Sensor Operation Research Articles

A tri-functional integrated hybrid energy harvester for the efficient collection of low-frequency environmental vibrational energy

Abstract To address the power supply challenges of Internet of Things (IoT) devices, this study proposes a novel tri-functional integrated hybrid energy harvester (TF-HEH) for efficiently capturing low-frequency environmental vibrations. The TF-HEH integrates triboelectric, electromagnetic, and piezoelectric mechanisms within a compact, 60 g cylindrical structure. Optimized for a 40 mm magnet length, the proposed TF-HEH exhibited high efficiency in converting vibrations into electricity, with peak outputs of 13 μW (pipe wall triboelectric generator), 17 μW (pipe cap triboelectric generator), 51.2 μW (piezoelectric nanogenerators), and 12.3 mW (electromagnetic generator) under a swing input with a 0.5 Hz frequency and 30° amplitude. The lightweight and portable design renders the device suitable for self-powered sensor operation in IoT applications.

Cybersecurity Challenges in UAV Systems: IEMI Attacks Targeting Inertial Measurement Units

The rapid expansion in unmanned aerial vehicles (UAVs) across various sectors, such as surveillance, agriculture, disaster management, and infrastructure inspection, highlights the growing need for robust navigation systems. However, this growth also exposes critical vulnerabilities, particularly in UAV package delivery operations, where intentional electromagnetic interference (IEMI) poses significant security and safety threats. This paper addresses IEMI attacks targeting inertial measurement units (IMUs) in UAVs, focusing on their susceptibility to medium-power electromagnetic interference. Our approach combines a comprehensive literature review and QuickField simulation with experimental validation using a commercially available 6-degree-of-freedom (DOF) IMU sensor. We propose a hardware-based electromagnetic shielding solution using mu-metal to mitigate IEMI’s impact on sensor performance. The study combines experimental testing with simulations to evaluate the shielding effectiveness under controlled conditions. The results of the measurements showed that medium-power IEMI significantly distorted IMU sensor readings, but our proposed shielding method effectively reduces the impact, improving sensor reliability. We demonstrate the mechanisms by which medium-power IEMI disrupts sensor operation, offering insights for future research directions. These findings also highlight the importance of integrating hardware-based shielding solutions to safeguard UAV systems against electromagnetic threats.

Research on collaborative reconstruction method of missing data and abnormal data in air handling units (AHU)system

The Air Handling Units (AHU), as critical components in building energy management, demand rigorous performance monitoring to ensure indoor comfort and energy efficiency. However, prolonged sensor operation often leads to malfunctions and measurement drifts due to insufficient maintenance, resulting in data gaps and anomalies. These issues pose significant challenges to system operational efficiency, occupant comfort, and energy consumption regulation. To address this dilemma, this paper innovatively leverages Bayesian inference techniques, developing a sophisticated likelihood function distance model. This model achieves precise imputation of missing data and effective rectification of anomalous data, thereby mitigating the issue of error accumulation associated with frequent reliance on historical data.Furthermore, the study exploits the intricate coupling relationships among multiple physical variables within the AHU systems to construct a comprehensive analytical model. This approach reduces the dependence on historical data for mathematical fitting and enhances the reliability of predicted data. Consequently, this paper presents an efficient methodology for handling missing and anomalous sensor data in AHU systems. Experimental results demonstrate that the relative error for reconstructing single-variable missing data remains below 7 %, while the relative error for reconstructing multi-variable missing data is even lower, at 3 %. These findings affirm the practical applicability of the methodology in calibrating anomalous sensor data within the building energy management domain.

A spin-refrigerated cavity quantum electrodynamic sensor

Quantum sensors based on solid-state defects, in particular nitrogen-vacancy (NV) centers in diamond, enable precise measurement of magnetic fields, temperature, rotation, and electric fields. Cavity quantum electrodynamic (cQED) readout, in which an NV ensemble is hybridized with a microwave mode, can overcome limitations in optical spin detection and has resulted in leading magnetic sensitivities at the pT-level. This approach, however, remains far from the intrinsic spin-projection noise limit due to thermal Johnson-Nyquist noise and spin saturation effects. Here we tackle these challenges by combining recently demonstrated spin refrigeration techniques with comprehensive nonlinear modeling of the cQED sensor operation. We demonstrate that the optically-polarized NV ensemble simultaneously provides magnetic sensitivity and acts as a heat sink for the deleterious thermal microwave noise background, even when actively probed by a microwave field. Optimizing the NV-cQED system, we demonstrate a broadband sensitivity of 576 ± 6 fT/Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{{{\\rm{Hz}}}}}$$\\end{document} around 15 kHz in ambient conditions. We then discuss the implications of this approach for the design of future magnetometers, including near-projection-limited devices approaching 3 fT/Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{{{\\rm{Hz}}}}}$$\\end{document} sensitivity enabled by spin refrigeration.

A Distributed RF Threat Sensing Architecture

The scope of this work is to propose a distributed RF sensing architecture that interconnects and utilizes a cyber security operations center (SOC) to support long-term RF threat monitoring, alerting, and further centralized processing. For the purpose of this work, RF threats refer mainly to RF jamming, since this can jeopardize multiple wireless systems, either directly as a Denial of Service (DoS) attack, or as a means to force a cellular or WiFi wireless client to connect to a malicious system. Furthermore, the possibility of the suggested architecture to monitor signals from malicious drones in short distances is also examined. The work proposes, develops, and examines the performance of RF sensing sensors that can monitor any frequency band within the range of 1 MHz to 8 GHz, through selective band pass RF filtering, and subsequently these sensors are connected to a remote SOC. The proposed sensors incorporate an automatic calibration and time-depended environment RF profiling algorithm and procedure for optimizing RF jamming detection in a dense RF spectrum, occupied by heterogeneous RF technologies, thus minimizing false-positive alerts. The overall architecture supports TCP/IP interconnections of multiple RF jamming detection sensors through an efficient MQTT protocol, allowing the collaborative operation of sensors that are distributed in different areas of interest, depending on the scenario of interest, offering holistic monitoring by the centralized SOC. The incorporation of the centralized SOC in the overall architecture allows also the centralized application of machine learning algorithms on all the received data.

Numerical model of N-level cascade systems for atomic Radio Frequency sensing applications

A ready-to-use numerical model has been developed for the atomic ladder (cascade) systems which are widely exploited in Rydberg Radio Frequency (RF) sensors. The model has been explicitly designed for user convenience and to be extensible to arbitrary N-level non-thermal systems. The versatility and adaptability of the model is validated up to 4-level atomic systems by direct comparison with experimental results from the prior art. The numerical model provides a good approximation to the experimental results and provides experimentalists with a convenient ready-to-use model to optimise the operation of an N-level Rydberg RF sensor. Current sensors exploit the 4-level atomic systems based on alkali metal atoms which require visible frequency lasers and these can be expensive and also suffer from high attenuation within optical fiber. The ability to quickly and simply explore more complex N-level systems offers the potential to use cheaper and lower-loss near-infrared lasers.

Additively manufactured microwave sensor for glucose level detection in saliva

In this paper, a novel realization of an ink-on-glass microwave sensor for biomedical applications is proposed. The Aerosol Jet Printing (AJP) technology is leveraged to implement a compact single-layer coplanar waveguide sensor featuring arc-shaped interdigital fingers that can accommodate a droplet of the Material-Under-Test (MUT). Such geometry provides a high sensitivity to even a very small deviation of MUT`s electrical properties when placed as a superstrate. An application towards the detection of trace amounts of glucose in saliva, which is a biomarker for diabetes, is showcased. The design and fabrication process of an exemplary sensor is discussed in detail. A circular geometry feature is introduced that helps a droplet to lie over the sensitive region due to wettability difference of glass substrate and silver ink. Sensor operating in K-band is developed providing a tradeoff between circuit size and droplet volume. The study is conducted for an artificial saliva requiring roughly a 0.5 µL droplet where changes in mixture content are proportional to relative changes of sensor`s transmission coefficient in a broad frequency range for occupied vs. empty states. The obtained results show that 10 mg of glucose per 100 ml of saliva can be easily distinguished in a frequency range of 20–30 GHz, whereas a monotonical change is visible for frequencies 20–26 GHz, which indicates the applicability of this sensor towards the detection of saliva-glucose levels and potential application in the detection of small amounts of other substances in liquids.

Monolayer Amphiphiles Hydrophobicize MoS2-Mediated Real-Time Water Removal for Efficient Waterproof Hydrogen Detection.

Ensuring water-fouling-free operation of semiconductor-based gas sensors is essential to maintaining their accuracy, reliability, and stability across diverse applications. Despite the use of hydrophobic strategies to prevent external water intrusion, addressing in situ-produced water transport during H2 detection remains a challenge. Herein, we construct a novel waterproof H2 sensor by integrating single-atom Ru(III) self-assembly with monolayer amphiphiles embedded in MoS2. The unique monolayer structure enables the sensor to detect H2 in the presence of water, as well as facilitate the self-transport of in situ-generated water from the H2-O2 reaction during H2 detection. Molecular dynamics simulations reveal that monolayer amphiphiles exhibit a higher water diffusion coefficient than multilayer amphiphiles, making them more advantageous for removing in situ-produced water. Deployable on mobile platforms, it enables wireless H2cat detection for up to 6 months, without the introduction of protective membranes against dust and water ingress. This work not only enhances the performance of H2 detection but also introduces a new concept for the advancement of stable water-sensitive sensors.

Wide-Range Operation of Microwave Photonic Sensor Using Recurrent Neural Network

Wide-Range Operation of Microwave Photonic Sensor Using Recurrent Neural Network

Guidance device for visually impaired people based on ultrasonic signals and open hardware

<span>Visual impairment is a complex challenge that affects people of all ages, and it is estimated that around 2.2 billion people worldwide lack adequate access to medical treatment and support. In Latin America, there is a lack of attention to people with visual disabilities, evidenced by poor urban infrastructure and lack of compliance with inclusion laws. Some projects stand out for the use of prototypes with artificial vision technology, global positioning system (GPS) and smart canes. Therefore, the objective of the project is to use ultrasonic sensors and a low-cost electronic device coupled to canes, for obstacle detection and mobility using an open hardware embedded system. The results confirmed the efficiency in the detection and operation of the ultrasonic sensor by activating the light emitting diode (LED), the buzzer and the vibrating motor according to the programmed distances. Challenges were identified, such as adapting the sensor to the tilt of the cane and the importance of accurate calibration of the ultrasonic sensor. The system met its objectives by detecting objects in a range of 2 to 50 cm and providing sound alerts to improve the perception of blind people.</span>

A 4H-SiC NMOSFET-Based Temperature Sensor Operating Between 14K and 481 K

A 4H-SiC NMOSFET-Based Temperature Sensor Operating Between 14K and 481 K

Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors.

Nonlinearity in sensor measurements reduces the sensor's accuracy. Therefore, accurate calibration is necessary for reliable sensor operation. This study proposes a segmented calibration method that divides the input range into multiple sections and calculates the optimized calibration functions for each one. This approach reduces the overall error rate and improves the calibration accuracy by isolating distinctive regions. The modified progressive polynomial calibration technique is used to calculate the calibration function. This algorithm addresses the computational complexity, allowing for reduced polynomial degrees and improving the accuracy. The segmented calibration method achieves a significantly lower error rate of 0.000006% compared to the original single calibration method, which has an error rate of 0.0823%, when using the same six calibration points and a fifth-degree polynomial function. This method maintains improved accuracy with fewer calibration points, and its ability to reduce the computational complexity and calculation time while using lower polynomial degrees is confirmed. Additionally, it can be extended to two dimensions to reduce the errors caused by cross-sensitivity. The results from a two-dimensional simulation show a reduction in the error rate ranging from 15.84% to 2.07% in an 8-bit signed fixed-point system. These results indicate that the segmented calibration method is an effective and scalable solution for various typical sensors.

Pd/PMMA Nanocomposite-Coated Optical Fiber Hydrogen Sensor Operating at Room Temperature With Humidity Tolerance

Pd/PMMA Nanocomposite-Coated Optical Fiber Hydrogen Sensor Operating at Room Temperature With Humidity Tolerance

Temperature field analysis and compensation improvement of load cell

During the operation of load cell, heat is generated by the strain gauge and the electronics on the PCB board, which leads to temperature gradients within the sensor itself. These temperature gradients are unstable at different ambient temperatures. Compensation inaccuracies can also occur when compensating for sensor measurements at different temperatures This paper proposes a method to change the position of temperature compensation resistors to address errors caused by the temperature field effect of the strain gauge sensor itself. Without affecting the sensor’s strain measurement, the correctness of the proposed method is demonstrated through steady-state thermal simulation results in ANSYS and experimental results, effectively addressing errors caused by unstable temperature gradients during the operation of strain gauge sensors.

Machine Learning for Improving Renewable Energy: Integrating Solar Power for Continuous Operation of Smart Sensors During Natural Calamities

This research tackles the critical issue of ensuring reliable data collection during natural disasters, such as floods, by calibrating and integrating smart sensors with renewable energy systems. Natural disasters often disrupt power supplies, posing significant challenges to the continuous operation and data availability of these sensors. To address this challenge, the study proposes leveraging renewable solar energy as a sustainable and reliable power source for smart sensors. By incorporating solar energy systems, the framework guarantees that sensors remain operational even in extreme conditions, ensuring that crucial environmental data is consistently accessible for rescue and recovery efforts. The research introduces an innovative approach that employs renewable energy to sustain the functionality of smart sensors during disasters. The framework includes models designed to manage uncertainties in disaster scenarios and optimize energy use for sensor operation. Additionally, the machine learning forecasting tool Prophet is utilized to improve the accuracy of predictions related to energy consumption and sensor performance. Prophet’s strength in handling time series data and generating reliable forecasts is vital for this application.

Triboelectric signal enhancement via interface structural design and integrated with deep learning for real-time online material recognition

Triboelectric nanogenerator (TENG) is a promising technology for material recognition due to a large amount of distinguishable feature information in triboelectric signal when the TENG device touches different materials. Nevertheless, the identification precision has only improved from the perspective of sensor construction and operation of design, rather than from the electrodes and the triboelectric interface structure. To prevent such issues, a design strategy of electrode/triboelectric material interface structure is applied to enhance the output performance and recognition accuracy. Electroless nickel was plated on a sponge to fabricate a TENG device electrode with a roughness of 179.1 µm. A coarse triboelectric material/electrode interface can improve the output performance of corresponding device. Hence, the nickel layer surface generates more the triboelectric charges, further leading to characteristic triboelectric signals, which can be distinguished by deep machine learning. As expected, the collected TENG signal has the unique characteristic of being able to identify the touched material with a recognition accuracy of 96 %. Based on this, combined with deep machine learning and the triboelectric effect, a material awareness system integrating TENG-based sensors, data collation, and display components was also designed and built for real-time material type identification with superior accuracy. The potential application advantages of the manufactured TENG device in nonvisual intelligent recognition can be foreseen.

Pt-Ta microhotplate with low resistance temperature coefficient and low resistance drift

Micro thermal plates are widely used in various sensors in the aerospace and automotive fields. With the reduction of chip size and the development of MEMS, micro thermal plates are also moving towards integration. The device failure caused by the Pt film at high temperatures seriously affects the stability of the sensor, and degradation determines the sensor's operating temperature and service life. In this paper, Pt, Pt-Ta, and Pt-Pd microcalorimetric plates were prepared and analyzed for their sintering behavior, microstructure, electrical properties, and high-temperature stability using co-firing of resistors and alumina substrates. The film layers prepared by co-firing were better matched to the alumina substrate, and the sintered network of the film layers was better. The Pt-10 %Ta microthermal plate has a 46 % lower resistance temperature coefficient TCR compared to the Pt microthermal plate, and the resistance drift at 800°C is reduced to 2.11 %/Day. And even at 1100°C, the Pt-Ta system also has a very low resistance drift −12 %/Day (Pt26.6 % /Day and Pt-Pd35.18 %/Day) with improved high temperature stability. The addition of Ta allows the membrane layer to have fewer voids at high temperatures, reinforcing the high temperature characteristics of the resistance thermometer. It improves the stability and accuracy of the heating plate in high temperature gas sensor applications.

Chemical durability of optical temperature sensors made of tellurite glass: Effect of the alumina concentration

Tellurite glass is an amorphous material commonly doped with rare earth ions for the development of optical temperature sensors. Considering the intrinsic properties of glasses, it is expected that these sensors could be used in acidic environments and withstand temperature changes. However, this has not been fully demonstrated since most of the literature is limited to demonstrate the sensor operation under normal conditions. Additionally, there is a lack of information regarding the chemical durability of these glasses and methods to improve it. In this work, a special tellurite glass composition was proposed for the development of optical temperature sensor. The effect of the alumina concentration on the structure, chemical durability and emission properties of the glasses was evaluated. Chemical durability was assessed by monitoring the weight loss of glass samples after immersion in hydrochloric acid solutions at room temperature, vegetable oil or air at 150 °C. The results indicate that the addition of alumina increases the chemical durability of the glass and improves its emission properties. It was found that the average temperature estimated by the optical sensor had a relative error of 1.3 % due to the degradation of the glass caused by remaining immersed in a 1 N HCl solution for 60 days.

Advancements in Flexible and Stretchable Electronics for Resistive Hydrogen Sensing: A Comprehensive Review.

Flexible and stretchable electronics have emerged as a groundbreaking technology with wide-ranging applications, including wearable devices, medical implants, and environmental monitoring systems. Among their numerous applications, hydrogen sensing represents a critical area of research, particularly due to hydrogen's role as a clean energy carrier and its explosive nature at high concentrations. This review paper provides a comprehensive overview of the recent advancements in flexible and stretchable electronics tailored for resistive hydrogen sensing applications. It begins by introducing the fundamental principles underlying the operation of flexible and stretchable resistive sensors, highlighting the innovative materials and fabrication techniques that enable their exceptional mechanical resilience and adaptability. Following this, the paper delves into the specific strategies employed in the integration of these resistive sensors into hydrogen detection systems, discussing the merits and limitations of various sensor designs, from nanoscale transducers to fully integrated wearable devices. Special attention is paid to the sensitivity, selectivity, and operational stability of these resistive sensors, as well as their performance under real-world conditions. Furthermore, the review explores the challenges and opportunities in this rapidly evolving field, including the scalability of manufacturing processes, the integration of resistive sensor networks, and the development of standards for safety and performance. Finally, the review concludes with a forward-looking perspective on the potential impacts of flexible and stretchable resistive electronics in hydrogen energy systems and safety applications, underscoring the need for interdisciplinary collaboration to realize the full potential of this innovative technology.

Self‐Powered Iontronic Capacitive Sensing Unit with High Sensitivity in Charge‐Output Mode

AbstractThe operation of iontronic capacitive sensors typically requires an external alternating current (AC) power source, resulting in additional energy consumption and AC‐frequency‐related sensing performance. Here, a class of self‐powered iontronic capacitive sensing units (SICSUs) is proposed based on a dynamic electric double layer (EDL), with a significant charge sensitivity of up to 24270 pC N−1, surpassing most piezoelectric materials by nearly 10 times. The effects of various design parameters and loading conditions on the sensing performance of the SICSUs are systematically investigated. The EDL at the hydrogel‐electrode interface is characterized in situ, revealing the underlying mechanism for high sensitivity and linearity. The capability of SICSUs in detecting diverse human‐related mechanical loads is demonstrated. Furthermore, a robotic hand equipped with a SICSU‐based artificial algesia sensor is fabricated to mimic the withdrawal reflex behavior of a human hand when its skin detects noxious stimuli caused by sharp objects.