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Wireless power transfer (WPT) technologies are advancing rapidly, yet their development trajectories within specific regional contexts remain underexplored. This review synthesizes India's contributions to both near-field and far-field WPT research. We conducted a systematic literature survey spanning 2018-2024 to identify dominant technological themes, benchmark performance against global standards, and analyze innovation patterns within India's research ecosystem. The review reveals a consistent focus on robust, cost-effective, and context-appropriate designs across both domains. In near-field WPT, Indian research emphasizes misalignment-tolerant magnetic coupling and high-frequency power converters for applications including electric vehicle charging and biomedical implants. In far-field WPT, progress is evident in rectenna architectures that enhance angular coverage and efficiency, particularly for IoT networks. We consolidate quantitative performance metrics from the literature to establish reference benchmarks and delineate persistent research gaps. We propose a forward-looking research agenda aimed at aligning WPT innovation with India's sustainable development goals and energy accessibility challenges. This analysis provides a foundation for understanding how regional ecosystems shape technological priorities and offers insights for global WPT development.

Real-time affine transformation, a core operation for image correction and registration of industrial cameras and scanners, faces challenges including the high computational cost of interpolation and inefficient data access. In this study, we propose a reconfigurable accelerator architecture based on a heterogeneous system-on-chip (SoC). The architecture decouples tasks into control and data paths: the ARM core in the processing system (PS) handles parameter matrix generation and scheduling, whereas the FPGA-based acceleration module in programmable logic (PL) implements the proposed PATRM algorithm. By integrating multiplication-free design and affine matrix properties, PATRM adopts Q15.16 fixed-point computation and AXI4 burst transmission for efficient block data prefetching and pipelined processing. Experimental results demonstrate 25 frames per second (FPS) for 2095×2448 resolution images, representing a 128.21 M pixel/s throughput, which is 5.3× faster than the Block AT baseline with a peak signal-to-noise ratio (PSNR) exceeding 26 dB. Featuring low resource consumption and dynamic reconfigurability, the accelerator meets the real-time requirements of industrial scanner correction and other high-performance image processing tasks.

To address the accuracy calibration issue of the high-precision FMCW laser scanning measurement system for the large-aperture antenna of the Fengyun-4 microwave sounding satellite in orbit, this paper proposes a system calibration method based on space ranging correction. First, by analyzing the geometric structure and optical axis offset errors of the FMCW measurement system, a comprehensive error model comprising 13 key parameters was established. Second, a calibration field was constructed using a high-precision reference scale and planar targets. The spatial ranging correction method was employed to eliminate reliance on the accuracy of reference point coordinates inherent in traditional approaches, and nonlinear least-squares optimization was used to estimate the error parameters. Finally, a calibration scheme involving four operational conditions was implemented, with validation performed under three independent operational conditions. Experimental results show that the RMS error in relative distance between two points decreased from 17.5 mm to 2.3 mm after calibration. The ICP registration residual for the spatial point cloud was reduced to 2.5 mm, and point cloud shape fidelity improved by 86.6%. This validates the effectiveness and generalization capability of the proposed method. This research provides a reliable technical approach for spatial 3D calibration of lidar systems.

LiDAR point cloud semantic segmentation is pivotal for scan-to-BIM workflows; however, contemporary deep learning approaches remain constrained by their reliance on extensive annotated datasets, which are challenging to acquire in actual construction environments due to prohibitive labeling costs, structural occlusion, and sensor noise. This study proposes a BIM-guided Virtual-to-Real (V2R) framework that requires no real annotations. The method is trained entirely on a large synthetic point cloud (SPC) dataset consisting of 132 scans and approximately 8.75×109 points, generated directly from BIM models with component-level labels. A multi-feature fusion network combines the global contextual modeling of PCT with the local geometric encoding of PointNet++, producing robust representations across scales. A learnable point cloud augmentation module and multi-level domain adaptation strategies are incorporated to mitigate differences in noise, density, occlusion, and structural variation between synthetic and real scans. Experiments on real construction floors from high-rise residential buildings, together with the BIM-Net benchmark, show that the proposed method achieves 70.89% overall accuracy, 53.14% mean IoU, 69.67% mean accuracy, 54.75% FWIoU, and 59.66% Cohen's κ, consistently outperforming baseline models. The Fusion model achieves 73 of 80 best scene-metric results and 31 of 70 best component-level scores, demonstrating stable performance across the evaluated scenes and floors. These results confirm the effectiveness of BIM-generated SPC and indicate the potential of the V2R framework for BIM-reality updates and automated site monitoring within similar building contexts.

In recent years, there has been increasing investment in the deployment of massive commercial Low Earth Orbit (LEO) constellations to provide global Internet connectivity. These constellations, now equipped with inter-satellite links, can serve as low-latency Internet backbones, requiring LEO satellites to act not only as access nodes for ground stations, but also as in-orbit core routers. Due to their high velocity and the resulting frequent handovers of ground gateways, LEO networks highly stress mobility procedures at both the sender and receiver endpoints. On the other hand, a growing trend in networking is the use of technologies based on the Information Centric Networking (ICN) paradigm for servicing IoT networks and sensor networks in general, as its addressing, storage, and security mechanisms are usually a good match for IoT needs. Furthermore, ICN networks possess additional characteristics that are beneficial for the massive LEO scenario. For instance, the mobility of the receiver is helped by the inherent data-forwarding procedures in their architectures. However, the mobility of the senders remains an open problem. This paper proposes a comprehensive solution to the mobility problem for massive LEO constellations using the Named-Data Networking (NDN) architecture, as it is probably the most mature ICN proposal. Our solution includes a scalable method to relate content to ground gateways and a way to address traffic to the gateway that does not require cooperation from the network routing algorithm. Moreover, our solution works without requiring modifications to the actual NDN protocol itself, so it is easy to test and deploy. Our results indicate that, for long enough handover lengths, traffic losses are negligible even for ground stations with just one satellite in sight.

Pumped-storage power stations, as a critical resource for supporting secure and stable grid operation, typically adopt a 'single-tunnel-multiple-unit' configuration, where hydraulic disturbance becomes a key operating condition affecting system security. Existing studies have primarily focused on the impact of the hydro-mechanical subsystem on the normally operating units, while the influence of the electrical subsystem on hydraulic disturbance has been insufficiently addressed. To bridge this gap, this study develops a coupled model of a grid-connected pumped-storage power station incorporating a detailed representation of the power system. The model comprehensively captures the multi-domain interactions among the hydraulic, mechanical, electrical, and grid subsystems, and its accuracy is validated using data from a physical model test platform. On this basis, the hydraulic transient responses under two modeling conditions-detailed grid representation and conventional simplified grid modeling-are systematically compared. Key parameters from the hydraulic, mechanical, and electrical domains are further examined to quantify their impacts on the dynamic characteristics of hydraulic disturbance. The results demonstrate that detailed grid modeling reveals novel characteristics of the hydraulic disturbance that cannot be simulated by the conventional model. Under the detailed model, the normally operating units compensate for the power deficit caused by the tripping unit, leading to reduced hydraulic pressure fluctuations and a significant increase in the maximum output of the operating units. Meanwhile, hydro-mechanical parameters strongly influence the transient behaviors of unit output and net head, whereas the guide vane regulation of the operating unit remains predominantly driven by grid-frequency deviations. Overall, this study enhances the understanding of hydraulic disturbance dynamics in grid-connected pumped-storage systems and provides important insights for ensuring their secure and stable operation.

Diffuse optical imaging (DOI) uses scattered light to non-invasively sense and image highly diffuse media, including biological tissues such as the breast and brain. Despite its clinical potential, widespread adoption remains limited because physical constraints, limited available datasets, and conventional reconstruction algorithms struggle with the strongly nonlinear, ill-posed inverse problem posed by multiple photon scattering. We introduce Diffuse optical Imaging using Genetic Programming (DI-GP), a physics-guided and fully interpretable genetic programming framework for DOI. Grounded in the diffusion equation, DI-GP evolves closed-form symbolic mappings that enable fast and accurate 2-D reconstructions in strongly scattering media. Unlike deep neural networks, Genetic Programming (GP) naturally produces symbolic expressions, explicit rules, and transparent computational pipelines-an increasingly important capability as regulatory and high-stakes domains (e.g., FDA/EMA, medical imaging regulation) demand explainable and auditable AI systems, and where training data are often scarce. DI-GP delivers substantially faster inference and improved qualitative and quantitative reconstruction performance compared to analytical baselines. We validate the approach in both simulations and tabletop experiments, recovering targets without prior knowledge of shape or location at depths exceeding ~25 transport mean-free paths. Additional experiments demonstrate centimeter-scale imaging in tissue-like media, highlighting the promise of DI-GP for non-invasive deep-tissue imaging and its potential as a foundation for practical DOI systems.

The impressive advancements in semi-supervised learning have driven researchers to explore its potential in object detection tasks within the field of computer vision. Semi-Supervised Object Detection (SSOD) leverages a combination of a small labeled dataset and a larger, unlabeled dataset. This approach effectively reduces the dependence on large labeled datasets, which are often expensive and time-consuming to obtain. Initially, SSOD models encountered challenges in effectively leveraging unlabeled data and managing noise in generated pseudo-labels for unlabeled data. However, numerous recent advancements have addressed these issues, resulting in substantial improvements in SSOD performance. This paper presents a comprehensive review of 28 cutting-edge developments in SSOD methodologies, from Convolutional Neural Networks (CNNs) to Transformers. We delve into the core components of semi-supervised learning and its integration into object detection frameworks, covering data augmentation techniques, pseudo-labeling strategies, consistency regularization, and adversarial training methods. Furthermore, we conduct a comparative analysis of various SSOD models, evaluating their performance and architectural differences. We aim to ignite further research interest in overcoming existing challenges and exploring new directions in semi-supervised learning for object detection.

The rising usage of IoT devices in everyday life has formed smart cities that require the adoption of decentralized systems for a secure and transparent mechanism to manage asset exchange across automotive supply chains. Several existing Blockchain-based models built on public chains focus on traceability while overlooking scalability limits, transaction fees, conditional payment trust, or real-time delivery validation. We introduce DeFiTrustChain, a DeFi-enabled framework that combines free NFTs, escrow-based automation, and IoT verification within a Hyperledger Fabric network. It represents each vehicle using a unique NFT to capture the details of manufacturing and ownership, along with immutable asset verification. The payment release between stakeholders is governed by a dedicated escrow contract responsible for IoT-based delivery confirmation. The proposed framework ensures authenticated access and prevents identity misuse through integration of the Fabric Certificate Authority. The experimental results demonstrate the coherent and dependable execution of NFT creation, escrow enforcement, and IoT-triggered validation, with low local transaction processing time and consistent behavior across peers.

Cycling-based rehabilitation is a non-invasive intervention for individuals with lower limb asymmetries. However, current cycling devices lack comprehensive biomechanical feedback and cannot assess asymmetry. Our lab has developed a novel cycle ergometer equipped with three-dimensional force pedals, a seat post and handlebar force sensors, which allow for a comprehensive analysis of asymmetry across a fatiguing task. This study assessed the reproducibility of the cycling kinetics and asymmetry index derived from this device during incremental and constant load cycling tasks to volitional failure. Eighteen participants completed incremental and constant-load tests, each across two identical sessions. Pedal forces and power were analyzed for each leg individually, and handlebar forces and seat post mediolateral sway were recorded during cycling. Normalized symmetry index (NSI), a metric quantifying the degree of asymmetry between limbs, was calculated for each variable. The reproducibility of the device was assessed using repeated measures analysis of variance and intraclass correlation coefficients (ICC). No significant session or interaction effects were found for pedal, handlebar, and seat post measures (all p > 0.05). Time effects were observed for pedal force and power in the incremental test (all p < 0.001). NSI values were reproducible with high ICC values (≥0.70) for force and power. The results suggest that this ergometer offers reproducible cycling kinetics and asymmetry measures across a fatiguing task. The findings support the application of this ergometer in research and rehabilitation settings.