This approach leverages a multi-label system-based cascade classifier structure, often abbreviated as CCM. The activity intensity labels would be initially categorized. The pre-layer's prediction dictates the division of the data flow into its specific activity type classifier. To analyze patterns of physical activity, an experiment was conducted using data collected from 110 participants. The novel approach, when contrasted with standard machine learning algorithms like Random Forest (RF), Sequential Minimal Optimization (SMO), and K Nearest Neighbors (KNN), leads to a substantial rise in the overall recognition accuracy of ten physical activities. A remarkable 9394% accuracy was attained by the RF-CCM classifier, exceeding the 8793% accuracy of the non-CCM system, which, in turn, could have better generalization. The proposed novel CCM system demonstrates superior effectiveness and stability in physical activity recognition compared to conventional classification methods, as evidenced by the comparison results.

Orbital angular momentum (OAM)-generating antennas promise substantial improvements in the channel capacity of future wireless communication systems. OAM modes, sharing a source aperture, are orthogonal. Therefore, every mode is capable of carrying a unique data stream. This enables the transmission of numerous data streams simultaneously and at the same frequency through a single OAM antenna system. The achievement of this necessitates the creation of antennas capable of generating a multitude of orthogonal antenna modes. This research utilizes a meticulously designed ultrathin, dual-polarized Huygens' metasurface to create a transmit array (TA) that produces a combination of orbital angular momentum (OAM) modes. Two concentrically-embedded TAs are strategically employed to stimulate the desired modes, the phase difference being precisely tailored to each unit cell's position in space. The prototype of the 28 GHz TA, with dimensions of 11×11 cm2, creates mixed OAM modes -1 and -2 using 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. A gain of 16 dBi represents the structural maximum.

Employing a large-stroke electrothermal micromirror, this paper proposes a portable photoacoustic microscopy (PAM) system designed to achieve high-resolution and swift imaging. A precise and efficient 2-axis control is a hallmark of the system's crucial micromirror. The mirror plate's four sides symmetrically incorporate two types of electrothermal actuators: O-shaped and Z-shaped. Employing a symmetrical design, the actuator produced a single-directional movement. check details Using finite element modeling, the two proposed micromirrors' performance revealed a large displacement exceeding 550 meters and a scan angle greater than 3043 degrees under 0-10 volts DC excitation. The steady-state response displays high linearity, and the transient-state response exhibits a swift response, which consequently results in fast and stable imaging. check details The system, utilizing the Linescan model, produces an effective imaging area of 1 mm by 3 mm in 14 seconds, and 1 mm by 4 mm in 12 seconds for the O and Z types. The proposed PAM systems' advantages in image resolution and control accuracy suggest considerable potential for their implementation in facial angiography.

Health problems frequently arise due to the presence of cardiac and respiratory diseases. An automated system for diagnosing irregular heart and lung sounds will lead to enhanced early detection of diseases and enable screening of a greater segment of the population than current manual methods. For simultaneous lung and heart sound diagnosis, we propose a model that is both lightweight and powerful, designed for deployment within low-cost embedded devices. This model is especially valuable in remote and developing nations, where internet access is often unreliable. The ICBHI and Yaseen datasets served as the foundation for training and rigorously testing the proposed model. Our 11-class prediction model, in experimental trials, demonstrated an accuracy rate of 99.94%, precision of 99.84%, specificity of 99.89%, sensitivity of 99.66%, and an F1 score of 99.72%. A digital stethoscope (approximately USD 5) was integrated with a low-cost Raspberry Pi Zero 2W (around USD 20) single-board computer, enabling our pre-trained model to run smoothly. For all individuals within the medical sector, this AI-powered digital stethoscope proves advantageous, enabling automatic diagnostic reports and digital audio documentation for detailed review.

In the electrical industry, asynchronous motors constitute a substantial proportion of the total motor count. Given the criticality of these motors in their operational functions, suitable predictive maintenance techniques are absolutely essential. To forestall motor disconnections and service disruptions, investigations into continuous, non-invasive monitoring procedures are warranted. Through the application of the online sweep frequency response analysis (SFRA) technique, this paper proposes a novel predictive monitoring system. Employing variable frequency sinusoidal signals, the testing system actuates the motors, then captures and analyzes both the input and output signals in the frequency spectrum. SFRA, in the literature, has been employed on power transformers and electric motors that are out of service and disconnected from the main grid. The innovative nature of the approach detailed in this work is noteworthy. Signals are introduced and collected using coupling circuits; grids, meanwhile, 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). Induction motor health monitoring, especially in mission-critical and safety-critical settings, appears to be a promising application for the online SFRA, as indicated by the results. The cost of the entire testing system, comprising the coupling filters and cables, is under EUR 400.

Although pinpointing small objects is crucial across numerous applications, the accuracy of neural network models, though designed and trained for general object detection, frequently degrades when dealing with the nuances of small object recognition. The popular Single Shot MultiBox Detector (SSD) performs inconsistently with small objects, and finding a method to balance performance across a range of object sizes remains a critical problem. 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. check details For enhanced SSD performance in discerning minute objects, we present a new matching strategy—'aligned matching'—which integrates aspect ratios and center-point distances alongside the Intersection over Union (IoU) metric. Experiments conducted on the TT100K and Pascal VOC datasets indicate that SSD, when utilizing aligned matching, noticeably improves the detection of small objects while maintaining performance on large objects without adding extra parameters.

Gauging the presence and movement of individuals or crowds within a given region offers significant understanding into genuine behavioral patterns and concealed trends. Hence, the implementation of proper policies and measures, alongside the advancement of sophisticated services and applications, is vital in areas such as public safety, transport systems, urban design, disaster response, and mass event management. This paper introduces a non-intrusive privacy-preserving method for detecting people's presence and movement patterns. This approach tracks WiFi-enabled personal devices carried by individuals, leveraging network management messages to associate those devices with available networks. Randomization procedures are in place within network management messages due to privacy regulations, making it challenging to discern devices through their addresses, message sequence numbers, data field contents, and the transmitted data amount. We presented a novel de-randomization method aimed at identifying individual devices by clustering analogous network management messages and their associated radio channel characteristics, employing a novel clustering and matching algorithm. The proposed method started with calibration via a labeled, publicly available dataset, followed by validation in a controlled rural and a semi-controlled indoor environment; its scalability and accuracy were assessed in an urban environment filled with people, without control Across the rural and indoor datasets, the proposed de-randomization method accurately detects over 96% of the devices when evaluated separately for each device. By grouping devices, the methodology's precision declines, however, it maintains an accuracy exceeding 70% in rural zones and 80% in indoor setups. In an urban setting, the final verification process of the non-intrusive, low-cost solution for analyzing the presence and movement patterns of people, providing clustered data for individual movement analysis, validated its accuracy, scalability, and robustness. The study's findings, however, unveiled a few shortcomings with respect to exponential computational complexity and the crucial task of determining and fine-tuning method parameters, necessitating further optimization and automated procedures.

Using open-source AutoML tools and statistical methods, this paper presents a novel approach to robustly predict tomato yield. Data from Sentinel-2 satellite imagery, taken every five days, provided the values of five chosen vegetation indices (VIs) for the 2021 growing season, running from April to September. In central Greece, the performance of Vis across diverse temporal scales was evaluated by collecting actual recorded yields from 108 fields covering 41,010 hectares of processing tomatoes. Besides, visual indicators were integrated with crop's developmental phases to establish the yearly changes in the crop's behavior.