Active Sensor Research Articles

Total variational noise reduction method for EBAPS image based on weighted nuclear norm minimization

The electron-bombarded active pixel sensor (EBAPS) is a highly sensitive vacuum-solid hybrid low-light imaging device capable of functioning in ultra-low illumination environments as low as 10-4 lx. However, this high sensitivity also causes problems, such as a low signal-to-noise ratio and complex noise. To enhance the quality of low-light night vision images captured by EBAPS and achieve effective imaging in ultra-low illumination, this study proposes a noise reduction algorithm based on the noise characteristics of EBAPS images. By utilizing the weighted nuclear norm minimization (WNNM) as the fundamental framework, several enhanced methods to address the multi-source noise of the EBAPS under ultra-low illumination have been proposed. These methods include outlier removal and variance-stabilizing transformation. To address the lack of edge preservation, a four-directional total variational regularization term has been incorporated into the WNNM model to maintain the image edge. Experimental results demonstrated that the improved method effectively eliminates various types of EBAPS noise while considerably returning the edges, ultimately enhancing the quality of low-light images. This study greatly facilitates subsequent applications of the low-light night vision technology.

Spatial Correlations Based Fault Tolerant Source Localization Using Wireless Sensor Networks

The source localization is a process to determine the location of a signal source based on measurements from sensor nodes or receivers that are spatially distributed in the area around the source. Localizing intruders and mobile users are some of the utilizations of wireless sensor networks (WSNs) based source localization problem. However, sensor faults and channel noise are the important issues that degrade the source localization performances of the systems. In this paper, we significantly study and improve our recent results; the representative fault tolerant hitting set and feature selection approaches for localizing a source in a decentralized WSN using the faulty sensor nodes and channel noise. First, we propose a technique to place some active sensor nodes in the sleep state such that the source localization performances of the hitting set and feature selection approaches do not significantly degrade. The proposed technique takes advantage of spatial correlations among sensor nodes, where nearby sensor nodes provide same decisions to the fusion center with a high probability. We observe that the performance gap for the results with and without sleeping sensor nodes decreases by increasing total sensor nodes. In addition, we improve our recent results by proposing a technique based on spatial correlations among sensor nodes to minimize the impact of channels’ noises, especially for a higher fault probability of sensor nodes. Finally, extensive simulations on synthetic and real-world data sets validate our theoretical results.

Do Activity Sensors Identify Physiological, Clinical and Behavioural Changes in Laying Hens Exposed to a Vaccine Challenge?

This study aimed to identify if sensor technology could be used to detect sickness-type signs (caused by a live vaccine) in laying hens compared to physiological and clinical sign scoring and behaviour observation. The experiment comprised 5 replicate batches (4 hens and 12 days per batch) using previously non-vaccinated hens (n = 20). Hens were moved on day 1 to a large experimental room with various designated zones (e.g., litter, perches, nest box), where they wore two sensors (FitBark, TrackLab). Saline was applied using ocular and nasal drops on day 3 as a control. A live vaccine (Infectious Laryngotracheitis, ILT, vaccine), applied using the same method on day 6, was used to induce mild respiratory and other responses. Physiological and clinical signs, and behaviour from videos were also recorded by a single observer. There were significant changes in body weight (p < 0.001), feed intake (p = 0.031), cloacal temperature (p < 0.001) and three out of five clinical signs (ocular discharge (p < 0.001), conjunctivitis (p < 0.001) and depression (p = 0.009)) over days. A significant decrease (p < 0.001) in activity level (FitBark) and distance travelled (both sensors) were identified over the study days, and activity and distance travelled were highly significantly associated (p < 0.001) with total clinical scores, with hens showing reduced activity and distance travelled with worsening total clinical scores. With behaviour observations from videos, the proportions of sitting, foraging and feeding behaviours (p = 0.044, 0.036 and 0.004, respectively), the proportion of total visit duration to the litter zone (p < 0.001) and perch (p = 0.037) with TrackLab and the proportions of visit counts of hens in the litter zone (p = 0.012) from video scanning changed significantly with days. This study suggests that the vaccine challenge caused associated changes in clinical/physiological signs and activity/distance travelled data from the sensors. Sensors may have a role in detecting changes in activity and movement in individual hens indicative of health or welfare problems.

Structural Fatigue Life Monitoring with Piezoelectric-Based Sensors: Fundamentals, Current Advances, and Future Directions

Structural fatigue can lead to catastrophic failures in various engineering applications and must be properly monitored and effectively managed. This paper provides a state-of-the-art review of recent developments in structural fatigue monitoring using piezoelectric-based sensors. Compared to alternative sensing technologies, piezoelectric sensors offer distinct advantages, including compact size, lightweight design, low cost, flexible formats, and high sensitivity to dynamic loads. The paper reviews the working principles and recent advancements in passive piezoelectric-based sensors, such as acoustic emission wave and strain measurements, and active piezoelectric-based sensors, including ultrasonic wave and dynamic characteristic measurements. These measurements, captured under in-service dynamic strain, can be correlated to the remaining structural fatigue life. Case studies are presented, highlighting applications of fatigue life monitoring in metals, polymeric composites, and reinforced concrete structures. The paper concludes by identifying challenges and opportunities for advancing piezoelectric-based sensors for fatigue life monitoring in engineering structures.

A methodology for timing performance optimizationof the pre-amplifier design in High Energy Physics

Particle colliders operating at an increasing luminosity for High Energy Physics (HEP) experiments often present greater challenges in the design of the front-end readout circuits, especially when exploring the possibility of employing Depleted Monolithic Active Pixel Sensors (DMAPS) for timing in HEP. This paper presents a methodology for the timing performance optimization in the context of the future collider experiments. First a simplified model is presented for theoretical performance analysis and the validity is tested. Then an optimization process is done to find the best parameters regarding the trade-offs from the application requirements.

Active and Passive Antenna Sensor Signal for the Prediction of Breast Cancer Cell Size Below 3mm Using Bayesian Optimized Regression Analysis

Active and Passive Antenna Sensor Signal for the Prediction of Breast Cancer Cell Size Below 3mm Using Bayesian Optimized Regression Analysis

Implantable Passive Sensors for Biomedical Applications

In recent years, implantable sensors have been extensively researched since they allow localized sensing at an area of interest (e.g., within the vicinity of a surgical site or other implant). They allow unobtrusive and potentially continuous sensing, enabling greater specificity, early warning capabilities, and thus timely clinical intervention. Wireless remote interrogation of the implanted sensor is typically achieved using radio frequency (RF), inductive coupling or ultrasound through an external device. Two categories of implantable sensors are available, namely active and passive. Active sensors offer greater capabilities, such as on-node signal and data processing, multiplexing and multimodal sensing, while also allowing lower detection limits, the possibility to encode patient sensitive information and bidirectional communication. However, they require an energy source to operate. Battery implantation, and maintenance, remains a very important constraint in many implantable applications even though energy can be provided wirelessly through the external device, in some cases. On the other hand, passive sensors offer the possibility of detection without the need for a local energy source or active electronics. They also offer significant advantages in the areas of system complexity, cost and size. In this review, implantable passive sensor technologies will be discussed along with their communication and readout schemes. Materials, detection strategies and clinical applications of passive sensors will be described. Advantages over active sensor technologies will be highlighted, as well as critical aspects related to packaging and biocompatibility.

Dynamics of affect modulation in neurodevelopmental disorders (DynAMoND) – study design of a prospective cohort study

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is a common neuro-developmental disorder that often persists into adulthood. Moreover, it is frequently accompanied by bipolar disorder (BD) as well as borderline personality disorder (BPD). It is unclear whether these disorders share underlying pathomechanisms, given that all three are characterized by alterations in affective states, either long or short-term. BD is characterized by infrequent but intense mood shifts, while ADHD and BPD involve more dynamic emotional fluctuations. It is yet to be determined whether these disorders represent distinct phenomena or different points on a spectrum of affective dysregulation.MethodsThis study seeks to distinguish the emotional dysregulation of BPD, ADHD, and BD by using digital phenotyping, a measurement burst electronic-diary method with different sampling rates, and accelerometry to measure participants’ activity. Our study will include 480 participants aged 14 to 50 (120 each from BPD, ADHD, BD, and healthy control groups) from five European sites. Participants’ smartphones will provide continuous data on their digital phenotypes, i.e., by indicators of physical activity and communication, for one year, along with daily evening ratings of mood and sleep. Moreover, five intensive measurement periods of five days each, called measurement bursts, will occur throughout the year, with electronic diaries asking participants to report on mood, self-esteem, impulsivity, life events, social interactions, and dysfunctional behaviors ten times a day. Moreover, participants will wear activity sensors during the five measurement bursts. Statistical analysis aims to identify whether affective dysregulation aspects share or differ across disorders. Specifically, data analysis aims to investigate the differences in parameters of affect fluctuation such as attractor strength and variability between disorders and to test the association of genetic risk factors for psychiatric disorders and resilience factors with critical parameters of affect modulation.DiscussionThe results of this study offer the potential to link patients’ external exposures with their affective state, reduce misdiagnosis, and determine the best timing for therapeutic interventions. Potential limitations of the study include insufficient recruitment of patients and drop-outs due to various protocol violations.Trial registrationStudy code: DRKS00028917, registered 27.07.2022, https://drks.de/search/de/trial/DRKS00028917.

Long-term Impact of Continuous Glucose Monitoring Assistance on Glycemic Control in Children and Adolescents with Type 1 Diabetes Following the 2023 Kahramanmaraş Earthquake.

This study aimed to evaluate the impact of continuous glucose monitoring (CGM) assistance on glycemic control in children with type 1 diabetes (T1D) in earthquake-affected regions, comparing those who benefited from CGM with those who did not. Additionally, the study assessed changes in CGM metrics over nine months of CGM use. A multicenter, cross-sectional study was conducted across 11 centers in Türkiye. Children with T1D were divided into two groups: those who received CGM support (CGM+) and those who continued with finger-stick glucose monitoring (CGM-). HbA1c levels were measured at four intervals: pre-earthquake, 3-6 months, 6-9 months, and 9-12 months post-earthquake. In the second phase, CGM metrics were analyzed over 90-day intervals in the CGM+ group with at least 85% sensor usage. A total of 532 children were included. Median HbA1c levels decreased from 9.1% pre-earthquake to 8.8% 3-6 months post-earthquake (p=0.027). In the CGM+ group, HbA1c levels significantly decreased from 8.8% to 8.3% (p<0.001), while no significant change was observed in the CGM- group. Of the 412 subjects with access to CGM reports, 105 (25.4%) had less than 85% sensor usage and were excluded. In the remaining 307 patients, there was a significant increase in active sensor time and daily glucose measurements, along with a reduction in hypoglycemia frequency over the 90-day intervals (p<0.001 for all three). CGM assistance improved glycemic control in children with T1D, even under the challenging conditions of the earthquake. These findings highlight the need for broader access to CGM devices to enhance diabetes management.

3D cloud masking across a broad swath using multi-angle polarimetry and deep learning

Abstract. Understanding the 3D structure of clouds is of crucial importance to modeling our changing climate. Both active and passive sensors are restricted to two dimensions: as a cross-section in the active case and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information requires computationally expensive radiative transfer techniques. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we develop a machine-learning model that predicts radar-based vertical cloud profiles from multi-angle polarimetric imagery. Notably, these models are trained only on center swath labels but can predict cloud profiles over the entire passive imagery swath. We compare with strong baselines and leverage the information–theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. Our experiments show that multi-angle sensors can recover surprisingly accurate vertical cloud profiles, with the skill strongly related to the number of viewing angles and spectral channels, with more angles yielding high performance, and with the oxygen A band strongly influencing skill. A relatively simple convolutional neural network shows nearly identical performance to the much more complicated U-Net architecture. The model also demonstrates relatively lower skill for multilayer clouds, horizontally small clouds, and low-altitude clouds over land, while being surprisingly accurate for tall cloud systems. These findings have promising implications for the utility of multi-angle sensors on Earth-observing systems, such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and Atmosphere Observing System (AOS), and encourage future applications of computer vision to atmospheric remote sensing.

Trait-focused low-cost and stereo-based 3D plant modeling for phenotyping via deep neural detection

Abstract. Phenotyping, the measurement of plants physical traits, plays a pivotal role in advancing sustainable agricultural practices. Therefore, developing efficient, low-cost, means to generate such measures is vital. Though image based 2D-driven methods are commonly applied for that purpose due to their processing simplicity, it is clear that only 3D information can offer the necessary plant details. Notwithstanding, the generation of such data is challenged by the requirement to acquire a large set of images or the use of active sensors, which exhibit sensitivity to illumination and require lengthy acquisition campaigns. Consequently, 3D plant phenotyping is presently limited to controlled laboratory conditions and is hardly applied in actual growth setups. To address this shortcoming, this paper argues that by focusing on relevant plant traits, modeling can be simplified and the need for detailed plant reconstruction can be relieved. Accordingly, only a minimal set of images, specifically a stereo pair, can suffice for the reconstruction, thereby providing a low-cost sensing solution. To facilitate the reconstruction, we adapt an anchor-free detection deep neural network and integrate low- and high-level features to accurately detect our plant traits of interest. As the paper demonstrates our adapted network facilitates a robust 3D reconstruction of the entities of interest. Performance analysis demonstrates how our detection is reliable and accurate compared to standard anchor-free frameworks, translating to accurate reconstruction, as we validate against 3D plant scans.

An Extreme Environment Capable Self‐Healing Single Active Layered Triboelectric Sensors as Fully Recyclable and Transparent Human‐Machine Interfaces

AbstractCurrent approaches to triboelectric devices require multiple layers. Here, a transparent ultra‐stretchable single active layered ionic‐based nanogenerator is shown that can self‐heal in both dry and wet environments. Despite being conductive, surprisingly the ionogel exhibits triboelectric properties and generates output voltages ranging from 1.7 to 70 V. By tuning the ionic liquid concentration, unique triboelectric devices with position‐detection and energy harvesting ability are developed. This design enables visualization of the mechanical force distribution through the mechano‐electric‐optical conversion process, which is achieved by changing it from an insulating dielectric material to a tribo‐negative ionic conducting material. The triboelectric device maintains its stability and self‐healing ability at extreme environments (−20 to 380 °C, pH 1.3 to 13), and the device can be fully closed‐loop recycled for reuse. Various human‐machine interface applications such as untethered self‐powered light‐emitting jellyfish‐like devices are demonstrated, a mechano‐thermal sensor array, and tactile recognition of object shapes with over 91% accuracy using convolution neural networks (CNN).

Triboelectric sensor with ultra-wide linear range based on water-containing elastomer and ion-rich interface

The incompatibility of the high sensitivity and wide linear range still restricts the further development of active sensors. Here we report a triboelectric pressure sensor based on water-containing triboelectric elastomer with gradient-based microchannels. Tiny amount of liquid is injected into the triboelectric elastomer and the pressure-induced water bridges can modulate the built-in electric field of the sensor, which enhance the signal linearity near the compression limit. Moreover, it has been found that liquid-solid contact electrification can be enhanced by triggering selective ionic transfer, while the prepared ion-rich interface in the microchannels boosts the sensitivity of the sensor. Hence, an ultra-wide linear range (5 kPa–1240 kPa) with a sensitivity of 0.023 V kPa−1 can be achieved, which is so far the widest linear range of active sensors to our knowledge. Our work can promote the practical application of triboelectric sensors and provide new insights for other sensory devices.

LEACH algorithm analysis and simulation using MATLAB

Research on Wireless Sensor Network (WSN) began to be carried out to meet various industrial needs including defense, health, environmental surveillance, and others. However, there are several obstacles in WSN, namely the problem of energy consumption which is the object of research by many researchers. The solution offered in this paper is to use the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a hierarchical protocol where this protocol focuses on saving energy use on WSN. This study used Matlab 2023 simulation software which used several measurement parameters to determine tissue life time, average residual energy, and throughput. The research scenario uses a homogeneous topology with three network sizes, namely 500 x 500, 750 x 750, and 1000 x 1000. Then also used three conditions for the number of sensor nodes, namely 100 nodes, 150 nodes, and 200 nodes. The results showed that the smaller the tissue size, the longer the life time and if the network size is wider, the network life time is shorter. The number of data packets transmitted depends on the number of active sensor nodes and sufficient energy to transmit.

Mobile sensing system for phenotyping of forest seedlings in container nurseries

The primary objective of this study was to develop a universal, lightweight, and mobile system that seamlessly integrates the scanner with the construction of the horizontal spray boom at a forest nursery. This innovative approach holds tremendous potential for advancing the assessment and management of forest seedlings, providing valuable insights and streamlining nursery operations. Multichannel spectral devices mounted on irrigation booms can be used to significantly enhance the quality assessment of forest seedlings in container nurseries. Contrary to traditional and time-consuming approaches such as laboratory analysis or destructive sampling, an active canopy optical sensor operates independently of ambient light conditions and does not necessitate spectral reference readings. Mobile irrigation systems in nurseries are ideal for incorporating plant scanners owing to their exceptional repeatability, ensuring consistently high-quality results. In our study, we rigorously tested a set of four ACS 435 Holland Scientific active crop canopy sensor along with the Vega Plus C21 laser sensor, all strategically positioned on a sprinkler boom above the nursery production field from May to September 2022. The seamless integration of these devices with the GeoSCOUT X Holland Scientific data acquisition unit formed the foundation of our system. The primary objective of this study was to develop a universal, lightweight, and mobile system that seamlessly integrates the scanner with the construction of the horizontal spray boom at a forest nursery. This innovative approach holds tremendous potential for advancing the assessment and management of forest seedlings, providing valuable insights and streamlining nursery operations.

An active learning-driven optimal sensor placement method considering sensor position distribution toward structural health monitoring

An active learning-driven optimal sensor placement method considering sensor position distribution toward structural health monitoring

Detection of Hydrogen Peroxide Vapors Using Acidified Titanium(IV)-Based Test Strips.

One method for the colorimetric detection of hydrogen peroxide vapor is based on a titanium-hydrogen peroxide complex. A color changing material based on a titania hydroxypropyl cellulose thin film was initially developed. However, as this material dries, the sensitivity of the material is significantly reduced. Thus, an alternative sensing material, based on titanium(IV) oxysulfate, an ionic liquid, and in some cases, triflouromethanesulfonic acid adsorbed onto low-cost silicon thin-layer chromatography (TLC) plates, was developed. TiO2 was heated with concentrated sulfuric acid in a controlled environment, usually at temperatures ranging from 100 °C to 250 °C. These sensors are disposable and single-use and are simple and inexpensive. When the resulting thin-film sensors are exposed to ppm levels of hydrogen peroxide vapor, they turn from a white reflective material to an intense yellow or orange. Ti(IV) oxysulfate combined with an acid catalyst and an ionic-liquid-based material provides an opportunity to enhance the sensor activity towards the peroxide vapor and decreases the detection limit. Kinetic measurements were made by the quantification of the intensity of the reflected light as a function of the exposure time from the sensor in a special cell using a low-cost web camera and a tungsten lamp. The measured rate of the color change indicates high sensitivity and first-order kinetics over a hydrogen peroxide concentration range of approximately 2 to 31 ppm. These new materials are a starting point for the preparation of more active sensor materials for hydrogen peroxide and organic peroxide vapor detection.

Data level fusion of acoustic emission sensors using deep learning

The acoustic emission (AE) technique is commonly utilized for identifying source mechanisms and material damage. In applications requiring numerous sensors and limited detection areas, achieving significant cost savings, weight reduction, and miniaturization of AE sensors is crucial. This prevents excessive weight burdens on structures while minimizing interference with structural integrity. Thin Piezoelectric Wafer Active Sensors (PWAS), compared to conventional commercially available sensors, offer a miniature, lightweight, and affordable alternative. The low signal-to-noise ratio (SNR) of PWAS sensors and their limited effectiveness in monitoring thick structures result in the decreased reliability of a single classical PWAS sensor for damage detection. This research aims to enhance the functionality of PWAS in AE applications by employing multiple thin PWAS and performing a data-level fusion of their outputs. To achieve this, as a first step, the selection of the optimal PWAS is performed and a configuration is designed for multiple sensors. Pencil break lead (PBL) tests were performed to investigate the compatibility between selected PWAS and traditional WSα and R15α sensors. The responses of all sensors from different AE sources were compared in both the time and frequency domains. After that, convolutional neural networks (CNNs) combined with principal component analysis (PCA) are proposed for signal processing and data fusion. The signals generated by the PBL tests were used for network training and evaluation. This approach, developed by the authors, fuses the signals from multiple PWAS and reconstructs the signals obtained from conventional bulky AE sensors for damage detection. Three CNNs with different architectures were built and tested to optimize the network. It is found that the proposed methodology can effectively reconstruct and identify the PBL signals with high precision. The results demonstrate the feasibility of using a deep-learning-based method for AE monitoring using PWAS for real engineering structures.

OpenBURST: Real-time air surveillance simulation and analysis for active and passive sensors

openBURST: Real-time air surveillance simulation and analysis for active and passive sensors

Silver nano-colloid particles embedded on Langmuir-Blodgett film matrix of stearic acid serving as a SERS active sensor for detecting the herbicide ‘Paraquat’

The herbicide Paraquat, widely used for efficient weed control, poses significant health risks to humans viz., severe toxicity to vital organs and induction of neurodegenerative disorder like Parkinson’s disease, underscoring the urgent need for developing sensitive detection methods for the herbicide. This study aims at fabricating a novel SERS-active substrate SA-LB/Ag (silver nano-colloids adsorbed on Langmuir-Blodgett film of stearic acid), as a SERS based sensor having high sensitivity, uniformity, and reproducibility to detect ultra-trace amounts of paraquat. The sensor was built up by immersing bilayer LB films of SA in silver nano-colloid suspension, prepared by mixing silver nitrate and sodium borohydride with vigorous stirring. Quantitative evaluations establish a significant linear regression across the concentration spectrum (100 ppm to 75 ppb) of paraquat, with a remarkable 15 ppb limit of detection (LOD) value. In practical terms, this SERS based sensor exhibits notable proficiency in detecting paraquat residues in (a) agricultural water from rice and vegetable fields and environmental water from adjoining water-logged areas, and (b) soil extracts of the agricultural fields. Moreover, the minimum toxic concentration of paraquat at the developmental stage of zebrafish embryos and larvae is found to be approximately 20 ppb, implying that the SA-LB/Ag sensor is sensitive enough to determine the limiting toxic level of paraquat for animal systems. Thus, the designed sensor holds far-reaching implications in ensuring food safety, monitoring the environment, and framing regulatory measures for paraquat use.