搜索结果：ACS sensors

We present a new approach to identify satellite trails (or other linear artifacts) in ACS/WFC imaging data using a modified Radon Transform. We demonstrate that this approach is sensitive to features with mean brightness significantly below the background noise level, and it is resistant to the influence of bright astronomical sources (e.g., stars, galaxies) in most cases. Comparing with a set of satellite trails identified by eye, we find a trail recovery rate of 85\% and a false detection rate (after removing diffraction spikes that are easily filtered) of 2.5\%. By performing an analysis using a much larger ACS/WFC data set where false trails are identified by their persistence across multiple images of the same field, we identify the Radon Transform parameter space and image properties where our algorithm is unreliable, and estimate a false detection rate of $\sim10\%$ elsewhere. We apply our method to ACS/WFC data taken between 2002 and 2022 to determine both the frequency of satellite trail contamination in science data and also the typical trail brightness as a function of time. We find the rate of satellite trail contamination has increased by approximately a factor of two

Deep neural networks (DNNs) have shown outstanding performance in visual recognition tasks within vision sensor networks; however, they are still vulnerable to adversarial manipulations and imperceptible perturbations that can lead to erroneous predictions. To address that, this paper presents AEGIS, a semantic aware and uncertainty guided adversarial detection framework designed for robust image classification in vision sensors pipelines. At its core, a SemantiGAN module functions as a multi class semantic discriminator, identifying and filtering visually inconsistent adversarial inputs before they propagate further in the pipeline. For inputs that pass this stage, a stochastic augmentation process generates test time variations, from which handcrafted instability metrics FlipScore, Prediction Inconsistency, Layerwise Cosine Similarity (early and mid layers), and Entropy are computed. These features are aggregated into a compact five dimensional vector and processed by an Evidential Deep Learning (EDL) classifier, which models output evidence using a Dirichlet distribution to yield both class predictions and calibrated uncertainty estimates. Evaluations on the Tiny ImageNet datase

Recently, the ACS team applied an Ubercal framework to assess the photometric repeatability of stars observed across the WFC detector using 15 years of post-SM4 calibration data in the globular cluster 47 Tuc (Ryan et al., 2024). A surprising finding was an apparent 0.05 mag global difference in sensitivity between the WFC1 and WFC2 chips, which had not been seen in prior tests of sensitivity variations around the field-of-view. Given the many degenerate variables within the Ubercal framework such as CTE losses, time-dependent sensitivity, and flat-field corrections, we obtained new calibration data to perform a straightforward test of the reported $\sim$5$\%$ flux offset between detectors. We observed three white dwarf standards with three filters at four positions on the detector (each on a different amplifier), but with the same number of x and y pixel transfers to mitigate differential CTE-related effects. For the F606W and F814W filters, the agreements are good to 0.4$\%$ on average, and always 1$\%$ or better in individual cases. The consistency of these two filters over all three stars and the four dither positions provides very strong evidence against the large global sensi

Using repeat imaging of a galaxy cluster taken over a seventeen-year baseline, we examine the impact that degraded Charge Transfer Efficiency (CTE) has on photometric measurements of extended sources using the ACS/WFC on HST. We examine how measured brightnesses depend on time since ACS installation, source location on the WFC detectors, source brightness, and local background level in individual exposures. We find that global brightness measurements using large apertures are generally reliable within $\sim$0.05 magnitudes across the WFC detectors if exposure backgrounds are above $20e^-/{pixel}$ and sources are brighter than $\sim300e^-$ in a single exposure. However, brightness measurements on smaller scales can suffer deficiencies in excess of 0.1 mags (sometimes, significantly more) in recent data unless sources are very close to the CCD serial registers ($\lesssim 512$ pixels), or brighter than $\sim3000\,e^-$ in a single exposure. We also show how degraded CTE can result in artificial asymmetries in galaxy light distributions, which are largely mitigated if backgrounds are $>20e^-/{pixel}$ and targets are not far ($>1536$ pixels) from the serial registers. As expected,

Electromagnetic waves are widely used including in defense, biomedicine, and fundamental science. Their efficient detection determines how we communicate, defend against adversaries, diagnose diseases and perform search and rescue operations. In this article, exploiting the precession of a levitated magnetic particle in vacuum, we show that weak electromagnetic waves down to the femtotesla level can be detected. It is also shown that such a sensor has a large dynamic range over a millitesla, is continuously tunable over many gigahertz and can detect frequencies with sub-hertz resolutions. The direction of arrival of the incoming electromagnetic wave can also be found relatively easily.

Many parts of human body generate internal sound during biological processes, which are rich sources of information for understanding health and wellbeing. Despite a long history of development and usage of stethoscopes, there is still a lack of proper tools for recording internal body sound together with complementary sensors for long term monitoring. In this paper, we show our development of a wearable electronic stethoscope, coined Patchkeeper (PK), that can be used for internal body sound recording over long periods of time. Patchkeeper also integrates several state-of-the-art biological sensors, including electrocardiogram (ECG), photoplethysmography (PPG), and inertial measurement unit (IMU) sensors. As a wearable device, Patchkeeper can be placed on various parts of the body to collect sound from particular organs, including heart, lung, stomach, and joints etc. We show in this paper that several vital signals can be recorded simultaneously with high quality. As Patchkeeper can be operated directly by the user, e.g. without involving health care professionals, we believe it could be a useful tool for telemedicine and remote diagnostics.

The paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous work to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators and all the process of interacting with the real world. After analysing previous work on the use of digital twins in cultural heritage, a summary description of the Heritage Digital Twin Ontology is provided, and the existing applications of digital twins to cultural heritage are overviewed, with references to reviews summarizing the large production of scientific contributions on the topic. Then a novel ontology, named Reactive Digital Twin Ontology is described, in which sensors, activators and the decision processes are also semantically described, turning the previous synchronic approach to cultural heritage documentation into a diachronic one. Some case studies exemplify this theory.

Forklifts are essential for transporting goods in industrial environments. These machines face wear and tear during field operations, along with rough terrain, tight spaces and complex handling scenarios. This increases the likelihood of unintended impacts, such as collisions with goods, infrastructure, or other machinery. In addition, deliberate misuse has been stated, compromising safety and equipment integrity. This paper presents a low-cost and low-power impact detection system based on multiple wireless sensor nodes measuring 3D accelerations. These were deployed in a measurement campaign covering realworld operational scenarios. An algorithm was developed, based on this collected data, to differentiate high-impact events from normal usage and to localize detected collisions on the forklift. The solution successfully detects and localizes impacts, while maintaining low power consumption, enabling reliable forklift monitoring with multi-year sensor autonomy.

Scientific CMOS (sCMOS) image sensors are a modern alternative to typical CCD detectors and are rapidly gaining popularity in observational astronomy due to their large sizes, low read-out noise, high frame rates, and cheap manufacturing. However, numerous challenges remain in using them due to fundamental differences between CCD and CMOS architectures, especially concerning the pixel-dependent and non-Gaussian nature of their read-out noise. One of the main components of the latter is the random telegraph noise (RTN) caused by the charge traps introduced by the defects close to the oxide-silicon interface in sCMOS image sensors, which manifests itself as discrete jumps in a pixel's output signal, degrading the overall image fidelity. In this work, we present a statistical method to detect and characterize RTN-affected pixels using a series of dark frames. Identifying RTN contaminated pixels enables post-processing strategies that mitigate their impact and the development of manufacturing quality metrics.

Accurate calibration of sensor extrinsic parameters for ground robotic systems (i.e., relative poses) is crucial for ensuring spatial alignment and achieving high-performance perception. However, existing calibration methods typically require complex and often human-operated processes to collect data. Moreover, most frameworks neglect acoustic sensors, thereby limiting the associated systems' auditory perception capabilities. To alleviate these issues, we propose an observability-aware active calibration method for ground robots with multimodal sensors, including a microphone array, a LiDAR (exteroceptive sensors), and wheel encoders (proprioceptive sensors). Unlike traditional approaches, our method enables active trajectory optimization for online data collection and calibration, contributing to the development of more intelligent robotic systems. Specifically, we leverage the Fisher information matrix (FIM) to quantify parameter observability and adopt its minimum eigenvalue as an optimization metric for trajectory generation via B-spline curves. Through planning and replanning of robot trajectory online, the method enhances the observability of multi-sensor extrinsic parameters

We report that considerable attention has been devoted to the integration of recognition elements with electronic elements to develop electrochemical sensors and biosensors.Various electrochemical devices, such as amperometric sensors, electrochemical impedance sensors, and electrochemical luminescence sensors as well as photoelectrochemical sensors, provide wide applications in the detection of chemical and biological targets in terms of electrochemical change of electrode interfaces. Here, this review focuses on recent advances in electrochemical sensors and biosensors based on nanomaterials and nanostructures during 2013 to 2014. The aim of this effort is to provide the reader with a clear and concise view of new advances in areas ranging from electrode engineering, strategies for electrochemical signal amplification, and novel electroanalytical techniques used in the miniaturization and integration of the sensors. Moreover, the authors have attempted to highlight areas of the latest and significant development of enhanced electrochemical nanosensors and nanobiosensors that inspire broader interests across various disciplines. Electrochemical sensors for small molecules, enzyme-based biosensors, genosensors, immunosensors, and cytosensors are reviewed herein (Figure 1). Such novel advances are important for the development of electrochemical sensors that open up new avenues and methods for future research. In conclusion, we recommend readers interested in the general principles of electrochemical sensors and electrochemical methods to refer to other excellent literature for a broad scope in this area.(3, 4) However, due to the explosion of publications in this active field, we do not claim that this Review includes all of the published works in the past two years and we apologize to the authors of excellent work, which is unintentionally left out.

The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and

Low-cost sensors (LCS) are affordable, compact, and often portable devices designed to measure various environmental parameters, including air quality. These sensors are intended to provide accessible and cost-effective solutions for monitoring pollution levels in different settings, such as indoor, outdoor and moving vehicles. However, the data produced by LCS is prone to various sources of error that can affect accuracy. Calibration is a well-known procedure to improve the reliability of the data produced by LCS, and several developments and efforts have been made to calibrate the LCS. This work proposes a novel Estimated Error Augmented Two-phase Calibration (\textit{EEATC}) approach to calibrate the LCS in stationary and mobile deployments. In contrast to the existing approaches, the \textit{EEATC} calibrates the LCS in two phases, where the error estimated in the first phase calibration is augmented with the input to the second phase, which helps the second phase to learn the distributional features better to produce more accurate results. We show that the \textit{EEATC} outperforms well-known single-phase calibration models such as linear regression models (single variable li

In this work, we experimentally investigate the frequency limit of Hall effect sensor designs based on a 2 dimensional electron gas (2DEG) gallium arsenide/aluminum gallium arsenide (GaAs/AlGaAs) heterostructure. The frequency limit is measured and compared for four GaAs/AlGaAs Hall effect sensor designs where the Ohmic contact length (contact geometry) is varied across the four devices. By varying the geometry, the trade-off in sensitivity and frequency limit is explored and the underlying causes of the frequency limit from the resistance and capacitance perspective is investigated. Current spinning, the traditional method to remove offset noise, imposes a practical frequency limit on Hall effect sensors. The frequency limit of the Hall effect sensor, without current spinning, is significantly higher. Wide-frequency Hall effect sensors can measure currents in power electronics that operate at higher frequencies is one such application.

Low-cost particulate matter sensors are transforming air quality monitoring because they have lower costs and greater mobility as compared to reference monitors. Calibration of these low-cost sensors requires training data from co-deployed reference monitors. Machine Learning based calibration gives better performance than conventional techniques, but requires a large amount of training data from the sensor, to be calibrated, co-deployed with a reference monitor. In this work, we propose novel transfer learning methods for quick calibration of sensors with minimal co-deployment with reference monitors. Transfer learning utilizes a large amount of data from other sensors along with a limited amount of data from the target sensor. Our extensive experimentation finds the proposed Model-Agnostic- Meta-Learning (MAML) based transfer learning method to be the most effective over other competitive baselines.

This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using data from wearable and non-wearable sensors. In particular, wearable sensor-based methods commonly use data from electroencephalography, electrocardiogram, galvanic skin response, electromyography, electrodermal activity, heart rate, heart rate variability, and photoplethysmography both individually and in multimodal fusion strategies. Whereas, methods based on non-wearable sensors include strategies such as analyzing pupil dilation and speech, smartphone data, eye movement, body posture, and thermal imaging. Whenever a stressful situation is encountered by an individual, physiological, physical, or behavioral change is induced which help in coping with the challenge at hand. A wide range of studies has attempted to establish a relationship between these stressful situations and the response of human beings by using different kinds of psychological, physiological, physi