Search Results (984)

Indoor navigation (IN) systems are increasingly essential in environments where GPS signals are unreliable, such as hospitals, airports, and large public buildings. This study explores a smartphone-based approach to indoor positioning that leverages inertial sensor data for accurate step detection and counting, which are fundamental components of pedestrian dead reckoning. A long short-term memory (LSTM) network was trained to recognize step patterns across a variety of indoor movement scenarios. The generalized model achieved an average step detection accuracy of 93%, while scenario-specific models tailored to particular movement types such as turning, stair use, or interrupted walking achieved up to 96% accuracy. The results demonstrate that incorporating activity-specific training improves performance, particularly under complex motion conditions. Challenges such as false positives from abrupt stops and non-walking activities were reduced through model specialization. Although the system performed well offline, real-time deployment on mobile devices requires further optimization to address latency constraints. The proposed approach contributes to the development of accessible and cost-effective indoor navigation systems using widely available smartphone hardware and offers a foundation for future improvements in real-time pedestrian tracking and localization. Full article

Photoplethysmogram (PPG) signals are increasingly utilized in wearable and mobile healthcare applications due to their non-invasive nature and ease of use in measuring physiological parameters, such as heart rate, blood pressure, and oxygen saturation. Recent advancements have highlighted green-light photoplethysmogram (gPPG) as offering superior signal quality and accuracy compared to traditional red-light photoplethysmogram (rPPG). Given the deterministic chaotic nature of PPG signals’ dynamics, nonlinear time series analysis has emerged as a powerful method for extracting health-related information not captured by conventional linear techniques. However, optimal data conditions, including appropriate sampling frequency and minimum required time series length for effective nonlinear analysis, remain insufficiently investigated. This study examines the impact of downsampling frequencies and reducing time series lengths on the accuracy of estimating dynamical characteristics from gPPG and rPPG signals. Results demonstrate that a sampling frequency of 200 Hz provides an optimal balance, maintaining robust correlations in dynamical indices while reducing computational load. Furthermore, analysis of varying time series lengths revealed that the dynamical properties stabilize sufficiently at around 170 s, achieving an error of less than 5%. A comparative analysis between gPPG and rPPG revealed no significant statistical differences, confirming their similar effectiveness in estimating dynamical properties under controlled conditions. These results enhance the reliability and applicability of PPG-based health monitoring technologies. Full article

Bridges are critical to national mobility and economic flow, making dependable structural health monitoring (SHM) systems essential for safety and durability. However, the SHM data quality is often affected by sensor faults, transmission noise, and environmental interference. To address these issues, anomaly detection methods are widely adopted. Despite their wide use and variety, there is a lack of systematic evaluation that comprehensively compares these techniques. Existing reviews are often constrained by limited scope, minimal comparative synthesis, and insufficient focus on real-time performance and multivariate analysis. Consequently, this systematic literature review (SLR) analyzes 36 peer-reviewed studies published between 2020 and 2025, sourced from eight reputable databases. Unlike prior reviews, this work presents a novel four-dimensional taxonomy covering real-time capability, multivariate support, analysis domain, and detection methods. Moreover, detection methods are further classified into three categories: distance-based, predictive, and image processing. A comparative evaluation of the reviewed detection methods is performed across five key dimensions: robustness, scalability, real-world deployment feasibility, interpretability, and data dependency. Findings reveal that image-processing methods are the most frequently applied (22 studies), providing high detection accuracy but facing scalability challenges due to computational intensity. Predictive models offer a trade-off between interpretability and performance, whereas distance-based methods remain less common due to their sensitivity to dimensionality and environmental factors. Notably, only 11 studies support real-time anomaly detection, and multivariate analysis is often overlooked. Moreover, time-domain signal processing dominates the field, while frequency and time-frequency domain methods remain rare despite their potential. Finally, this review highlights key challenges such as scalability, interpretability, robustness, and practicality of current models. Further research should focus on developing adaptive and interpretable anomaly detection frameworks that are efficient enough for real-world SHM deployment. These models should combine multi-modal strategies, handle uncertainty, and follow standardized evaluation protocols across varied monitoring environments. Full article

Short-term passenger flow prediction provides critical data-driven support for optimizing resource allocation, guiding passenger mobility, and enhancing risk response capabilities in urban rail transit systems. To further improve prediction accuracy, this study proposes a hybrid SMA-VMD-Informer-BiLSTM prediction model. Addressing the challenge of error propagation caused by non-stationary components (e.g., noise and abrupt fluctuations) in conventional passenger flow signals, the Variational Mode Decomposition (VMD) method is introduced to decompose raw flow data into multiple intrinsic mode functions (IMFs). A Slime Mould Algorithm (SMA)-based optimization mechanism is designed to adaptively tune VMD parameters, effectively mitigating mode redundancy and information loss. Furthermore, to circumvent error accumulation inherent in serial modeling frameworks, a parallel prediction architecture is developed: the Informer branch captures long-term dependencies through its ProbSparse self-attention mechanism, while the Bidirectional Long Short-Term Memory (BiLSTM) network extracts localized short-term temporal patterns. The outputs of both branches are fused via a fully connected layer, balancing global trend adherence and local fluctuation characterization. Experimental validation using historical entry flow data from Weihouzhuang Station on Xi’an Metro demonstrated the superior performance of the SMA-VMD-Informer-BiLSTM model. Compared to benchmark models (CNN-BiLSTM, CNN-BiGRU, Transformer-LSTM, ARIMA-LSTM), the proposed model achieved reductions of 7.14–53.33% in $f_{mse}$, 3.81–31.14% in $f_{rmse}$, and 8.87–38.08% in $f_{mae}$, alongside a 4.11–5.48% improvement in $R^2$. Cross-station validation across multiple Xi’an Metro hubs further confirmed robust spatial generalizability, with prediction errors bounded within $f_{mse}$: 0.0009–0.01, $f_{rmse}$: 0.0303–0.1, $f_{mae}$: 0.0196–0.0697, and $R^2$: 0.9011–0.9971. Furthermore, the model demonstrated favorable predictive performance when applied to forecasting passenger inflows at multiple stations in Nanjing and Zhengzhou, showcasing its excellent spatial transferability. By integrating multi-level, multi-scale data processing and adaptive feature extraction mechanisms, the proposed model significantly mitigates error accumulation observed in traditional approaches. These findings collectively indicate its potential as a scientific foundation for refined operational decision-making in urban rail transit management, thereby significantly promoting the sustainable development and long-term stable operation of urban rail transit systems. Full article

The article addresses the challenges of beam position tracking in Free-Space Optical Communication (FSOC) systems. A review of available photodetector technologies is presented, highlighting their operating principles and applications in optical links. The analysis indicates that most current monitoring devices function with the visible and near- or short-infrared ranges. However, due to the propagation characteristics of radiation in terrestrial environments, the mid-wave infrared (MWIR) region offers particularly promising opportunities. To the end, the work introduces a novel detector module based on an MWIR quadrant detector capable of simultaneously performing two essential tasks: monitoring beam position and receiving transmitted data. Such an integrated approach has the potential to significantly simplify the design of mobile FSOC systems, especially those requiring accurate transceivers’ tracking. The concept was validated through laboratory experiments on an MWIR link model, where both the signal bandwidth and position transfer function of the quadrant detector were examined. Full article

Information Systems (ISs) research frequently relies on digital trace data, often using simple activity counts as proxies for complex latent constructs like ‘experience’. However, the validity of such proxies is often assumed rather than critically scrutinized. This study problematizes this practice by treating a common proxy—a creator’s prior project count on Kickstarter—not as a measure of experience, but as a focal signal whose meaning is inherently ambiguous and context-dependent. By analyzing large-scale data (N ≈ 16,407 projects), we uncover a nuanced ‘experience paradox.’ The proxy exhibits a significant inverted-U association with backer mobilization and non-linearly moderates the value of other positive signals. Strikingly, it also maintains a persistent negative direct association with total funding, with its meaning varying significantly across project categories. These findings reveal the profound ambiguity of seemingly objective digital traces. Our primary contribution is methodological and theoretical: we provide a robust empirical critique of naive proxy use and refine signaling theory for digital contexts by integrating it with cognitive limitations and contextual factors. We urge IS scholars to develop more sophisticated measurement models and offer specific, evidence-based cautions for platform managers against the simplistic use of activity metrics in the digital economy. Full article

Against a global backdrop of industrialization and urbanization, precise measurement of multifactor flows and systematic identification of barriers and drivers are critical for optimizing resource allocation in smart regional development. This study develops an integrated geospatial analytic framework that incorporates mobile signaling data and POI data to quantify the intensity, barriers, and driving mechanisms of urban–rural factor flows in Huzhou City at the township scale. Key findings reveal the following. (1) Urban–rural factor flows exhibit significant spatial polarization, with less than 20% of connections accounting for the majority of flow intensity. The structure shows clear core–periphery differentiation, further shaped by inner heterogeneity and metropolitan spillovers. (2) Barriers demonstrate complex and uneven spatial distributions, with 45.37% of the integrated flow intervals experiencing impediments. Critically, some nodes act as both facilitators and obstacles, depending on the flow type and direction, revealing a metamodern tension between promotion and impairment. (3) Economic vitality plays a crucial role in driving urban–rural factor flow, with different factors having complex, often synergistic or nonlinear effects on both single and integrated flows. The study advances the theoretical understanding of heterogeneous spatial structures in urban–rural systems and provides a replicable analytical framework for diagnosing factor flows in small and medium-sized cities. These insights form a critical basis for designing targeted and adaptive regional governance strategies. Full article

This paper proposes a novel electric wheelchair that uses the surface electromyographic signal (sEMG) signals generated by the occlusal muscles to control the wheelchair during occlusion, instead of the traditional electric wheelchair that requires users to use their hands or feet for control. In this work, the myoelectric signal controls the electric wheelchair so that users with limited mobility and paraplegia can operate the electric wheelchair using the myoelectric signal generated during clenching. This is achieved through the seamless transmission of user data and GPS paths to the cloud and is facilitated by the state-of-the-art Wi-Fi 6E communication technology. By leveraging cloud connectivity, the system can instantly relay critical information, such as the user’s location and movement patterns, ensuring a prompt emergency response. Furthermore, several standard methods exist to set up the myoelectric signal electrodes and analyze the signals. This novel electric wheelchair can change the daily activities of many users who have difficulty walking. This work is presented as a proof-of-concept feasibility study rather than a comprehensive clinical validation. Full article

As smart cities evolve, integrating new technologies into school transportation is becoming increasingly important to ensure student comfort and safety. Monitoring and enhancing comfort during daily commutes can significantly influence well-being and learning readiness. However, most existing research addresses isolated factors, which limits the development of comprehensive and scalable solutions. This study presents the design and implementation of a low-cost, generalized IoT-based system for monitoring comfort in school transportation. The system processes multiple environmental and operational signals, and these data are transmitted to a cloud computing platform for real-time analysis. Signal processing incorporates standardized metrics, such as root mean square (RMS) values from ISO 2631-1 for vibration assessment. In addition, machine learning techniques, including a Random Forest classifier and ensemble-based models, are applied to classify ride comfort levels using both road roughness and environmental variables. The results show that stacked multisensor fusion achieved a significant improvement in classification performance compared with vibration-only models. The platform also integrates route visualization with commuting time per student, providing valuable information to assess the impact of travel duration on school mobility. Full article

Monitoring vital signs in the Neonatal Intensive Care Unit (NICU) typically relies on wired skin sensors, which can limit mobility, cause skin issues, and interfere with parent–infant bonding. Wireless sensors offer promising alternatives, but evaluations to date often emphasize accuracy alone, lack NICU-specific validation, and rarely use standardized frameworks. Our objective was to develop and apply a comprehensive framework for evaluating the feasibility, safety, and accuracy of wireless monitoring technologies using a wireless pulse oximeter, the Anne limb (Sibel Health, USA), in real-world NICU conditions. A prospective study was conducted on a diverse NICU population. A custom system enabled synchronized data recordings from both standard and wireless devices. Feasibility was assessed as signal coverage across a variety of daily care activities and during routine procedures. Safety was evaluated through skin assessments after extended wear. Accuracy was examined sample-by-sample and interpreted using the Clarke Error Grid for clinical relevance. The wireless oximeter device showed high feasibility with reliable Bluetooth connection across a range of patients and activities (median wireless PPG coverage = 100%, IQR: 99.85–100%). Skin assessments showed no significant adverse effects. Accuracy was strong overall (median bias 1.34%, 95% LoA −3.63 to 6.41), with most data points within clinically acceptable Clarke error grid zones A and B, though performance declined for infants on supplemental oxygen. This study presents a robust, multidimensional framework for evaluating wireless monitoring devices in NICUs and offers recommendations for future research design and reporting. Full article

Despite extensive research, indoor localization techniques remain an open problem, with Bluetooth Low Energy (BLE) continuing to be a dominant technology even in the presence of ultrawideband and Bluetooth 5.1. This study proposes a novel approach for indoor mobile device localization using BLE. Unlike traditional methods relying on the Received Signal Strength Indicator (RSSI), this technique employs spatial signal coverage analysis from multi-power Bluetooth emulators, with data collected by an array of receivers. These coverage patterns form a probability grid, which is processed to accurately determine the mobile device’s location. The method accounts for the intrinsic properties of antennas and the operational ranges of multiple beacon emulators, thereby enhancing localization precision. By utilizing receiver range data rather than RSSI, localization outcomes demonstrate greater consistency. Static measurements show an average error of 1.83 m, a median error of 1.73 m, and a mode error of 2.35 m. In dynamic settings, a moving robot exhibited a measurement error of 3.6 m for 70% of samples and 4.6 m for 94% of samples. This solution is currently being implemented to track attendees at trade fairs, providing metrics to inform stand rental pricing and insights for optimizing stand distribution to encourage visitor exploration. Full article

Waveforms define the shape, structure, and frequency characteristics of signals, whereas modulation schemes determine how information symbols are mapped onto these waveforms for transmission. Their appropriate selection plays a critical role in determining the efficiency, robustness, and reliability of data transmission. In wireless communications, the choice of waveform influences key factors, such as network capacity, coverage, performance, power consumption, battery life, spectral efficiency (SE), bandwidth utilization, and the system’s resistance to noise and electromagnetic interference. This paper provides a comprehensive analysis of the waveforms and modulation schemes used across successive generations of mobile cellular networks, exploring their fundamental differences, structural characteristics, and trade-offs for various communication scenarios. It also situates this analysis within the historical evolution of mobile standards, highlighting how advances in modulation and waveform technologies have shaped the development and proliferation of cellular networks. It further examines criteria for waveform selection—such as SE, bit error rate (BER), throughput, and latency—and discusses methods for assessing waveform performance. Finally, this study presents a comparative evaluation of modulation schemes across multiple mobile generations, focusing on key performance metrics, with the BER analysis conducted through MATLAB simulations. Full article

Accurate and non-invasive assessment of fat and muscle composition is crucial for biomedical monitoring to track health conditions in humans and pets, as well as for classifying meats in the meat industry. This study introduces a cost-effective, multifunctional ultra-wideband microwave system operating from 2.4 to 4.4 GHz, designed for rapid and non-destructive quantification of fat thickness, muscle thickness, and fat-to-muscle ratio in diverse ex vivo samples, including pork, beef, and oil–water mixtures. The compact handheld device integrates essential RF components such as a frequency synthesizer, directional coupler, logarithmic power detector, and a dual-polarized Vivaldi antenna. Bluetooth telemetry enables seamless real-time data transmission to mobile- or PC-based platforms, with each measurement completed in a few seconds. To enhance signal quality, a two-stage denoising pipeline combining low-pass filtering and Savitzky–Golay smoothing was applied, effectively suppressing noise while preserving key spectral features. Using a random forest regression model trained on resonance frequency and signal-loss features, the system demonstrates high predictive performance even under limited sample conditions. Correlation coefficients for fat thickness, muscle thickness, and fat-to-muscle ratio consistently exceeded 0.90 across all sample types, while mean absolute errors remained below 3.5 mm. The highest prediction accuracy was achieved in homogeneous oil–water samples, whereas biologically complex tissues like pork and beef introduced greater variability, particularly in muscle-related measurements. The proposed microwave system is highlighted as a highly portable and time-efficient solution, with measurements completed within seconds. Its low cost, ability to analyze multiple tissue types using a single device, and non-invasive nature without the need for sample pre-treatment or anesthesia make it well suited for applications in agri-food quality control, point-of-care diagnostics, and broader biomedical fields. Full article

Modern inspection mobile robots can carry multiple sensors that can provide opportunities to take advantage of the fusion of information obtained from different sensors. In real-world condition monitoring, harsh environmental conditions can significantly affect the sensor’s accuracy. To address this issue in this paper, we introduced a fusion approach around information gaps to handle the portion of false information that can be captured by the employed sensors. To test our idea, we looked at various types of data, such as sounds, color images, and infrared images taken by a mobile robot inspecting a mining site to check the condition of the belt conveyor idlers. The RGB images are used to classify the rotating idlers as stuck ones (late-stage faults); on the other hand, the acoustic signals are employed to identify early-stage faults. In this work, the cyclostationary analysis approach is employed to process the captured acoustic data to visualize the bearing fault signature in the form of Cyclic Spectral Coherence. Since convolutional neural networks (CNNs) and their transfer learning (TL) forms are popular approaches for performing classification tasks, a comparison study of eight CNN-TL models was conducted to find the best models to classify different fault signatures in captured RGB images and acquired Cyclic Spectral Coherence. Finally, to combine the collected information, we suggest a method called dynamic weighted majority voting, where each model’s importance is regularly adjusted for each sample based on the surface temperature of the idler taken from IR images. We demonstrate that our method of combining information from multiple classifiers can work better than using just one sensor for monitoring conditions in real-world situations. Full article

Electroencephalography (EEG) communication technology with ultra-low power consumption, high transmission data rates, and low latency plays a significant role in mHealth, telemedicine, and Internet of Medical Things (IoMT). In this paper, generalized frequency division multiplexing (GFDM)-based direct mapping (DM) multi-input—multi-output (MIMO) mobile EEG communication technology (MECT) is proposed for implementation with the above-mentioned applications. The (2000, 1000) low-density parity-check (LDPC) code, four-quadrature amplitude modulation (4-QAM), a power assignment mechanism, and the 3rd Generation Partnership Project (3GPP) cluster delay line (CDL) channel model D were integrated into the proposed EEGCT. The transmission bit error rates (BERs), mean square errors (MSEs), and Pearson-correlation coefficients (PCCs) of the original and received EEG signals were evaluated. Simulation results show that, with a signal to noise ratio (SNR) of 14.51 dB, with a channel estimation error (CEE) of 5%, the BER, MSE, and PCC of the original and received EEG signals were 9.9777 × 10⁻⁸, 1.440 × 10⁻⁵ and 0.999999998, respectively, whereas, with an SNR of 15.0004 dB and a CEE of 10%, they were 9.9777 × 10⁻⁸, 1.4368 × 10⁻⁵, and 0.999999997622151, respectively. As the BER value, and PS saving are 9.9777 × 10⁻⁸, and 40%, respectively. With the CEE changes from 0% to 5%, and 5% to 10%, the N₀ values of the proposed MECT decrease by approximately 0.0022 and 0.002, respectively. The MECT has excellent EEG signal transmission performance. Full article