Genetic Algorithm-based Scheme Research Articles

Minimum-order filter design for partial state estimation of linear systems with multiple time-varying state delays and unknown input delays

Summary Designing delay-dependent functional observers for LTI systems with multiple known time-varying state delays and unknown time-varying input delays is studied. The input delays are arbitrary, but the state delays should be upper-bounded. In addition, two scenarios of slow-varying and fast-varying state delays are investigated. The results of the paper can also be considered as one of the first contributions considering unknown-input functional observer design for linear systems with multiple time-varying state delays. Based on the Lyapunov Krasovskii approach, delay-dependent sufficient conditions of the exponential stability of the observer in each scenario are established in terms of linear matrix inequalities. Because of using effective techniques, such as the descriptor transformation and an advanced weighted integral inequality, the proposed stability criteria can result in larger stability regions compared with the other papers that study functional observers for time-varying delay systems. Furthermore, to help with the design procedure, a genetic algorithm-based scheme is proposed to adjust a weighting matrix in the established linear matrix inequalities. Two numerical examples illustrate the design procedure and demonstrate the efficacy of the proposed observer in each scenario. Copyright © 2016 John Wiley & Sons, Ltd.

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.

Energy efficiency‐based device‐to‐device uplink resource allocation with multiple resource reusing

Device-to-device communication fulfils its potential in terms of high resource utilisation, thus it has been a hot topic in the recent long-term evolution standardisation process. The general scenario with multiple resource pool multiplexing instead of the one-time reusing is considered. Meanwhile, the optimisation target focuses on device energy efficiency under certain system throughput insurance rather than the traditional system throughput. A modified genetic algorithm-based scheme is applied to address the facing non-deterministic polynomial-time hard problem with higher convergence and lower complexity.

Genetic Algorithm-Based Approach to Spectrum Allocation and Power Control with Constraints in Cognitive Radio Networks

This study presents a genetic algorithm for spectrum allocation and power control, both with constraints, in cognitive radio networks. The models for spectrum allocation and power control with constraints are formulated in a partially distributed manner, then the scheme based on genetic algorithms is presented. Simulation results demonstrate that the proposed genetic algorithm-based scheme is efficient for spectrum allocation and power control in cognitive radio networks.