Machine learning has been a popular topic in research field for many applications. One of the applications is traffic surveillance system. In many areas, traffic surveillance system is installed in order to gather and estimate important traffic information. Nowadays, there are several systems used for information’s extracting, and classifying. One of the well-known approaches is decision tree. It uses a tree-like model that decides consequences outcomes from events. However, in some application, decision tree does not perform well. Another widely used approach is neural network, which has promising performance. It has been developed and become one of the most popular computing systems in research field. The traditional approach in training neural network is backpropagation. However, it has several drawbacks. One of them is the training time. In recent decades, Extreme learning machine (ELM) was proposed for training single hidden layer feed-forward neural network (SLFN) in the extremely fast way. It minimizes training error by utilizing dataset in one-shot calculation. This paper focuses on classifiers in traffic surveillance system. The classification divides into two main tasks. One is vehicle types’ classification. Another is vehicle colors’ classification. Neural networks trained with ELM are applied to the dataset. The performance are then compared to decision tree based approaches with ensemble methods. The experimental results show that ELM achieves better accuracy than of decision tree based approaches in both tasks.

Application of Neural Networks for Vehicle Classifiers: Extreme Learning Machine Approach

This manuscript builds a HMD-based virtual display environment and conducts an experiment on a relation between viewing distances to the display and task performance. The result implies that longer viewing distances could improve speed, accuracy and precision of mouse manipulation.

A HMD-Based Virtual Display Environment with Adjustable Viewing Distance for Improving Task Performance

This paper discusses VDT syndrome from the point of view of the viewing distance between a computer screen and user’s eyes. This paper conducts a series of experiments to show an impact of the viewing distance on task performance. In the experiments, two different viewing distances of 50 cm and 350 cm with the same viewing angle of 30 degrees are taken into consideration. The results show that the long viewing distance enables people to manipulate the mouse more slowly, more correctly and more precisely than the short. key words: viewing distance, physical fatigue, physiological stress, task performance, VDT syndrome

Impact of Viewing Distance on Task Performance and Its Properties

Impact of Longer Viewing Distance to Virtual Screen upon Mouse-Manipulation Performance

This manuscript conducts a series of experiments on an impact of longer viewing distances upon mouse manipulation and calculation tasks on performance and psychological fatigue. The results show that the accuracy of tracing tends to increase while taking more time, resulting in slower speed for longer viewing distances. In addition, the speed of calculation and its accuracy tends to increase for longer viewing distances where users would relax more. 

A Longer Viewing Distance to a Virtual Screen Could Improve Task Performance

This paper discusses VDT syndrome from the point of view of viewing distance between the surface of a computer screen and the user's eyes. This paper conducts a pilot experiment to show an impact of viewing distance to task performance. In the experiment, two different viewing distances of 50 cm and 350 cm with the same viewing angle of 30 degrees, resulting in 24 inch computer screen at 50 cm away from the user and 200 inch projection screen at 350 cm away from the user, are taken into consideration. Subjects were asked to perform two tasks: a simple mouse manipulation task of Tapping test and a simple calculation task of Kraepelin test. The result shows that viewing distance tends to affect accuracy of mouse manipulation and calculation tasks.

A pilot study on impact of viewing distance to task performance

In this study, we build a learning system on LINE app that is provided by LINE Corporation. The system incorporates three elements of Self-determination theory: autonomy, competence and relatedness in order to keep people intrinsically motivated for long. Recently the increase of phishing mails draws attention to immediate countermeasures for cybersecurity. Accessing such mails could trigger the onset of virus infection and confidential information leakage, which affects not only individuals but also the companies they work for. In reality, eliciting personal information from the recipients is sometimes not hard if they don’t perform security practices as well as the mails are made deceptive and appear to be sent from well-known senders. Thus, it is necessary that people learn security practices on a daily basis and perform those all times. To provide a platform that facilitates learning in this way, we decide to build a learning system on LINE app and incorporate into it the three elements of Self-determination theory.

LINE Based Learning System for IT Security Practices Through Intrinsic Motivation

In this study, we discuss a way of correcting the distortion of views that look into fish tanks. It is widely known that those views are all seen distorted due to light distortion. That is, light rays traveling inside the tank towards an observer are refracted on the boundaries between different media: water, glass and air. Therefore, those rays come away from the observer, which are however necessary for constructing the observer’s view without distortion. To capture the rays, a trapezoidal glass and microlens array are placed at specific positions and an image formed behind the array is taken by a camera. Finally, the observer’s view is constructed from the single image without distortion.

Peripheral Distortion on Views in a Fish Tank Could Be Corrected Using a Trapezoidal Glass and Microlens Array

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