The cascade classifier structure of this approach, built on a multi-label system, is referred to as CCM. Prior to any other analysis, the labels representing activity intensity would be categorized. Following pre-layer prediction output, the data stream is categorized into its respective activity type classifier. To analyze patterns of physical activity, an experiment was conducted using data collected from 110 participants. Relative to traditional machine learning methods such as Random Forest (RF), Sequential Minimal Optimization (SMO), and K Nearest Neighbors (KNN), the proposed method exhibits a marked improvement in the overall recognition accuracy for ten physical activities. A 9394% accuracy rate for the RF-CCM classifier surpasses the 8793% accuracy of the non-CCM system, indicating improved generalization performance. The novel CCM system, in the comparison results, outperforms conventional classification methods in physical activity recognition by exhibiting greater effectiveness and stability.

Upcoming wireless systems will likely benefit from a considerable boost in channel capacity, thanks to the use of antennas that generate orbital angular momentum (OAM). Due to the orthogonal nature of different OAM modes triggered from a single aperture, each mode is able to transmit its own individual data stream. Therefore, a unified OAM antenna system facilitates the simultaneous transmission of multiple data streams at a shared frequency. To realize this, there is a demand for antennas that can produce numerous orthogonal azimuthal modes. The current study deploys an ultrathin dual-polarized Huygens' metasurface to fabricate a transmit array (TA) for the purpose of generating mixed orbital angular momentum (OAM) modes. To achieve the requisite phase difference, two concentrically-embedded TAs are used to stimulate the desired modes, taking into account the coordinate of each unit cell. The 28 GHz TA prototype, measuring 11×11 cm2, generates mixed OAM modes -1 and -2 through dual-band Huygens' metasurfaces. To the best of the authors' knowledge, this represents the first instance of a dual-polarized, low-profile OAM carrying mixed vortex beams designed with TAs. The structure's maximum gain is 16 decibels, or 16 dBi.

A large-stroke electrothermal micromirror forms the foundation of the portable photoacoustic microscopy (PAM) system presented in this paper, enabling high-resolution and fast imaging. A precise and efficient 2-axis control is a hallmark of the system's crucial micromirror. Two electrothermal actuators, one in an O-shape and the other in a Z-shape, are uniformly distributed about the four compass points of the mirror plate. The actuator's symmetrical configuration allowed only a single directional operation. IU1 A finite element modeling study of the two proposed micromirrors established a large displacement exceeding 550 meters and a scan angle exceeding 3043 degrees at 0-10 volts DC excitation. Additionally, the system exhibits high linearity in the steady-state response, and a quick response in the transient-state, allowing for fast and stable imaging. IU1 The system, employing the Linescan model, achieves a 1 mm by 3 mm imaging area in 14 seconds for O-type subjects and a 1 mm by 4 mm imaging area in 12 seconds for Z-type subjects. The advantages of the proposed PAM systems lie in enhanced image resolution and control accuracy, signifying a considerable potential for facial angiography.

A significant contributor to health problems are cardiac and respiratory diseases. Automating the diagnosis of abnormal heart and lung sounds will enable earlier disease detection and expand screening to a larger population than manual methods allow. A novel, simultaneous lung and heart sound diagnostic model, lightweight and robust, is developed. The model is optimized for deployment in low-cost, embedded devices and provides considerable utility in underserved remote and developing nations lacking reliable internet connections. Our proposed model was subjected to training and testing using the ICBHI and Yaseen datasets. The experimental assessment of our 11-class prediction model highlighted a noteworthy performance, with results of 99.94% accuracy, 99.84% precision, 99.89% specificity, 99.66% sensitivity, and a 99.72% F1-score. Our team constructed a digital stethoscope at a cost of approximately USD 5, and linked it with a low-cost, single-board computer, the Raspberry Pi Zero 2W (approximating USD 20), that seamlessly supports our pre-trained model’s execution. This digital stethoscope, empowered by AI technology, offers a substantial advantage to those in the medical field, automatically producing diagnostic results and creating digital audio records for further review.

Asynchronous motors dominate a large segment of the electrical industry's motor market. Suitable predictive maintenance techniques are unequivocally required when these motors are central to their operations. Examining continuous, non-invasive monitoring techniques can mitigate motor disconnections, thus averting service disruptions. An innovative predictive monitoring system, built on the online sweep frequency response analysis (SFRA) technique, is proposed in this paper. Motor testing involves the system's application of variable frequency sinusoidal signals, followed by the acquisition and frequency-domain processing of the input and output signals. The application of SFRA to power transformers and electric motors, which are offline and disconnected from the primary grid, is documented in the literature. This work's approach stands out due to its originality. The injection and capture of signals is accomplished through coupling circuits, whereas grids supply the motors with power. Using a group of 15 kW, four-pole induction motors, some healthy and some with minor damage, the technique's performance was assessed by analyzing the difference in their respective transfer functions (TFs). The results imply that the online SFRA method may be suitable for monitoring the health conditions of induction motors, notably in safety-critical and mission-critical circumstances. Coupling filters and cables are included in the overall cost of the entire testing system, which amounts to less than EUR 400.

While the identification of minuscule objects is essential across diverse applications, standard object detection neural networks, despite their design and training for general object recognition, often exhibit inaccuracies when dealing with these tiny targets. The Single Shot MultiBox Detector (SSD) tends to struggle with small-object detection, with the problem of achieving balanced performance across varying object scales remaining a significant issue. This study contends that SSD's current IoU-matching approach negatively impacts the training efficiency of small objects, arising from mismatches between default boxes and ground truth targets. IU1 To boost the accuracy of SSD's small object detection, we present a new matching technique, 'aligned matching,' that improves upon the IoU calculation by factoring in aspect ratios and the distance between object centers. Analysis of experiments conducted on the TT100K and Pascal VOC datasets shows SSD with aligned matching to offer superior detection of small objects without diminishing performance on large objects, nor increasing the number of required parameters.

Monitoring the positions and trajectories of individuals or crowds in a particular area provides valuable insights into observed behavioral patterns and concealed trends. Subsequently, the adoption of appropriate policies and strategies, together with the advancement of advanced services and applications, is paramount in fields such as public safety, transportation, city planning, disaster response, and large-scale event coordination. A non-intrusive, privacy-preserving system for recognizing people's presence and motion patterns is presented in this paper. This system utilizes WiFi-enabled personal devices and the corresponding network management messages to establish associations with the available networks. Randomization techniques are applied to network management messages, safeguarding against privacy violations. These safeguards include randomization of device addresses, message sequence numbers, data fields, and message content size. To achieve this objective, we introduced a novel de-randomization technique that identifies distinct devices by grouping related network management messages and their corresponding radio channel attributes using a novel clustering and matching process. The proposed approach began with calibrating it using a publicly available labeled dataset, confirming its accuracy through controlled rural and semi-controlled indoor measurements, and finally assessing its scalability and accuracy in an uncontrolled, densely populated urban setting. Across the rural and indoor datasets, the proposed de-randomization method accurately detects over 96% of the devices when evaluated separately for each device. The method's accuracy decreases when devices are clustered together, but still surpasses 70% in rural areas and maintains 80% in indoor settings. The urban environment's people movement and presence analysis, using a non-intrusive, low-cost solution, confirmed its accuracy, scalability, and robustness via a final verification, including the generation of clustered data useful for analyzing individual movements. Despite yielding beneficial results, the method unveiled certain drawbacks, including exponential computational complexity and the demanding task of determining and fine-tuning method parameters, which necessitates further optimization and automation.

This paper introduces a novel method for robustly predicting tomato yield based on open-source AutoML and statistical analysis. Sentinel-2 satellite imagery was utilized to gather data on five selected vegetation indices (VIs) during the 2021 growing season, from April through September, at five-day intervals. Actual recorded yields across 108 fields in central Greece, encompassing a total area of 41,010 hectares devoted to processing tomatoes, were used to gauge the performance of Vis at differing temporal scales. Besides, visual indicators were integrated with crop's developmental phases to establish the yearly changes in the crop's behavior.