Real‐time monitoring of successful shots during basketball training is crucial for coaches to assess player performance and formulate training plans. Existing visual monitoring systems are greatly affected by variations in illumination intensity and color differences, resulting in significant detection errors. To address this limitation, this study presents a self‐powered basketball hoop goal sensor based on a triboelectric nanogenerator (TENG). The sensor utilizes the basketball’s gravity to induce friction between internal aluminum and polyimide electrodes, achieving both goal detection and power generation. Experimental results demonstrate that the sensor exhibits desirable measurement characteristics, with output signal voltage and current amplitudes of 5.5 V and 60 nA, respectively. The measurement error is less than 1.5%, and the sensor operates reliably within a temperature range of −20 to 70°C and a relative humidity range of 5% to 95%. Furthermore, the sensor incorporates a power generation function, achieving a maximum output power of 46.5 nW when an external 20 MΩ resistor is connected in series.

This study evaluates the performance of a pulse‐excited printed circuit board (PCB) resolver in comparison with the traditional square‐wave excitation method. Although the pulse‐excited resolver exhibits slightly higher error levels than the square‐wave excitation approach, these differences remain acceptable for applications that prioritize reduced power consumption and improved thermal performance. Experimental results indicate that the average position errors of the resolver are 1.109° at low speeds and 0.338° at high speeds. These error levels are considered acceptable for a wide range of industrial applications. Thermal analysis demonstrates that the proposed pulse excitation method substantially reduces thermal stress on the resolver, yielding a maximum temperature of approximately 32.0°C during testing, compared with nearly 59.7°C under square‐wave excitation. Overall, the findings highlight the advantages of the proposed method, particularly for applications requiring effective heat management. The results suggest that the pulse‐excited resolver represents a viable alternative, combining operational efficiency with enhanced thermal performance.

Accurate calibration of triaxial magnetometers is critical for magnetic anomaly detection, yet existing optimization‐based calibration methods often suffer from premature convergence and suboptimal parameter estimation. To address these limitations, this article proposes a Tent map and Selection‐phase enhanced African Vulture Optimization Algorithm (TS‐AVOA) for efficient magnetometer error correction. The proposed TS‐AVOA enhances global search diversity and local convergence accuracy by integrating a Bald Eagle Search (BES)‐inspired selection mechanism and embedding Tent chaotic mapping into both the exploration and exploitation phases. Numerical simulations demonstrate that the maximum absolute error between the calibrated and theoretical magnetic field amplitude is reduced to just 1.1 nT, with parameter estimation accuracy exceeding 99.9%. Comparative analysis against PSO, GA, and original AVOA confirms that TS‐AVOA outperforms all baselines in convergence speed and calibration accuracy. Field experiments further validate that total geomagnetic fluctuation is suppressed from 282.47 to 9.79 nT, achieving a 28.86‐fold reduction. These results highlight the practical potential of TS‐AVOA in enhancing the reliability of fluxgate magnetometers for geomagnetic applications.

Mapping invasive alien plants (IAPs) has become essential for land and biodiversity conservation authorities, as these species can transform the areas they invade. Fortunately, advances in remote sensing using publicly available products such as Sentinel‐2 have improved this process, especially in hard‐to‐access mountainous regions. In South Africa, poplar (Populus alba) is among the IAPs of concern and is found in the eastern Free State and elsewhere in the country, but remote sensing has not yet been used to map this species. Using Sentinel‐2 imagery and the random forest (RF) algorithm, this study allowed us to: (a) map and distinguish poplar trees from other land covers throughout the year in the eastern Free State’s mountainous region, (b) evaluate influential bands and their combinations in classification, and (c) assess the accuracy of the classification for the first and second halves of the year. The results showed that images from the first half of the year (January–June) had higher classification accuracy (overall accuracy [OA] = 91% and kappa = 0.89) than those from the second half (Jul–Dec) (OA = 87% and kappa = 0.84). Poplar and other classes were separable, with poplar mostly found in riparian areas. The study identified variables such as short‐wave infrared‐1 (SWIR‐1), normalized difference vegetation index (NDVI), blue, poplar detection index‐1 (PI‐1), modified normalized difference water index (MNDWI), near‐infrared (NIR), and PI‐3 as key parameters for classifying poplar trees in mountainous regions. Overall, our findings demonstrate that Sentinel‐2 bands and indices combined with an RF classifier provide an effective method for mapping poplar invasive trees in mountainous ecosystems.

Ultra‐wideband (UWB) antennas play a crucial role in biomedical and microwave imaging applications because they provide accurate imaging and diagnostics with a wide frequency range, high resolution, and reliable signal integrity. This study presents a coplanar waveguide (CPW)‐fed Helm‐shaped printed antenna with UWB characteristics for biomedical and microwave imaging applications. The antenna is compact and has total dimensions of 30 × 25 mm 2 and demonstrates an impressive UWB with a fractional bandwidth (FBW) of 122.15%, covering the frequency range from 2.9 to 12 GHz. Additionally, it achieves a peak gain of 4.8 dBi @ 11 GHz and has stable radiation characteristics across the operating bandwidth. The proposed antenna is developed in the form of a prototype, and measured results are obtained using in‐house facilities. It is observed that the measured performance is agreed well with simulations. The time domain analysis of the proposed structure is also conducted, and it is observed that the group delay is <1 ns for both side‐by‐side (SbS) and face‐to‐face configurations. Through the obtained results, it is worth mentioning that the proposed antenna design is suitable for biomedical, microwave imaging, and hyperthermia treatment applications.

In this article, we present the fabrication and TCAD–based optimization of a p‐NiO/n‐β‐Ga 2 O 3 heterojunction deep ultraviolet (DUV) photodetector. Most previous studies focused on device fabrication and performance; by contrast, our study developed a calibrated simulation framework incorporating charge separation and recombination mechanisms for determining the optimal configuration. The fabricated device was characterized using current–voltage ( I – V ) and capacitance–voltage ( C – V ) measurements to evaluate its photoresponse performance. Based on the results, a calibrated TCAD model was developed and then used to systematically optimize key device parameters, including the doping concentration and thickness of the β‐Ga 2 O 3 epitaxial layer, as well as the design and placement of the electrodes. The optimized structure is that of a double electrode, exhibiting significantly enhanced photoresponse characteristics at an epitaxial doping concentration of 1 × 10 16 cm −3 and epitaxial thickness of 1 μm. Thus, the proposed photodetector design demonstrates a 4% improvement in photoresponse compared with that of the initial fabricated device. These findings provide valuable guidelines for the development of high‐performance and wide‐bandgap DUV photodetectors.

The implementation of advanced communication technologies with intelligent systems under Industry 5.0 demands ultralow‐latency 6G networks to support seamless human–machine connections. This study introduces SNN optimization for low‐latency 6G (SNN‐LL6G) Communication, which transforms delayed interactions into high‐speed communications suited for Industry 5.0 applications. The SNN‐LL6G methodology exists as an event‐based processing framework with spiking neural networks (SNNs) temporal dynamics for executing real‐time operations through low‐latency execution. Through spike‐timing‐dependent plasticity (STDP)‐based biologically‐inspired learning, the model develops real‐time adaptable strategies for communication path selection while optimizing resource utilization based on its acquired industrial frame experience. The efficiency of SNN operating systems is enhanced through federated learning (FL) strategies, which conduct model adaptations on local system storage to decrease transmission time delays. In addition, graph neural networks (GNNs) enhance the performance of 6G network optimization by applying routing strategy refinement to topological structures, thus reducing latency levels. The SNN‐LL6G method demonstrated experimental findings that proved its capability to reduce latency by 70%, which promotes improved network efficiency, better energy utilization, and strengthened network robustness.

Identifying objects in surveillance and reconnaissance systems with the human eye can be challenging, underscoring the growing importance of employing deep learning models for the recognition of enemy weapon systems. These systems, leveraging deep neural networks known for their strong performance in image recognition and classification, are currently under extensive research. However, it is crucial to acknowledge that surveillance and reconnaissance systems utilizing deep neural networks are susceptible to vulnerabilities posed by adversarial examples. While prior adversarial example research has mainly utilized publicly available internet data, there has been a significant absence of studies concerning adversarial attacks on data and models specific to real military scenarios. In this paper, we introduce an adversarial example designed for a binary classifier tasked with recognizing helicopters. Our approach generates an adversarial example that is misclassified by the model, despite appearing unproblematic to the human eye. To conduct our experiments, we gathered real attack and transport helicopters and employed TensorFlow as the machine learning library of choice. Our experimental findings demonstrate that the average attack success rate of the proposed method is 81.9%. Additionally, when epsilon is 0.4, the attack success rate is 90.1%. Before epsilon reaches 0.4, the attack success rate increases rapidly, and then we can see that epsilon increases little by little thereafter.

Realization of screen-printed disposable microchip based on organic membrane sensitive layer highly responsive to lead has been demonstrated for the first time. Fabrication, potentiometric characterization and analytical application of the novel microchip have been reported. A sensitive layer comprises TiO2 nanoparticles and multiwalled carbon nanotubes “MWCNTs” composite incorporated in PVC membrane has uploaded on the plastic screen-printed microelectrode substrate surface using novel protocol. The new chip provided a linear behavior for Pb2+ ions over the lead concentration range of 1 × 10−6–1 × 10−1 mole L−1 with super Nernstian sensitivity (49 mV), relatively long life span (>4 months), and a fast response time (10 s). The advantages showed by the microchip include simple fabrication, small size, mass production, cost effectiveness, and automation and integration feasibility. The realized new microchip has been successfully utilized in the quantification of some lead (II) samples with average recovery of 101.9% and the RDS was <3.