In this article, a compact 4-port UWB (Ultra-Wide Band) MIMO (Multiple Input Multiple Output) antenna is proposed. A low profile FR-4 substrate is used as a dielectric material with the dimensions of 58 × 58 mm² (0.52λ × 0.52λ) at 2.8 GHz and a standard thickness of 1.6 mm. The proposed design characterizes an impedance bandwidth starting from 2.8 to 12.1 GHz (124.1%). Each of the four elements of the proposed MIMO antenna configuration consists of a monopole antenna with PG (partial ground) that has a slot at its center. The corner of each patch (radiator) and ground slot are rounded for impedance matching. Each unit cell is in an orthogonal orientation, forming a quad-port MIMO antenna system. For reference, the partial ground of each unit cell is connected meticulously with the others. The simulated results of the proposed quad-port MIMO antenna design were configured and validated by fabrication and testing. The proposed Quad-port MIMO design has a 6.57 dBi peak gain and 97% radiation efficiency. The proposed design has good isolation below 15 dB in the lower frequency range and below 20 dB in the higher frequency range. The design has a measured ECC (Envelop Correlation Co-efficient) of 0.03 and DG (Diversity Gain) of 10 dB. The value of TARC (Total Active Reflection Coefficient) over the entire operating band is less than 10 dB. Moreover, the design maintained CCL (Channel Capacity Loss) < 0.4 bits/sec/Hz and MEG (Mean Effective Gain) < 3 dB. Based on the obtained results, the proposed design is suitable for the intended high data rate UWB wireless communication portable devices.

A wideband circularly polarized rectangular dielectric resonator antenna (DRA) fed by a single feeding mechanism has been studied theoretically and experimentally. The purpose of the study is to determine how adding a parasitic strip next to the flat surface metallic feed would affect various far- and near-field antenna characteristics. Initially, the basic antenna design, i.e., the T-shape feed known as antenna A, produced a 4.81% impedance matching bandwidth (|S₁₁| −10 dB). Due to the narrow and undesirable results of the initial antenna design, antenna-A was updated to the antenna-B design, i.e., Yagi-Uda. The antenna-B produced a decent result (7.89% S₁₁) as compared to antenna-A but still needed the bandwidth widened, for this, a parasitic patch was introduced next to the Yagi-Uda antenna on the rectangular DRA at an optimized location to further improve the results. This arrangement produced circular polarization (CP) waves spanning a broad bandwidth of 28.21% (3.59–3.44 GHz) and a broad impedance |S₁₁| bandwidth of around 29.74% (3.71–3.62 GHz). These findings show that, in addition to producing CP, parasite patches also cause the return loss to rise by a factor of almost three times when compared to results obtained with the Yagi-Uda-shape feed alone. Computer simulation technology was used for the simulation (CST-2017). The planned antenna geometry prototype was fabricated and measured. Performance indicators show that the suggested antenna is a good fit for 5G applications. The simulated outcomes and measurements match up reasonably.

Vector Quantization (VQ) is a classical block coding technique used for image compression which achieves high compression using simple encoding and decoding process. Codebook generation is an important factor in VQ design, which directly influences computational cost and the quality of the reconstructed image. Linde-Buzo-Gray (LBG) is considered as a state of art technique, which uses k-mean clustering algorithm for codebook design. Various optimization techniques are applied for searching the optimal codebook, such as Bat Algorithm (BA), Particle swarm optimization (PSO), and Firefly Algorithm (FA). These algorithm suffers mainly with high time consumption due to unavailability of the optimal solution in search space. This research proposes a novel approach, where peak values of the histogram are applied to predefined pattern masks to predict the image patterns for codebook design. From the experimental results, it is indicated that when compared with other algorithms, the proposed pattern based masking (PBM) algorithm requires fewer iterations and converges at a faster speed, particularly at the bitrates ≥0.375 without compromising on peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM).

Vector quantization (VQ) is a block coding method that is famous for its high compression ratio and simple encoder and decoder implementation. Linde–Buzo–Gray (LBG) is a renowned technique for VQ that uses a clustering-based approach for finding the optimum codebook. Numerous algorithms, such as Particle Swarm Optimization (PSO), the Cuckoo search algorithm (CS), bat algorithm, and firefly algorithm (FA), are used for codebook design. These algorithms are primarily focused on improving the image quality in terms of the PSNR and SSIM but use exhaustive searching to find the optimum codebook, which causes the computational time to be very high. In our study, our algorithm enhances LBG by minimizing the computational complexity by reducing the total number of comparisons among the codebook and training vectors using a match function. The input image is taken as a training vector at the encoder side, which is initialized with the random selection of the vectors from the input image. Rescaling using bilinear interpolation through the nearest neighborhood method is performed to reduce the comparison of the codebook with the training vector. The compressed image is first downsized by the encoder, which is then upscaled at the decoder side during decompression. Based on the results, it is demonstrated that the proposed method reduces the computational complexity by 50.2% compared to LBG and above 97% compared to the other LBG-based algorithms. Moreover, a 20% reduction in the memory size is also obtained, with no significant loss in the image quality compared to the LBG algorithm.

Received: 9 July 2025 | Revised: 27 August 2025 | Accepted: 30 August 2025 | Published Online: 15 September 2025 Our research focuses on IoT-integrated hydrogen fuel cells for clean energy in Inner Mongolia and Hebei, China. The system features a 100 kW PEMFC array (65–80 ℃, 98% pure H₂, 850 mbar) paired with an optimized AWS IoT Cloud using MQTT/LoRaWAN for 1 Hz data communication. AI-driven EMS optimizes operations (35–85% load, 30–80% battery charge), while predictive maintenance via Random Forests achieves 96% fault detection, maintaining 5 mΩ membrane resistance and 0.8% voltage deviation. The system consumes 4.1 kg/h of H₂, operates for 720 h (−25 ℃ to 38 ℃, 20–100 kW load), and reduces unscheduled downtime by 68%. Estimated results show 58% efficiency, 28% battery cycle reduction, and 814 t of annual CO₂ savings per unit. Advanced security (AES-256/TLS, blockchain) ensures data integrity, supporting clean energy access and industrial growth in remote regions. The proposed system holds significance in adding sustainable energy, but it is challenged by high initial cost, extreme range of weather adaptability, complications in scalability, and industrial acceptance. These challenges are addressed by feasible solutions, including the cost reduction through mass production, advanced hydrogen infrastructure development, data security with cybersecurity enhancements (AES-256/TLS, blockchain), and thermal management. The research paper aims to add a feasible and sustainable technology helping in achieving energy goals set for China’s futuristic industrial growth, and ensuring sustainable energy access in Inner Mongolia and the Hebei region.

With the requirement for new and effective procedures to improve crop cultivation, blending of modern technology, such as the Internet of Things (IoT), and ancient agricultural methods holds greater relevance. In the following study, the feasibility and importance of implementing an IoT-based smart crop cultivation and monitoring system are discussed. The smart agricultural technology will provide control, anti-toxic, and an optimum level of environment to yield crops in any remote or short on resources areas across the globe and beyond. The smart agricultural system enables professionals to manage, monitor, and analyze crop yields remotely with the help of an integrated mobile application. With limitations in natural resources and time, crop cultivation needs to be robust but safe and technologically empowered through smart agricultural systems such as the one we are presenting. The research work aims to mitigate the carbon footprint associated with conventional agriculture, add more precision to the crop yield by offering an optimized farming space, educate the farmers about advanced technology, and most importantly, enable quality crop cultivation in remote regions. With the help of IoT, the proposed procedure for crop cultivation gets precision in results, scalability for resources, remote control, and an optimized environment for cultivating multiple crops.