Kolmogorov-Arnold Networks (KANs) have recently gained increasing attention as an alternative to conventional neural architectures, mainly because they replace fixed activation functions with learnable univariate mappings defined along network edges. This design not only increases modeling flexibility but also makes it easier to interpret how inputs are transformed within the network while maintaining parameter efficiency. KANs are particularly well suited for sensor-driven systems where transparency, robustness, and computational constraints are critical. This study provides a survey of KAN-based approaches for processing sensor data. A literature review conducted from 2024 to 2026 examined the deployment of KAN models in industrial and mechanical sensing, medical and biomedical sensing, and remote sensing and environmental monitoring, utilizing a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based methodology. We first revisit the theoretical foundations of KANs and their main architectural variants, including spline-based, polynomial-based, monotonic, and hybrid formulations, to structure the discussion. From a practical standpoint, we then examine how KAN modules are integrated into modern deep learning pipelines, such as convolutional, recurrent, transformer-based, graph-based, and physics-informed architectures. KAN-based models demonstrate comparable predictive performance as conventional machine learning models, while having fewer parameters and more interpretable representations. Several limitations persist, including computational overhead, sensitivity to noisy signals, and resource-constrained device deployment challenges. Real-world sensor systems encounter significant challenges in adopting KAN-based models, including scalability in large-scale sensor networks, integration with hardware architectures, automated model development, resilience to out-of-distribution conditions, and the need for standardized evaluation metrics. Collectively, these observations provide a clearer understanding of the current and potential limitations of KAN-based models, offering practical guidance on the development of interpretable and efficient learning systems for future sensor equipment applications.
Three-Dimensional (3D) face reconstruction from monocular Red-Green-Blue (RGB) imagery remains a fundamental yet ill-posed challenge in computer vision, with applications in biometrics, augmented reality/virtual reality (AR/VR), and intelligent visual sensing systems. While deep learning has significantly improved reconstruction fidelity and realism, existing surveys primarily focus on network architectures in isolation, often overlooking how sensing conditions, data acquisition protocols, and geometric calibration influence reconstruction reliability and evaluation outcomes. This paper presents a sensor-aware, end-to-end review of deep learning-based 3D face reconstruction and introduces a unified modular framework that connects sensing hardware, data acquisition, calibration, representation learning, and geometric refinement within a coherent pipeline. The reconstruction process is organized into four stages: sensor-driven acquisition and calibration, landmark estimation and feature extraction, 3D representation and parameter regression, and iterative refinement via differentiable rendering. Within this framework, we examine how sensor characteristics, calibration accuracy, representation models, and supervision strategies affect reconstruction accuracy, perceptual quality, robustness, and computational efficiency. We further synthesize the reported results across widely used benchmarks using both geometric and perceptual metrics, highlighting trade-offs between reconstruction fidelity and deployment constraints. By integrating sensing-aware analysis with architectural evaluation, this survey provides practical insights for developing scalable and reliable 3D face reconstruction systems under real-world conditions.
Measurement of material thermodynamic parameters plays a crucial role in understanding the interactions between host materials and guest species. Therefore, developing a general-purpose system for thermodynamic parameter measurement is of great significance. In this work, a complete gas-solid interface thermodynamic parameter measurement platform was developed based on isothermal adsorption and a resonant microcantilever testing platform. Unlike conventional adsorption measurement systems that rely on manual, multi-cycle adsorption-desorption processes, the proposed platform integrates an automated hardware-software architecture together with a stepwise concentration-gradient protocol and on-chip thermal desorption, enabling continuous and efficient acquisition of adsorption isotherms. The study includes: (i) construction of an improved thermodynamic parameter extraction model based on the Sips model, (ii) development of an integrated resonant microcantilever control and acquisition module using a modified Fourier algorithm, and (iii) implementation of an automated testing and data analysis software framework developed in LabVIEW based on the Queued Message Handler (QMH) architecture. The system was validated from both hardware performance and material testing perspectives using CO2 adsorption on H-SSZ-13 as a representative case. The results show that the system achieves a maximum sampling rate of 10,000 pts (points per second), with minimum root-mean-square (RMS) noise levels of 0.0083 Hz for frequency and 0.0109 °C for temperature. The PID temperature-control settling time (0.1%) is 24.9 ms, and the frequency-response settling time (0.01%) is 9.6 ms. Thermodynamic parameters including entropy change (ΔS), enthalpy change (ΔH), and Gibbs free energy change (ΔG) were successfully extracted during CO2 adsorption at 294.15 K under different relative uptakes. Reproducibility was verified across three independent samples, yielding a standard deviation of 9.1 J·mol-1 for ΔS at 2% relative uptake and relative standard deviations of 6.85% and 8.12% for ΔH and ΔG, respectively. These results demonstrate that the proposed thermodynamic measurement platform features a simple architecture, superior performance, and high reproducibility in gas-solid interface thermodynamic studies, showing strong potential for future commercialization.
Deep brain stimulation has demonstrated efficacy in treating various neurological disorders. However, its invasiveness and the associated surgical risks have motivated noninvasive approaches that can selectively modulate deep targets. Conventional transcranial electrical stimulation techniques, however, have limited capability to reach deep brain regions with high spatial focality. Temporal interference stimulation (TIS) has emerged as a promising solution to overcome these challenges, using two slightly different high-frequency carriers to generate a low-frequency envelope with improved spatial focality in tissue. Currently, TIS is being extensively validated in rodent models and has been expanded to studies using cadaveric human heads and clinical trials for various neurological disorders. However, the precision and safety of TIS strongly depends on the underlying hardware implementation. Therefore, a systematic understanding of circuit and system design is required for practical device development. This paper covers comprehensive hardware design considerations and implementation strategies for TIS devices. Major TIS waveform schemes are categorized and their impact on system complexity, channel synchronization, and stimulation performance is analyzed. For the output stage architecture, various circuit topologies are discussed regarding their voltage compliance and current driving capability. In addition, essential safety features, including charge balancing techniques and impedance monitoring methods tailored to TIS operation are reviewed. Finally, experimental validation approaches using tissue phantoms are summarized to provide guidelines for developing robust and reliable TIS systems.
CubeSats have emerged as an enabling technology for a new generation of space missions, offering relatively low cost and rapid development opportunities for scientific, educational, and technology demonstration. The reliable operation of a CubeSat depends critically on its electrical power system (EPS), which serves as the primary energy backbone for all onboard subsystems and payloads. Several studies have indicated that EPS as a subsystem is most susceptible to failure in the satellite missions, as it is under tight power, volume, and reliability constraints exposed to harsh and variable orbital conditions. In this context, this paper proposes the design, hardware implementation and experimental validation of an autonomous EPS architecture to enhance the operational lifetime of CubeSats. The proposed EPS autonomously manages energy to deal with solar irradiance variations in the low earth orbit (LEO). It combines maximum power point tracking (MPPT), battery management system (BMS), regulated power distribution and sensor based telemetry based on a microcontroller unit (MCU) controlled system. A real time decision making algorithm autonomously monitors the photovoltaic (PV) array voltage. The supervisory algorithm implements a three mode graduated control strategy, i.e. normal, moderate, and power-down. It is governed by two discrete PV voltage thresholds, enabling more precise and graduated load management compared to binary single threshold schemes reported in prior work. When nominal irradiance level is regained, the system returns autonomously to desired operational mode with no interference needed from the ground station. The modular design of the system allows to upgrade its components easily without the need to redevelop entire architecture. A detailed power budget analysis yields a total system load of 1,460mW, divided between telemetry (22mW), payload (1,400mW) and communication subsystems (38mW). The deployed EPS uses a Li-ion 3-cell battery pack (11.1V, each cell 1,800mAh) and PV panels of (12V, 2,100mW). Experimental tests validate the acquisition of data from various sensors and importantly accurate mode transition from normal to power down mode and vice-versa under fluctuating irradiance conditions. Furthermore, dynamic experiments involving controlled variation of PV voltage are also conducted to evaluate both degradation and recovery behavior of the system. The results demonstrate stable, repeatable, and threshold consistent mode transitions under varying input power scenarios. The results collectively demonstrate that autonomous mode transition and load management can be achieved using low cost commercial off the shelf components (COTS), making the proposed EPS a practical, reproducible, and scalable testbed for academic and small mission CubeSat platforms.
Background/Objectives: Magnetic nanoparticles have emerged as powerful tools for biomedical imaging, targeted drug delivery, and hyperthermia therapy. Magnetic particle imaging (MPI) is among the most promising technologies built around its properties: a radiation-free, quantitative tomographic modality that detects superparamagnetic iron oxide nanoparticles (SPIONs) directly against a biologically silent background. This review synthesizes MPI's physical principles, nanoparticle design strategies, and preclinical applications within the broader landscape of magnetic material engineering for biomedical use. Methods: A systematic review was conducted covering MPI signal generation and image reconstruction, nanoparticle core synthesis and surface coating approaches, and preclinical applications, spanning cell tracking, oncological imaging, vascular perfusion, neuroimaging, and MPI-guided theranostics. Studies were selected to provide quantitative benchmarks and direct comparisons with competing modalities where available. Results: MPI delivers signal-to-background ratios above 1000:1, iron-mass linearity at R2 ≥ 0.99, regardless of tissue depth, and acquisition rates up to 46 volumes per second. Tracer architecture-encompassing single-core particles, multicore nanoflowers, and stimuli-responsive cluster designs-is the primary determinant of sensitivity, environmental robustness, and theranostic capability. Preclinical results include detection of cell populations in the low thousands, earlier ischaemia identification than diffusion-weighted MRI, real-time drug release quantification, and spatially confined tumour hyperthermia. Three translational bottlenecks are identified: the absence of a clinically approved tracer with optimal relaxation dynamics, hardware performance losses when scaling to human-bore systems, and overestimation of passive tumour accumulation in murine models. Conclusions: MPI illustrates how progress in magnetic material design directly expands clinical imaging and theranostic possibilities. Successful translation will require indication-driven, interdisciplinary development that integrates materials science, scanner engineering, and regulatory strategy in parallel.
To meet the future demands of high-rate transmission and full-coverage networks, radio frequency-underwater wireless optical communication (RF-UWOC) relaying systems are considered a promising heterogeneous communication architecture. The rate-splitting (RS) scheme, through its power allocation (PA) mechanism, provides a generalized framework for the performance evaluation of such systems. Based on this, this paper analyzes the performance of an RS-based RF-UWOC system under hardware impairments (HIs) and interference. Analytical expressions of the outage probability (OP) and ergodic capacity (EC) for the considered system are formulated within a generalized framework, which encompasses the conventional RF-UWOC system as a special case. The results indicate that the OP and EC are affected by HIs, interference transmit power, the PA coefficients, channel fading, pointing errors (PEs), and detection types of the UWOC link. Furthermore, the asymptotic results for the OP and the diversity gain (DG) are explicitly characterized. For a fixed interference transmit power, the DG is mainly dominated by the channel fading severity, PEs effect, and the detection scheme. When the interference transmit power is comparable to the desired signal power, the system operates in an interference-limited regime, and the DG decreases to zero. It is also revealed that HIs and PA coefficients affect the coding gain but not the DG. Moreover, the existence of an optimal PA scheme improves the reliability of the RS-based system.
Early-stage detection of cancer is a key factor for successful treatment and improved survival. Yet, current screening approaches are often invasive, expensive, and limited to single biomarkers, which constrains their applicability at a global scale. Breath analysis offers a promising, non-invasive alternative capable of detecting multiple cancer biomarkers simultaneously, with potential to reduce diagnostic inequality across low- and high-income countries. Among available technologies, electronic noses (E-noses) have emerged as powerful platforms for detecting volatile organic compounds (VOCs) in exhaled breath. This review critically discusses advances in metal oxide (MOX)-based E-nose systems, highlighting the transition from individual sensors to integrated multisensor array chip (MSAC) architectures, advanced signal conditioning, and machine-learning (ML)-assisted data analysis pipelines. The working principle of ML-assisted MOX E-noses, including sensor array response acquisition, feature extraction, dimensionality reduction, and classification of complex VOC mixtures, is systematically analyzed, with particular attention to drift, cross-sensitivity, and real-world variability. Distinct from prior reviews, this work integrates a systematic analysis of cancer-related VOCs with recent breakthroughs (2025-2026) in hybrid sensing modalities, hardware-level drift mitigation, and clinical translation barriers. By bridging material-level innovations with system-level performance metrics and real-world deployment challenges, this work provides a critical framework for the development of reliable, scalable, and real-time E-nose technologies for multicancer diagnostics.
The advent of next-generation sequencing (NGS) has revolutionized genomic research by enabling cost-effective, high-throughput sequencing of a diverse range of organisms. This breakthrough has unleashed a "Cambrian explosion" in genomic data volume and diversity. This volume of workloads places genomics among the top four big data challenges anticipated for this decade. In this context, pairwise sequence alignment represents a very time- and energy-intensive step in common bioinformatics pipelines. Speeding up these computations requires the implementation of heuristic approaches, optimized algorithms, and/or hardware acceleration. Among the metrics used in sequence comparison, edit distance is an adopted measure of sequence similarity. Although state-of-the-art CPU and GPU implementations have demonstrated significant performance gains, recent FPGA implementations have shown improved energy efficiency. However, the latter often suffer from limited read-length scalability due to constraints on hardware resources, with some reported designs supporting comparison matrices for sequences of only up to 227 nucleotides. In this work, we present a flexible FPGA-based accelerator template that implements Myers's algorithm to compute exact unit-cost edit-distance up to 1000 bp using high-level synthesis and a worker-based architecture. GeneTEK, a set of instances of this accelerator template in a Xilinx Zynq UltraScale+ FPGA, achieves up to 113% increase in execution speed and up to 111× reduction in energy consumption compared to leading CPU and GPU solutions, while fitting comparison matrices up to 13× larger than previous FPGA-based systolic-array solutions. By following a SW-HW co-design approach, GeneTEK implements efficient memory access and exploits parallelization at multiple levels. These results reaffirm the potential of FPGAs as an energy-efficient platform for computing the exact unit-cost edit distance used in sequence comparisons of read-lengths up to 1000 bp.
As global interconnectivity continues to intensify across digital and physical infrastructures, the pursuit of sophisticated hardware-level security mechanisms that seamlessly intertwine these domains has become increasingly vital. Physically unclonable functions (PUFs) have emerged as intrinsic identifiers that exploit unavoidable physical variations to ensure authenticity and tamper resistance. Early generations of PUFs-implemented through single-mode architectures such as electrical or optical configurations-demonstrated the foundational potential of device-intrinsic randomness for secure authentication. Electrical PUFs capitalize on stochastic charge transport and interface disorder, while optical PUFs harness complex light-matter interactions to achieve high entropy and physical uniqueness. Building upon these single-domain systems, recent advances have driven the evolution toward multidimensional and reconfigurable PUFs, integrating multiple transduction pathways and tunable material responses. Such hybrid architectures expand the challenge-response landscape, enhance adaptability under varying conditions, and enable programable security characteristics. This review traces the progression from conventional single-domain PUFs to emerging multidimensional systems, highlighting advances in materials, device integration, and adaptive design. Finally, we discuss persisting limitations and outline prospects for developing intelligent, scalable, and resilient PUF platforms for next-generation cyber-physical networks.
This paper addresses the critical communication barrier experienced by deaf and hearing-impaired individuals in the Arab world through the development of an affordable, video-based Arabic Sign Language (ArSL) recognition system. Designed for broad accessibility, the system eliminates specialized hardware by leveraging standard mobile or laptop cameras. Our methodology employs Mediapipe for real-time extraction of hand, face, and pose landmarks from video streams. These anatomical features are then processed by a hybrid deep learning model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), specifically Bidirectional Long Short-Term Memory (BiLSTM) layers. The CNN component captures spatial features, such as intricate hand shapes and body movements, within individual frames. Concurrently, BiLSTMs model long-term temporal dependencies and motion trajectories across consecutive frames. This integrated CNN-BiLSTM architecture is critical for generating a comprehensive spatiotemporal representation, enabling accurate differentiation of complex signs where meaning relies on both static gestures and dynamic transitions, thus preventing misclassification that CNN-only or RNN-only models would incur. Rigorously evaluated on the author-created JUST-SL dataset and the publicly available KArSL dataset, the system achieved 96% overall accuracy for JUST-SL and an impressive 99% for KArSL. These results demonstrate the system's superior accuracy compared to previous research, particularly for recognizing full Arabic words, thereby significantly enhancing communication accessibility for the deaf and hearing-impaired community.
We present an experimentally validated measurement-domain steganography framework based on orthogonal ghost imaging (OGI). In the proposed scheme, a compact single-pixel OGI front-end with orthogonal illumination patterns generates the cover measurements, while a hash-initialized logistic map drives a chaotic keystream that is embedded in the least significant bits (LSBs) of the bucket intensities. This integration into a single optical-digital pipeline avoids holographic components, deep-learning-based reconstruction, and multi-channel architectures, and keeps both the hardware and computational complexity moderate enough for resource-constrained optical platforms. A combination of numerical simulations and single-pixel optical experiments shows that the framework supports imperceptible embedding with stable statistical behaviour. Under the reported operating conditions, the stego ghost images retain PSNR values above 50 dB, SSIM exceeding 0.996, and NPCR close to 99.98%, while intensity histograms, pixel correlations, higher-order moments, and chi-square statistics of the LSB plane remain almost unchanged. These results indicate that chaos-encrypted, bucket-domain LSB modulation can be effectively hidden within the speckle-like statistics and correlation-based reconstruction of OGI, allowing reliable message recovery without visible artifacts while exhibiting statistical consistency with basic steganalysis tests in the OGI domain. Overall, the proposed approach provides a proof-of-concept route to measurement-domain information hiding with hardware-generated covers. Owing to the use of a compact single-pixel architecture and lightweight digital processing, the framework suggests potential relevance to optical scenarios in which system complexity and processing resources are constrained. These considerations are based on architectural simplicity rather than measured power, volume, or embedded runtime metrics. A full system-level implementation and experimental validation on specific embedded or mobile platforms remain beyond the scope of this work and are left for future investigation.
The increasing energy and bandwidth demand of modern AI workloads highlight the need for hardware that mitigates the data-movement bottleneck of von Neumann architectures. Compute-in-memory and neuromorphic systems offer a compelling solution, yet reliable multi-tier 3D integration of analog synaptic devices remains challenging. Here, we report a monolithic 3D (M3D) integration platform that vertically stacks In-Ga-Zn-O (IGZO) access transistors and Hf0.5Zr0.5O (HZO)-based ferroelectric transistors to realize compact, energy-efficient neuromorphic hardware. Two-tier and four-tier IGZO/ferroelectric field effect transistors (FeFET) architectures were fabricated with excellent structural integrity, uniform elemental profiles, and preserved orthorhombic HZO ferroelectricity across all tiers. The devices exhibit reproducible switching, >10-year retention, endurance up to 101 1 cycles, and stable multilevel conductance states suitable for synaptic computing. Mapping device characteristics to a convolutional neural network (CNN) for Canadian Institute for Advanced Research (CIFAR-10) inference yields 95.0% (tier-2) and 95.5% (tier-4) accuracy, approaching the 96.1% software baseline. Analog-domain convolution was further demonstrated by encoding kernel weights into FeFET conductance states for edge-aware image processing. These results establish M3D-integrated FeFETs as a scalable and reliable platform for next-generation compute-in-memory and neuromorphic vision applications.
Deep learning models for medical image analysis often rely on large-scale parameterization, which may limit their practical use in resource-constrained settings. This study aims to design a structurally compact multi-source framework capable of delivering competitive diagnostic performance with reduced computational overhead. We propose ML-ConvNet, a lightweight architecture comprising approximately 4.2 K parameters and 924 M FLOPs at 512×512 input resolution. The network incorporates Multi-Branch Re-parameterized Convolutions for scale-aware feature extraction, Hierarchical Dual-Path Attention for feature localization, Feature Self- Transformation for cross-feature interaction, and a Local Variance Weighted optimization strategy to address class imbalance. The framework is evaluated independently on three publicly available benchmark datasets representing heterogeneous imaging modalities: brain MRI, lung CT, and chest X-ray. Ablation studies, precision-recall analysis, cross-modality validation, and computational benchmarking are conducted to assess performance, stability, and efficiency under controlled experimental conditions. Within the evaluated settings, results indicate competitive diagnostic accuracy relative to established lightweight baselines, including EfficientNet and MobileNet variants, while substantially reducing parameter count. Class-wise F1-scores and PR-AUC values suggest relatively stable minority-class performance under repeated cross-validation sampling. Attention visualizations show activations concentrated over regions broadly associated with pathological findings, though these observations are qualitative in nature. Inference latency measurements on CPU and mobile hardware suggest feasibility for low-latency deployment under the tested single-image batch configurations, though real-world throughput may differ depending on hardware and operational conditions. These findings suggest that careful architectural design and domain-informed inductive biases may support competitive medical image classification on public benchmark datasets without extensive parameter scaling. The framework was evaluated exclusively under controlled conditions on publicly available data, and multi-institutional external validation is required before conclusions regarding generalizability or clinical applicability can be drawn.
Spiking Neural Networks (SNNs) executed on neuromorphic hardware promise energyefficient, low-latency inference well-suited to edge deployment in size, weight, and powerconstrained environments such as autonomous vehicles, wearable devices, and unmanned aerial platforms. However, a coherent research pathway to deployment of neuromorphic devices remains elusive. This paper presents a structured review and position on the state of SNN-based vision across four interconnected dimensions: network architectures, training methodologies, event-based datasets and simulation techniques, and neuromorphic computing hardware. We survey the evolution from shallow convolutional SNNs to spiking Transformers and hybrid designs which leverage the advantages of SNNs and conventional artificial neural networks. We also examine surrogate gradient training and ANN-to-SNN conversion approaches, catalogue real-world and simulated event-based datasets, and assess the landscape of neuromorphic platforms ranging from rigid mixed-signal architectures to fully-configurable digital systems. Our analysis reveals that while each area has matured considerably in isolation, critical integration challenges persist. In particular, event-based datasets remain scarce and lack standardisation, training methodologies introduce systematic gaps relative to deployment hardware, and access to neuromorphic platforms is restricted by proprietary toolchains and limited development kit availability. We conclude that bridging these integration gaps, rather than advancing individual components alone, represents the most important and least addressed work required to realise the potential of SNN-based vision at the edge.
We demonstrate how the natural amplification of defect signatures via Fresnel diffraction can be harnessed for optical detection without imaging or lenses. In this work, we use the diffraction pattern itself as the detection domain, establishing a direct pathway from physical wavefront modulation to defect detection tasks that bypasses conventional image formation. In this paradigm, subtle defects are naturally amplified into distinctive diffraction fringes during propagation, a physical expansion that enhances their detectability without lenses. Computationally, we focus this scattered field using only a single inverse diffraction step, which relocalizes the expanded signal into a sharp saliency map at the defect site. This tight coupling of physical expansion and computational localization eliminates the need for imaging optics, phase retrieval or annotated data and enables a simple and low cost architecture with inherently high reliability. Because the detection operates directly on diffraction phenomena rather than reconstructed images, the method achieves fast processing and maintains strong sensitivity to phase type defects. Experimental and numerical results on semiconductor wafers and display panels at visible wavelengths under strong noise (σ2 = 0.05) and extremely low contrast (C = 0.005) show that the method reliably detects defects at the scale of the working wavelength level. Under bandwidth and sampling constraints, the underlying diffraction model can be transferred naturally to the near infrared and even the extreme ultraviolet, which indicates strong potential for spectral scalability. These results position diffraction saliency as a scalable framework with simple hardware implementation for inline inspection in complex environments.
Spread spectrum is a leading solution for covert communications, but is currently constrained by the limited bandwidth and sampling rates of pure electronic hardware. To address this issue, an ultra-wideband microwave covert communication system using dual optical frequency combs (OFCs) with doubled spreading efficiency is proposed in this paper. A joint optimization design that combines the electro-optic integrated spread spectrum processing and the spectrum stitching technology doubles the spectrum spreading efficiency. It significantly simplifies the system hardware complexity with a dual-OFCs architecture comprising only 37 channels, and successfully generates a spreading signal with an instantaneous bandwidth of 11.68 GHz, a frequency-domain flatness within 10 dB, and a spreading factor of up to 1825. Moreover, the center frequency of the spreading signal is tunable from 13.63 to 34.16 GHz, greatly enhancing the flexibility and the scalability of the system. At the receiver, a two-stage processing architecture consisting of an optical pre-despreading followed by a digital post-despreading is designed. A substantial processing gain of 29.5 dB is confirmed, which enables high-fidelity and reliable signal reception with a signal-to-noise ratio as low as -21 dB. Overall, the proposed system constitutes a promising solution for the advancement of covert communication technologies.
High-throughput plant phenotyping (HTPP) is increasingly limited by the mismatch between the need for field-relevant, fine-grained phenotypic information and the restricted capability of conventional observation platforms under complex agricultural conditions. Ground mobile robots are emerging as the key carrier for resolving this gap because they combine close-range sensing, autonomous mobility, and physical interaction within real field environments. In this paper, a structured scoping review is presented using a closed-loop perception-decision-action pipeline as the organizing principle. Within this framework, recent advances are synthesized from the perspectives of multimodal fusion, localization-aware sensing, motion planning, deep-learning-based phenotypic analysis, active observation, robotic intervention, and edge deployment. The review further clarifies the complementary roles of Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), and air-ground collaboration in multiscale phenotyping workflows. Beyond summarizing technologies, the article provides three concrete deliverables: a structured taxonomy of mobile phenotyping systems; comparative tables covering sensing modalities, localization/navigation methods, and AI models; and a research agenda linking technical progress to field deployability. The synthesis highlights four persistent bottlenecks, namely environmental generalization, annotation scarcity, limited standardization and reproducibility, and the gap between advanced models and agricultural edge hardware. Overall, ground robots are identified not merely as sensing platforms, but as the central system architecture for advancing mobile phenotyping toward autonomous, fine-grained, and field-deployable operation.
Expressive piano performance poses extreme challenges for robotic manipulation, necessitating high-speed repetitive impacts, substantial force output, and coordinated multi-joint control under stringent dynamic constraints. However, existing robotic systems exhibit significant limitations in replicating human-level dexterity, as well as achievable motion speed and force output. This work presents a data-driven, bio-inspired dexterous robotic hand designed specifically for high-fidelity piano performance. We first extract kinematic primitives and stable inter-joint coupling patterns from large-scale motion capture data of professional pianists. These human motion priors are directly embedded into the mechanical architecture through morphological coupling and actuator allocation. Actuator selection is further guided by empirically measured human peak velocities and force profiles from biomechanics literature, ensuring sufficient bandwidth for high-speed repetitive motion and adequate force transmission. Experimental results demonstrate that the proposed hand replicates human-like joint coordination, achieves peak joint velocities of 53.88 rad/s, and provides sufficient fingertip force for authentic piano interaction. As a demonstration of its capabilities, the hand successfully performs a Grade 7 piano piece, Croatian Rhapsody, illustrating its potential for expressive musical performance. This research establishes a principled pathway from human motion statistics to embodied robotic intelligence, providing a high-performance hardware foundation for autonomous musical performance.
Falls are a leading cause of injury and mortality among older adults, motivating growing interest in video-based computer vision (CV) fall detection systems. This study presents a systematic mapping of vision-based fall detection in video, synthesizing evidence from 433 primary studies published through 2025 and retrieved from five databases using explicit eligibility criteria and independent dual screening within a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-inspired workflow. We characterize the field through: (i) a structured three-level taxonomy grouping approaches into Feature Engineering, Deep Learning, and hybrid models; (ii) a quantitative analysis of commonly used algorithmic components and their reported performance across datasets and metrics; and (iii) a dedicated assessment of efficiency and deployment evidence (e.g., frames per second (FPS), latency, and hardware/platform reporting). Our findings indicate the predominance of Deep Learning pipelines-particularly convolutional neural network (CNN)-based backbones-together with a sustained prevalence of hybrid designs, while Transformers/Attention architectures show accelerated adoption in recent years. Despite frequent real-time claims, efficiency metrics and hardware specifications remain inconsistently reported, limiting reproducibility and clinical translatability. Overall, this mapping consolidates trends, benchmarks, and reporting practices, and identifies research gaps that hinder reproducibility and clinical translation.