Current Issue : October-December Volume : 2024 Issue Number : 4 Articles : 5 Articles
Microstrip patch antennas (MPAs) are compact and easy-to-fabricate antennas, widely used in long-distance communications. MPAs are commonly fabricated using subtractive methods such as photolithographic etching of metals previously deposited using sputtering or evaporation. Despite being an established technique, subtractive manufacturing requires various process steps and generates material waste. Additive manufacturing (AM) techniques instead allow optimal use of material, besides enabling rapid prototyping. AM methods are thus especially interesting for the fabrication of electronic components such as MPAs. AM methods include both 2D and 3D techniques, which can also be combined to embed components within 3D-printed enclosures, protecting them from hazards and/or developing haptic interfaces. In this work, we exploit the combination of 2D and 3D printing AM techniques to realize three MPA configurations: flat, curved (at 45◦), and embedded. First, the MPAs were designed and simulated at 2.3 GHz with a −16.25 dB S11 value. Then, the MPA dielectric substrate was 3D-printed using polylactic acid via fused deposition modeling, while the antenna material (conductive silver ink) was deposited using three different AM methods: screen printing, water transfer, and syringe-based injection. The fabricated MPAs were fully operational between 2.2–2.4 GHz, with the flat MPA having a higher S11 peak value compared to the curved and embedded MPAs. Development of such AM MPAs in various configurations demonstrated in this work can enable rapid development of long-range antennas for novel applications in e.g. aerospace and Internet of Things sectors....
This paper works on detecting a person in bed for sleep routine and sleep pattern monitoring based on the Micro-Electro-Mechanical Systems (MEMS) accelerometer and Internet of Things (IoT) embedded system board. This work provides sleep information, patient assessment, and elderly care for patients who live alone via tele-distance to doctors or family members. About 216,000 pieces of acceleration data were collected, including three classes: no person in bed, a static laying position, and a moving state for Artificial Intelligence (AI) application. Six well-known Machine-Learning (ML) algorithms were evaluated with precision, recall, F1-score, and accuracy in the workstation before implementing in the STM32-microcontroller for real-time state classification. The four best algorithms were selected to be programmed into the IoT board and applied for real-time testing. The results demonstrate the high accuracy of the ML performance, more than 99%, and the Classification and Regression Tree algorithm is among the best models with a light code size of 1583 bytes. The smart bed information is sent to the IoT dashboard of Node-RED via a Message Queuing Telemetry broker (MQTT)....
Due to their ability to analyse the behaviour of slender structures with reasonable accuracy and moderate computational expense, embedded finite elements have attracted widespread interest from the geotechnical engineering community. Related formulations describe the soil-structure interaction behaviour at implicit interaction domains to circumvent expensive surface-to-surface mesh tying problems between the slender structure surfaces and the corresponding solid surfaces. Essentially, this requires the implementation of stress recovery techniques to consider the development of effective normal stresses acting along the shaft, for example, to employ Coulomb-type failure criterions. To this date, reliable information on this research aspect is limited, which decreases the confidence in the results obtained with embedded finite elements. This lack of reliable information has motivated the development of three conceptually different normal stress recovery techniques. These techniques are general in a sense that they can be applied to different types of embedded finite elements, irrespective of their implicit interaction domain geometry. It is found that the presented stress recovery schemes are computationally efficient and capture the numerical benchmark response with high fidelity. Potential lines of research in the context of embedded finite element models are explored throughout this work and may serve as valuable reference in future research....
Advances in artificial intelligence (AI) have led to its application in many areas of everyday life. In the context of control engineering, reinforcement learning (RL) represents a particularly promising approach as it is centred around the idea of allowing an agent to freely interact with its environment to find an optimal strategy. One of the challenges professionals face when training and deploying RL agents is that the latter often have to run on dedicated embedded devices. This could be to integrate them into an existing toolchain or to satisfy certain performance criteria like real-time constraints. Conventional RL libraries, however, cannot be easily utilised in conjunction with that kind of hardware. In this paper, we present a framework named LExCI, the Learning and Experiencing Cycle Interface, which bridges this gap and provides end-users with a free and open-source tool for training agents on embedded systems using the open-source library RLlib. Its operability is demonstrated with two state-of-the-art RL-algorithms and a rapid control prototyping system....
Background: Previous investigations have shown a positive relationship between baseball pitching velocity and the kinetic chain involved in pitching motion. However, no study has examined the influence of finger characteristics on pitching velocity and rate of spin via a sensor-embedded baseball. Methods: Twenty-one pitchers volunteered and were recruited for this study. An experimental baseball embedded with a force sensor and an inertial measurement unit was designed for pitching performance measurement. Finger length and strength were measured as dependent variables. Spin rate and velocity were independent variables. Pearson product–moment correlations (r) and intraclass correlation coefficients (ICCs) determined the relationship between finger characteristics and pitching performance. Results: Finger length discrepancy, two-point pinch strength, index finger RFD (rate of force development), middle finger impulse, and force discrepancy had significant correlations with spin rate (r = 0.500~0.576, p ≤ 0.05). Finger length discrepancy, two-point pinch, three-point pinch strength, index and middle finger RFD, middle finger impulse, and force combination had significant correlations with fastball pitching velocity (r = 0.491~0.584, p ≤ 0.05). Conclusions: Finger length discrepancy, finger pinch strength, and pitching finger force including maximal force and RFD may be factors that impact fastball spin rate and fastball pitching velocity....
Loading....