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