Search Results (4,488)

The prevalence of radio frequency signals in indoor environments has in recent years given rise to new technologies across many domains such as robotics, healthcare, and surveillance. Radio frequency signals propagate in the wireless medium through multiple paths and carry useful environment-dependent information. Capturing and analyzing these signal patterns can offer new solutions for a number of applications relevant to ranging, tracking, perception and recognition. In this work we propose a novel architecture, separating physical, back-bone networks, and inference layers, towards fully ubiquitous passive recognition systems that scale with the number of environments and applications. We propose a back-bone architecture that utilizes a novel Cross Dual-Path Attention (CDPA) block to capture spatial and temporal correlations from Channel State Information (CSI) for device-free, multi-task applications. Subsequently, a distill and transfer algorithm is proposed to generalize the inference capabilities of CDPA over multiple target environments for scalable training and reduced computational costs. By sharing knowledge between models across a shared network, experimentation shows that edge devices can be deployed with improved performance while simultaneously meeting strict computation and memory requirements. Our distributed learning paradigm demonstrates that CDPA-based models are capable of using passive signals in a non-intrusive and privacy-protecting manner, in order to achieve ubiquitous recognition at scale in smart environments. Full article

Assistive devices in recent years have transitioned from a passive mode of operation to the integration of smart solutions that enable humans to interact with active and robotic platforms. The main problems in the evolution of this kind of device are accessibility in terms of price and the functional limitations of the smart integrated solutions. This project proposes an armrest prototype for integration into smart walkers or wheelchairs that can detect the user’s intentions at a low development cost. The smart principle of operation is based on Hall-effect sensors, strategically positioned to measure the Center of Pressure (CoP) of the user’s forearm and to classify motor intention using machine learning algorithms such as Random Forest and Leave-One-Subject-Out (LOSO). The detection and correct classification of the user’s intention is a tool that can be integrated as a control system for both motorized and passive assistive devices. Full article

In the era of Industry 4.0, Zero-Defect Manufacturing is critical for injection molding but faces three major hurdles: severe class imbalance, the “black-box” nature of AI models, and the lack of scalability across machines. This study presents a comprehensive framework addressing these challenges. Using industrial datasets, we evaluated state-of-the-art supervised algorithms. Results show that CatBoost outperforms other architectures. Crucially, we demonstrate that maximizing accuracy is insufficient; instead, we introduce a cost-sensitive threshold optimization that minimizes economic risk, identifying an optimal classification threshold significantly lower than the standard. To enhance trust, SHAP analysis reveals that motor power and specific nozzle temperatures are the primary defect drivers. Finally, we validate a transfer learning approach using LightGBM, proving that models can be adapted to new datasets with minimal retraining. The implementation of cost-sensitive thresholding reduces total failure costs by over 75% compared to standard classification, while the transfer learning approach cuts the data requirements for new machine adaptation by more than half, providing a high-impact, scalable solution for sustainable smart manufacturing. Full article

Breaking the talent bottleneck in agriculture and forestry and establishing an effective channel for transmitting intellectual achievements from university graduates to rural areas are crucial for building a high-quality rural revitalization workforce. This study employs a mixed-methods approach, combining systematic surveys based on a five-point Likert scale (Cronbach’s α = 0.982) with machine learning modeling to analyze the factors influencing the employment of graduates from agricultural and forestry institutions. Key findings indicate that respondents generally recognize the importance of salary and benefits, express high satisfaction with occupational environments and living conditions, and acknowledge the effectiveness of training systems and promotion channels. The Genetic Algorithm-Back Propagation (GA-BP) predictive model constructed in this study demonstrates outstanding performance, achieving coefficients of determination (R²) of 0.983 and 0.960 on the training and test sets, significantly outperforming traditional measurement methods. This research not only provides data-driven support for optimizing employment policies in agricultural and forestry institutions but also showcases an innovative application of artificial intelligence in analyzing employment factors, offering an interdisciplinary research paradigm for talent strategies aimed at advancing smart agriculture. Full article

Accurate and in-situ monitoring of frost growth on plant leaves is crucial for disaster prevention in smart agriculture. To address the limitations of traditional methods in quantification and continuity, this study proposes a novel monitoring paradigm integrating dynamic dielectric spectrum analysis with hybrid intelligent algorithms. A mesh-electrode-based capacitive sensor was designed to capture in-situ and continuous dielectric spectrum changes on leaf surfaces. Subsequently, a hybrid SWT-SSA-LSTM model was constructed for high-fidelity denoising and prediction of the original signals. Field experiments demonstrated that this system could quantify frost layer mass and thickness with high precision. The established nonlinear regression models achieved coefficients of determination of 0.924 and 0.975, respectively. The prediction model exhibited outstanding performance, with a root mean square error as low as 1.475. This study establishes a complete technical closed-loop from physical perception to intelligent prediction, providing an innovative solution for precise frost monitoring in agriculture. Full article

Predictive pest management in urban sewer networks represents a sustainable alternative to reactive, biocide-based methods. Using data collected through an IoT architecture and validated with manual inspections across eight manholes over 113 days, we implemented a rigorous comparative framework evaluating eleven data mining algorithms, including classical methods (KNN, SVM, decision trees) and advanced ensemble techniques (XGBoost, LightGBM, CatBoost) optimized for unbalanced datasets. Gradient boosting models with explicit handling of class imbalance—where the absence of pests exceeds 77% of observations—showed exceptional performance, achieving a Macro-F1 score above 0.92 and high precision in identifying the minority high-risk class. Explainability analysis using SHAP consistently revealed that elevated CO₂ concentrations are the primary predictor of infestation, enabling early identification of critical zones. This study demonstrates that carbon dioxide (CO₂) acts as the most robust bioindicator for predicting severe infestations of Periplaneta americana, significantly outperforming conventional environmental variables such as temperature and humidity. The implementation of the model in a real-time monitoring platform generates interpretable heat maps that support proactive and localized interventions, optimizing resource use and reducing dependence on biocides. This study presents a scalable, operationally viable predictive system designed for direct integration into municipal asset management workflows, offering a concrete, industry-ready solution to transform pest control from a reactive, labor-intensive process into a data-driven, proactive operational paradigm. This approach not only transforms pest management from reactive to predictive but also aligns with the Sustainable Development Goals, offering a scalable, interpretable, and operationally viable system for smart cities. Full article

Renewable energy sources have been widely utilized in many applications worldwide in recent years, particularly to support sustainable and energy-efficient systems. One of the most vital applications of these sources is the photovoltaic (PV) traffic light system (TF-LS), which represents a sustainable alternative to conventional grid-powered traffic infrastructure. This paper presents the design and experimental implementation of a stand-alone PV TF-LS, consisting of a PV power system and an integrated TF-LS that operate autonomously while ensuring reliable and efficient energy utilization. The proposed control of the PV power system accomplishes two main functions: maximum power point tracking (MPPT) of the PV module and battery charging management. The MPPT system is implemented using a perturb and observe (P&O)-based PI algorithm with reduced step size and is experimentally validated using a dSPACE 1104 real-time control platform. In addition, this paper proposes and experimentally implements a novel intelligent control strategy for the TF-Ls that relies on vehicle counting and real-time comparison of traffic densities at road intersections instead of the traditional fixed-time scheduling approach, using LabVIEW software and an Arduino microcontroller. Experimental results demonstrate the effectiveness of the proposed control techniques for MPPT, battery charging, and traffic signal operation. Moreover, the proposed TF-LS control demonstrates fast and efficient operation under real-time traffic conditions, providing a simple and practical solution for mitigating traffic congestion. Full article

The high rate of bike commuting around the globe has greatly transformed the mode of transportation in cities, but the high speeds of motorized cycling have contributed to a high rate of serious road trauma. Although conventional helmets offer necessary passive structural protection, they do not consider the most important aspect of the emergency response, which is the Golden Hour the time frame during which medical intervention can have the most significant impact. This paper is a development and validation of an autonomous, low-cost smart helmet architecture that is programmed to operate in real-time to detect accidents and autonomously inform the operator of accidents. The system is built up of an ESP32 microcontroller with a multi-modal sensor package, which comprises an inertial measurement unit (IMU), force-impact sensors, and MQ-3 alcohol sensors to conduct proactive safety screening. To overcome the single threshold limitation of unreliable systems, a time-windowed sensor-fusion algorithm was applied in order to distinguish between normal riding dynamics and bona fide collisions. This reasoning involves concurrent cues of high-G inertial rotations and physical impacting features over a time window of 500 ms to reduce spurious activations. The architecture of the system is completely self-sufficient and employs an in-built GPS-GSM module to send the geographical location through SMS without the need to have a smartphone connection. The prototype was also put through 150 experimental tests, with some conducted in laboratories, and real-world running tests in diverse terrains. The findings reveal an accuracy in detection of 93.7, a false positive rate (FPR) of 2.6 and a mean emergency alert latency of 2.8 s. In addition, it was found that structural integrity was confirmed at ECE 22.05 impact conditions using Finite Element Analysis (FEA), with a safety factor of 1.38. These quantitative results mean that the proposed system is an effective way to address a cultural shift between passive structural protection and active rescue intervention as a statistical and computationally efficient safety measure of modern micro-mobility. Full article

The rapid growth of renewable integration and active consumer participation has made modern power grids increasingly complex and dynamic, where maintaining balanced and efficient energy distribution remains a central challenge. This paper introduces a symmetry-aware optimized fuzzy deep reinforcement learning-gated recurrent unit (OF-DRL-GRU) model that exploits the natural symmetry and asymmetry in demand–generation behavior to achieve stable and adaptive load balancing. The proposed architecture consists of four core modules: a fuzzy logic layer that formulates symmetrically distributed membership functions for interpretable and balanced state transitions; a DRL agent that governs decision actions through a symmetry-preserving reward mechanism balancing exploration and exploitation; a GRU network that models temporal symmetries while performing controlled symmetry-breaking during dynamic fluctuations to enhance generalization; and an improved multi-objective biogeography-based optimization (IMOBBO) algorithm that optimizes fuzzy parameters and model hyper-parameters through adaptive migration alternating between symmetry preservation and deliberate asymmetry, ensuring efficient convergence and global diversity. The synergy among these modules forms a unified symmetry-aware optimization paradigm, reflecting how symmetric structures sustain stability while purposeful asymmetry enhances robustness and adaptivity. The proposed framework is evaluated using three benchmark datasets (UK-DALE, Pecan Street, and REDD) and compared against several advanced and competitive models. Experimental outcomes show that the proposed OF-DRL-GRU model achieves 99.23% accuracy, 99.69% recall, and 99.83% area under the curve (AUC), alongside faster runtime, lower variance, and improved convergence stability. These results demonstrate that incorporating symmetry–asymmetry principles within AI-driven optimization significantly enhances interpretability, resilience, and energy efficiency, paving the way for intelligent, self-adaptive load management in next-generation smart grids. Full article

The creation of unified, open, secure, reliable, and agile data spaces is essential for collecting, storing, and sharing data in a standardized and accessible manner, promoting data reuse and addressing current interoperability limitations. In this context, this research presents a proof of concept for a unified agronomic data space based on the structured integration of heterogeneous open data sources. The central hypothesis is that the automated acquisition, preprocessing, and harmonization of publicly available agronomic data can significantly improve accessibility, usability, and interoperability for agricultural decision support applications. To this end, a comprehensive analysis of relevant open data sources was conducted, followed by the design and implementation of configurable algorithms for automated data downloading, cleaning, validation, and integration. The proposed approach explicitly addresses key challenges such as heterogeneous data formats, inconsistent spatial and temporal resolutions, missing values, and outlier detection. As a result, a unified access point was developed, providing reliable agronomic information, including (i) preprocessed climatological time series, (ii) crop and phytosanitary data, (iii) high-resolution aerial orthophotography, (iv) remote-sensing imagery, (v) pest-related information, and (vi) time series of major vegetation indices. The proof of concept was implemented for olive groves in the Andalusian region of Spain; however, the methodology is fully transferable to other crops, regions, and institutional contexts where comparable open data sources are available. The results demonstrate the potential of shared agronomic data spaces to enhance data reuse, support scalable analytics, and facilitate interoperable, data-driven agricultural management beyond the specific regional case study. Full article

With the rapid development of the Internet of Vehicles (IoV), the explosive growth of data traffic within the system has led to a surge in demand for spectrum resources. However, the strict limitations on spectrum supply make the construction of an efficient and reasonable resource allocation scheme crucial for IoV. To maximize social benefits and improve security in the resource allocation process under IoV spectrum scarcity, this paper proposes a dynamic spectrum allocation (DSA) scheme based on a consortium blockchain framework. In this scheme, we design a demand-based vehicle priority classification method and propose a novel hybrid consensus mechanism—PhDPoR—which integrates practical byzantine fault tolerance (PBFT) and Hierarchical Delegated Proof of Reputation. Furthermore, we construct a multi-leader, multi-follower (MLMF) Stackelberg game model and utilize smart contracts to implement an immutable on-chain record of spectrum resource allocation, thereby deriving the optimal spectrum pricing and purchase strategy. Experimental results show that the proposed scheme not only effectively optimizes the utility of both supply and demand sides and improves overall social benefits while ensuring efficiency, but also significantly outperforms baseline algorithms in identifying and mitigating malicious nodes, thus verifying its feasibility and application value in complex IoV environments. Full article

In complex, semi-structured orchard environments, mobile inspection robots often suffer from excessive turning points, low search efficiency, limited trajectory-tracking accuracy, and poor adaptability to dynamic obstacles. To address these issues, this study proposes an integrated autonomous navigation method that employs an improved A* algorithm for global path planning, a Fuzzy-Weighted Dynamic Window Approach (FW-DWA) for local path optimization, and a model predictive control (MPC)-based trajectory-tracking controller. First, a dynamic heuristic-weight adjustment strategy is introduced into the conventional A* algorithm, in which a correction factor adaptively tunes the heuristic weight; a two-stage node optimization procedure then removes hazardous and redundant nodes to improve path smoothness and safety. Second, the FW-DWA, grounded in fuzzy control theory, uses goal distance and obstacle distance to update the weights of the heading, clearance, and velocity evaluation functions in real time, thereby enhancing obstacle avoidance in dynamic environments. Finally, a discrete kinematic model is established to design the MPC Controller, which achieves high-precision tracking through receding-horizon optimization and feedback correction. Experiments conducted in real orchards demonstrate that the proposed method reduces path length by 5.79%, shortens planning time by 3.64%, and increases the minimum safety distance by 50%. Comparative results further show that the MPC Controller attains a mean position error of 0.032 m and a mean heading error of 3.14°, clearly outperforming a conventional Proportional–Integral–Derivative (PID) controller. These findings provide an effective solution for reliable autonomous navigation of orchard inspection robots and offer a valuable reference for smart agricultural robotics applications. Full article

Smart homes are becoming increasingly common, bringing convenience to users but also raising serious security concerns. As the number of connected devices grows, so does the research interest in securing smart homes. However, the literature is broad, making it difficult to understand the main research directions and how they are connected. Given the scope and diversity of existing research, a systematic mapping study was chosen to provide a high-level overview of smart home security research by mapping research communities, identifying dominant themes, and examining their evolution over time. We retrieved articles from the Scopus database published between 2000 and April 2025, resulting in approximately 13,600 articles. After filtering out unrelated domains such as smart vehicles, smart industry, and general IoT, a final set of 6313 publications specifically focused on smart home security was used for analysis. We applied a citation-based network analysis approach, constructed an author citation graph, and used the Louvain community detection algorithm to identify 12 main research communities. Each community was further analyzed based on its keywords, most-cited publications, leading authors, and institutions. Our results provide a structured overview of the field, highlighting its key themes and evolution over time. This work can help researchers better navigate the smart home security landscape and identify future research opportunities. Full article

This work presents a curated dataset of multimodal sensor measurements from Internet of Things (IoT) units mounted on waste collection vehicles. Each unit records multiple data streams including GPS position, vehicle velocity, radar-based container presence, accelerometer readings of the lifting arm, and RFID tag identifiers of the bins. The dataset provides two complementary forms of annotation: (1) algorithmically generated events that were manually cleaned through visual inspection of sensor signals, offering large-scale coverage across 5 vehicles over a total of 25 collection days, and (2) manually validated events derived from synchronized video recordings, representing ground truth for 3 vehicles over 8 collection days. In total, the dataset contains 12,391 annotated waste collection events. The dataset spans diverse operational conditions with varying container sizes and includes both RFID-equipped and non-RFID bins. It can be used to train and evaluate machine learning models for event detection, anomaly recognition, or explainability studies, and to support practical applications such as Pay-as-you-throw (PAYT) waste management schemes. By combining multimodal sensor signals with reliable annotations, the dataset represents a unique resource for advancing research in smart waste collection and the broader field of IoT-enabled urban services. Full article

Smart suspensions have significantly improved passenger comfort and vehicle stability compared to their passive counterparts. This manuscript explores the integration of artificial intelligence (AI) into hybrid suspension control systems to enhance vehicle stability and ride comfort under conditions where suspended mass changes. A one-quarter-vehicle model is employed to simulate and evaluate the performance of a hybrid control strategy, which combines skyhook and groundhook methods using a dynamic weighting factor ($\alpha$). This investigation considers an everyday situation where the sprung mass of a vehicle changes considerably when passengers enter or exit the automobile, impacting the suspension performance. Reinforcement learning techniques are utilized to optimize $\alpha$, achieving an acceptable balance between passenger comfort and vehicle stability. Simulation results show significant improvements in the dynamic response of the sprung mass compared to traditional passive systems, while keeping vehicle stability. Although improvements in road holding are incremental, simulation effort validates the AI-based hybrid system’s potential for refinement and practical application. Validation in MATLAB-Simulink demonstrates the system’s adaptability to varying road conditions and load distributions. The findings highlight the transformative role of AI in suspension control, paving the way for real-time implementation, advanced algorithms, and integration into full-vehicle models. This study contributes to the ongoing development of intelligent suspension systems toward vehicle performance advancement by improving passenger comfort and road holding. Full article