Search Results (12)

The precise identification and non-destructive measurement of structural features and defects in semiconductor wafers are essential for ensuring process integrity and sustaining high yield in advanced manufacturing environments. Unlike conventional measurement techniques, scanning acoustic microscopy (SAM) is an advanced method that provides detailed visualizations of both surface and internal wafer structures. However, in practical industrial applications, the scanning time and image quality of SAM significantly impact its overall performance and utility. Prolonged scanning durations can lead to production bottlenecks, while suboptimal image quality can compromise the accuracy of defect detection. To address these challenges, this study proposes LinearTGAN, an improved generative adversarial network (GAN)-based model specifically designed to improve the resolution of linear acoustic wafer images acquired by the breakthrough rotary scanning acoustic microscopy (R-SAM) system. Empirical evaluations demonstrate that the proposed model significantly outperforms conventional GAN-based approaches, achieving a Peak Signal-to-Noise Ratio (PSNR) of 29.479 dB, a Structural Similarity Index Measure (SSIM) of 0.874, a Learned Perceptual Image Patch Similarity (LPIPS) of 0.095, and a Fréchet Inception Distance (FID) of 0.445. To assess the measurement aspect of LinearTGAN, a lightweight defect segmentation module was integrated and tested on annotated wafer datasets. The super-resolved images produced by LinearTGAN significantly enhanced segmentation accuracy and improved the sensitivity of microcrack detection. Furthermore, the deployment of LinearTGAN within the R-SAM system yielded a 92% improvement in scanning performance for 12-inch wafers while simultaneously enhancing image fidelity. The integration of super-resolution techniques into R-SAM significantly advances the precision, robustness, and efficiency of non-destructive measurements, highlighting their potential to have a transformative impact in semiconductor metrology and quality assurance. Full article

The COVID-19 pandemic has been linked to numerous threats to public health. Of these, physical inactivity became increasingly prevalent, mainly due to the widespread closure of indoor gyms. Home-based exercise alternatives were created as potential solutions, but little research is available validating their efficacy to improve long-term health and fitness. This case study investigated the longitudinal effects of ≥three weekly exercise sessions with a smart home-based fitness platform on anthropometric, fitness, and cardiometabolic measures. Three participants were annually assessed over a five-year period spanning before and after the COVID-19 pandemic. Reductions in body fat percentage occurred simultaneously with increases in fat-free mass. Improvements in physical performance measures, including VO₂ max and both one-repetition maximum (1-RM) and 85% 1-RM for chest press and squat press, were identified. Cardiometabolic measures also demonstrated notable improvements, as borderline hypertension was reduced along with resting heart rate while resting metabolic rate (RMR) and heart rate variability (HRV) increased. Beyond these metrics, volitional exercise frequency grew without compromising exercise program adherence. Although physical activity on a global scale decreased during the COVID-19 pandemic, the long-term cardiometabolic and fitness benefits observed with this home-based exercise platform highlight its potential to improve health and fitness. Full article

Background: GaitSmart (GS) is a sensor-based digital medical device that can be used with the integrated app vGym to provide a personalised rehabilitation programme for older people undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA). This study aimed to determine whether the GS intervention used in the rehabilitation of older people undergoing THA or TKA is potentially cost-effective compared to the current standard of care (SoC). Methods: Decision-analytic modelling was conducted to estimate the cost-effectiveness over a seventeen-week time horizon from an NHS perspective. UK clinical and cost data from the GaitSmart randomised clinical trial was used to obtain the input parameters, and a sensitivity analysis was performed to address uncertainties. Results: Over a seventeen-week time horizon, GS incurred cost savings of GBP 450.56 and a 0.02 gain in quality-adjusted life years (QALYs) compared to the SoC. These results indicate that GS is the dominant intervention because the device demonstrated greater effectiveness and lower costs. Probabilistic sensitivity analyses confirm the robustness of our results. Conclusions: GS appears to offer short-term efficiency benefits and demonstrates cost-effectiveness for the improvement in gait in people undergoing THA or TKA, compared to the SoC. Full article

A scanning acoustic microscopy (SAM) system is a common non-destructive instrument which is used to evaluate the material quality in scientific and industrial applications. Technically, the tested sample is immersed in water during the scanning process. Therefore, a robot arm is incorporated into the SAM system to transfer the sample for in-line inspection, which makes the system complex and increases time consumption. The main aim of this study is to develop a novel water probe for the SAM system, that is, a waterstream. During the scanning process, water was supplied using a waterstream instead of immersing the sample in the water, which leads to a simple design of an automotive SAM system and a reduction in time consumption. In addition, using a waterstream in the SAM system can avoid contamination of the sample due to immersion in water for long-time scanning. Waterstream was designed based on the measured focal length calculation of the transducer and simulated to investigate the internal flow characteristics. To validate the simulation results, the waterstream was prototyped and applied to the TSAM-400 and W-FSAM traditional and fast SAM systems to successfully image some samples such as carbon fiber-reinforced polymers, a printed circuit board, and a 6-inch wafer. These results demonstrate the design method of the water probe applied to the SAM system. Full article

This study presents two simple physiotherapy programs that were implemented for five weeks and showed positive changes in balance, coordination, and motor skills in kindergarteners with ASD. Physiotherapy programs in a gym and games on a smart board with balance plates and an unstable base were applied to improve the physical condition of children with ASD. Thirty children with ASD (4–6 years old) attending special needs kindergarten were enrolled in the study. Three tests were used to assess participants’ physical condition before and after the study: the modified Berg Balance Scale, the Imbalance Coordination Sample, and the Bruininks–Oseretsky Motor Proficiency Test (BOTMP). The resulting mean change, calculated from each group’s scores, shows that the participants who received physical therapy sessions at the smart board had the greatest change of 1.58 points. It shows that the opportunity to play games on a smart board motivates children with ASD to work harder; therefore, it is a simple and easy way to engage children in different types of physical exercise. A slightly smaller change of 1.51 was obtained in the group that received gym sessions. However, working in the gym was more psychologically challenging for the children with ASD due to their lack of desire and motivation. Both methods are relatively simple and easy to apply at home; therefore, parents can make a significant contribution to improving children’s physical condition and that can be an effective tool to assist these individuals with activities in daily life. Full article

In response to the challenges posed by climate change, including extreme weather events, such as heavy rainfall and droughts, the agricultural sector is increasingly seeking solutions for the efficient use of resources, particularly water. Pivotal aspects of smart agriculture include the establishment of weather-independent systems and the implementation of precise monitoring and control of plant growth and environmental conditions. Hydroponic cultivation techniques have emerged as transformative solutions with the potential to reduce water consumption for cultivation and offer a sheltered environment for crops, protecting them from the unpredictable impacts of climate change. However, a significant challenge lies in the frequent need for human intervention to ensure the efficiency and effectiveness of these systems. This paper introduces a novel system with a modular architecture, offering the ability to incorporate new functionalities without necessitating a complete system redesign. The autonomous hydroponic greenhouse, designed and implemented in this study, maintains stable environmental parameters to create an ideal environment for cultivating tomato plants. Actuators, receiving commands from a cloud application situated at the network’s edge, automatically regulate environmental conditions. Decision-making within this application is facilitated by a PID control algorithm, ensuring precision in control commands transmitted through the MQTT protocol and the NGSI-LD message format. The system transitioned from a single virtual machine in the public cloud to edge computing, specifically on a Raspberry Pi 3, to address latency concerns. In this study, we analyzed various delay aspects and network latency to better understand their significance in delays. This transition resulted in a significant reduction in communication latency and a reduction in total service delay, enhancing the system’s real-time responsiveness. The utilization of LoRa communication technology connects IoT devices to a gateway, typically located at the main farm building, addressing the challenge of limited Internet connectivity in remote greenhouse locations. Monitoring data are made accessible to end-users through a smartphone app, offering real-time insights into the greenhouse environment. Furthermore, end-users have the capability to modify system parameters manually and remotely when necessary. This approach not only provides a robust solution to climate-induced challenges but also enhances the efficiency and intelligence of agricultural practices. The transition to digitization poses a significant challenge for farmers. Our proposed system not only represents a step forward toward sustainable and precise agriculture but also serves as a practical demonstrator, providing farmers with a key tool during this crucial digital transition. The demonstrator enables farmers to optimize crop growth and resource management, concretely showcasing the benefits of smart and precise agriculture. Full article

The optical modal gain of Cd_0.6Zn_0.4Te/ZnTe double quantum dots was measured using a variable stripe length method, where large and small quantum dots are separated with a ZnTe layer. With a large (~18 nm) separation layer thickness of ZnTe, two gain spectra were observed, which correspond to the confined exciton levels of the large and small quantum dots, respectively. With a small (~6 nm) separation layer thickness of ZnTe, a merged single gain spectrum was observed. This can be attributed to a coupled state between large and small quantum dots. Because the density of large quantum dots (4 × 10¹⁰ cm⁻²) is twice the density of small quantum dots (2 × 10¹⁰ cm⁻²), the density of the coupled quantum dots is determined by that of small quantum dots. As a result, we found that the peak gain (123.9 ± 9.2 cm⁻¹) with the 6 nm separation layer is comparable to that (125.2 ± 29.2 cm⁻¹) of the small quantum dots with the 18 nm separation layer. Full article

Localized states in an anisotropic single GaAs quantum ring were investigated in terms of polarization dependence of micro-photoluminescence spectrum at 5K. Given four Stokes parameters measured with a pair of linear polarizers and waveplates, the elliptical polarization states of two different vertical confinement states ($k=1$ and $k=2$) were compared with phase, rotation, and ellipticity angles. While the polarized emission intensity of the $k=2$ states becomes enhanced along [1,1,0] compared to that along [1,$\overline{1}$,0], the polarization asymmetry of the $k=1$ states shows the opposite result. We conclude the polarization state is determined by the shape of the lateral wavefunctions. In the $k=2$ state, crescent-like wavefunctions are strongly localized, but the $k=1$ state consists of two crescent-like wavefunctions, which are connected weakly through quantum tunneling. Full article

Physical activity is a crucial factor for maintaining not only physical health status, but vast amounts of research have shown its link with better mental health. Supporting the use of gyms for the safety of its practitioners is vital in the new norm and living with COVID-19. Therefore, in this study we show research supporting the development of a framework for a Total Safe-Care Fitness Solution based on a multimodal COVID-19 tracking system integrating computer vision and data from wearable sensors. We propose a framework with three areas that need to be integrated: a COVID-19 vaccine and health status recognition system (QR code scan prior to entry to the gym, and physiological signals monitored by a smart-band and a health questionnaire filled in prior to entry to the gym); an accident detection system (video and smart-band based); and a gym-user digital tracking system (CCTV and smart-band based). We show the proposed architecture for the integration of these systems and provide practical tips on how to implement it in testbeds for feasibility testing. To the best of our knowledge, this is the first proposed COVID-19 tracking system of use in gyms that includes a predictive model for accident detection for safer exercise participation through health monitoring. Full article

Automatic tracking and quantification of exercises not only helps in motivating people but also contributes towards improving health conditions. Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. Excellent trackers are available for aerobic exercises but, in contrast, tracking free weight exercises is still performed manually. This study presents the details of our data acquisition effort using a single chest-mounted tri-axial accelerometer, followed by a novel method for the recognition of a wide range of gym-based free weight exercises. Exercises are recognized using LSTM neural networks and the reported results confirm the feasibility of the proposed approach. We train and test several LSTM-based gym exercise recognition models. More specifically, in one set of experiments, we experiment with separate models, one for each muscle group. In another experiment, we develop a universal model for all exercises. We believe that the promising results will potentially contribute to the vision of an automated system for comprehensive monitoring and analysis of gym-based exercises and create a new experience for exercising by freeing the exerciser from manual record-keeping. Full article

Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today’s time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature. Full article

Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to support the development and deployment of web-enabled embedded sensor and actuator devices with two major objectives: (i) to integrate sensing and actuating functionalities into everyday objects, and (ii) to easily allow a diversity of devices to plug into the Internet. Currently, developers who are applying this Internet-oriented approach need to have solid understanding about specific platforms and web technologies. In order to alleviate this development process, this research proposes a Resource-Oriented and Ontology-Driven Development (ROOD) methodology based on the Model Driven Architecture (MDA). This methodology aims at enabling the development of smart spaces through a set of modeling tools and semantic technologies that support the definition of the smart space and the automatic generation of code at hardware level. ROOD feasibility is demonstrated by building an adaptive health monitoring service for a Smart Gym. Full article