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A proposed dual size design for energy minimization in sub-threshold circuits
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Sub-threshold operation has received a lot of attention in limited performance applications.However, energy optimization of sub-threshold circuits should be performed with the concern of the performance limitation of such circuit. In this paper, a dual size design is proposed for energy minimization of sub-threshold CMOS circuits. The optimal downsizing factor is determined and assigned for some gates on the off-critical paths to minimize the energy at the maximum allowable performance. This assignment is performed using the proposed slack based genetic algorithm which is a heuristic-mixed evolutionary algorithm. Some gates are heuristically assigned to the original and the downsized design based on their slack time determined by static timing analysis. Other gates are subjected to the genetic algorithm to perform an optimal downsizing assignment taking into account the previous assignments. The algorithm is applied for different downsizing factors to determine the optimal dual size for low energy operation without a performance degradation. Experimental results are obtained for some ISCAS-85 benchmark circuits such as 74283, 74L85, ALU74181, and 16 bit ripple carry adder. The proposed design shows an energy per cycle saving ranged from (29.6% to 56.59%) depending on the utilization of available slack time from the off-critical paths. © School of Engineering, Taylor’s University.

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Publication Date
Wed Sep 05 2007
Journal Name
Neural Network World
A canonical generic algorithm for likelihood estimator of first order moving average model parameter
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The increasing availability of computing power in the past two decades has been use to develop new techniques for optimizing solution of estimation problem. Today's computational capacity and the widespread availability of computers have enabled development of new generation of intelligent computing techniques, such as our interest algorithm, this paper presents one of new class of stochastic search algorithm (known as Canonical Genetic' Algorithm ‘CGA’) for optimizing the maximum likelihood function strategy is composed of three main steps: recombination, mutation, and selection. The experimental design is based on simulating the CGA with different values of are compared with those of moment method. Based on MSE value obtained from bot

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
ZnO: MWCNT optical hybrid filter a promising nanomaterial for wastewater treatment and antimicrobial applications
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Publication Date
Fri Dec 01 2023
Journal Name
Case Studies In Chemical And Environmental Engineering
Kinetic modeling of a solar photo-electro-Fenton process for treating petroleum refinery wastewater
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Publication Date
Wed Feb 05 2003
Journal Name
. Sc. Conf. Of The College 5th Of Eng. Univ. Of Baghdad 2003
COMPUTATIONAL AND EXPERIMENTAL INVESTIGATION OF THE AERODYNAMIC CHARACTERISTICS FOR A FORWARD SWEPT WING AIRCRAFT
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The aerodynamic characteristics of the forward swept wing aircraft have been studied theoretically and experimentally. Low order panel method with the Dirichlet boundary condition have been used to solve the case of the steady, inviscid and compressible flow. Experimentally, a model was manufactured from wood to carry out the tests. The primary objective of the experimental work was the measurements of the wake dimensions and orientation, velocity defect along the wake and the wake thickness. A blower type low speed (open jet) wind tunnel was used in the experimental work. The mean velocity at the test section was (9.3 m/s) and the Reynolds number based on the mean aerodynamic chord and the mean velocity was (0.46x105). The measurements sho

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Publication Date
Wed Jan 01 2014
Journal Name
International Journal Of Computer Applications
Enhancing the Delta Training Rule for a Single Layer Feedforward Heteroassociative Memory Neural Network
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In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

Publication Date
Fri Sep 02 2022
Journal Name
Military Medical Science Letters
SOLID LIPID NANOPARTICLES AS A PROMISING APPROACH FOR DELIVERY OF ANTICANCER AGENTS: REVIEW ARTICLE
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Cancer disease has a complicated pathophysiology and is one of the major causes of death and morbidity. Classical cancer therapies include chemotherapy, radiation therapy, and immunotherapy. A typical treatment is chemotherapy, which delivers cytotoxic medications to patients to suppress the uncontrolled growth of cancerous cells. Conventional oral medication has a number of drawbacks, including a lack of selectivity, cytotoxicity, and multi-drug resistance, all of which offer significant obstacles to effective cancer treatment. Multidrug resistance (MDR) remains a major challenge for effective cancer chemotherapeutic interventions. The advent of nanotechnology approach has developed the field of tumor diagnosis and treatment. Cancer nanote

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Publication Date
Tue Oct 19 2021
Journal Name
Biochem
A Neuro-Fuzzy Technique for the Modeling of β-Glucosidase Activity from Agaricus bisporus
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This paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership functions based on previously published experimental data for β-glucosidase. Second, each input’s optimized membership functions from the ANF

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Publication Date
Wed Aug 31 2022
Journal Name
Al-kindy College Medical Journal
A comparison of cross sections for Selenium -73 radioisotopes produced by accelerators and reactors
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Background: Selenium-73 with half- life of 7.15 hour emits β+ in nature and has six stable isotopes which are ( 74Se,76Se,77Se,78Se,80Se and 82Se ). Selenium-73 has many applications in technology and radioselenium compounds of metallic have found various applications in medicine.

Objective:  To make a comparison between different reactions that produced cross sections of Se-73 radioisotopes.

Subjects and methods: The feasibility of the production of Selenium -73 via various nuclear reactions was investigated. Excitation functions of 73Se production by the re

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Publication Date
Thu Sep 28 2023
Journal Name
Iraqi Journal Of Computer, Communication, Control And System Engineering
Distinct Architectures of Feed Forward Neural Network for Implementing A Human Swing Leg System
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The artificial intelligence techniques such as neural networks and fuzzy systems play an important role to disconnect flexion & expansion of the swing leg, the earth response force of the other foot has been redesigned. Under that paper, we think the fuzzy controller plan issue for yield following flawed genuine investigation of nonlinear systems. For examination, an essential fuzzy control plot has been bristly developed dependent on a current methodology delegate under the field. In this paper, the Feedforward Neural Network has been implemented with integer, fixed point and floating point data representations. Additionally, The Fuzzy Logic Controllers in both analog and digital forms has been implemented in hardware. Both designs use les

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Publication Date
Fri Mar 01 2024
Journal Name
Partial Differential Equations In Applied Mathematics
A hybrid technique for solving fractional delay variational problems by the shifted Legendre polynomials
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This study presents a practical method for solving fractional order delay variational problems. The fractional derivative is given in the Caputo sense. The suggested approach is based on the Laplace transform and the shifted Legendre polynomials by approximating the candidate function by the shifted Legendre series with unknown coefficients yet to be determined. The proposed method converts the fractional order delay variational problem into a set of (n + 1) algebraic equations, where the solution to the resultant equation provides us the unknown coefficients of the terminated series that have been utilized to approximate the solution to the considered variational problem. Illustrative examples are given to show that the recommended appro

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