Small Sensor Research Articles

Comparison of foot pressure distribution and foot kinematics in undulatory underwater swimming between performance levels

ABSTRACT This study aimed to elucidate the foot kinematics and foot pressure difference characteristics of faster swimmers in undulatory underwater swimming (UUS). In total, eight faster and eight slower swimmers performed UUS in a water flume at a flow velocity set at 80% of the maximal effort swimming velocity. The toe velocity and foot angle of attack were measured using a motion capture system. A total of eight small pressure sensors were attached to the surface of the left foot to calculate the pressure difference between the plantar and dorsal sides of the foot. Differences in the mean values of each variable between the groups were analysed. Compared to the slower swimmers, the faster swimmers exhibited a significantly higher swimming velocity (1.53 ± 0.06 m/s vs. 1.31 ± 0.08 m/s) and a larger mean pressure difference in the phase from the start of the up-kick until the toe moved forward relative to the body (3.88 ± 0.65 kPa vs. 2.66 ± 1.19 kPa). The faster group showed higher toe vertical velocity and toe direction of movement, switching from lateral to medial at the time of generating the larger foot pressure difference in the up-kick, providing insight into the reasons behind the foot kinematics of high UUS performance swimmers.

Wireless wearable system based on multifunctional conductive PEG-HEMA hydrogel with anti-freeze, anti-UV, self-healing, and self-adhesive performance for health monitoring

Polymer gels, such as hydrogels, have been widely applied in biomedicine, flexible electronics, and soft robotics. However, due to the complexity of application environments, preparing hydrogels that can maintain comprehensive performance under harsh conditions remains a challenge. Herein, based on polyethylene glycol (PEG) and water as a mixed solvent and using hydroxyethyl methacrylate (HEMA) as a monomer, a conductive dual-network PEG-HEMA hydrogel with anti-freezing, anti-ultraviolet, self-healing, and self-adhesive functions was prepared. The results show that PEG-HEMA hydrogel remained unfrozen even at -20°C, with tensile strength and elongation at break of 49.946 KPa and 529.7%, high resolution (able to measure strains as low as 0.2%), good sensitivity and linearity (GF=1.076, R2=0.999), fatigue resistance (over 1000 cycles), and long-term storage capability (over one week). Furthermore, a small wireless wearable strain sensor system was developed by integrating the hydrogel with a microcontroller, which is capable of monitoring various human motion and physiological signals in real time and exhibiting excellent sensing performance. This work provides a straightforward approach for multifunctional hydrogels, and it is expected to attract widespread attention and applications in fields such as electronic skin, human-machine interaction, and human health monitoring.

Image Reconstruction Requirements for Short-Range Inductive Sensors Used in Single-Coil MIT.

MIT (magnetic induction tomography) image reconstruction from data acquired with a single, small inductive sensor has unique requirements not found in other imaging modalities. During the course of scanning over a target, measured inductive loss decreases rapidly with distance from the target boundary. Since inductive loss exists even at infinite separation due to losses internal to the sensor, all other measurements made in the vicinity of the target require subtraction of the infinite-separation loss. This is accomplished naturally by treating infinite-separation loss as an unknown. Furthermore, since contributions to inductive loss decline with greater depth into a conductive target, regularization penalties must be decreased with depth. A pair of squared L2 penalty norms are combined to form a 2-term Sobolev norm, including a zero-order penalty that penalizes solution departures from a default solution and a first-order penalty that promotes smoothness. While constraining the solution to be non-negative and bounded from above, the algorithm is used to perform image reconstruction on scan data obtained over a 4.3 cm thick phantom consisting of bone-like features embedded in agarose gel, with the latter having a nominal conductivity of 1.4 S/m.

HoloDiffusion: Sparse Digital Holographic Reconstruction via Diffusion Modeling

In digital holography, reconstructed image quality can be primarily limited due to the inability of a single small aperture sensor to cover the entire field of a hologram. The use of multi-sensor arrays in synthetic aperture digital holographic imaging technology contributes to overcoming the limitations of sensor coverage by expanding the area for detection. However, imaging accuracy is affected by the gap size between sensors and the resolution of sensors, especially when dealing with a limited number of sensors. An image reconstruction method is proposed that combines physical constraint characteristics of the imaging object with a score-based diffusion model, aiming to enhance the imaging accuracy of digital holography technology with extremely sparse sensor arrays. Prior information of the sample is learned by the neural network in the diffusion model to obtain a score function, which alternately constrains the iterative reconstruction process with the underlying physical model. The results demonstrate that the structural similarity and peak signal-to-noise ratio of the reconstructed images using this method are higher than the traditional method, along with a strong generalization ability.

A Single-Longitudinal-Mode S + C Band Wavelength-Tunable Fiber Laser.

An external cavity wavelength-fiber ring laser (ECWTFL) based on a semiconductor optical amplifier and a combined wavelength scanning filter in the Littrow configuration is proposed and experimentally demonstrated. With the benefit of the combination of an external cavity wavelength filter and a Lyot filter, the laser achieves a single-mode narrow linewidth output with a linewidth of 1.75 kHz. The wavelength tuning range reaches 133 nm, covering the entire S + C band. The proposed ECWTFL is used for demodulation of a fiber EFPI sensor; the result shows that the proposed ECWTFL has the ability to demodulate the small cavity-length FPI sensor.

Optics miniaturization strategy for demanding Raman spectroscopy applications

Raman spectroscopy provides non-destructive, label-free quantitative studies of chemical compositions at the microscale as used on NASA’s Perseverance rover on Mars. Such capabilities come at the cost of high requirements for instrumentation. Here we present a centimeter-scale miniaturization of a Raman spectrometer using cheap non-stabilized laser diodes, densely packed optics, and non-cooled small sensors. The performance is comparable with expensive bulky research-grade Raman systems. It has excellent sensitivity, low power consumption, perfect wavenumber, intensity calibration, and 7 cm−1 resolution within the 400–4000 cm−1 range using a built-in reference. High performance and versatility are demonstrated in use cases including quantification of methanol in beverages, in-vivo Raman measurements of human skin, fermentation monitoring, chemical Raman mapping at sub-micrometer resolution, quantitative SERS mapping of the anti-cancer drug methotrexate and in-vitro bacteria identification. We foresee that the miniaturization will allow realization of super-compact Raman spectrometers for integration in smartphones and medical devices, democratizing Raman technology.

Portable luminescent fiber- and glove-based nanosensor for multicolor visual detection of tetracycline in food samples.

Anintelligent fluorescent nanoprobe (lignite-CDs-Eu) was constructed by an effective and facile method based on lignite-derived carbon dots (CDs) and lanthanide europium ions (Eu3+), which exhibited high sensitivity, low detection limit (13.35nM) and visual color variation (from blue to red) under ultraviolet light towards tetracycline (TC) detection. Significantly, portable and economical sensors were developed using lignite-CDs-Eu immobilized fiber material of filter paper and wearable glove with the aid of colorextractingandimage processing application (APP) in the smartphone. Facile, fast and real-time visual detection of TC in food samples was realized. Moreover, logic gate circuit was also designed to achieve intelligent and semi-quantitative inspection of TC. To some extent, this study extended the cross-application of intelligent computer software in food analytical science, and provided a certain reference for the development of small portable detection sensors which were suitable for convenience and non-professional use in daily life.

Cardiac Multi-Frequency Vibration Signal Sensor Module and Feature Extraction Method Based on Vibration Modeling.

Cardiovascular diseases pose a long-term risk to human health. This study focuses on the rich-spectrum mechanical vibrations generated during cardiac activity. By combining Fourier series theory, we propose a multi-frequency vibration model for the heart, decomposing cardiac vibration into frequency bands and establishing a systematic interpretation for detecting multi-frequency cardiac vibrations. Based on this, we develop a small multi-frequency vibration sensor module based on flexible polyvinylidene fluoride (PVDF) films, which is capable of synchronously collecting ultra-low-frequency seismocardiography (ULF-SCG), seismocardiography (SCG), and phonocardiography (PCG) signals with high sensitivity. Comparative experiments validate the sensor's performance and we further develop an algorithm framework for feature extraction based on 1D-CNN models, achieving continuous recognition of multiple vibration features. Testing shows that the recognition coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the 8 features are 0.95, 2.18 ms, and 4.89 ms, respectively, with an average prediction speed of 60.18 us/point, meeting the re-quirements for online monitoring while ensuring accuracy in extracting multiple feature points. Finally, integrating the vibration model, sensor, and feature extraction algorithm, we propose a dynamic monitoring system for multi-frequency cardiac vibration, which can be applied to portable monitoring devices for daily dynamic cardiac monitoring, providing a new approach for the early diagnosis and prevention of cardiovascular diseases.

Energy efficient cluster routing protocol for wireless sensor networks using hybrid metaheuristic approache’s

The development of wireless sensor networks (WSNs) has been particularly notable in the context of smart computing, where various application areas have been identified. These networks consist of self-configured, small sensor nodes with battery power. However, sensors have limited energy and resources at their disposal. When unbalanced nodes exist within the network, it adversely affects the network’s lifetime due to increased power consumption. Designing efficient routing for wireless sensor networks remains a challenging task, particularly in terms of energy efficiency. Optimal solutions to address these challenges involve reducing node energy consumption through the implementation of clustering techniques. The current clustering schemes do not take into account node energy balancing, node density, and scalability when selecting low-energy nodes as cluster heads. The existing system is limited to exploring global opportunities within specified search zones. To enhance the performance of WSNs, a hybrid approach combining artificial bee colony (ABC) and ant colony optimization (ACO) has been developed. This approach helps in selecting an ideal cluster head from a group of terminals. Several factors are considered in the cluster head election, including residual energy at the nodes, distance to neighbors, distance to the base station, node degree, and node centrality. ACO determines the path between the cluster leader and the base station (BS) by selecting the most efficient route in terms of distance, remaining power, and node degrees. The performance of this proposed methodology has been analyzed in terms of energy consumption, network lifetime, and data packets at the base station. A comparison has been made between the outputs of the proposed methods and traditional benchmarking methods. The results reveal a substantial improvement in the average network lifetime, showcasing an increase of 40.50%, 33.17%, 25.00%, and 15.49% when compared to LEACH, Beecluster, iABC, and BeeSensor, respectively. In terms of alive nodes, our solution surpasses LEACH, Beecluster, iABC, and BeeSensor by 28.7%, 22.51%, 20.95%, and 12.47%, respectively. Additionally, the energy consumption of our approach proves to be significantly lower than LEACH, Beecluster, iABC, and BeeSensor, recording reductions of 47.25%, 32.40%, 27.38%, and 22.20%, respectively.

Development and Testing of a Rocket-Based Sensor for Atmospheric Sensing Using an Unmanned Aerial System.

Measurements of the vertical structure of the lower atmosphere are important to the understanding of air quality. Unmanned Aerial Systems (UASs, drones) can provide low cost, repeatable measurements of the temperature, pressure, and relative humidity. A set of inexpensive sensors controlled with an Arduino microprocessor board were tested on a UAS against a meteorology grade sensor. Two modes of operation for sampling were tested: a forward moving sampler and a vertical ascent sampler. A small particle sensor (Sensiron SPS30) was integrated and was capable of retrieving vertical aerosol distributions during an inversion event. The thermocouple-based temperature probe and the relative humidity measurement on the Bosch BME280 sensor correlated well with the meteorological sensor. The temperature and relative humidity sensors were then deployed on a rocket sounding platform. The rocket sounding system performed well up to a height of 400 m. The inexpensive sensors were found to perform adequately for low-cost development and uses in education and research.

Study on the mechanism of vibration signal generation in water supply pipeline leaks

This study addresses water supply network leakage by employing numerical simulations to overcome challenges related to too small orifice hindering sensor deployment. Utilizing computational fluid dynamics and finite element methods, coupled with large eddy simulation, flow field distribution within pipes and at leakage orifice is investigated. The formation of leakage flow fields is categorized into three stages: leakage formation, unstable leakage, and stable leakage. During the leakage stage, the flow field is analyzed, and four regions—bottom central, bottom vortex, upper central, and upper vortex—are identified based on spatial characteristics. Detailed variable distribution patterns (velocity, pressure, vorticity) are provided in various planes. Understanding these distribution characteristics, six excitation sources responsible for generating leakage vibration signals are identified: pulsating velocity, pulsating pressure, bubble rupture, cavity pulsation, vortex-induced vibration, and gas expansion. The relationships among these excitation sources and their intensities concerning pipeline pressure and leak orifice radius are analyzed. Notably, a positive correlation between pressure and the intensity of all excitation sources is observed, while the impact of leak orifice size on these sources is found to be intricate. This research significantly contributes to an enhanced understanding of the physical processes associated with pipeline leakage.

Magnetic Attitude Control Scheme for Drag Sail Deorbiting with Limited Sensors

As concerns over space debris in the Low Earth orbit (LEO) intensify, deorbit strategies for small satellites becomes evident because many lack an effective deorbit mechanism. After successfully demonstrating their effectiveness in flight tests, aerodynamic drag sails emerge as a superior deorbit solution, significantly reducing the time required for deorbiting. To maximize the aerodynamic drag, it is crucial to maintain the drag sail perpendicular to the atmospheric flow. This becomes especially challenging with the potential threat of sensor failure. This paper introduces a magnetic control scheme for deorbiting using drag sails. Relying solely on magnetometers as sensors, the control scheme uses magnetorquers, bias momentum wheels, and an onboard geomagnetic field model to keep the sail perpendicular to the satellite’s velocity. Furthermore, a backup control scheme that does not require the onboard geomagnetic field model is proposed. This control scheme uses magnetometers in conjunction with just another common small satellite sensor, such as the GPS, gyroscopes, or sun sensors as sensors. A stability criterion, based on a linearized analytical model, validates the feasibility of this backup control scheme. Numerical simulations confirm the efficacy of both control schemes. Both schemes reduce the deorbit time by approximately 40% compared to the uncontrolled satellite deorbiting.

A general-purpose spectroscopic camera for single-board computers

A low-cost consumer-grade spectroscopic system offers potential applications in many fields. Linear variable optical filters (LVOFs) placed in front of imaging sensors are one of the most promising avenues to realize this goal. In this work, we built a spectroscopic system by depositing an LVOF directly onto the protective window of OV5647 color and OV9281 monochrome imaging sensors for the Raspberry Pi. The ¼” sensors cost $15-$80 CAD at the time of writing and have linear dimensions of only 2.7 – 3.6 mm. To achieve a suitable gradient over such small sensors, an ultra-slow shutting system was built for the physical vapor deposition of an SiO2 gradient layer between two partially transparent silver mirrors to form a miniature LVOF. The resulting devices operated as effective low-cost spectrometers that were single-moded in the visible spectrum. These simple systems were finally used to measure spectra from sample light sources, clearly illustrating the advantages and limitations of this simple and economical approach towards everyday consumer spectroscopy.

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals.

Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, which is not a trivial task. This study proposes a method for an accurate recognition and localization of heart sounds in Forcecardiography (FCG) recordings. FCG is a novel technique able to measure subsonic vibrations and sounds via small force sensors placed onto a subject's thorax, allowing continuous cardio-respiratory monitoring. In this study, a template-matching technique based on normalized cross-correlation was used to automatically recognize heart sounds in FCG signals recorded from six healthy subjects at rest. Distinct templates were manually selected from each FCG recording and used to separately localize S1 and S2 sounds, as well as S1-S2 pairs. A simultaneously recorded electrocardiography (ECG) trace was used for performance evaluation. The results show that the template matching approach proved capable of separately classifying S1 and S2 sounds in more than 96% of all heartbeats. Linear regression, correlation, and Bland-Altman analyses showed that inter-beat intervals were estimated with high accuracy. Indeed, the estimation error was confined within 10 ms, with negligible impact on heart rate estimation. Heart rate variability (HRV) indices were also computed and turned out to be almost comparable with those obtained from ECG. The preliminary yet encouraging results of this study suggest that the template matching approach based on normalized cross-correlation allows very accurate heart sounds localization and inter-beat intervals estimation.

The high-performance DIRC for the ePIC detector at the EIC

The next-generation nuclear physics facility in the United States will be the Electron-Ion Collider (EIC), scheduled to be built at Brookhaven National Laboratory (BNL). Excellent particle identification (PID) is one of the key requirements for the EIC central detector called ePIC. Identification of the hadrons in the final state is critical to study how different quark flavors contribute to nucleon properties. A detector using the Detection of Internally Reflected Cherenkov light (DIRC) principle, with a radial size of only 7-8 cm, was chosen as the PID system for the ePIC barrel region. Within the framework of the EIC R&D programs, the development of a high-performance DIRC (hpDIRC) detector was undertaken with the aim of significantly advancing state-of-the-art performance. Key components of the hpDIRC detector are a 3-layer compound lens and small pixel-size photo sensors. Currently, the hpDIRC R&D program is focused on developing and validating the radiation hard 3-layer lens, quality assurance of the BaBar DIRC radiation bars, and the early stage of the hpDIRC prototype program with Cosmic Ray Telescope at Stony Brook University, in preparation for beam tests at Fermilab in 2024.

A Rolled Organic Thermoelectric Generator with High Thermocouple Density

AbstractThe surge in the number of distributed microelectronics and sensors requires versatile, scalable, and affordable power sources. Heat‐harvesting organic thermoelectric generators (TEGs) are regarded as potential key components of the future energy landscape. Recent advances in the performance of organic thermoelectric materials have made practical applications of organic TEGs more feasible than ever before, yet the challenges of designing and fabricating organic TEGs suitable for real scenarios are scarcely addressed. Specifically, small sensors and wearables demand for micro‐thermoelectric generators (µTEGs) with high power density architectures and small form factors, while typical demonstrations of organic TEGs are characterized by < 10 thermocouples (TCs) per cm2. This work presents a rolled, organic µTEG architecture combining large‐area, solution‐based deposition techniques, such as inkjet and spray‐coating, and an ultrathin parylene substrate to achieve a thermocouple density of 1842 TCs cm−2. Such demonstrative µTEG reaches a thermoelectric conversion performance of 0.15 µW cm−2 at ΔT = 50 K. Such power output is well in line with finite element method simulations, which highlight the benefit of the architecture and show that remarkable power densities, in the mW cm−2 range at ΔT = 10 K, are realistically achievable with geometrical improvements and already ongoing advancements in organic thermoelectric inks.

Fluid Oscillation‐Driven Bi‐Directional Air Turbine Triboelectric Nanogenerator for Ocean Wave Energy Harvesting

AbstractOcean wave energy is one of the important renewable energy sources. However, random, low‐frequency, and micro‐amplitude characteristics of ocean waves make it difficult for traditional electromagnetic generators to collect wave energy efficiently. The emergence of triboelectric nanogenerator (TENG) provides an extremely effective technical means for the collection of low‐frequency and micro‐amplitude wave energy. In this work, a non‐contact turntable‐structured oscillating water column TENG (OWC‐TENG) is designed by combining an OWC wave energy conversion mechanism with a TENG for the first time. The OWC is optimized through simulation experiments according to the principles of ship hydrostatics and wave theory. The output performances of the OWC‐TENGs with different structural parameters under different water wave excitation conditions are then tested. The TENG delivers a maximum output current of 55.45 µA and an output power of 5.28 mW, which corresponds to a power density of 114.8 W m−3, enabling a stable power supply for small sensors at sea. This work provides a new solution to the efficient collection of low‐frequency micro‐amplitude ocean wave energy for powering various offshore equipments, presenting broad application prospects in ocean blue energy development and offshore Internet of Things.

Synthesis of tetraphenylethylene-based small molecular sensor for the selective “turn-on” detection of pyrophosphoric acid in the aqueous solution

Pyrophosphoric acid (PPi) is a crucial indicator for monitoring adenosine triphosphate hydrolysis processes, and abnormal PPi levels in the human body seriously threaten human health. Thus the efficient detection of the concentration of PPi in the aqueous solution is important and urgent. This paper described the successful synthesis of a tetraphenylethylene (TPE) derivative, named as TPE-4B, which contained four chelate pyridinium groups exhibiting aggregation-induced emission characteristics. TPE-4B was explicitly developed for the selective and sensitive fluorescence detection of PPi in aqueous solutions, showing a fluorescence “turn-on” response, and the detection limit was 65 nM. The four chelate pyridinium moieties of TPE-4B exhibited robust electrostatic interactions and binding capacity towards PPi, leading to the formation of aggregations, which was confirmed by zeta potential, dynamic light scattering, and scanning electron microscopy. Compared with free TPE-4B in the aqueous solution, the zeta potential of aggregations decreased from 20.7 to 4.2 mV, the average diameter increased from 155 to 403 nm, and the morphology transformed from porous nanostructures into a block-like format. Leveraging these properties, TPE-4B is a promising candidate for a “turn-on” fluorescence sensor designed to detect PPi in the aqueous solution.

3D-integrated pixel circuit for a low power and small pitch SOI sensor

Targeting on low power consumption and high spatial resolution, the CPV-4 SOI pixel sensor has been developed. Its pixel circuit requires about 120 transistors to implement the analog-digital mixed signal processing functionality within a compact pixel area of 17 μm × 21 μm. By utilizing 3D vertical integration, sensing diode and analog front-end are realized in the lower-tier chip, while hit information storage and sparse readout are achieved in the upper-tier chip, thereby minimizing its pixel size and power consumption. This work presents the pixel circuit design and the test results on the completed 3D chips. The feature of SOI pixel process and the 3D integration are also highlighted in this article.

Gallium phosphide nanoribbon-based carbon monoxide sensors: insights from first principles study

The priority of research efforts over the past two decades has been focused on the effective detection of harmful gases and the development of small, efficient, and reliable nanodimensional sensors. The adsorption of carbon monoxide (CO) gas molecules on zigzag gallium phosphide nanoribbons (zGaPNRs) has been studied using first-principle calculations within the context of Density Functional Theory (DFT). Here, we have evaluated the potential of single-atom thick zGaPNRs in different configurations of nanoribbons for the detection of CO. Many possible configurations of studying the CO molecule adsorption on zGaPNRs have been explored. It is established that the interaction of CO molecules has an impact on the electrical and transport properties of zGaPNRs. The H-GaP-H is the most stable structure with the binding energy ([Formula: see text]) of −5.70[Formula: see text]eV. The stability is compromised with CO adsorption with CO-GaP-CO being the least stable structure. Pristine structure is semiconducting with the energy band gap ([Formula: see text]) of −3.95[Formula: see text]eV. The computed sensitivity (S) values are found to be highest for Co-GaPNRs with the S value of [Formula: see text] and the least sensitive structure is H-GaP-CO with the computed S as [Formula: see text]. Additionally, it is noted that CO molecules always establish a stable chemical bond with the nanoribbon edges through the C-side. The unique behavior is revealed by the transport characteristics, which demonstrates that when CO adsorption occurs near the Ga edge, the current magnitude is noticeably greater. Our research demonstrates the potential of specific CO adsorption and detection for the development of nanosensors.