ability of finding the global minimum. Also, we compared results with CCA, which indicates SOCCA superiority. Our findings show that SOCCA may provide better performance for some other applications.

关键词： evolutionary optimization algorithms Heuristic search SOCCA CCA Numerical optimization

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Secure Scheduling of Scientific Workflows in Cloud 12

Secure Scheduling of Scientific Workflows in Cloud

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12th International Conference on Cloud Computing and Services Science (CLOSER)

作者： Roy, Shubhro Ramamurthy, Arun Pawar, Anand Gharote, Mangesh Lodha, Sachin TCS Res Tata Consultancy Serv Gurgaon Haryana India Indian Inst Informat Technol Pune Maharashtra India TCS Res Gurgaon Haryana India

ISBN: (纸本)9789897585708

Scheduling of tasks in scientific workflow has been challenging due to heterogeneous and interdependent tasks in workflow. The scheduling involves selection of different types of virtual machines (VM) belonging to different instance series (computing, memory, storage) to minimize the overall execution cost and time (makespan). Apart from VM selection, selection of security services (such as authentication, integrity, confidentiality) is critical. In this paper, we propose OptReUse - a workflow schedule generation algorithm for efficient reuse of VMs. Our approach of OptReUse algorithm along with combinatorial optimization approach gives lower cost of scheduling compared to the prior art without incurring delay in the makespan. Further, we enhance the security model by accurate estimation of risks. Our experiments using standard scientific workflows demonstrate that the proposed method gives lower costs compared to the prior VM resource utilization methods.

关键词： Scientific Workflow Scheduling Cloud Computing Security in Cloud Task Scheduling evolutionary optimization algorithms

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Efficient prediction of fog-related low-visibility events with Machine Learning and evolutionary algorithms

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ATMOSPHERIC RESEARCH 2023年 295卷

作者： Pelaez-Rodriguez, C. Perez-Aracil, J. Casanova-Mateo, C. Salcedo-Sanz, S. Univ Alcala Dept Signal Proc & Commun Alcala De Henares 28805 Spain Univ Politecn Madrid Dept Comp Syst Engn Madrid 28038 Spain

Low visibility events are a severe problem for road transport, causing accidents and major economic losses. Their accurate prediction may help prevent these problems. For that purpose, machine and deep learning techniques have been applied for fog prediction using in situ meteorological data and persistence variables as baseline predictors. These techniques have been evaluated for different prediction time-horizons: 1 h, 3 h and 6 h. The effect of including data extracted from ERA5 Reanalysis as predictive variables has been studied. A database, covering 23 months, has been used, which contains visibility and other meteorogical variables measured in Mondon similar to edo, Galicia, Spain. A 222000 km2 region around Mondon similar to edo has been delimited. Thus, a proposed iterative forward selection algorithm based on evolutionary algorithms has been applied to determine the optimal variables and nodes in the region for each regressor model. Both Differential Evolution and Particle Swarm optimization have been used as optimization algorithms, and an improvement of up to 17.3% with respect to the baseline databases have been obtained. Finally, an analysis of the most frequently selected variables by the evolutionary algorithms has been conducted, leading us to conclude which variables and geographical nodes provide better information to the prediction models.

关键词： Low -visibility events Orographic fog Machine Learning algorithms Reanalysis data evolutionary optimization algorithms Iterative forward selection

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An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 90卷

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad *** Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad *** Iran

Artificial neural network (ANN) is an information processing paradigm that loosely models the thinking patterns of the human brain with specifications such as real-time learning, self-adaption, and self -organization. The learning process of ANNs is complex and tackles shortcomings such as a slow convergence rate, learning timeconsuming, and local minimum trapping, especially when using gradient -based optimization techniques. Although many metaheuristics have been proposed to arm ANN and solve these weaknesses, ANN learning still needs solution quality. Therefore, this study proposes an evolutionary crow search algorithm (ECSA) to optimize the hyperparameters of ANNs for diagnosing chronic diseases. ECSA introduces an evolutionary search strategy, self -adaptive adaptive flight length, and an interactive memory mechanism to alleviate the canonical crow search algorithm's shortcomings. The evolutionary search strategy and self -adaptive flight length provide a meaningful search strategy in which crows effectively explore and exploit problem spaces by maintaining population diversity. During the search process, the interactive memory mechanism records the best solution obtained during optimization. The performance of ECSA was evaluated and compared with well-known metaheuristic algorithms in terms of local and global search abilities, local optima avoidance, and convergence speed towards the promising area. Then, the results were statistically analyzed. Ultimately, the effectiveness of an adaptation of ECSA named ECSA-MLP for optimizing hyperparameters of the multilayer perceptron network for diagnosing diseases including coronavirus, breast cancer, diabetes, cardiovascular, cervical cancer, Parkinson's, mammography, and acute inflammation was compared with state-of-the-art competitor algorithms. The experimental results indicated the superiority of ECSA over competitor algorithms in optimizing the network.

关键词： Artificial Neural Networks Multilayer perceptron network Hyperparameters Metaheuristic algorithm Swarm intelligence algorithm evolutionary optimization algorithms

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Efficiency of the slime mold algorithm for damage detection of large-scale structures

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2022年第14期31卷 e1967-e1967页

作者： Ghannadi, Parsa Kourehli, Seyed Sina Islamic Azad Univ Dept Civil Engn Ahar Branch Ahar Iran Azarbaijan Shahid Madani Univ Dept Civil Engn Tabriz Iran

optimization-based methods are increasingly being implemented for structural damage detection problems through the minimization of the objective functions based on vibration data. The adopted optimization algorithm and objective function play an important role in the accurate detection and quantification of damages. Meanwhile, the challenge of long computational time is another aspect of structural damage identification problems, especially upon addressing large-scale structures. In this paper, recently developed optimization techniques called slime mold algorithm (SMA) and marine predators algorithm (MPA) are applied to damage assessment of large-scale structures for the first time. The performance of these algorithms is compared with those obtained using other well-known optimization techniques such as ant lion optimizer (ALO), whale optimization algorithm (WOA), and grasshopper optimization algorithm (GOA). Furthermore, the sensitivity of three objective functions based on modal assurance criterion (MAC), modified total modal assurance criterion (MTMAC), and natural frequency vector assurance criterion (NFVAC) are examined. Two numerical studies, including the 53-bar planar truss and the Guangzhou New TV tower, and a full-scale three-story frame as an experimental investigation are conducted to present a statistical comparison. The overall results show that the combination of SMA and objective function based on MTMAC provides an accurate tool for damage identification. However, improved SMA (ISMA) has been introduced to enhance the capability of standard SMA for damage detection, and especially finite element model updating in the experimental example. Five benchmark functions are also used to evaluate the global optimization capacity of ISMA and SMA. The results show that the ISMA has many benefits in terms of tackling global optimization problems. MPA-MTMAC can provide promising results compared with ALO-MTMAC, WOA-MTMAC, and GOA-MTMAC. However, excessive compu

关键词： damage identification evolutionary optimization algorithms FEM updating marine predators algorithm MTMAC slime mold algorithm

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An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets

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SENSORS 2022年第21期22卷 8128-8128页

作者： Khare, Smith K. Gaikwad, Nikhil Bokde, Neeraj Dhanraj Aarhus Univ Dept Elect & Comp Engn DK-8000 Aarhus Denmark Aarhus Univ Ctr Quantitat Genet & Genom DK-8000 Aarhus Denmark

Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.

关键词： electroencephalogram signals intelligent system evolutionary optimization algorithms adaptive wavelets Laplacian score support vector classifiers

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Multi-Objective optimization of a Wireless Body Area Network for Varying Body Positions

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SENSORS 2018年第10期18卷 3406-3406页

作者： Januszkiewicz, Lukasz Di Barba, Paolo Hausman, Slawomir Lodz Univ Technol Inst Elect PL-90924 Lodz Poland Univ Pavia Dept Elect Comp & Biomed Engn I-27100 Pavia Italy

The purpose of this research was to improve the performance of a wireless body area sensor network, operating on a person in the seated and standing positions. optimization-focused on both the on-body transmission channel and off-body link performance. The system consists of three nodes. One node (on the user's head) is fixed, while the positions of the other two (one on the user's trunk and the other on one leg) with respect to the body (local coordinates) are design variables. The objective function used in the design process is characterized by two components: the first controls the wireless channel for on-body data transmission between the three sensor nodes, while the second controls the off-body transmission between the nodes and a remote transceiver. The optimal design procedure exploits a low-cost Estra, which is an evolutionary strategy optimization algorithm linked with Remcom XFdtd, a full-wave Finite-Difference Time-Domain (FDTD) electromagnetic field analysis package. The Pareto-like approach applied in this study searches for a non-dominated solution that gives the best compromise between on-body and off-body performance.

关键词： WBAN wearable antenna human body model FDTD analysis evolutionary optimization algorithms computational electromagnetics

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