A field-programmable gate array (FPGA) device with a Zynq architecture integrates a processing system (PS) and programmable logic (PL) into a single chip. Although the PL performance is typically considered, the PS load cannot be completely ignored. In this study, using an FPGA board with a Zynq architecture, the conditions under which an object detection system performs the best, while considering the PS and PL load balance, are explored.

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As businesses increasingly embrace digitalization, the transformative impact of technology becomes evident, making various aspects more streamlined and manageable. This, in turn, has the potential to alleviate workloads and contribute to Sustainable Development Goals (SDGs) and Environmental, Social, and Governance (ESG) principles. The primary objective of this study was to conduct research and develop a web-based application for efficient business stock management, aligning with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). This application empowers employees to monitor their stock levels, helping to prevent stockouts and improve business performance, thereby addressing SDG 8 (Decent Work and Economic Growth). The development process adhered to the Software Development Life Cycle (SDLC) model and utilized technologies such as the Vue 2 framework, JavaScript, HTML5, and Tailwind CSS to code the web-based application, contributing to SDG 9 by promoting technological innovation. In addition, a cloud-based database was established to store shop-related data securely, reducing the risk of data loss due to disasters or human errors, aligning with SDG 11 (Sustainable Cities and Communities). Furthermore, the web-based system was designed to conduct stock checks and monitoring, providing valuable insights into product statistics and notifying shopkeepers when stock levels are low, which supports SDG 12 by promoting responsible consumption and production. As part of the sustainability efforts, a digital receipt system was implemented as the default option for customers instead of traditional hardcopy receipts. This initiative aligns with SDG 13 (Climate Action) by reducing paper consumption and waste, as hardcopy receipts often contain non-recyclable chemical substances. In summary, the web-based application's contributions encompass promoting business efficiency, preventing stock shortages, and reducing paper usage, all of which align with various SDGs and ESG principles, ultimately fostering sustainable and responsible business practices.

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Map construction, or mapping, plays an important role in robotic applications. Mapping relies on inherently noisy sensor measurements to construct an accurate representation of a surrounding environment. Generally, individual sensors suffer from performance degradation issues under certain conditions in the environment. Sensor fusion enables to obtain statistically more accurate perception and to cope with performance degradation issues by combining data from multiple sensors of different modalities. This paper describes the latest developments in data fusion and state-of-the-art mapping methods using data fusion.

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Accurate localization of Autonomous Underwater Vehicles (AUVs) is fundamental to navigation accuracy, and robust control which leads to an overall success of vehicles’ mission. Conventional positioning systems are expensive and cannot be implemented for lightweight/budget AUV systems. This paper describes the design and performance of an inexpensive acoustic positioning system (APS) used to guide the mission strategy of a lightweight AUV (KYUBIC) for the Underwater Robotics Competition in Okinawa. Using Position and Thrust control strategy, the AUV velocity is used for self-positioning estimation, and this is calculated from the equation of motion without integrating the acceleration. The acoustic positioning method is based on Supershort baseline (SSBL) principle comprising of 2 self-made hydrophone module using MEMS microphone. Our competition strategy searches and locates the position of a submerged Pinger using detection and angle estimates that are integrated for dynamic control of the AUV. Performance analysis show that the controller and low-cost acoustic system can search and localize at Pinger position within the accepted boundary.

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Degradation in underwater imaging is as a result of absorption and scattering of light. Propagated visible light rays through the water column are absorbed at rates that vary depending on the wavelength of light. Large suspended particles also scatter the propagated light rays, as can be observed in an underwater environment. Furthermore, color is distorted due to the inverse ratio of attenuation that is proportional to the wavelength of light through a unit of length through the water column. These phenomena distort underwater images by making them appear dark and have low contrast. Conventional underwater image restoration techniques are largely based on the image formation model (IMF) which restores the image based on estimates from the degraded images. The results are solutions that are limited to specific underwater conditions. In this paper, we propose a novel restoration strategy by considering the optical properties in the underwater environment at the time of image capture, a robust restoration technique can be applied to images captured in different underwater conditions. In so doing, we design a turbidity meter that capture wavelength-dependent absorption data which are applied as parameters to restore the distorted images based on the RGB channels. To validate our proposed technique, we conduct experiments in a controlled underwater environment while varying the concentration of suspended particles based on degree of kaolin mixture.

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Generally, dementia is defined as a general term for sustainable decline of memory, language and other thinking abilities, therefore, the early detection is important. However, it is often failed because it is difficult to articulate dementia problem verbally. In this paper, from the perspective of narrative approach, I analyzed semi-structured interview data stored in DIPEx-Japan, a database of individual experiences of health and illness, and discussed the difficulty of early detection of dementia. I concluded the importance of narratives without specific events because they are less influenced by cognitive bias.

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Various industries have adopted 5G Non-Public Networks to take advantage of improved connectivity while remaining separate from public networks. As these networks support private operations, they demand stringent security measures to prevent potential harm. This research paper focuses on securing 5G non-public networks against fake base station attacks, which pose a significant threat to the security of mobile networking. The study analyzes the risks associated with fake base stations and reviews various protection methods. Our findings underscore the importance of deploying effective countermeasures to mitigate the threats posed by fake base stations in 5G Non-Public Networks.

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Underwater communications research is significant to researchers because of the challenges and difficulties in transmitting wireless signals within this environment. Underwater Acoustic Communications (UWA) are characterized by the fact that they can travel long distances compared to the radio signal, which makes it very effective compared to other types, in addition to the lack of costs in establishing these types of systems. OFDM technique has been used widely in 4G communication systems, and recently it was approved to be successfully implemented in 5G. One of the most important aims of developing communications systems is to reduce the bit error rate (BER), and since the underwater communication signal is subject to many influencing factors that reduce the accuracy of the signal when it reaches the receiver, such as absorption and multipathing, researchers must improve the performance of the OFDM system by removing this effect. The channel effect is reduced by selecting some subcarriers from the OFDM system and assigning them as pilots. These Pilots are given higher power to determine the channel effect and then use the appropriate equalizer to reduce the effect and improve signal accuracy. In this study we tested the appropriate number of pilots to estimate the channel response, with a linear equalizer to reduce the effect at the receiver, then compared the BER before and after adding the equalizer.

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Testing is a crucial aspect of robotic systems development. New robotic concepts should be proven to be safe and effective before running on real robotic platforms in real environments. Robotic simulation allows creating virtual environments and repeating real-world scenarios. 3D modelling has become a standard way for creating on-demand compound digital 3D models with a high level of realism. This research paper presents a guide on prototyping a real office environment in Gazebo simulator. Blender modelling suite is used for creating high-quality 3D models. The validation approach applied to the virtual environment is to perform lidar-based SLAM task for a mobile robot. Human-environment interaction tests were executed using extended Gazebo actors.

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In the coal mining industry, numerous hazardous risks such as gas explosions, temperature fluctuations, and humidity variations pose challenges in terms of monitoring and control. This paper introduces a proposed method that utilizes Li-Fi technology for data transmission. Equipped with sensors, this system provides real-time situation updates to coal miners, ensuring their safety. Among the dangerous gases in coal mining, carbon monoxide (CO) is a significant concern. To address this, specific sensors are employed and connected to a core component, the microcontroller, which is strategically placed throughout the coal mine. When the concentration level exceeds a predetermined threshold, an alarm is triggered, and a notification is sent to the central database, alerting the workers. By overcoming issues like slow data transmission or data loss, Li-Fi offers a solution that enhances safety and improves working conditions for laborers in the coal mining sector.

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Industrial control systems currently heavily rely on the Internet of Things, which poses a higher risk of cyber attacks. This is particularly concerning in critical infrastructure, as an attack could endanger lives and result in military actions. A software-defined network is a common centralized network architecture in the Internet. It has a lot of uses and is easy to manage. In our research, we applied the SDN (Software-defined network) on the ICS (Industrial Control System) to ensure the critical flow between industrial equipment.

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We discuss a system of robotic assembly for gearmotors which are also called gear motors or geared motors. A gearmotor consists of a reducer, a rotor, a stator, a bracket, bolts, and so on. This paper focuses on an assembly task of the stator to the rotor and reducer. The task is complicated because the fit diameter of the stator and rotor is clearance fit. Currently, the task is performed by manual handwork of skilled workers. To accomplish the task, the workers utilize various information such as visual and hand sensation. We develop assembly system using robots, vision, and force torque sensors to automatize the task. Two main methods, key point matching and contact position estimation, are applied to perform the task. Keypoint matching is utilized to detect the pose (position and orientation) of the stator by using visual information. Contact position estimation is utilized to detect the contact between the parts during the insertion process by using force information and to prevent failure of the insertion operation. The validity of our proposed system is verified using experiments of the automatic insertion task of the stator.

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Distorted images due to microscopy lens distortion can cause errors when acquiring images of parasites in water samples during inspection. Given the critical nature of inspecting treated water, it is important to monitor the quality of microscopic parasite images to prevent errors during inspection. To this end, this study involved both subjective and objective evaluations of parasite images, specifically Cryptosporidium and Giardia (oo)cysts. The evaluation utilized a parasite image database comprising 380 images where 20 are reference images and 360 are distorted images. For the subjective assessment, 20 subjects assessed the distorted images, and Mean Opinion Scores (MOS) were obtained. To perform an unbiased evaluation, six Full Reference- IQA (FR-IQA) metrics and three Blind-IQA metrics were employed to appraise the distorted images. The Mean Opinion Scores (MOS) were used as a reference point to ascertain the most appropriate objective IQA method for evaluating the parasite images. The study analyzed the relationship between the MOS ratings and the objective IQA techniques using PLCC and RMSE as performance metrics. The results of the investigation revealed that the MSSIM method was the most effective IQA approach for evaluating parasite images affected by Gaussian White Noise (GWN) and Gaussian Blur (GB).

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In the analysis based on multibody dynamics (MBD), the MBD models are described by ordinary differential equations, and the numerical integration methods applied to the analysis nonnegligibly affect the accuracy of the results. In consideration of models containing dynamic components such as spring dampers and flexible elements, the selection of integration methods is highly important not only for accuracy but also computational costs of the analysis, because it easily causes integration errors. For the development of exoskeleton-type assistive devices using flexible elements, an accurate assessment of the effect of their elastic properties is required. In the present study, we applied the implicit integration method in order to improve the accuracy of MBD-based analysis, and numerical errors in computer experiments were significantly reduced in the case of implicit methods with respect to those caused by explicit methods. This verification result can lead to the improvement of the accuracy of the inverse dynamics analysis based on the flexible multibody dynamics (fMBD) theory.

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Development of neuromorphic chips with biologically plausible learning mechanisms is vital for investigating brain-like learning processes. One such mechanism is Spike-timing dependent plasticity (STDP), but implementing its multi-bit circuitry requires significant silicon area. In a prior study, we introduced a hardware-friendly learning rule named adaptive STDP. Through experiments, we demonstrated its performance similarity to the ideal STDP rule in a basic biologically plausible spike pattern detection task involving a single neuron. Building upon this, our present study extends the adaptive STDP learning rule to encompass lateral inhibition, a prevalent motif in the brain. We apply it to a spike pattern detection model featuring multiple neurons that engage in competition to detect multiple patterns. Furthermore, we investigate the performance of the ideal STDP rule using 4-bit and 6-bit synapse resolution and present a comparative analysis of the results.

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