a b s t r a c tAccurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure.

The main objective of this project is to investigate the performance conventional method of perturb and observe (P&O) and soft computing techniques (fuzzy logic and adaptive neuro-fuzzy inference system) of maximum power point tracking (MPPT) for photovoltaic (PV) module. In this paper, the MSX-64 PV module and boost DC-DC converter are used for simulation and modeling the MPPT system. This work demonstrates the performance of those three types of MPPT techniques, which subjected to a partial shading pattern as well as a non-shaded and shaded of real weather profile. These MPPT techniques are compare in terms of power extracted, MPPT efficiency, rise time, its ability to track global maximum power point (MPP), and the response to varied weather. Simulation results of soft computing MPPT techniques have shown the ability to track the MPP during partial shading conditions and the response to weather changes when nonshaded real weather profile applied to the system. The performance of the conventional P&O based MPPT has depicted the failure of this controller to track the global MPP during partial shading and lack response to real weather changes. The proposed system has simulated using MATLAB SIMULINK.

<span>The field of automated vehicle technology is developing rapidly developing. While it is likely to be many years before self-driving cars are commercially viable and used in a wide range of conditions by the general public, technological advances are speeding along the automated technology continuum towards this destination. Automated vehicle technologies troth with significant social benefits such as reduced injuries and deaths, increased road efficiency, mobility. Automated vehicles can improve traffic safety, balance traffic flows, maximize road usage by offering driver warnings and/or assuming vehicle control in dangerous situations, as well as provide motorists with the best end-to-end transportation experience and reduce emissions, which are the most important goals of modern smart traffic control infrastructures. Exchanging data and integration of such systems with Vehicle-to-Vehicle (V2V) may be a keystone to successful readying of vehicular ad-hoc networks (VANETs) and will simply be the following step of this evolution, with dynamic period of time data exchange between all the players of the traffic dominant system and fostering cooperative urban quality. One of the applications of this concept is to provide vehicles and roads with the ability to make road time more enjoyable and also to make roads safer. These applications are typical examples of what an Intelligent Transportation System (ITS) is called, whose objective is to improve security by using new information and communication technologies (NTIC). In this paper, we will focus on the study of the main component in ITS systems and present a review of the major V2V benefits related to driver safety by focusing primarily on the recent developments of these systems.</span>

<span lang="EN-MY">The idea of Internet of Things (IoT) based traffic management &amp; routing solution for parking space is due to the vehicle parking has become major issue in urban areas. The growing number of vehicles has contributed to the traffic problem and vehicle parking issue nowadays. The main purpose of this project is to assist the user to locate the vacant parking space, which help to reduce time and fuel consumption on searching the parking space. This proposed system was used online system via website application, which assist people to find the available parking slot. In fact, the system counted the capacity of the available parking space and notified the user through the website application. Frankly, the system was equipped with an ultrasonic sensor, which acts as the detector that sent data to the microcontroller in order to update into UBIDOTS cloud server for data logger purposes. This system could lessen or solve the time management problem at the parking area, which user could save their time by checking the available parking slots in advance through the website application.</span>

Emergence of Industry 4.0 in current economic trend promotes the usage of Internet of Things (IoT) in product development. Counting people on streets or at entrances of places is indeed beneficial for security, tracking and marketing purposes. The usage of cameras or closed-circuit television (CCTV) for surveillance purposes has emerged the need of tools for the digital imagery content analysis to improve the system. The purpose of this project is to design a cloud-based people counter using Raspberry Pi embedded system and send the received data to ThingSpeak, IoT platform. The initial stage of the project is simulation and coding development using OpenCV and Python. For the hardware development, a Pi camera is used to capture the video footage and monitor the people movement. Raspberry Pi acts as the microcontroller for the system and process the video to perform people counting. Experiment have been conducted to measure the performance of the system in the actual environment, people counting on saved video footage and visualized the data on ThingSpeak platform.