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Home appliances are designed and manufactured for the market. Most users find that APPs are installed on smartphones or tablets to control home appliances with individual APP. Even light-bulbs with different brands or models need exclusive control software to corresponding light-bulbs. Thus, the smart and intelligent technology for smart-home is difficult to use due to different brands home-appliances from different manufactures. It lacks a standard to coordinate the control way like traditional light-bulbs-based E27 specs. For the light-bulbs of home appliances as an example, the traditional light-bulbs-based E27 lamp-holder have different fixtures and power-supply. Thus, new light-bulbs of home appliances for smart-home need a complex control-way besides electronic power to achieve smart functions and remote-control based on APPs on users' smart-phone. In this research, we try to integrate the internet of thing and message queue telemetry transport (MQTT) to design a mechanism and to implement a prototype web system to achieve an instant and visualized digital light switch of smart-home to solve the problems. For the validation of this research, we used a NodeMCU-32 microcontroller unit as a micro-controller to integrate with WS2812B LED strips to design light tube and light-bulb as the proof of concept (PoC) to demonstrate it in smart-home.

Edge computing is a decentralized computing paradigm with low latency and high response time. This paradigm provides services for end-devices at the network edge and brings together IoT, big data, and mobile computing into an integrated and ubiquitous computing platform. The on-demand computing power and the processing ability for data from enddevices are needed at the network edge. More recently, edge computing capable with artificial intelligence technologies has received much attention in the research literature. This paper will review the recent research literature about using edge computing in bioinformatics and health informatics, and provide challenges, opportunities and further research directions.

The injury resulted from the repetitive and load-bearing works is the most frequent work-related musculoskeletal disorders (WMSD) or cumulative trauma disorders (CTD). It comes from the overload of repetitive load-bearing actions, which resulting in fatigue, inflammation, even injuries of musculoskeletal system. According to the annular report of Labor Insurance Bureau in Taiwan, WMSD is up to 85-88% payment. Thus, the aim of this study is to evaluate the risk of WMSD during work by using the simple, quick, and correct methods by using the deep learning algorithms. In the proposed research method, after collection the videos of hand repeated movements, the ergonomic injuries are evaluated by using the 2D human pose estimation method, which is based on the Key Indicator Method - Manual Handling Operations (KIM-MHO). Then, a model of predefined classifications through deep learning approaches for manual handling operating tasks is built. The analysis results show that the classification accuracy is more than 80%, compared with the doctor's judgment. The goal of this study is to get the accuracy up to 90%, so as to achieve fast and accurate assistance for deciding the risk of ergonomics, and immediately give proper feedback.

Blockchain is a technology that does not rely on third parties to store, verify, transfer and communicate network data through its own decentralized nodes. Important features of the blockchain include decentralization, independence, security, and anonymity. This study mainly uses Ethereum blockchain technology to establish a combination of money flow and transparency for implementing a rental trading platform, such that the platform can trade, control and autonomously rent a house without third-party management. In addition, all the transaction information can be recorded in the blockchain for everyone to search. For the landlords, the platform can save their cost of promotion. For the tenants, they can check the past transaction records of the house, confirm the past rental process of the house, and find a more suitable house.

In the modern industry, machine health monitoring systems are critical to the modern manufacturing industry. In the machine health monitoring, due to the advanced sensor technologies, the data-driven approach has became the most popular way. However, how to deal with the noise data and realize the spatial and temporal correlations presented in the data is a challenge. The traditional models focus on using the feature extraction method to solve these problems, but it deeply relies on high-quality expertise for achieving it. With the rapid development of deep learning methods, the capture of data features has been achieved effectively. For example, the LSTM method can effectively capture the long-term dependence of data. The purpose of this paper is to predict tool wear by using the proposed Holistic-Local LSTM Model (HLLSTM). With the proposed method, learning local characteristics by cutting data into fragments. The training is divided into holistic training and local training to capture the characteristics implied by data, so as to realize the timing of changing the wear tool. Finally, the experimental results show the performance of our proposed approach is better than other deep learning approaches in the metric of root-mean-square error (RMSE).

In recent years, consumers have paid more and more attention to the reliability of commodity sources, leading to the demand for traceability of supply chains. In the traditional supply chain, the commodities are managed centrally. However, when multiple parties are involved, the data of commodities in the traditional supply chain are isolated and are lacking trust among the parties. In addition, there are few supply chain systems which record the complete production process of commodities currently, such that it is difficult to get the production history/process and to achieve more secure, reliable and trust traceability management in the supply chain. Thus, how to construct a novel framework is needed to provide a more secure, reliable, trust, and traceable transaction environment is the main focus of this work. In this paper, we propose an integrated framework based on blockchain and smart contract technologies to achieve the traceability management of commodities in the supply chain. In the system, the raw materials of commodities are not only transformed into the tokens which can record all the history of commodities, but also the supply chain management can be achieved. With the proposed framework, the traceability of product conversion can be retained and the ingredients of processed products can be traced.

The living environment monitoring is a critical issue today. Many related researches focused on developing the living environment monitoring systems by using the web-based technology, but such information systems would increase the difficulty of promotion and migration to others, because of needing well-trained engineers to achieve and maintain such information system. This research focuses on developing the weather monitoring system. The main goal of the research is to use the technology of message queuing telemetry transport (MQTT) as roles of the communication layer instead of direct-connecting database, which can isolate the system migration complexity from heterogeneous relational database management system (RDBMS) and construct a distributed information system easily. Finally the contributions are demonstrated as implementation of the IoT-based weather monitoring system. The prototyping of the weather monitoring system is implemented and deployed at Wu-Tso elementary school for the students' learning of nature science and activity environment. In addition, the system is implemented using open sources and is easy to be deployed and scalable.