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:
Slope Detection with road lane detection using Image Processing is one of the methods used to detect boundaries of the road using a different range of color from the road environment to distinguish road lane and non-road lane. This research using the road lane detection method that is inspired and based on previous research that we modified and develop to detect the slope of the road using OpenCV libraries and Linear Regression Method. Detection is done by making an intersection line from upper and down line that detected from Image Processing. This Research using a truck prototype with a 1:11 dimension to simulate the system and using prototype road to with the same dimension of the truck. The result shows that system can detect the slope of the road with accuracy reaching 90%
Generally, the green technology has been taken for granted in terms of providing clean and cheap energy without realising the costs. However, there are many trade-offs concurrent with enabling such technology. Accordingly, this paper evaluates and compares the green energy based networks with traditional counterparts. It presents a mathematical model which helps understand-ing the different variables that are necessary to advocate the green/renewable method over tra-ditional, or vice versa. This research shows that the cost efficiency (CE) of green networks can be relatively high, about twice the traditional, that is represented by cloud radio access network (C-RAN). Based on experimental data, this research shows that green technology requires more operational caring than traditional to produce same amount of power. With variant sites, cities, countries, geographical areas and equipment’s manufacturing characteristics, the model can pre-dict the total green systems’ trade-offs in the future. By doing so, the service providers, investors or network vendors are able to re-consider and decide the shifting between both types of networks
