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OPTIMIZED REVERSIBLE ARITHMETIC LOGIC UNITS

Journal of Electronics(China) 2014年第5期31卷 394-405页

作者： Payman Moallem Maryam Ehsanpour ali bolhasani Mehrdad Montazeri Department of Electrical Engineering University of Isfahan Department of Computer Falavarjan Branch Islamic Azad University Department of Computer Arak Branch Islamic Azad University Department of Computer Faculty of Engineering Polytechnic University of Turin

Arithmetic Logic Unit(ALU) as one of the main parts of any computing hardware plays an important role in digital computers. In quantum computers which can be realized by reversible logics and circuits, reversible ALUs should be designed. In this paper, we proposed three different designs for reversible 1-bit ALUs using our proposed 3×3 and 4×4 reversible gates called MEB3 and MEB4(Moallem Ehsanpour bolhasani) gates, respectively. The first proposed reversible ALU consists of six logical operations. The second proposed ALU consists of eight operations, two arithmetic, and six logical operations. And finally, the third proposed ALU consists of sixteen operations, four arithmetic operations, and twelve logical operations. Our proposed ALUs can be used to construct efficient quantum computers in nanotechnology, because the proposed designs are better than the existing designs in terms of quantum cost, constant input, reversible gates used, hardware complexity, and functions generated.

关键词： Reversible Arithmetic Logic Unit(ALU) Full Adder(FA) Control unit Reversible logic gates

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Depression diagnosis by deep learning using EEG signals:A systematic review

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Medicine in Novel Technology and Devices 2021年第4期12卷 16-31页

作者： Atefeh Safayari Hamidreza bolhasani Department of Computer Science Kharazmi University of TehranTehranIran Department of Computer Engineering Islamic Azad University Science and Research BranchTehranIran

Depression is considered by WHO as the main contributor to global disability and it poses dangerous threats to approximately all aspects of human life,in particular public and private *** mental disorder is usually characterized by considerable changes in feelings,routines,or *** respect to the fact that early diagnosis of this illness would be of the critical importance in effective treatment,some developments have occurred in the purpose of depression *** signals reflect the working status of the human brain which are considered the most proper tools for a depression *** learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into *** to this significant characteristic of deep learning,recently,these methods have intensely utilized in the diverse research fields,specifically medicine and ***,in this article,we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input *** the adopted search method,we have finally evaluated 22 articles between 2016 and *** article which is organized according to the systematic literature review(SLR)method,provides complete summaries of all exploited studies and compares the noticeable aspects of ***,some statistical analyses have been performed to gain a depth perception of the general ideas of the latest pieces of research in this area.A pattern of a five-step procedure has also been established by which almost all reviewed articles have fulfilled the goal of depression ***,open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions have been discussed.

关键词： Deep learning Depression Electroencephalogram EEG Depressive disorders Systematic literature review

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