Deceleration in The Micro Traffic Model and Its Application to Simulation for Evacuation from Disaster Area

Referring to the Nagel–Schreckenberg’s (NaSch) model, we have studied the impact of agent and diligent driver into the micro traffic model in the case of evacuation. This study is attention to the deceleration that added in the micro traffic model. The effect of deceleration to simulation for evacuation from disaster area is considered. The traffic flow property is studied by analyzing the time-space diagram. The simulation results show that deceleration caused the evacuation time increases when we compare it by without deceleration

A Review of Chinese Academy of Sciences (CASIA) Gait Database As a Human Gait Recognition Dataset

Human Gait as the recognition object is the famous biometrics system recently. Many researchers had focused this subject to consider for a new recognition system. One of the important advantage in this recognition compare to other is it does not require observed subject’s attention and cooperation. There are many human gait datasets created within the last 10 years. Some databases that are widely used are University Of South Florida (USF) Gait Dataset, Chinese Academy of Sciences (CASIA) Gait Dataset, and Southampton University (SOTON) Gait Dataset. This paper will analyze the CASIA Gait Dataset in order to see their characteristics. There are 2 pre-processing subsystems; model based and model free approach. We will use 2D Discrete Wavelet Transform (DWT). We select Haar wavelets to reduce and extract the feature

Global environmental monitoring from space

we are facing global environmental issues such as global warming, ozone depletion, deforestation, air pollution, ocean pollution, desertification, etc. Homeostasis among geosphere, atmosphere, cryosphere, hydrosphere, biosphere has to be maintained. In order for Global Sustainability, global environmental monitoring from space is needed. The monitoring system does work for not only global environmental monitoring, but also disaster mitigation such as Sulawesi Big Earthquake and Tsunami which was occurred on 28 September 2018. Other than environmental monitoring from space, modeling and simulation, big data analysis, Artificial intelligence are also needed

Traffic Density Prediction using IoT-based Double Exponential Smoothing

The number of vehicles and currents that tend to increase causes traffic density. A system is proposed to calculate the number of vehicles and predict real-time traffic density. This research uses Haar Cascade to detect the number of cars and motorcycles and the Double Exponential Smoothing (DES) for forecasting the number of vehicles on the road. MAPE describes forecasting accuracy as a base for selecting the best smoothing constant (Alpha). The best test results from June 13 to 20, 2020, are cars on June 14, 2020 (alpha 0.5, MAPE 0%) and Motorcylecycles on June 18, 2020 (alpha 0.5, MAPE 0.1134% ). The most significant MAPE results of the car were on June 15, 2020, with alpha 0.5 and MAPE 2.1073%. The 3 minutes haar cascade detects 72.58% of cars and 81.90% of motorcycles

Ultrafast single-channel machine vision based on neuro-inspired photonic computing

High-speed machine vision is increasing its importance in both scientific and technological applications. Neuro-inspired photonic computing is a promising approach to speed-up machine vision processing with ultralow latency. However, the processing rate is fundamentally limited by the low frame rate of image sensors, typically operating at tens of hertz. Here, we propose an image-sensor-free machine vision framework, which optically processes real-world visual information with only a single input channel, based on a random temporal encoding technique. This approach allows for compressive acquisitions of visual information with a single channel at gigahertz rates, outperforming conventional approaches, and enables its direct photonic processing using a photonic reservoir computer in a time domain. We experimentally demonstrate that the proposed approach is capable of high-speed image recognition and anomaly detection, and furthermore, it can be used for high-speed imaging. The proposed approach is multipurpose and can be extended for a wide range of applications, including tracking, controlling, and capturing sub-nanosecond phenomena.Comment: 30 pages, 12 figure

Interactive M-Learning Media Technology to Enhance the Learning Process of Basic Logic Gate Topics in Vocational School and Engineering Education

The process of learning to use smartphones is now highly promoted. Almost everyone has a smartphone. The latest trend in learning is known as Mobile Learning (M-Learning). M-Learning can be used anywhere and anytime. Thus, we propose the use of the M-Learning application for computer system subjects in the basic logic gate topics so that students can be motivated to learn. We call this application BLG-LeMed. The focus of this research is on the process of using BLG-LeMed applications on classroom learning that is used directly by vocational high school students, then testing with alpha testing, User Acceptance Tests (UAT), usability evaluations, and knowing the effect of motivating students to use five dimensions of motivation and student learning outcomes. The development model used is Extreme Programming (XP). The design used in this study, by conducting trials in one class and observing students using the BLG-LeMed application as learning media, 38 students consisted of 26 men and 12 women involved in this study, with a duration of 135 minutes at one time of the meeting. We conclude that using the BLG-LeMed application based on M-Learning in the learning process of this basic logic gate, strongly supported by the testing team, can be accepted by users, has a usefulness as a interactive learning media, can have an effect in motivating students to learn, and provide results very satisfying learning