Review of Safety Evaluation of Thermal Wearable Power Harvesting Device

Thermal wearable power harvesting device is developing fast nowadays. The increasing demand on simple and easily handled devices forcing researches to find a better on improving the performance and safety of the devices. Thermal power harvesting is using the heat from the surrounding and human body to generate power. So, the safety precaution needs to be taken in order to keep it safe to use. This paper reviews the use of wearable technology, the basic concept, methods and future of power harvesting technology, ideas of thermoelectric power generators and its related work as well the safety evaluation for international standard of wearable devices

Face recognition on bag locking mechanism

With the emergent of biometric technology, people are no longer afraid to keep their important things in the safe box or room or even facility. This is because; human beings have unique features that distinguish them with other people. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called ‘Eigenfaces’, which are actually the principal components of the initial training set of face images. In this report, thorough explanation on design process of face recognition on bags locking mechanism will be elucidated. The results and analysis of the proposed design prototype also presented and explained. The platform for executing the algorithm is on the Raspberry Pi. There are two artificial intelligent techniques applied to manipulate and processing data which is fuzzy logic and neural networks. Both systems are interdependent with each other, so that it can calculate and analyse data precisely. The receive image from the camera is analysed through the Eigenfaces algorithm. The algorithm is using Principal Component Analysis (PCA) method which comprise of artificial neural network paradigm and also statistical paradigm

A conceptual model of the automated credibility assessment of the volunteered geographic information

The use of Volunteered Geographic Information (VGI) in collecting, sharing and disseminating geospatially referenced information on the Web is increasingly common. The potentials of this localized and collective information have been seen to complement the maintenance process of authoritative mapping data sources and in realizing the development of Digital Earth. The main barrier to the use of this data in supporting this bottom up approach is the credibility (trust), completeness, accuracy, and quality of both the data input and outputs generated. The only feasible approach to assess these data is by relying on an automated process. This paper describes a conceptual model of indicators (parameters) and practical approaches to automated assess the credibility of information contributed through the VGI including map mashups, Geo Web and crowd-sourced based applications. There are two main components proposed to be assessed in the conceptual model-metadata and data. The metadata component comprises the indicator of the hosting (websites) and the sources of data / information. The data component comprises the indicators to assess absolute and relative data positioning, attribute, thematic, temporal and geometric correctness and consistency. This paper suggests approaches to assess the components. To assess the metadata component, automated text categorization using supervised machine learning is proposed. To assess the correctness and consistency in the data component, we suggest a matching validation approach using the current emerging technologies from Linked Data infrastructures and using third party reviews validation. This study contributes to the research domain that focuses on the credibility, trust and quality issues of data contributed by web citizen provider

The spatial epidemiology of jackfruit pest and diseases: a review

Jackfruit is identified as targeted produced for premium fruit and vegetable (EPP 7). Meanwhile in Johor, jackfruit is the third biggest fruit produced in 2016. Jackfruit contains a lot of benefits which certainly good for living things and have been used in various sector such as medicine, food, anti-bacterial and anti-oxidant, antifungal effect, immunomodulatory effect and else. However, the existence of pests and diseases have threatened the productivity of jackfruit plant particularly in tropical countries including Malaysia. There are many factors that can affect the occurrence of pests and plant diseases of jackfruit such as shoot borers, bark borers, mealy bug and scale insects, blossoms and fruit rots and bacterial die-back. Several studies have been devoted to model the plant pests and diseases epidemiology, though the contexts that focus in tropical environment and jackfruit plant are limited. Therefore, this paper aims to discuss abiotic factors and spatial methods that have been used to define dispersal pattern and relationship between abiotic factors including major climatic variables with plant pests and diseases occurrence data, particularly in tropical climate. This paper could be used as a basis to understand the epidemiological models in combating pest and plant disease and to support towards the effective management of jackfruit pests and diseases in tropical countries, particularly Malaysia

Travel Time Patterns of Students with Special Needs to Special Education Integrated Program-based Schools in Johor Bahru, Malaysia: An Initial Finding

Education for all has been a global priority to ensure that all students have equal access to high-quality education regardless of disability or minority status. In Malaysia, the special education integrated programme (SEIP) is designed to close the inequality gap by integrating special education into existing government and vernacular schools. Numerous studies examine the travel patterns of regular students to school, resulting in a dearth of research on the travel patterns of special needs students to formal school. Thus, this paper uses spatial analysis to demonstrate the travel patterns of students with special needs to SEIP schools. This paper demonstrated that the majority of SEIP schools in the Johor Bahru district can be reached within a 5 to 10 minute drive. Individual travel time analyses between origin (home) and destination (current versus ideal school) indicate that the majority of secondary school students attend their ideal neighbourhood schools, but not primary school students. The average travel time is 12 minutes, with 89 percent of them travelling by car. The travel time clustering analysis revealed that the majority of students who commute to school live within a radius of 2 to 10 km and within a time range of 10 to 20 minutes. However, a small group of these special students commutes to school for 20 to 25 minutes each day. The preliminary findings can be improved and may aid in the design of carpool and transit schedules, as the majority of these students heavily rely on their cars for transportation. The effects of the lengthy commute to school could be further investigated, as these children are vulnerable and any negative impact on their mental, emotional, or physical development must be addressed

Tweet data extractor for creating a twitter traffic map mashup

Over recent years, there has been a growth of interest in the use of social media including Facebook and Twitter by the authorities to share and updates current information to the general public. The technology has been used for a variety of purposes including traffic control and transportation planning. There is a concern that the use of new technologies, including social media will lead to data abundance that requires effective operational resources to interpret the big data. This paper proposes a tweet data extractor to extract the traffic tweet by the authority and visualise the reports and mash up on top of online map, namely Twitter map. Visualisation of traffic tweet on a map could assist a user to effectively interpret the text based Twitter report by a location based map viewer. Hence, it could ease the process of planning itinerary by the road users

Human driving skill for human adaptive mechatronics applications by using neural network system

The existence of the new improvement system for Human Machine System (HMS) is called as Human Adaptive Mechatronic (HAM) system. The main difference between these two systems is the relationship between human and machine in the system. HMS is one way relationship between human and machine while HAM is a two way relationship between human and machine. In HAM, not only human need to adapt the characteristics of machine but the machine also has to learn on human characteristics. As a part of mechatronics system, HAM has an ability to adapt with human skill to improve the performance of machine. Driving a car is one of the examples of application where HAM can be applied. One of the important elements in HAM is the quantification of human skill. Therefore, this project proposed a method to quantify the driving skill by using Artificial Neural Network (ANN) system. Feedforward neural network is used to create a multilayer neural network and five models of network were designed and tested using MATLAB Simulink software. Then, the best model from five models is chosen and compared with other method of quantification skill for verification. Based on results, the critical stage in designing the network of the system is to set the number of neurons in the hidden layer that affects an accuracy of the output

Robotic arm control using internet of things (IoT)

Robotic arm is a reprogrammable and multifunctional manipulator design to assist human in various surroundings. It is able to overcome human inefficiency in performing repetitive task such as pick and place operation. Thus, industrial in assembly and manufacturing have widely integrated robotic arm into their assembling line to overcome the problem of human inefficiency. Internet of things (IoT) allow data to be exchange between devices through the connection of many devices. The integration of internet of things with robotic arm allows smart industry to be realized. The purpose of this research is to design and build a three degree of freedom robotic arm with a mechanical gripper. The robotic arm can be controlled remotely through android mobile device to perform pick and place operation while Matlab provides the graphical movement of the robotic arm as a feedback

Talent development program towards enriching future ready Industry Revolution 4.0 in Johor

The aim of this paper is to examine the relationship between talent development program and future ready Industry Revolution 4.0 in Johor. The variables consist of talent ecosystem (EC) as independent variable and industry demand of IR 4.0 is the dependent variable. This study adopted quantitative approach using IBM SPSS version 26. The survey was conducted on the future talents in higher education in Johor. Total of 350 respondents were selected in this study. The expected finding will ensure that the transfer new talent to industry and at the same time improve practical capabilities and employment competitiveness by having integration between Industry and Academia to meet industry demand

Advancement of a smart fibrous capillary irrigation management system with an Internet of Things integration

This paper presents the development work for integrating an Internet of Things (IoT) with a fibrous capillary irrigation system based on the climatic demand estimated by the weather condition. The monitoring and control using an IoT system is critical for such application that is targeted for precision irrigation. The fibrous capillary irrigation system is managed by manipulating a water supply depth using the potential evapotranspiration (ETo). A soil mositure sensor was used to monitor the progress of the root water uptake and input the fuzzy logic system, to determine the water requirements for the crop medium. Experiment was conducted by using a Choy sum plant as the test crop grown in a greenhouse. The monitoring of the demand and management of the watering system was successful. The ETo data was able to approximate the crop water requirement in near real time