Publications by authors named "Nasser Mehrshad"

3 Publications

  • Page 1 of 1

Semisupervised classification of hyperspectral images with low-rank representation kernel.

J Opt Soc Am A Opt Image Sci Vis 2020 Apr;37(4):606-613

A semisupervised deformed kernel function, using low-rank representation with consideration of local geometrical structure of data, is presented for the classification of hyperspectral images. The proposed method incorporates the wealth of unlabeled information to deal with the limited labeled samples situation as a common case in HSIs applications. The proposed kernel needs to be computed before training the classifier, e.g., a support vector machine, and it relies on combining the standard radial basis function kernel based on labeled information and the low-rank representation kernel obtained using all available (labeled and unlabeled) information. The low-rank representation kernel can overcome the difficulties of clustering methods that are used to construct the kernels such as bagged kernel and multi-scale bagged kernel. The experimental results of two well-known HSIs data sets demonstrate the effectiveness of the proposed method in comparison with cluster kernels obtained using traditional clustering methods and graph learning methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1364/JOSAA.381158DOI Listing
April 2020

Multi-objective optimization of MOSFETs channel widths and supply voltage in the proposed dual edge-triggered static D flip-flop with minimum average power and delay by using fuzzy non-dominated sorting genetic algorithm-II.

Springerplus 2016 22;5(1):1391. Epub 2016 Aug 22.

Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.

Background: D Flip-Flop as a digital circuit can be used as a timing element in many sophisticated circuits. Therefore the optimum performance with the lowest power consumption and acceptable delay time will be critical issue in electronics circuits.

Findings: The newly proposed Dual-Edge Triggered Static D Flip-Flop circuit layout is defined as a multi-objective optimization problem. For this, an optimum fuzzy inference system with fuzzy rules is proposed to enhance the performance and convergence of non-dominated sorting Genetic Algorithm-II by adaptive control of the exploration and exploitation parameters. By using proposed Fuzzy NSGA-II algorithm, the more optimum values for MOSFET channel widths and power supply are discovered in search space than ordinary NSGA types. What is more, the design parameters involving NMOS and PMOS channel widths and power supply voltage and the performance parameters including average power consumption and propagation delay time are linked. To do this, the required mathematical backgrounds are presented in this study.

Conclusion: The optimum values for the design parameters of MOSFETs channel widths and power supply are discovered. Based on them the power delay product quantity (PDP) is 6.32 PJ at 125 MHz Clock Frequency, L = 0.18 µm, and T = 27 °C.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s40064-016-2987-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4993748PMC
September 2016

Electrocardiogram Based Identification using a New Effective Intelligent Selection of Fused Features.

J Med Signals Sens 2015 Jan-Mar;5(1):30-9

Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.

Over the years, the feasibility of using Electrocardiogram (ECG) signal for human identification issue has been investigated, and some methods have been suggested. In this research, a new effective intelligent feature selection method from ECG signals has been proposed. This method is developed in such a way that it is able to select important features that are necessary for identification using analysis of the ECG signals. For this purpose, after ECG signal preprocessing, its characterizing features were extracted and then compressed using the cosine transform. The more effective features in the identification, among the characterizing features, are selected using a combination of the genetic algorithm and artificial neural networks. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database, MITBIH Normal Sinus Rhythm Database and The European ST-T Database, in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Identification rates of 99.89% and 99.84% and 99.99% are obtained for these databases respectively. The proposed algorithm exhibits remarkable identification accuracies not only with normal ECG signals, but also in the presence of various arrhythmias. Simulation results showed that the proposed method despite the low number of selected features has a high performance in identification task.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4335143PMC
February 2015