Search Results (69,135)

Wireless surface electromyography (sEMG) sensors are very practical in that they can be worn freely, but the radio link between them and the receiver might cause unpredictable latencies that hinder the accurate synchronization of time between multiple sensors, which is an important aspect to study, e.g., the correlation between signals sampled at different sites. Moreover, to minimize power consumption, it can be useful to design a sensor with multiple clock domains so that each subsystem only runs at the minimum frequency for correct operation, thus saving energy. This paper presents the design, implementation, and test results of an sEMG sensor that uses Bluetooth Low Energy (BLE) communication and operates in three different clock domains to save power. In particular, this work focuses on the synchronization problem that arises from these design choices. It was solved through a detailed study of the timings experimentally observed over the BLE connection, and through the use of a dual-stage filtering mechanism to remove timestamp measurement noise. Time synchronization through three different clock domains (receiver, microcontroller, and ADC) was thus achieved, with a resulting total jitter of just 47 µs RMS for a 1.25 ms sampling period, while the dedicated ADC clock domain saved between 10% to 50% of power, depending on the selected data rate. Full article

Gas pipeline networks are vital urban infrastructure, susceptible to leaks caused by natural disasters and adverse weather, posing significant safety risks. Detecting and localizing these leaks is crucial for mitigating hazards. However, existing methods often fail to effectively model the time-varying structural data of pipelines, limiting their detection capabilities. This study introduces a novel approach for leak detection using a spatial–temporal attention network (STAN) tailored for small-hole leakage conditions. A graph attention network (GAT) is first used to model the spatial dependencies between sensors, capturing the dynamic patterns of adjacent nodes. An LSTM model is then employed for encoding and decoding time series data, incorporating a temporal attention mechanism to capture evolving changes over time, thus improving detection accuracy. The proposed model is evaluated using Pipeline Studio software and compared with state-of-the-art models on a gas pipeline simulation dataset. Results demonstrate competitive precision (91.7%), recall (96.5%), and F1-score (0.94). Furthermore, the method effectively identifies sensor statuses and temporal dynamics, reducing leakage risks and enhancing model performance. This study highlights the potential of deep learning techniques in addressing the challenges of leak detection and emphasizes the effectiveness of spatial–temporal modeling for improved detection accuracy. Full article

This paper presents an optoelectronic transceiver (OTRx) realized in a 180 nm CMOS technology for applications of short-range LiDAR sensors, in which a modified current-mode single-ended VCSEL driver (m-CMVD) is exploited as a transmitter (Tx) and a voltage-mode fully differential transimpedance amplifier (FD-TIA) is employed as a receiver (Rx). Especially for Tx, a concurrent automatic power control (APC) circuit is incorporated to compensate for the inevitable increase in the threshold current in a VCSEL diode. For Rx, two on-chip spatially modulated P⁺/N- well avalanche photodiodes (APDs) are integrated with the FD-TIA to achieve circuit symmetry. Also, an extra APD is added to facilitate the APC operations in Tx, i.e., concurrently adjusting the bias current of the VCSEL diode by the action of the newly proposed APC path in Rx. Measured results of test chips demonstrate that the proposed OTRx causes the DC bias current to increase from 0.93 mA to 1.42 mA as the input current decreases from 250 µA_pp to 3 µA_pp, highlighting its suitability for short-range sensor applications utilizing a cost-effective CMOS process. Full article

This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The system leverages advanced computer vision algorithms and an extended Kalman filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive maneuver computation. A novel hardware-in-the-loop (HIL) simulation environment was developed and used for validation, enabling the evaluation of closed-loop real-time interaction between the “Sense” and “Avoid” subsystems. Extensive numerical simulations and a flight test campaign demonstrate the system’s effectiveness in real-time detection and the avoidance of non-cooperative obstacles, ensuring compliance with UAV aero mechanical and safety constraints in terms of minimum separation requirements. The novelty of this research lies in (1) the design of an innovative and efficient visual processing pipeline tailored for SWaP-constrained mini-UAVs, (2) the formulation an EKF-based data fusion strategy integrating optical data with a custom-built Doppler radar, and (3) the development of a unique HIL simulation environment with realistic scenery generation for comprehensive system evaluation. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, significantly contributing to the safety of flight in non-segregated airspaces Full article

Incorrect sensor readings can cause serious problems in Wireless Sensor Networks (WSNs), potentially disrupting the operation of the entire system. As shown in the literature, they can arise from various reasons; therefore, addressing this issue has been a significant challenge for the scientific community over the past few decades. In this paper, we examine the applicability of seven distributed consensus gossip-based algorithms for sensor fusion (namely, the Randomized Gossip algorithm, the Geographic Gossip algorithm, three initial configurations of the Broadcast Gossip algorithm, the Push-Sum protocol, and the Push-Pull protocol) to compensate for incorrect data in WSNs. More specifically, we consider a scenario where the sensor-measured data (measured by a set of independent sensor nodes) are skewed due to Gaussian noise with a various standard deviation $\sigma$, resulting in discrepancies between the measured values and the true value of observed physical quantities. Subsequently, the aforementioned algorithms are employed to mitigate this skewness in order to improve the accuracy of the measured data. In this paper, WSNs are modeled as random geometric graphs with various connectivity, and the performance of the algorithms is evaluated using two metrics (specifically, the mean square error (MSE) and the number of sent messages required for an algorithm to be completed). Based on the presented results, it is identified that all the examined algorithms can significantly suppress incorrect sensor readings (MSE without sensor fusion = −0.42 dB if $\sigma$ = 1, and MSE without sensor fusion = 14.05 dB if $\sigma$ = 5), and the best performance is achieved by PS in dense graphs and by GG in sparse graphs (both algorithms achieve the maximum precision MSE = −24.87 dB if $\sigma$ = 1 and MSE = −21.02 dB if $\sigma$ = 5). Additionally, the performance of the analyzed distributed consensus gossip algorithms is compared to the best deterministic consensus algorithm applied for the same purpose. Full article

Single-photon avalanche detectors (SPADs) have significant applications in fields such as autonomous driving. However, processing massive amounts of background data requires substantial storage and computational resources. This paper designs a linear SPAD sensor capable of three detection modes: 2D intensity detection, 3D synchronous detection, and 3D asynchronous detection. A configurable coincidence circuit is used to effectively suppress background light. To overcome the significant resource demands for storage and computation, this paper designs a histogram circuit that simultaneously possesses data storage and shifting capabilities. This circuit can not only perform statistical counting on time data but also shift data to quickly complete computational analysis. The chip is fabricated using a 0.13 μm mixed-signal CMOS process, with a pixel scale of 64 elements, a time resolution of 132 ps, and a power consumption of 12.9 mW. Test results indicate that the chip has good detection capabilities and good background light suppression. When the background light intensity is 6000 lux, the maximum background data are suppressed by 95.4%, and the average suppression rate increases to 86% as the coincidence threshold is raised from 0 to 1. Full article

Currently, semiconductor gas sensors are being actively studied and used in various fields, including ecology, industry, and medical diagnostics. One of the major challenges is to reduce their operating temperature to room temperature. To address this issue, sensor layers based on WO_3−x/WS₂ nanostructures synthesized by the hydrothermal method have been proposed. In this paper, the morphology of the material’s surface and its elemental composition were investigated, as well as the optical band gap. Additionally, changes in the resistance of the WO_3−x/WS₂ sensor layers under the influence of alcohol vapors at room temperature were analyzed. The results showed that the layers exhibited a significant response, with short response and recovery times. The achieved response value to 1000 ppm of isopropanol was 1.25, with a response time of 13 s and a recovery time of 12 s. The response to 1000 ppm of ethanol was 1.35, and the response and recovery times were 20 s. This indicates that these sensor layers have promising potential for various applications. Full article

Wearable electronic devices have shown great application prospects in the fields of tactile sensing, electronic skin, and soft robots. However, the existing wearable electronic devices face limitations such as power supply challenges, lack of portability, and discomfort, which restrict their applications. The invention of triboelectric nanogenerators (TENGs) with dual functions of energy harvesting and sensing provides an innovative solution to address these issues. This study prepared a highly stretchable conductive hydrogel using doped conducting polymer poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) as a strain sensor, demonstrating high sensitivity (GF = 4.31), an ultra-wide sensing range (0–1690%), ultra-fast response speed (0.15 s), excellent durability, and repeatability. A high-performance triboelectric nanogenerator was constructed using the hydrogel as an electrode, achieving an output performance of up to 192 V. Furthermore, the TENG fixed in the hands, wrists, legs, and feet of the human body can be used as a wearable electronic device to monitor human motion, which is conducive to promoting the development of triboelectric nanogenerators based on conductive hydrogels in strain sensors and self-powered wearable devices. Full article

Our limb prostheses aim to restore Activities of Daily Living (ADLs) for amputees, with the socket being a critical component of trans-tibial prostheses influencing both comfort and functionality. Despite technological advancements, challenges such as fit, weight, and durability remain. This study investigates an additive manufacturing method for Total Surface Bearing (TSB) sockets, leveraging CT scans to create a Computer-Aided Design (CAD) and finite element (FE) model. Biomechanical behavior under static loading conditions were analyzed using FE analysis and resistive-based pressure sensors. The study found consistent pressure distribution across the residual limb, with deviations of 8.53 kPa and 4.46 kPa between FE analysis and experimental measurements. Mean pressures of 44.6 kPa and 22.11 kPa were observed under Full Body Weight (FBW) and Half Body Weight (HBW) conditions, respectively. The FE analysis demonstrated a uniform stress distribution in the prosthetic socket, with a maximum stress of 0.15 MPa and a deformation of 0.008 mm, highlighting the effectiveness of this approach in enhancing socket design. Full article

A wavelength-swept laser (WSL) demodulation system offers a unique time-domain analysis solution for high-sensitivity optical fiber sensors, providing a high-resolution and high-speed method compared to optical spectrum analysis. However, most traditional WSL-demodulated sensing systems require a synchronous trigger signal or an additional optical dispersion link for sensing analysis and typically use a fiber Bragg grating (FBG) as the sensing unit, which limits displacement sensitivity and increases fabrication costs. We present a novel displacement sensing system that combines a trigger-free WSL demodulation method with a cascaded balloon-like interferometer, featuring a simple structure, high sensitivity, and low temperature cross-sensitivity. The sensor is implemented by bending a short length of single-mode fiber with an optimal radius of around 4 mm to excite cladding modes, which form an interference spectral response with the core mode. Experimental findings reveal that the system achieves a high sensitivity of 397.6 pm/μm for displacement variation, corresponding to 19.88 ms/μm when demodulated using a WSL with a sweeping speed of 20 nm/s. At the same time, the temperature cross-sensitivity is as low as 5 pm/°C or 0.25 ms/°C, making it a strong candidate for displacement sensing in harsh environments with significant temperature interference. Full article

The Internet of Things produces vast amounts of data that require specialized algorithms in order to secure them. Lightweight cryptography requires ciphers designed to work on resource-constrained devices like sensors and smart things. A new encryption scheme is introduced based on a blend of the best-performing algorithms, SIMECK and TEA. A selection of software-oriented Addition–Rotation–XOR (ARX) block ciphers are augmented with a dynamic substitution security layer. The performance is compared against other lightweight approaches. The US National Institute of Standards and Technology (NIST) SP800-22 Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications and the German AIS.31 of the Federal Office for Information Security (BSI) are used to validate the output of the proposed encryption scheme. The law of iterated logarithm (LIL) for randomness is verified in all three forms. The total variance (TV), the Hellinger Distance (HD), and the root-mean-square deviation (RMSD) show values smaller than the required limit for 10.000 sequences of ciphertext. The performance evaluation is analyzed on a Raspberry PICO 2040. Several security metrics are compared against other ciphers, like ${\chi }^2$ and encryption quality (EQ). The results show that SIMECK-T is a powerful and fast, software-oriented, lightweight cryptography solution. Full article

This paper presents the concept of a radiofrequency (RF) sensor designed to estimate the mass of the regolith acquired by a sampling device or excavator in planetary environments. The sensor utilizes a microstrip line with an open end as the sensing element, with the mass estimation based on measurements of the phase of the reflection coefficient (S11 of the scattering matrix) for the line immersed in the regolith. The Rotary Clamshell Excavator (RCE) was employed for the experimental evaluation of the sensor’s performance. The RCE successfully passed an environmental test campaign, demonstrating its suitability for future lunar missions. The test results indicate that the RF sensor can estimate the mass of the acquired regolith with reasonable accuracy, approximately 15%, making it a viable solution for rough mass estimation in sampling devices and excavators. Full article

Although the construction sector significantly bolsters the Australian economy, it is widely recognised for lagging behind other industries in adopting emerging technologies. This study reviews existing literature to explore the current state of technology integration in the Australian construction industry, focusing on its impact on safety, cost, quality, and project timelines. The research methodology involved conducting interviews and surveys with industry stakeholders to collect qualitative and quantitative data regarding technology integration. The analysis of survey data provided insights into the current and anticipated future adoption of emerging technologies in Australia, identifying significant obstacles that impede the industry’s digital transformation. Based on the survey results, a specialised system was developed for Tier 1 and Tier 2 construction firms, enabling them to evaluate their present and predicted future use of emerging technologies. Additionally, this system helps university graduates gauge their understanding and awareness of emerging technologies to meet the expectations of leading industry professionals. The findings of this study aim to enhance the understanding and implementation of emerging technologies within the construction sector, thereby fostering a new generation of professionals who recognise the significant potential of these technologies to revolutionise construction practices. Full article

Fatigue management is critical for high-risk professions such as pilots, firefighters, and healthcare workers, where physical and mental exhaustion can lead to catastrophic accidents and loss of life. Traditional fatigue assessment methods, including surveys and physiological measurements, are limited in real-time monitoring and user convenience. To address these issues, this study introduces a novel contactless fatigue level diagnosis system leveraging multimodal sensor data, including video, thermal imaging, and audio. The system integrates non-contact biometric data collection with an AI-driven classification model capable of diagnosing fatigue levels on a 1 to 5 scale with an average accuracy of 89%. Key features include real-time feedback, adaptive retraining for personalized accuracy improvement, and compatibility with high-stress environments. Experimental results demonstrate that retraining with user feedback enhances classification accuracy by 11 percentage points. The system’s hardware is validated for robustness under diverse operational conditions, including temperature and electromagnetic compliance. This innovation provides a practical solution for improving operational safety and performance in critical sectors by enabling precise, non-invasive, and efficient fatigue monitoring. Full article

Urban green-blue spaces (UGBS) are increasingly recognized for their benefits to physical and mental well-being. However, research on real-time gender-specific emotional responses to UGBS remains limited. To address this gap, a dual-method approach combining facial expression recognition (FER) and self-reported measures to investigate gender differences in real-time emotional evaluations of UGBS was developed. Using static images from Google Street View as stimuli, a self-reporting experiment involving 108 participants provided insights into subjective emotional experiences. Subsequently, a FER experiment, utilizing 360-degree video stimuli, captured over two million data points, validating the feasibility and advantages of real-time emotion monitoring. The findings revealed distinct gender-specific emotional patterns: women experienced stronger pleasant emotions and preferred scenes evoking higher arousal, while men demonstrated sharper responses and rated scenes with peak valence emotions more favorably. Grass elicited relaxation and delight in women and arousal in men, whereas blue spaces induced calmness across genders, with men reporting greater relaxation as water content increased. The study underscores the potential of FER technology in assessing real-time emotional responses, providing actionable insights for inclusive urban planning. By integrating advanced tools and participatory design approaches, urban planners can develop strategies that enhance emotional well-being and create livable cities that support diverse user needs. Full article