Search Results (417)

This study presents a structured methodology for optimizing the placement and selection of accelerometer sensors for structural health monitoring in civil infrastructures. The approach integrates both ambient and forced vibration testing data, followed by a unified analysis of sensor energy distribution through singular value decomposition of the cross power spectral density. The energy associated with each sensor is normalized and decomposed into its vertical, longitudinal, and transversal components, allowing for detailed ranking and visualization across different structural elements such as the deck and supporting piers. A comparative analysis between the energy distributions obtained from ambient and forced vibrations is conducted to identify consistent sensor locations. The sensor configuration is then iteratively refined using a combination of global dynamic criteria and local spatial constraints to ensure both stability and optimal spatial distribution. The resulting framework enables the systematic design of sensor layouts that combine energy-based robustness with optimal spatial distribution across all three spatial components, while significantly reducing the number of required sensors, ensuring the preservation of damage detection capability and long-term structural health monitoring. Full article

In this study, an active vibration control (AVC) strategy is effectively used on a system made of a honeycomb polymer–paper core and carbon fiber-reinforced polymer (CFRP) plates. A cost-effective and practical solution based on an AVC system has been developed and tested using a microcontroller unit (MCU) from Texas Instruments. The control system is studied by applying out-of-plane disturbances to the composite panel via an electrodynamic shaker, by exciting the identified mode shapes obtained through experimental modal analysis, i.e., impact tests. The actuator chosen for the AVC system is a Macro Fiber Composite (MFC) patch. Multiple analog signal processing circuits were developed to scale and shift the signal at the input and output of the MCU. The proposed control algorithm is based on a positive position feedback (PPF) technique. Modal analysis was performed to identify the natural frequencies and mode shapes of the structure, which are essential for the design and tuning of the modal-based PPF controller. This analysis also enabled optimal sensor and actuator placement, ensuring effective targeting and control of the dominant vibration modes. Then, a series of tests were performed using pure sine excitations at frequencies of interest, close to the 2nd and 8th mode at 25.13 Hz and 129 Hz, respectively. The results of the experiments revealed a velocity attenuation of 55.8% to 76.9% and a Power Spectral Density (PSD) attenuation of 5.8 dB to 12.8 dB, depending on the mode under study. Owing to the size and mass properties of the Macro Fiber Composite (MFC) patches, the control system is very much suitable for automobile and aerospace applications. Full article

This review examines the current state of development and application of artificial intelligence (AI) tools for monitoring nutrition and physical activity in individuals with obesity, with a focus on the physiological complexity of energy balance and the role of chrono-nutrition. Energy intake and expenditure are dynamically coupled and circadian-regulated: meal timing and movement patterns influence insulin sensitivity, thermogenesis, and Non-Exercise Activity Thermogenesis within the same day. Traditional monitoring methods suffer from recall bias and low granularity, while isolated sensors operate in data silos, limiting accuracy. Effective solutions require multimodal, continuous, and temporally aligned data streams. Current AI models exhibit critical limitations in obesity-specific contexts: inaccurate gait and energy expenditure estimates due to biomechanical differences, dietary models underestimating glycemic variability, poor performance on mixed dishes, sauces, and culturally diverse foods, and a lack of validation against gold standards such as doubly labelled water (DLW) and weighed food records. This review proposes a paradigm shift toward obesity-specific AI design, including enriched datasets and multimodal integration. Physical activity monitoring faces similar challenges: systematic measurement bias in wearables, sensor placement issues, and algorithms trained on normal-weight cohorts. In the GLP-1/GIP era, if transparency, ethical safeguards, and equitable access are ensured, AI will act as a catalyst for personalized care, remote monitoring, trial optimization, and next-generation drug discovery. In conclusion, the integration of AI with rigorous validation procedures and inclusive sampling strategies is essential to achieve reliable, fair, and clinically relevant monitoring approaches for obesity management. Full article

Background: Solid waste collection is a relevant issue for municipalities and can be improved by installing volumetric sensors inside dumpsters. Sensors generate a maintenance cost but provide additional information to decide which dumpsters to empty in a given day when visiting all of them is expensive. Moreover, dumpsters close to each other are expected to follow similar filling trends, as they serve the same catchment area; hence, equipping them all with sensors may be inconvenient. This leads to the problem of finding sensor locations that minimize routing, waste overflow, and sensor maintenance costs. Methods: We tackle the problem using a heuristic based on adaptive large neighborhood search and a one-step look-ahead policy, performed through a rolling horizon method to approximate the multi-stage stochastic programming problem, in order to compute the number and locations of sensors to be installed, minimizing the total cost. Results: We apply the proposed approach to a realistic setting with 50 dumpsters in Torino. The results show that placing sensors in 21 dumpsters at optimized locations allowed saving about 17,000 € per year and reduced vehicle emissions by 15.5%. Conclusions: The proposed approach enables more cost-effective and sustainable waste collection operations. Full article

Accurate thermal conductivity measurement requires temperature control systems to establish stable operating conditions within a limited time. In practical thermal conductivity performance testing systems, large thermal inertia, complex heat transfer paths, and input time delays arising from thermal propagation and sensor placement often degrade dynamic response and control accuracy. To overcome these limitations, a composite PI–RLS–MPC control strategy is proposed for thermal systems with inertia and time delay. A proportional–integral (PI) controller serves as the baseline stabilizing controller, while model predictive control (MPC) is utilized to optimize the control input by explicitly considering system delay and input constraints. To enhance robustness against model uncertainty and parameter variations, recursive least squares (RLS) is adopted for online parameter identification and adaptive PI tuning, and a steady-state parameter freezing mechanism is introduced to suppress unnecessary parameter updates after convergence. Simulation studies are performed on an identified thermal process model with a 20 s input time delay. The results indicate that the proposed strategy reduces overshoot, shortens settling time, and improves disturbance rejection compared with conventional controllers. Overall, the proposed PI–RLS–MPC approach provides a practical solution for improving temperature control performance in thermal conductivity testing systems. Full article

In the field of structural health monitoring systems, sensors serve as the fundamental components for assessing infrastructure integrity. The rationality of their spatial configuration significantly influences the precision of structural performance assessment, the efficacy of damage detection algorithms, and the operational reliability of the system throughout its designated lifecycle. A robust optimization methodology for the placement of multi-type sensors is proposed in this study, explicitly formulated to mitigate the negative impact of sensor malfunctions during long-term operation. First, a rigorous evaluation framework for sensor placement schemes is established based on Bayesian inference and the minimization of information entropy, thereby quantifying the uncertainty inherent in parameter identification. Then, a probabilistic model of sensor failure is developed utilizing the Weibull distribution to capture time-dependent reliability characteristics, combined with a modified information entropy calculation method that mathematically assimilates these failure probabilities into the optimization objective. Finally, a heuristic search strategy is employed to achieve the robust optimal placement of multi-type sensors, efficiently navigating the complex combinatorial search space. In contrast to deterministic information entropy (DIE) methodologies, which assume ideal sensor functionality, the robust information entropy (RIE) approach comprehensively accounts for the stochastic nature of sensor failures and their impact on the information content of the monitoring network, thereby significantly augmenting the robustness and redundancy of the sensor configuration. Validations utilizing a numerical frame structure and a finite element bridge model demonstrate that the RIE method effectively integrates the sensor failure probability model to yield robust optimal placement schemes, minimizing the risk of information loss and ensuring reliable structural health monitoring throughout the engineering lifecycle. Full article

The optimal placement of samplers and sensors in water distribution systems (WDSs) and wastewater collection systems (WCSs) is fundamental to effective monitoring, early contamination detection, and system protection. The goal of optimal sensor/sampling placement (OSP) is to maximize the ability to detect, monitor, and track critical variables, such as contaminants or temperature, while maintaining cost-effectiveness and operational efficiency. In practice, OSP problems are inherently multi-objective and typically involve trade-offs between cost minimization, spatial and temporal coverage, detection accuracy, and robustness under uncertainty. This paper presents a comprehensive review of recent single- and multi-objective optimization strategies for source detection and monitoring, drawing on approaches developed in various research fields. The reviewed literature is systematically organized according to problem formulation, objective functions, optimization techniques, and decision-making strategies, paying particular attention to their applicability in real-world WDSs and WCSs. Beyond summarizing existing methods, this review critically examines key methodological assumptions and limitations that hinder practical implementation. These include sparse sensor deployment, budget constraints, and modeling and sensor uncertainty. Finally, the paper identifies open challenges and outlines potential directions for future research aimed at improving the robustness, scalability, and practical relevance of OSP strategies. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article

Optimal sensor placement (OSP) for structural health monitoring of large-scale space grid structures must enable reliable identification of localized member deterioration with sparse instrumentation. Modal-based OSP criteria optimize observability of a healthy model but do not directly minimize uncertainty in substructure-level damage parameters. We partition the structure into substructures, simulate axial and biaxial bending stiffness-loss cases, and compute truncated modal flexibility. Each element is encoded by stacked end-node flexibility submatrices over $m=6$ modes. A multi-task, zero-anchored multi-layer perceptron is trained to regress three nonnegative damage parameters and classify damage presence using losses tailored for small-damage accuracy. Sensor sensitivities are obtained by automatic differentiation of the surrogate with respect to flexibility features and aggregated with scenario weights emphasizing critical bending and neighbor-substructure interference scenarios. A greedy D-optimal design then maximizes the log-determinant of a regularized Fisher information matrix under practical coverage constraints; substructure selections are merged into a globally feasible layout. On a representative space grid, the method improves task-oriented identifiability over EFI and MKE across budgets $K_{\mathrm{tot}}=30$–60 (higher-damage D-optimality, lower A-optimality trace, and reduced proxy variance indicators), while yielding lower modal log-determinants. These findings indicate risk-weighted, substructure-first task design as an alternative to purely modal criteria for substructure-level damage-parameter identification. Full article

Wireless Sensor and Actor Networks (WSANs) are critical for industrial automation in the context of Industry 4.0, yet the optimal placement of actors to ensure connectivity and coverage remains an NP-hard problem. This study addresses the Actor Placement Problem (APP) in constrained, two-zone industrial environments. We propose a hybrid system, the PSO-HC-DGA hybrid system, which integrates Particle Swarm Optimization (PSO), Hill Climbing (HC), and the Distributed Genetic Algorithm (DGA). We evaluate four crossover methods (UNDX, SPX, BLX-$\alpha$, and psBLX) combined with two actor replacement methods (RIWM and FC-RDVM) for small-, medium-, and large-scale scenarios. The simulation results demonstrate that psBLX is the most effective of the four crossover methods. In the small-scale scenario, it achieved better load balancing combined with RIWM, while in the medium-scale scenario, psBLX achieved full sensor coverage with RIWM and good load balancing with FC-RDVM. For the large-scale scenario, we compared the performance of the implemented hybrid system with that of a PSO system. The hybrid system showed 100% connectivity and achieved better sensor coverage than the PSO system. The Kruskal–Wallis test confirmed that the performance differences in load balancing were statistically significant. We conclude that the proposed hybrid system using psBLX enables robust and high-performance deployment in two-zone industrial WSANs. Full article

Continuous plantar-pressure monitoring is important for objective gait analysis and early detection of abnormal loading; however, many existing solutions remain laboratory-bound (force plates and instrumented walkways) or rely on costly in-shoe multilayer sensor arrays. Here, we developed and optimized a fully screen-printed pressure-sensing insole based on carbon–polymer nanocomposite layers, with an emphasis on manufacturability and process control to bridge the gap between proof-of-concept force-sensitive resistor (FSR)-based insoles and scalable printed-electronics manufacturing workflows. Composite pastes containing carbon fillers (graphene nanoplatelets, carbon black, and graphite) were formulated to improve sensor repeatability and sensitivity. Sensors were characterized under compression loads from 100 N to 1300 N, showing a sensitivity of 10.5 ± 2.8 Ω per 100 N and a sheet-to-sheet coefficient of variation of 22.1% in resistance response. The effects of paste composition, screen mesh density, electrode layout, and lamination on sensitivity and repeatability were systematically evaluated. In addition, correlation analysis of resistance values from integrated quality-control meanders proved useful for monitoring screen-printing process stability. The final insole integrates printed carbon sensing pads and contacts, a dielectric spacer, and an adhesive layer in a thin, flexible format suitable for integration with wearable electronics. In practical static-load tests, repeated manual placement of weights yielded coefficients of variation as low as 4% at 500 g and a detection limit of ~0.1 N, comparable to a very light finger touch. These results demonstrate that low-cost screen-printed electronics can provide robust pressure sensing for wearable plantar-pressure monitoring. Full article

This study proposes a Kalman filter-based method to optimize the placement of accelerometers in buildings, formulated as a multi-objective problem that simultaneously minimizes the number of sensors and the state estimation error. State-space equations of 3-, 9-, 15-, and 30-story buildings were developed from a simplified continuous beam model, allowing the method to be evaluated across different structural conditions. The trace of the state error covariance matrix (Tr(P)) was employed as the performance metric, showing a strong correlation with the signal-to-noise ratio (SNR) and the normalized absolute estimation error. The results highlight that measurement noise critically affects sensor placement. As the noise covariance increases, estimation uncertainty grows, and more sensors are required, often concentrated in specific structural regions. Conversely, high-sensitivity low-noise sensors reduce uncertainty, though at a higher sensor unit cost. Maintaining an SNR above 10 dB proved essential to ensure reliable operational modal analysis. Optimal layouts tended to concentrate on upper floors, where accelerations and SNR are higher, avoiding redundant sensors at modal nodes or lower levels. Validation under real and synthetic excitations, including the 2010 Concepción ground motion record and band-limited white noise, confirmed that the method can accurately identify the fundamental frequencies of the structures. These findings demonstrate the effectiveness of the proposed Kalman filter-based methodology for optimizing sensor layouts in structural health monitoring applications under realistic operational conditions. Full article

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) offers the flexibility to position sensors closer to the scalp, which improves the signal-to-noise ratio compared to conventional superconducting quantum interference device (SQUID) systems. However, the spatial resolution of OPM-MEG critically depends on sensor placement, especially when the number of sensors is limited. In this study, we present a methodology for optimizing OPM-MEG sensor layouts using each subject’s anatomical information derived from individual magnetic resonance imaging (MRI). We generated realistic forward models from reconstructed head surfaces and simulated magnetic fields produced by equivalent current dipoles (ECDs). We compared multiple simulation strategies, including ECDs randomly distributed across the cortical surface and ECDs constrained to regions of interest. For each simulated magnetic field map (MFM) database, we applied the sequential selection algorithm (SSA) to identify sensor positions that maximized information capture. Unlike previous approaches relying on large measurement databases, this simulation-driven strategy eliminates the need for extensive pre-existing recordings. We benchmarked the performance of the personalized layouts using OPM-MEG datasets of auditory evoked fields (AEFs) derived from real whole-head SQUID-MEG measurements. Our results show that simulation-based SSA optimization improves the coverage of cortical regions of interest, reduces the number of sensors required for accurate source reconstruction, and yields sensor configurations that perform comparably to layouts optimized using measured data. To evaluate the quality of estimated MFMs, we applied metrics such as the correlation coefficient (CC), root-mean-square error, and relative error. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95) capture most of the information contained in full-head MFMs. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles and found that localization errors were < 5 mm. The results further indicate that SSA performance is insensitive to individualized head geometry, supporting the feasibility of using representative anatomical models and highlighting the potential of this approach for clinical OPM-MEG applications. Full article

LiDAR sensors are becoming more common and are going to be widely adopted in vehicles in the future by reducing the production cost of the time-of-flight units. Manufacturers are uncertain about the placement, cover material, and shape of the assembly to achieve the optimal performance of the LiDAR, especially in rainy conditions. Although there are existing methodologies for evaluating the visibility and signal intensity of point clouds, there are no indexing approaches available since they would require a broad and comprehensive dataset and realistic and repeatable conditions to perform parametric studies. A matrix of rain conditions with quantified raindrop distribution characteristics is simulated using a wind tunnel via the wind-driven rain concept to produce the realistic impact of raindrops onto the sensor assembly surface at various wind speeds. This paper presents a performance prediction model method for LiDAR sensors and showcases the capability of such a model to provide insights quantitatively when comparing variations. The model is 3-dimensional, including rain conditions perceived by a moving vehicle at different speeds, material properties of surface wettability, and LiDAR visibility in rain compared to dry conditions. The observed LiDAR signal degradation follows an exponential manner, for which this study provides experimentally derived coefficients, enabling quantitative prediction across materials, topologies, rain, and driving speed conditions. Full article

This study presents a hybrid digital twin system designed for real-time indoor air quality (IAQ) monitoring and filtration optimization within a residential environment. Using a network of low-cost sensors, physics-based simulations, and machine learning models, the system dynamically replicates the indoor environment to enable continuous assessment and optimization of key pollutants, including particulate matter, volatile organic compounds, and carbon dioxide. The system architecture integrates mass balance and decay models, computational fluid dynamics simulations, regression models, and neural network algorithms, all evaluated under both filtering and non-filtering conditions. A graphical user interface allows users to interact with the system, test air purifier placements, and visualize air quality dynamics in real time. The results demonstrate that, within this system, simpler models, such as linear regression, outperform more complex architectures under data-limited conditions, achieving test-set coefficients of determination ranging from 0.97 to 0.99 across multiple IAQ parameters. At the same time, the hybrid modelling approach enhances interpretability and robustness. Overall, this digital twin system contributes to smart building management by offering a scalable, interpretable, and cost-effective solution for proactive IAQ control and personalized decision-making. Full article