2020年度 大学院 特別研究


研究テーマ

テー マ:Recognition of Driving Environment Using Deep Learning
担当: G19601 Adhikari Ashish Ashish Adhikari(修士2年)
概要:
 In recent years, there are a bunch of autonomous driving technologies to help drivers on road. These technologies are highly expensive for ordinary people to afford. Technologies like road condition recognition, surrounding recognition, speed control notifications, sudden acceleration prevention, signal detection, and notification technologies are available in the market. Most of those systems come with the automobile itself. In this research, we propose an image analysis-based smartphone application model and the dataset to train. We used the Caffe implementation of the MobileNet-SSD detection network, with pre-trained weights on Visual Object Classes Challenge 2012 (VOC2012)  with the dataset we prepared. Counting depthwise and pointwise convolutions as separate layers, MobileNet has 28 layers. Android terminals were used to demonstrate the model efficiency and the experiment was conducted in the daytime on the public roads as well as by showing the screen. Although, the system was not fully efficient. Analysis and detection tasks validation was performed manually as well to avoid mechanical errors. While validating misrecognized and undiscovered objects, images are manually taken for re-train. As a result, the mean average was 0.712. The upper level was the farthest so the detection was also the lowest as 0.370, the middle level was 0.889 and the lower past was 0.750 in the single-lane road. Double lane road was 0.458, 0.944, 0.857 for upper, middle, and lower levels respectively.



テーマ:Mental State Classification by Using Brainwave Sensors
担当:Sukh Sagar Subedi(修士2年)
概要:
In recent researches, BCI (Brain-Computer Interface) is being pulled as a research topic. Development of BCI can be applied in various places from home to medical sectors. Basically, BCI is the interconnection of human brain and computer. BCI is the communication pathway between the external peripheral devices. We classify our research in two parts. Firstly, we tried to control the IoT device named MaBeee with the help of Neurosky Mindwave EEG sensor. The combination of these two different sensors is the first approach among the researchers. In another research, two different brainwave sensors Neurosky Mindwave Mobile and Emotiv EPOC+ sensors have been used. The purpose of this research is to analyze the brainwave data for the best accuracy result and also to compare the accuracy result between both sensors.




inserted by FC2 system