Volume 62, Issue 10 will be published on 02 December 2020

Technology Reports of Kansai University

Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.

Submission Deadline

Volume - 62 , Issue 11
09 Dec 2020
Day
Hour
Min
Sec

Upcoming Publication

Volume - 62 , Issue 10
30 Nov 2020

Aim and Scope

Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to:

Electrical Engineering and Telecommunication Section:

Electrical Engineering, Telecommunication Engineering, Electro-mechanical System Engineering, Biological Biosystem Engineering, Integrated Engineering, Electronic Engineering, Hardware-software co-design and interfacing, Semiconductor chip, Peripheral equipments, Nanotechnology, Advanced control theories and applications, Machine design and optimization , Turbines micro-turbines, FACTS devices , Insulation systems , Power quality , High voltage engineering, Electrical actuators , Energy optimization , Electric drives , Electrical machines, HVDC transmission, Power electronics.

Computer Science Section :

Software Engineering, Data Security , Computer Vision , Image Processing, Cryptography, Computer Networking, Database system and Management, Data mining, Big Data, Robotics , Parallel and distributed processing , Artificial Intelligence , Natural language processing , Neural Networking, Distributed Systems , Fuzzy logic, Advance programming, Machine learning, Internet & the Web, Information Technology , Computer architecture, Virtual vision and virtual simulations, Operating systems, Cryptosystems and data compression, Security and privacy, Algorithms, Sensors and ad-hoc networks, Graph theory, Pattern/image recognition, Neural networks.

Civil and architectural engineering :

Architectural Drawing, Architectural Style, Architectural Theory, Biomechanics, Building Materials, Coastal Engineering, Construction Engineering, Control Engineering, Earthquake Engineering, Environmental Engineering, Geotechnical Engineering, Materials Engineering, Municipal Or Urban Engineering, Organic Architecture, Sociology of Architecture, Structural Engineering, Surveying, Transportation Engineering.

Mechanical and Materials Engineering :

kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.

Chemical Engineering :

Chemical engineering fundamentals, Physical, Theoretical and Computational Chemistry, Chemical engineering educational challenges and development, Chemical reaction engineering, Chemical engineering equipment design and process design, Thermodynamics, Catalysis & reaction engineering, Particulate systems, Rheology, Multifase flows, Interfacial & colloidal phenomena, Transport phenomena in porous/granular media, Membranes and membrane science, Crystallization, distillation, absorption and extraction, Ionic liquids/electrolyte solutions.

Food Engineering :

Food science, Food engineering, Food microbiology, Food packaging, Food preservation, Food technology, Aseptic processing, Food fortification, Food rheology, Dietary supplement, Food safety, Food chemistry. AMA, Agricultural Mechanization in Asia, Africa and Latin America Teikyo Medical Journal

Physics Section:

Astrophysics, Atomic and molecular physics, Biophysics, Chemical physics, Civil engineering, Cluster physics, Computational physics, Condensed matter, Cosmology, Device physics, Fluid dynamics, Geophysics, High energy particle physics, Laser, Mechanical engineering, Medical physics, Nanotechnology, Nonlinear science, Nuclear physics, Optics, Photonics, Plasma and fluid physics, Quantum physics, Robotics, Soft matter and polymers.

Mathematics Section:

Actuarial science, Algebra, Algebraic geometry, Analysis and advanced calculus, Approximation theory, Boundry layer theory, Calculus of variations, Combinatorics, Complex analysis, Continuum mechanics, Cryptography, Demography, Differential equations, Differential geometry, Dynamical systems, Econometrics, Fluid mechanics, Functional analysis, Game theory, General topology, Geometry, Graph theory, Group theory, Industrial mathematics, Information theory, Integral transforms and integral equations, Lie algebras, Logic, Magnetohydrodynamics, Mathematical analysis.

Latest Articles of

Technology Reports of Kansai University

Journal ID : TRKU-18-09-2020-11120
Total View : 397

Title : An Analysis of FPGA-based Home Automation System for Elderly and Disabled

Abstract :

Home automation system refers to the automated or remotely controlled household features that are generally aimed to improve the quality of life. In the technology era, various smartphone-based home automation system is developed. However, this kind of design might not be suitable for the elderly and disabled population due to their technological literacy in handling smart devices. The motivation of this project is to assist their daily activities when they are alone or not under care to help them live more independently. This paper presents the designing of a home automation system for elderly and disabled using verilog algorithm. The developed home automation system included front-end digital Integrated Circuit (IC) design up to logic synthesis process using Synopsys and was implemented on an Altera Field Programmable Gate Array (FPGA) using Quartus II. This system incorporated alert features besides common household controlling features and it was successfully prototyped using FPGA. The RTL was synthesized into a gate-level netlist and the lowest clock period 8 ns while slack for setup time and hold time are 1.64 ns and 0.18 ns

Full article
Journal ID : TRKU-18-09-2020-11118
Total View : 402

Title : Efficient Selection of Training Candidates in Improving Sparse Representation Classifier (SRC) for Heart Sound Biometric Recognition

Abstract :

Sparse Representation Classifier (SRC) is one of the popular and efficient methods of classification for biometric traits. Redundant dictionaries for training samples are created in SRC classification which requires complex mathematical computation. This complexity further increases when the entire training set of samples is used for the classification of the test sample. In this paper, an efficient heart sound recognition algorithm is proposed based on a combination of the two classification methods, namely, Nearest Centroid Neighbor and Sparse Representation Classification (kNCN-SRC). In this method, firstly, k nearest centroid neighbors for the test sample are computed, and then the test sample is classified by sparse representation classification with respect to the k selected nearest neighbors. The proposed kNCN-SRC method showed a significantly increased recognition rate of 8.63% when compared to that of the SRC. This improved recognition rate is due to the selection of nearest neighbors as training signals for classification by SRC. Also, as the selection of training signals is based on the nearest centroid neighbor, this improves the recognition rate as the best training signals are selected for classification by SRC. The findings of the present study showed that the kNCN-SRC classification method demonstrated an improved recognition rate and was found to be a more suitable classifier than SRC for heart sound biometric systems

Full article

Certificates