PMCC PMCC

Search tips
Search criteria

Advanced

Important Notice

PubMed Central Canada to be taken offline in February 2018

On February 23, 2018, PubMed Central Canada (PMC Canada) will be taken offline permanently. No author manuscripts will be deleted, and the approximately 2,900 manuscripts authored by Canadian Institutes of Health Research (CIHR)-funded researchers currently in the archive will be copied to the National Research Council’s (NRC) Digital Repository over the coming months. These manuscripts along with all other content will also remain publicly searchable on PubMed Central (US) and Europe PubMed Central, meaning such manuscripts will continue to be compliant with the Tri-Agency Open Access Policy on Publications.

Read more

Results 1-2 (2)
 

Clipboard (0)
None

Select a Filter Below

Journals
Authors
Year of Publication
Document Types
1.  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;5(1):1391.
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.
doi:10.1186/s40064-016-2987-6
PMCID: PMC4993748  PMID: 27610310
Optimum MOSFETs channel widths and power supply; Proposed Dual Edge-Triggered Static D Flip-Flop; Minimization of average power and delay; Power delay product; Fuzzy NSGA-II
2.  Electrocardiogram Based Identification using a New Effective Intelligent Selection of Fused Features 
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.
PMCID: PMC4335143  PMID: 25709939
Biometrics; identification; electrocardiogram; genetic algorithm; neural networks

Results 1-2 (2)