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:
Hypertension is a silent killer disease, the incidence of hypertension is increasing, especially in menopause women. Hypnosis and acupressure are non-pharmaceutical techniques to treat hypertension, but the combination of the two therapies has never been done. The aim of this research is to develop a digital hypnopressure device and its effect on hypertension in menopause women. The research used an experimental study with the stages of developing digital hypnopressure devices and the application on menopause women. The sample size was 40 people with purposive, 20 people as the intervention group and 20 people as the control group. The intervention is carried out for 30 minutes. Analysis of data with Mann Whitney U test. The results show that the creation of a digital hypnopressure device which is a combination of five-finger hypnosis and acupressure therapy. There were differences in systolic and diastolic blood pressure between the intervention group and the control group. Therefore, digital hypnopressure devices are effective for lowering blood pressure
In this study, a human face recognition technique based on statistical features using a neural network technique is presented. In the pre-processing stage image edges have been detected. Subsequently, a new technique for two-dimension gray image to one-dimension vector is proposed. Then, seven features have been extracted depending on statistical analysis. This work describes is based on four statistical characteristics (mean, standard deviation, skewness, kurtosis) for feature extraction. These features can address image capture problems because it is main tasks are not affected by the rotation, zoom, and transfer of images taken from before the control cameras. After that, the image details have been extracted using wavelet transformations. Elman Neural Network (ENN) is used in this study for face identification. Finally, the proposed study has been implemented using MATLAB R2013a and Microsoft Excel database to preserve the information of the required people, this can be achieved by utilizing the principle of distance between facial points
