This study introduces an innovative and compact wearable Long-Range (LoRa) patch antenna developed for monitoring vital signs, with a focus on heart rate and body temperature, in medical applications of Wireless Body Area Networks (WBAN). The antenna functions within the 868 MHz and 915 MHz LoRa bands, filling a notable gap in current literature regarding compact, wearable antennas operating below 1 GHz. Fabricated on a Rogers Duroid RO3003™ substrate, the antenna incorporates a U-slot on a conventional rectangular patch, a matching stub, and a partial ground plane to enhance impedance matching and performance efficiency. Furthermore, the antenna displays a bidirectional radiation pattern in the E -plane and an omnidirectional pattern in the H -plane at both frequencies, achieving a peak gain of 2.12 dBi and a radiation efficiency of 99.8% at 868 MHz. The antenna, measuring 80×60mm ² (0.23 λ ₀ × 0.17 λ ₀ ), was designed, simulated, and optimized using CST Microwave Studio (MWS) software. The performance under bending conditions was also assessed, revealing a bending an excellent efficiency with minimal impact on bandwidth and gain. Specific Absorption Rate (SAR) analysis indicated that all values were within the safety limits set by FCC and ICNIRP standards. Supervised regression machine learning models, specifically the ensemble regression model, were employed to predict resonance frequencies based on various antenna parameters, resulting in an R-squared score of 87.68%. This approach significantly reduced the computational time required for full-wave simulations, streamlining the design process. Real-world experimental validation involved open-field testing of the fabricated prototype for WBAN LoRa applications. The performance, evaluated on a LoRa transceiver system utilizing the LoRa SX1276, demonstrated the superior capabilities of the proposed antenna in heart rate and temperature monitoring, with an average RSSI improvement of -5 dBm at various points within a range of up to 1 km. This confirmed its improved signal transmission and reception capabilities in vital sign monitoring. The proposed antenna shows strong performance metrics and significant potential for WBAN in long-range applications, as evidenced by thorough experimental validations.

Gastrointestinal (GI) tract diseases are among the most common diseases in the world, resulting in more than 8 million deaths. The majority of these deaths occur due to cancer or tumors. Early detection of these tumors can greatly lower the mortality rate. In this work, an implantable multiple-input-multiple-output (MIMO) antenna sensor is constructed for GI tract devices to detect the tumor. The implantable MIMO antenna sensor has two embedded antennas, each operating at 915 MHz. Both elements of the system are placed 0.6 mm apart from each other (edge-to-edge). The volume consumed by this design is measured to be 7 × 7 × 0.25 = 12.25 mm³. It occupies a very small volume due to miniaturization achieved using meandered resonating structures and a high-permittivity substrate. It maintains stable radiation performance (gain = −26.2 dBi at resonance). The antenna units are decoupled by maintaining a proper gap between them and adding a slot on the bottom side. An isolation level greater than 28.7 dB is achieved using these approaches. Since the MIMO system utilizes two antenna elements, its effectiveness is verified using MIMO parameters. At SNR = 20 dB, the channel capacity reaches 8.75 bps/Hz. The proposed antenna ensures high channel capacity and enables seamless communication while simultaneously acting as a sensor to monitor internal changes in the observed region. The frequency response change with variations in the permittivity of human tissue, enabling its sensing capability. Moreover, the antenna sensor maintains stable radiation and S-parameter performance throughout the sensing mechanism. Thus, the proposed solution is suitable for biomedical implants requiring both high-data-rate communication and sensing.

Skin cancer is the dangerous life‐threatening disease and is increasing rapidly due to environmental changes and lifestyle factors. The treatment for skin cancer is possible if detected at an early stage, but if not detected early, the possibility of successful treatment is reduced. Dermatologists use visual examinations to detect the type of skin cancer. However, this method is time‐consuming, requires high skill, and has limited accuracy, especially at early stages when melanoma and benign lesions look similar and are difficult to differentiate. In recent years, deep learning (DL) has gained popularity in image processing by producing excellent results. This research proposes a novel hybrid DL model, dual path ConvNeXt‐Tiny–vision transformer (ViT) with effective feature refinement. ConvNeXt‐Tiny extracts spatial and local features, while ViT extracts global contextual features. Feature refinement fuses all the features and selects the most important ones for classification. Two public datasets, ISIC‐2019 and HAM‐10000, are employed to evaluate the proposed network. The collected dataset is affected by class imbalance. To address this issue, a novel variational autoencoder generative adversarial network (VAE–GAN) network is introduced, which improves the quality of generated images and enhances classification accuracy. The proposed network is compared with other DL models, including AlexNet, DenseNet, and Transformer. The results show that the proposed network outperforms other DL models, achieving 99.38% accuracy on ISIC‐2019 and 99% on HAM‐10000. To assess the significance of the proposed system, an ablation study is conducted. The experimental outcomes of the ablation study highlight the effectiveness of the proposed network. Additionally, comparisons with state‐of‐the‐art (SOTA) methods are performed. After extensive evaluation, the proposed network is finalized and deployed on a field‐programmable gate array (FPGA) to serve as a low‐power, high‐speed edge device. A hardware accelerator is designed with high processing efficiency, optimized data transfer, dual cache, and efficient scheduling. The FPGA’s performance is evaluated using metrics such as power consumption, inference time, and throughput, and it is compared with GPU and CPU performance. The proposed accelerator classifies skin cancer 6.88× faster than a CPU and 2.29× faster than a GPU, while consuming only 2.8 W of power. The results demonstrate that the proposed FPGA‐based DL network is suitable for real‐time skin cancer classification, offering high speed, maximum accuracy, and low power consumption.

In this work, the design and experimental validation of passive UHF RFID tag antennas are presented with the objective of evaluating the impact of chip placement and miniaturization approaches on tag performance. Four initial antenna layouts were developed by varying the position of the RFID integrated circuit within a coupling loop. The results show that chip placement directly affects the coupling-loop efficiency, the antenna–chip matching condition, and the practical tolerance of the structure to fabrication-related variations. Simulations and measurements identified Antenna 1 as the best-performing reference configuration, exhibiting the most favorable impedance behavior around 866 MHz and a measured power sensitivity of $-16.3$ dBm. Based on this reference design, a miniaturized version (Antenna 5) was obtained by integrating meander lines and capacitive end-loading, reducing the physical size while maintaining resonance at 866 MHz. Both structures were fabricated and evaluated using a Voyantic Tagformance measurement system, with read-range measurements performed under free-space conditions and in proximity to dielectric and conductive materials. The results demonstrate a maximum read range of $8.6$ m for Antenna 1 in free space, while Antenna 5 preserved a read range of $6.3$ m. In the presence of copper, Antenna 1 maintained a read range of 3 m, whereas Antenna 5 achieved approximately $0.5$ m, highlighting the trade-off between miniaturization and robustness under conductive loading.

In this work, the design and experimental validation of passive UHF RFID tag antennas are presented with the objective of evaluating the impact of chip placement and miniaturization approaches on tag performance. Four initial antenna layouts were developed by varying the position of the RFID integrated circuit within a coupling loop. Simulations and measurements confirmed that Antenna 1 achieved the best impedance matching, with a minimum reflection coefficient of −40 dB at 866 MHz and a power sensitivity of −16.3 dBm. Based on this reference design, a miniaturized version (Antenna 5) was obtained by integrating meander lines and capacitive end-loading, reducing the physical size while maintaining resonance at 866 MHz. Both structures were fabricated and evaluated using a Voyantic Tagformance measurement system, with read-range measurements performed under freespace conditions and in proximity to dielectric and metallic materials. The results demonstrated a maximum read range of 8.6 m for Antenna 1 in free space, while Antenna 5 preserved a read range of 6.3 m. In the presence of copper, Antenna 1 maintained a read range of 3 m, whereas Antenna 5 achieved approximately 0.5 m, confirming the robustness of the proposed designs in representative industrial environments.