The most common algorithm for detecting straight lines in a digital image is the Hough Transform method. The method involves two consecutive steps: transforming the image edge location onto parameter space and voting procedure to acquire line characteristics. The former deals in continuous space, and the latter counts votes in a discrete matrix, making the algorithm sensitive to noise. We transformed each image pixel location to the parameter space as a Hough line and generated a nonlinear oscillator to attract them into the intersection with other lines. This concept could contribute to a line detection problem in image processing using a consistent mathematical description.

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Artificial intelligence (AI) has revolutionized the way we interact with technology, and one of its key subcategories is the automated emotion recognition system (ERS). ERS is a technology that enables AI to recognize human emotional states, and it has the potential to transform human-computer interaction (HCI) as we move towards the fifth industrial revolution (IR 5.0). ERS has significant implications for various sectors, including healthcare, education, and the workplace. For example, in healthcare, ERS can be used to identify and treat mental health disorders, while in the workplace, it can be used to monitor employee well-being and improve productivity. However, the adoption of ERS is crucial for its success, and there is a need to understand the factors that affect its adoption. This paper aims to explore the landscape of ERS adoption in Malaysia by conducting a systematic literature review of studies published over the past decade between 2011 and 2022. The review will focus on understanding the current levels of adoption of ERS in Malaysia, identifying the factors that hinder or facilitate its adoption, and discussing the potential benefits and drawbacks of using ERS in different contexts. The findings of this review will provide insights into the current ERS research landscape in Malaysia and inform future research on the adoption and use of ERS in the country.

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Programmable logic controllers, or PLCs for short, is ubiquitous in the field of industrial automation. These devices are used to control and monitor a variety of processes related to production, ensuring that everything runs smoothly and efficiently. Unfortunately, there are currently no reliable means of verifying the accuracy and reliability of these devices, which can lead to serious consequences in the event of an error or malfunction. To address this issue, researchers are exploring the use of advanced technologies such as blockchain to provide greater transparency and security PLC operations.

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This study developed an optimization algorithm to balance the Quality of Service (QoS) rates and buffer size for robot data communication in the Aggregated Robot Processing (ARP) architecture. Robot Operating System 2 (ROS 2) is robotic software that uses a set of QoS policies to control the quality of data transmission in robotic networks, such as DEADLINE to determine the rates, and DEPTH to determine the buffer size. An unbalanced DEADLINE and DEPTH configuration in ROS 2 node communication can result in high packet latency and packet loss in RELIABLE connections. This happens when the DEADLINE sets the rates at high frequencies, and the DEPTH sets the buffers with small sizes. The results of this study show that the optimization algorithm developed in this study can determine the rate and buffer size through a balancing configuration for better quality robot data transmission in ARP architecture, influence the improvement of latency and reduce the packet loss.

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The rapid economic development has fueled the demand for enhancing lifestyle and home aesthetics, leading to the growing popularity of leisure activities and home decoration. As a response, the ornamental fish industry has flourished, prompting fish enthusiasts to seek efficient ways to care for their fish. Smart fish box has emerged as popular solutions, offering features such as remote control and monitoring. Smart fish box incorporates machine vision and Internet of Things technologies, allowing users to remotely control lighting, water changing, feeding, and oxygen pump operations. Temperature sensors transmit data to a mobile app, enabling users to monitor and adjust water temperature. These boxes also features built-in cameras for real-time monitoring and send notifications when fish food is running low. This innovative design addresses several challenges in ornamental fish care. This paper presents the mechanical structure, control circuitry, and vision algorithm of the smart fish box. By utilizing collected data, a neural network is trained on the Raspberry Pi platform, successfully recognizing fish health status.

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Datasets are one of the key elements which determine the performance of a deep learning network. Urban environments datasets receive much attention nowadays due to the rise of autonomous cars but off-road environment on the other hand lacks quality datasets. Offroad environments need equal attention as only 55% of the world’s population lives in urban areas. This paper tackles this issue to close the gap of robotic visual perception on the beach, one of the common offroad environments that lack attention by presenting a real and synthetic dataset, namely BCRobo..

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In recent years, robots have played an important role in various places, including factories of the manufacturing industry as well as homes where people live. The number of robotic tasks with a high degree of difficulty is increasing because they are required to perform various types of tasks, and failures are likely to occur. Therefore, there is a growing demand for error recovery techniques. We propose an error recovery method that considers task stratification and error classification. This allows various error-recovery paths to be derived for a single error. In this study, recovery paths were classified into several patterns to facilitate selection of the optimal path.

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This paper proposed a deep learning-based beach litter detector specifically designed for assessing litter levels on beaches effectively. This litter detector was created utilizing a HTC, also known as the Hybrid Task Cascade network, and its performance was compared to that of the traditional mask R-CNN network in order to judge its effectiveness. The findings uncovered that the HTC network possessed heightened sensitivity towards small and tiny litters taken within the RGB colored images.

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RoboCup Middle-sized-League soccer robots are mainly equipped with the three omni-roller mechanism. These mobile robots are expected to function efficiently in fields, such as logistics. These systems are easy to control and allow omnidirectional movement. However, theoretical research on these systems’ exercise efficiencies has not been conducted. In this research, we assume a mechanism that can arbitrarily change the roller arrangement based on a circular mechanism and evaluate the roller arrangement from a speed efficiency perspective. Additionally, we define an evaluation function using the theory of linear transformation to examine the roller arrangement.

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In this paper, development of IoT control device using Wi-Fi. In recent years, IoT has been attracting attention, and there are growing expectations in the industrial world for the utilization of data obtained from many sensors. These data are stored in databases in real time through communication between sensors and the cloud and communicating with the cloud. Meanwhile, digital controllers are widely used in the process industry as general-purpose controllers. However, it is difficult to incorporate AI, machine learning, and databases into general-purpose controllers due to data memory limitations. Therefore, in this paper, we develop an IoT self-tuning controller using Wi-Fi. As a result of experiments, the controller and computer were connected via Wi-Fi, self-tuning was performed on the computer side, and the calculated PID gains could be sent to the controller to achieve control.

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