scholarly journals A Particle Swarm Optimization Based Approach to Pre-tune Programmable Hyperspectral Sensors

2021 ◽  
Vol 13 (16) ◽  
pp. 3295
Author(s):  
Bikram Pratap Banerjee ◽  
Simit Raval
Keyword(s):  
Particle Swarm Optimization ◽  
Hyperspectral Imaging ◽  
Data Storage ◽  
Particle Swarm ◽  
Limiting Factor ◽  
Target Class ◽  
Swarm Optimization ◽  
Spectral Bands ◽  
Hyperspectral Sensors ◽  
Vegetation Species

Identification of optimal spectral bands often involves collecting in-field spectral signatures followed by thorough analysis. Such rigorous field sampling exercises are tedious, cumbersome, and often impractical on challenging terrain, which is a limiting factor for programmable hyperspectral sensors mounted on unmanned aerial vehicles (UAV-hyperspectral systems), requiring a pre-selection of optimal bands when mapping new environments with new target classes with unknown spectra. An innovative workflow has been designed and implemented to simplify the process of in-field spectral sampling and its realtime analysis for the identification of optimal spectral wavelengths. The band selection optimization workflow involves particle swarm optimization with minimum estimated abundance covariance (PSO-MEAC) for the identification of a set of bands most appropriate for UAV-hyperspectral imaging, in a given environment. The criterion function, MEAC, greatly simplifies the in-field spectral data acquisition process by requiring a few target class signatures and not requiring extensive training samples for each class. The metaheuristic method was tested on an experimental site with diversity in vegetation species and communities. The optimal set of bands were found to suitably capture the spectral variations between target vegetation species and communities. The approach streamlines the pre-tuning of wavelengths in programmable hyperspectral sensors in mapping applications. This will additionally reduce the total flight time in UAV-hyperspectral imaging, as obtaining information for an optimal subset of wavelengths is more efficient, and requires less data storage and computational resources for post-processing the data.

scholarly journals A Novel Weighted Support Vector Machine Based on Particle Swarm Optimization for Gene Selection and Tumor Classification

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad Javad Abdi ◽  
Seyed Mohammad Hosseini ◽  
Mansoor Rezghi
Keyword(s):  
Particle Swarm Optimization ◽  
Dna Microarrays ◽  
Gene Selection ◽  
Particle Swarm ◽  
Tumor Classification ◽  
Support Vector ◽  
Classification Problems ◽  
Target Class ◽  
Swarm Optimization ◽  
Kernel Parameters

We develop a detection model based on support vector machines (SVMs) and particle swarm optimization (PSO) for gene selection and tumor classification problems. The proposed model consists of two stages: first, the well-known minimum redundancy-maximum relevance (mRMR) method is applied to preselect genes that have the highest relevance with the target class and are maximally dissimilar to each other. Then, PSO is proposed to form a novel weighted SVM (WSVM) to classify samples. In this WSVM, PSO not only discards redundant genes, but also especially takes into account the degree of importance of each gene and assigns diverse weights to the different genes. We also use PSO to find appropriate kernel parameters since the choice of gene weights influences the optimal kernel parameters and vice versa. Experimental results show that the proposed mRMR-PSO-WSVM model achieves highest classification accuracy on two popular leukemia and colon gene expression datasets obtained from DNA microarrays. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.

scholarly journals Multi-objective Pareto front and particle swarm optimization algorithms for power dissipation reduction in microprocessors

2020 ◽  
Vol 10 (6) ◽  
pp. 6549
Author(s):  
Diary R. Sulaiman
Keyword(s):  
Particle Swarm Optimization ◽  
Power Dissipation ◽  
High Performance ◽  
Pareto Front ◽  
Particle Swarm ◽  
Limiting Factor ◽  
Clock Frequency ◽  
Swarm Optimization ◽  
Multi Objective ◽  
System Temperature

The progress of microelectronics making possible higher integration densities, and a considerable development of on-board systems are currently undergoing, this growth comes up against a limiting factor of power dissipation. Higher power dissipation will cause an immediate spread of generated heat which causes thermal problems. Consequently, the system's total consumed energy will increase as the system temperature increase. High temperatures in microprocessors and large thermal energy of computer systems produce huge problems of system confidence, performance, and cooling expenses. Power consumed by processors are mainly due to the increase in number of cores and the clock frequency, which is dissipated in the form of heat and causes thermal challenges for chip designers. As the microprocessor’s performance has increased remarkably in Nano-meter technology, power dissipation is becoming non-negligible. To solve this problem, this article addresses power dissipation reduction issues for high performance processors using multi-objective Pareto front (PF), and particle swarm optimization (PSO) algorithms to achieve power dissipation as a prior computation that reduces the real delay of a target microprocessor unit. Simulation is verified the conceptual fundamentals and optimization of joint body and supply voltages (Vth-VDD) which showing satisfactory findings.

scholarly journals Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Xiyang Liu ◽  
Lei Fan ◽  
Liming Wang ◽  
Sha Meng
Keyword(s):  
Particle Swarm Optimization ◽  
Data Storage ◽  
Volume Growth ◽  
Real Life ◽  
Particle Swarm ◽  
Experimental Results ◽  
Digital Data ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Multiobjective Particle Swarm Optimization

Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS) of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO) algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods.

scholarly journals A Particle Swarm Optimization based Approach to Pre-tune Programmable Hyperspectral Sensors

2021 ◽  
Author(s):  
Bikram Banerjee ◽  
Simit Raval
Keyword(s):  
Particle Swarm Optimization ◽  
Field Survey ◽  
Particle Swarm ◽  
Trial And Error ◽  
Swarm Optimization ◽  
Hyperspectral Sensors ◽  
Significant Wavelength ◽  
First Time ◽  
Selection Of ◽  
Hyperspectral Sensor

This article presents development of an innovative approach to identify spectrally significant wavelength bands, for a given environment, to tune hyperspectral sensor acquisition before UAV borne surveys. As several programmable hyperspectral sensors are now available, it is often a challenge to consider the suitable wavelengths of interest. Researchers often conduct a thorough field survey to identify the composition of target endmembers in an area to identify suitable wavelengths before UAV survey, which is difficult and cumbersome. Otherwise, the selection of wavelengths by trial-and-error is error-prone. <br>To our knowledge, this is the first time a technique for optimal hyperspectral band (or feature) selection has been proposed to pre-tune UAV-hyperspectral sensors before the survey. A metaheuristic evolutionary workflow using Particle Swarm Optimisation was used for this. The method is easy in the field and efficient to identify optimal bands before UAV-hyperspectral surveys.<br>

scholarly journals A Particle Swarm Optimization based Approach to Pre-tune Programmable Hyperspectral Sensors

2021 ◽  
Author(s):  
Bikram Banerjee ◽  
Simit Raval
Keyword(s):  
Particle Swarm Optimization ◽  
Field Survey ◽  
Particle Swarm ◽  
Trial And Error ◽  
Swarm Optimization ◽  
Hyperspectral Sensors ◽  
Significant Wavelength ◽  
First Time ◽  
Selection Of ◽  
Hyperspectral Sensor

This article presents development of an innovative approach to identify spectrally significant wavelength bands, for a given environment, to tune hyperspectral sensor acquisition before UAV borne surveys. As several programmable hyperspectral sensors are now available, it is often a challenge to consider the suitable wavelengths of interest. Researchers often conduct a thorough field survey to identify the composition of target endmembers in an area to identify suitable wavelengths before UAV survey, which is difficult and cumbersome. Otherwise, the selection of wavelengths by trial-and-error is error-prone. <br>To our knowledge, this is the first time a technique for optimal hyperspectral band (or feature) selection has been proposed to pre-tune UAV-hyperspectral sensors before the survey. A metaheuristic evolutionary workflow using Particle Swarm Optimisation was used for this. The method is easy in the field and efficient to identify optimal bands before UAV-hyperspectral surveys.<br>

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
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