The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

Purpose: In this advanced era of Industrial Revolution 4.0, as an element of cyber physical systems, the Internet of Things (IoT) has been applied in many different industries; however, its adoption in the construction industry is still limited to a few applications. This study uncovers, identifies and assesses the challenges of adopting IOT in construction projects. The challenges have been identified through the briefed literature review and a survey instrument from construction industries in Malaysia. Design/methodology/approach: In this study, the quantitative research approach has been used and data have been collected through a questionnaire survey for construction practitioners. Whereas, respondents to the questionnaire are practitioners from the Malaysian construction industry. The method of sampling implied is random sampling technique whereby the final sample size is 132 participants. Moreover, the gathered data has been analysed using univariate approach via standard deviation and average index in SPSS Software 22.0. Findings: The results of this study indicated the most dominant challenges are lack of safety and security, lack of documented standards, lack of benefit awareness, improper introduction of IOT and lack of robustness in connectivity. This study also examined that the awareness of construction practitioners toward the understanding of IOT and its possibility to be applied and extended in construction projects and determined that construction practitioners are aware of IOT benefits to improve the efficiency of construction projects. Practical implications: The study presents a better understanding of IOT in the construction industry and their potential challenges, which helps the construction policymakers to avoid encounters related to challenges and provide education-based campaigns to educate construction practitioners on the concept and importance of using IOT in the construction sector. Originality/value: This is a unique study because of its nature in this advance era of industry 4.0. Furthermore, it is specifying the latest trend associated with IOT in the construction industry and addressing the challenges of implementation, which is crucial to exploit and take advantage of the full potential of IoT benefits.

This paper reviews and studies the state-of the-art of available maximum power point tracking (MPPT) algorithms. Due to the nature of the wind that is instantaneously changing, hence, there is only one optimal generator speed is desirable at one time that ensures the maximum energy is harvested from the available wind. Therefore, it is essential to include a controller that is able to track the maximum peak regardless of any wind speed. The available maximum power point tracking (MPPT) algorithms can be classified according to the control variable, namely with and without sensor, and also the technique used to locate the maximum peak. A comparison has been made on the performance of the selected MPPT algorithms on the basis of various speed responses and the ability to achieve the maximum energy yield. The tracking performance is performed by simulating wind energy system using MATLAB/Simulink simulation package. Besides that, a brief and critical discussion is made on the differences of available MPPT algorithms for wind energy system. Finally, a conclusion is drawn.

Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.

This paper proposes an improved one-power-point (OPP) maximum power point tracking (MPPT) algorithm for wind energy conversion system (WECS) to overcome the problems of the conventional OPP MPPT algorithm, namely, the difficulty in getting a precise value of the optimum coefficient, requiring pre-knowledge of system parameters, and non-uniqueness of the optimum curve. The solution is based on combining the particle swarm optimization (PSO) and optimum-relation-based (ORB) MPPT algorithms. The PSO MPPT algorithm is used to search for the optimum coefficient. Once the optimum coefficient is obtained, the proposed algorithm switches to the ORB MPPT mode of operation. The proposed algorithm neither requires knowledge of system parameters nor mechanical sensors. In addition, it improves the efficiency of the WECS. The proposed algorithm is studied for two different wind speed profiles, and its tracking performance is compared with conventional optimum torque control (OTC) and conventional ORB MPPT algorithms under identical conditions. The improved performance of the algorithm in terms of tracking efficiency is validated through simulation using MATLAB/Simulink. The simulation results confirm that the proposed algorithm has a better performance in terms of tracking efficiency and energy extracted. The tracking efficiency of the PSO-ORB MPPT algorithm could reach up to 99.4% with 1.9% more harvested electrical energy than the conventional OTC and ORB MPPT algorithms. Experiments have been carried out to demonstrate the validity of the proposed MPPT algorithm. The experimental results compare well with system simulation results, and the proposed algorithm performs well, as expected.