Living in a healthy environment is a need for every human being whether indoor or outdoor. However, pollutions occur everywhere and most people are merely mindful of the importance of having clean outdoor air to breathe and are not concerned about the indoor air quality. Indoor air quality refers to the quality within the building, and relates to the health and comfort of the building occupants. Dangerous particles exist in the outside air, pollute the indoor environment and produce harmful conditions as the polluted air travels into the house or building through windows or doors. Therefore, a wireless Internet of Things-based air quality device is developed to monitor the air quality in the indoor environment. The proposed system integrates a low-cost air quality sensor, temperature and humidity sensors, a single-board computer (Raspberry Pi 2 microprocessor) and cloud storage. The system provides realtime air quality reading, transfers the data through a wireless network to the Internet and displays the data in dedicated webpage. Furthermore, it stores records in cloud storage and sends e-mail notification message to the user when unhealthy condition is met. The study has a significant impact on promoting affordable and portable smart pollution monitoring system as the development of the device utilizing low-cost and off-the-shelf components.

Considerable attention to coordinate the system between buyer and vendor has become an interesting issue to efficiently increase the performance of supply chain activities. Joint economic lot size model (JELS) has been introduced by many researchers as the spirit of coordinating the flow of material from the vendor to its downstream. As an inventory replenishment technique, JELS model is centered on reducing joint total cost of vendor and buyer by simultaneously deciding optimal delivery lot size, number of deliveries, and batch production lot. It is appropriate to take into account transportation costs as the function shipping weight and distance since delivery lot size has interrelated with shipping weight. Hence, this study constitutes an effort to develop the model of JELS by incorporating transportation cost. The solution procedure of the model is developed for solving two problems which are incapacitated and capacitated model. In addition, numerical examples were provided to illustrate the feasibility of the solution procedure in deriving optimal solution. The result presents central decision making which is useful for coordination and collaboration between vendor and buyer.

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Nowadays, the utilization of cameras as an inspection tool has been increasing. The flexibility functions of camera fits to get different kind of information. This research is focusing on developing a robust visual inspection system for corrosion detection that is able to detect corrosion in any environment, and the corrosion detection will be using visual data as primary tools. A review on current pipeline inspection would give a brief detail on the improvement of the proposed inspection system. Furthermore, the inadequacies of the proposed visual corrosion detection are identified and discussed from the reviewing process on existing researches and analysis on preliminary data obtained. It is expected that the output of the proposed system will be a new method of corrosion detection and pioneer for the inspection system on robust environment.

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Currently, the Brain Computer Interfaces (BCI) system was designed mostly to be implemented for control purpose or navigation which are mostly being employed for mobile robot, manipulator robot and humanoid robot by using Motor Imagery. This study presents an implementation of BCI system to Modular Self-Reconfigurable (MSR) Dtto robot so the robot able to propagate multiple configurations based on EEGbased brain signals. In this paper, a Neurosky's Mindwave Mobile EEG headset is being used and a framework of controlling the Dtto robot by EEG signals, processed by OpenViBE software are built. The connection being established between Neurosky's headsets to the OpenViBE, where a Motor Imagery BCI is created to receive and process the EEG data in real time. The main idea for system developed is to associate a direction (Left, Right, Up and Down) based on Hand and Feet Motor Imagery as a command for Dtto robot control. The Direction from OpenViBE were sent via Lab Streaming Layer (LSL) and transmitted via Python software to Arduino controller in the robots. To test the system performance, this study was conducted in Real time experiments. The results are being discussed in this paper.

The management of pest insects is the critical component of agricultural production especially in the fertigation based farm. Although the fertigation farm in Malaysia has advantages in the fertilization and irrigation management system, it still lacking with the pest management system. Since almost the insect and pests are living under the crop's leaves, it is difficult and hard labor work to spray under the leaves of the crop. Almost agricultural plants are damaged, weakened, or killed by insect pests especially. These results in reduced yields, lowered quality, and damaged plants or plant products that cannot be sold. Even after harvest, insects continue their damage in stored or processed products. Therefore, the aim of this study is to design and develop an autonomous pesticide sprayer for the chili fertigation system. Then, this study intends to implement a flexible sprayer arm to spray the pesticide under the crop's leaves, respectively. This study involves the development of unmanned pesticide sprayer that can be mobilized autonomously. It is because the pesticide is a hazardous component that can be affected human health in the future if it exposed during manual spraying method especially in a closed area such as in the greenhouse. The flexible sprayer boom also can be flexibly controlled in the greenhouse and outdoor environment such as open space farms. It is expected to have a successful pesticide management system in the fertigation based farm by using the autonomous pesticide sprayer robot. Besides, the proposed autonomous pesticide sprayer also can be used for various types of crops such as rockmelon, tomato, papaya. pineapples, vegetables and etc.

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