Large Sensor Research Articles

MENTO: Automated near real-time data analysis at PETRA III

With the advent of next-generation X-ray detectors with large sensor areas and high sampling rates, photon science experiments are now having to deal with a data explosion. We present a scalable, near real-time data processing toolkit developed to address this challenge at PETRA III, which is currently in use at the coherence applications beamline P10 and at the macromolecular crystallography beamline P11. This toolkit runs automated analysis pipelines on high-volume data concurrently with data acquisition, thus providing quick feedback during experiments at the beamlines. Named mento (Maxwell-Enhanced Near real-Time Online analysis), the toolkit leverages the computing resources of the in-house HPC cluster ‘Maxwell’ to enhance analysis performance, and additionally takes advantage of the available distributed data storage to provide analysis results directly back to the experimenter with minimal delay. mento works seamlessly with the experiment control mechanisms used at P10, P11, and other PETRA III beamlines, and ensures that analysis on the HPC cluster is triggered automatically during data acquisition at the beamline, and that results are available for visualization at the beamline control hutch even though the analysis itself is performed remotely. mento thus helps the human-in-the-loop concentrate on novel science aspects of the experiment, without needing to manage the computational workload in a high data-rate regime. mento has been used with both in-house and commercial analysis software to achieve speedups of 50x-100x relative to the existing analysis pipelines, thus proving its utility for different kinds of experiments at PETRA III. The source code for mento is available at https://gitlab.desy.de/fs-sc/mento.

Generalized and wavelength-dependent temperature calibration function for multipoint regenerated fiber Bragg grating sensors.

A new calibration methodology for regenerated fiber Bragg grating (RFBG) temperature sensors up to 700 °C is proposed and demonstrated. A generalized, wavelength-dependent temperature calibration function is experimentally determined that describes the temperature-induced wavelength shifts for all RFBG sensor elements that are manufactured with the same fabrication parameters in the wavelength range from 1465 nm to 1605 nm. Using this generalized calibration function for absolute temperature measurements, each RFBG sensor element only needs to be calibrated at one reference temperature, representing a considerable simplification of the conventional calibration procedure. The new calibration methodology was validated with 7 RFBGs, and uncertainties were found to be compliant with those of Class 1 thermocouples (< ±1.5 K or < ±0.4% of the measured temperature). The proposed calibration technique overcomes difficulties with the calibration of spatially extended multipoint RFBG sensor arrays, where setting up an adequate calibration facility for large sensor fibers is challenging and costly. We assume that this calibration method can also be adapted to other types of FBG temperature sensors besides RFBGs. An accurate and practical calibration approach is essential for the acceptance and dissemination of the fiber-optic multipoint temperature sensing technology.

RSSI Analysis on CSS Modulation in the 433 MHz Frequency Band Using Lora in Flood Sensor

Long Range (LoRa) low power is a transmission technology capable of connecting multiple sensors to the Internet of Things in the future. LoRa uses Chirp Spread Spectrum (CSS) frequency modulation which promises to achieve remote connectivity at very low power. More specifically, this paper focuses on the analysis of Receive Signal Strength Indicator (RSSI) on CSS modulation on flood sensors. This paper presents a detailed model of using LoRa on flood sensors, showing that LoRa is capable of transmitting sensor data over long distances with perfect RSSI values. The results showed the potential for a close communication range with a narrow band network at a frequency of 433 MHz and an average RSSI of -50 dB at a maximum distance of 600 meters without obstruction. Although LoRa communication uses CSS modulation and has good RSSI values, in long-distance communication with data in the form of video or large sensor data, this toughness is not sufficient due to long distances and large bandwidth usage, while in this study LoRa type Ai Thinker Ra- 02 SX 1278 can only be set up to 125 KHz according to the datasheet. The disadvantages of using large sensor data over long distances and a larger footprint make CSS vulnerable to other, possibly larger, resources.

Reducing the Latency of Touch Tracking on Ad-hoc Surfaces

Touch sensing on ad-hoc surfaces has the potential to transform everyday surfaces in the environment - desks, tables and walls - into tactile, touch-interactive surfaces, creating large, comfortable interactive spaces without the cost of large touch sensors. Depth sensors are a promising way to provide touch sensing on arbitrary surfaces, but past systems have suffered from high latency and poor touch detection accuracy. We apply a novel state machine-based approach to analyzing touch events, combined with a machine-learning approach to predictively classify touch events from depth data with lower latency and higher touch accuracy than previous approaches. Our system can reduce end-to-end touch latency to under 70ms, comparable to conventional capacitive touchscreens. Additionally, we open-source our dataset of over 30,000 touch events recorded in depth, infrared and RGB for the benefit of future researchers.

Dynamic modeling and analysis of flexible micro-porous piezoelectric sensors applicable in soft robotics

With recent advances in system integration technologies, numerous efforts have been made to develop soft piezoelectric sensors for various engineering and healthcare applications. Using flexible and sensitive materials is crucial for designing soft sensors in order to maximize their efficiency and integrability. Micro-porous PU-PZT composite is a recently designed piezoelectric particulate composite material with an improved flexibility and piezoelectric voltage coefficient over common piezoelectric ceramics that makes it a promising candidate for application in soft sensors. In this study, we investigate the dynamic response and sensitivity of the micro-porous PU-PZT composite for applications in soft sensors in both 33 and 31 modes using energy methods. By using the effective field method, the micro-porous PU-PZT composite material properties were extracted and optimized based on the partially experimentally measured properties in order to get a complete picture of the properties of the material. In addition, the effects of changing the sensor geometry by varying the thickness and adding an extra layer between the piezoelectric layers are studied. Finally, a large area sensor based on micro-porous PU-PZT composite is simulated in finite element software, and the effect of several parameters on sensor’s performance is investigated. Dynamic analysis of the sensor shows high sensitivity in both 31 and 33 modes which is a significant improvement compared to the commonly used bulk piezoelectric ceramics. This work has demonstrated that due to the high output voltage and structural flexibility of the micro-porous PU-PZT composite, a flexible large-area sensor would be a suitable choice for artificial skins and smart gloves.

A Flexible Capacitance Strain Sensor with Stitched Contact Terminals

AbstractFlexible strain sensing in wearables and textiles, employs simple resistive sensor elements which suffer from high hysteresis. Textile compatible capacitive strain sensors that have low hysteresis and negligible cross‐axis sensitivity will be ideal for sensing human movement and shape change. In this work, a non‐MEMS based flexible interdigital capacitive strain sensor is proposed. The proffered sensor is built using flexible elastomer material with stitched conductive threads serving as internal and external contacts thereby making the sensor easily customizable. The developed sensor is amenable for fabrication using large scale textile sensor manufacturing techniques. The low sensitivity due to the low dielectric constant of most elastomers is addressed by employing high dielectric constant additives while molding the elastomer sections. The developed sensor is virtually insensitive to cross‐axis strain by design and is capable of withstanding stretching up to 160%. The prototype sensor built and tested has a sensitivity of and portrays a worst‐case nonlinearity error of &lt;±2.9%. An application, where the developed sensor was stitched on to a glove to sense finger movement is presented and discussed. The proposed fully stitch‐able sensors have the versatility to perform fabric‐based wearable strain sensing for body shape and movement analysis.

Performance of Intrusive Phase-Detection Probe with Large Sensor Size in Air-Water Flow Measurement and Application to Prototype Hydraulic Jump Study

Performance of Intrusive Phase-Detection Probe with Large Sensor Size in Air-Water Flow Measurement and Application to Prototype Hydraulic Jump Study

Trident: A Deep Learning Framework for High-Resolution Bridge Vibration Monitoring

Bridges are the essential components in lifeline transportation systems, and their safe operation is of great importance. Information on structural damage could assist in timely repairs and reduce downtime. With the latest advancements in sensing technology, collecting vibration data from bridges has become more accessible. However, effective vibration processing is still a challenge, given the high dimensionality and massive size of vibration data. Existing studies have shown that machine/deep learning techniques can be valuable tools for this task. However, the learning and computational capacities of these models are challenged in the presence of large sensor arrays. We propose Trident as a novel deep learning framework that enables automatic damage feature extraction by simultaneously learning from temporal and three-dimensional (3D) spatial variations of 6D input data in instrumented bridges. Trident is equipped with 3 ConvLSTM3D branches to achieve this goal. A 3D steel truss bridge subject to dynamic traffic loads is monitored for its vibrations to evaluate Trident’s robustness in finding damaged elements. A damage dataset of 52,800 vehicle passing simulations is generated leveraging a database of 528 passenger vehicles in the United States, obtained from the National Highway and Traffic Safety Administration. Bayesian optimization is utilized to tune the model’s hyperparameters, achieving a test Node Average Geometric Mean Accuracy of 86%. This level of performance is promising given the high dimensionality and complexities of the output space in vibration-based monitoring. Trident’s concept can be extended to other vibration monitoring tasks with different time series data and damage labeling strategies.

A large bandwidth optical fiber magnetic field sensor based on Sagnac interferometer

In this paper, a fiber magnetic field sensor is proposed based on Terfenol-D and Sagnac interferometer. The mechanism of the sensor is deduced theoretically. As the frequency of the magnetic field increases, on the one hand, the sensitivity is enhanced because the response to the high-frequency non-reciprocal phase differences is intensified in the Sagnac interferometer. On the other hand, the sensitivity is suppressed by the eddy current effect. Then it exhibits significant linear response to alternating magnetic field in a certain frequency range. The sensor performances, such as frequency response, linear range, and noise, are analyzed theoretically and demonstrated experimentally. Experimental results show that the sensor has a 3 dB bandwidth from 100 Hz to 2500 Hz, and the excellent linearity and reversibility from 0 to 274.05 μT (rms). The high sensitivity of 1.34 mV/μT (rms) and minimum detectable magnetic field of 25 nT/√Hz (rms) are achieved at 500 Hz. The sensor with large bandwidth and wide measuring range is suitable for detecting weak alternating magnetic field.

Design of a 2 m Primary Mirror Assembly Considering Fatigue Characteristics

The design requirements of a 2 m mirror assembly installed on a large space optical remote sensor are investigated in this study. The mirror body and rigid connectors are designed using topology and size optimization methods. The initial design scheme of flexible supports is then proposed through an in-depth exploration of fatigue failure mechanism caused by impacts of size parameters, thermal–mechanical vibration, and surface abrasion. The comprehensive analysis mode of the flexible support structure is established with the help of finite element and fatigue analysis software programs. The designed mirror surface error under composited force is 5.6 nm, first natural vibration mode is 119.16 Hz, and fatigue life synthesizing thermal–mechanical vibration is about 21,790,000 cycles, thereby meeting the design requirements. The first natural vibration mode, acceleration response, and stress responses of sine and random vibration are verified with experiments, and the findings are compared with the theoretical analysis results. The analysis mode can successfully and significantly improve the reliability of the mirror assembly and help optimize the design of flexible supports.

IOT BASED NON-INVASIVE TECHNIQUE TO DETECT THYROID

This system presents a thyroid monitoring sensor for Internet of Things (IoT) applications. The proposed architecture is energy efficient and user friendly. Thyroid monitoring can be done either by monitoring the basal body conductance or through blood samples. In this work basal body conductance is the quantity sensed for thyroid monitoring. The conductance acquisition is performed by an electrode along with an amplifier and a Node MCU. The designed module implements the conductance acquisition module along with the calibration method to dynamically optimize the sensor design. The sensor should be non-intrusive to avoid possible disturbs and also because the person who wears it could feel uncomfortable. Furthermore, the system acquisition should be designed by wireless technology. Finally, the sampling rate of the acquisition and the material of the sensor have to be revised. A large majority or sensors provide signals with artifacts and out layers that make the difficult information extraction.

Autonomous Flight of High-Endurance UAVs to Monitor Powerlines

In this project, the authors will develop a highly efficient UAV (unmanned aerial vehicle) architecture for long endurance flights. The authors propose an IoT (internet of things) model that will connect a network of drones using a 4G LTE (long-term evolution) connection. The researchers also propose the use of MEC (mobile edge computing) stations, smart GCSs (ground control stations) for battery replacement, and wireless charging via powerline magnetic field harvesting. The authors will be applying these ideas to a powerline monitoring mission profile, creating a large sensor network with computer vision capabilities to detect powerline faults and report them to the proper authorities.

Microwave Synthesized 2D WO3 Nanosheets for VOCs Gas Sensors.

As an n-type semiconductor material, tungsten oxide (WO3) has good application prospects in the field of gas sensing. Herein, using oxalic acid (OA), citric acid (CA) and tartaric acid (TA) as auxiliary agents, three homogeneous tungsten oxide nanosheets were prepared by the rapid microwave-assisted hydrothermal method. The potential exhaled gases of various diseases were screened for the gas sensitivity test. Compared with WO3-OA and WO3-TA, WO3-CA exhibits significant sensitivity to formaldehyde, acetone and various alkanes. Photoluminescence (PL) chromatography and photoelectric properties show that its excellent gas sensitivity is due to its abundant oxygen vacancies and high surface charge migration rate, which can provide more preferential reaction sites with gas molecules. The experiment is of great significance for the sensor selection of the large disease exhaled gas sensor array.

Design of a focused light field fundus camera for retinal imaging

Light field cameras show great potential for use in retinal imaging. The ability to refocus images and remove glare post-acquisition provides great benefits over current fundus cameras. Previous light field fundus cameras did not have the spatial resolution required for retinal screening. In this work, we design a light field fundus camera based on the specifications prescribed by the ISO standard for fundus cameras. We build a camera according to the developed specifications and test it using the method prescribed by the standard. We validate the design procedure by showing that the resolutions achieved by the constructed camera were similar to those achieved by the theoretical design. We additionally show that we can achieve a spatial resolution of 33.3 lp/mm which is a significant improvement over 17.96 lp/mm obtained in previous work. Given a larger sensor and custom microlens array, the proposed design procedure could feasibly provide the spatial resolution required for diagnostic retinal imaging while keeping the preferable qualities of a light field camera.

Design of a large Stokes shift ratiometric fluorescent sensor with hypochlorite detection towards the potential application as invisible security ink

Hypochlorite (ClO-) as a well-known highly reactive oxygen species (ROS), is widely used as preservative and household disinfectant in daily life. Although many fluorescence imaging sensors for ClO- have been reported, the development of ClO- ratio fluorescence sensors with large Stokes shift is still quite limited. This sensor shows obvious benefits including minimizing environmental intervention and improving signal-to-noise ratio. In the present project, we report an innovative conjugated pyrene-based system, 1-B, as a chlorine fluorescence sensor. The detector exhibits ratio detection performance, large Stokes and emission shifts. Furthermore, the system has desired sensitivity as well as selectivity for ClO-. Based on these excellent properties, the sensor 1-B was successfully used as ink to encrypt patterns and anti-counterfeiting information through inkjet printing technology. Compared with the existing probes, the probe shows some superior characteristics, which provides a promising tool for exploring the role of ClO- response sensor in the field of anti-counterfeiting.

Characterization of large LGAD sensors for proton counting in particle therapy

A proton counter prototype based on Low Gain Avalanche Detector (LGAD) technology is being developed for the online monitoring of the fluence rate of therapeutic proton beams. The laboratory characterization of thin (45 μm and 60 μm) LGAD sensors segmented in 146 strips with an unprecedented large area of 2.6 × 2.6 cm2, covering the entire beam cross-section, is presented and discussed. The production includes 14 wafers with different characteristics, designed and produced at Fondazione Bruno Kessler (FBK) of Trento in 2020. The laboratory characterization was carried out at FBK, right after production, and at the University of Torino, after cutting the sensors, using a probe station with a power analyzer for the static DC electrical tests. The tests proved that the production was of very high quality. From 16 sensors randomly selected from different wafers, we observed consistency between the measurements performed at FBK and at the University of Torino, indicating that the cut did not degrade the performance. The sensors were also exposed to the clinical proton beam of the National Center for Oncological Hadrontherapy (CNAO, Pavia, Italy). The results show that LGADs allow achieving, in a very thin active thickness, a good separation between the proton signal, a peak of a very short duration, and the noise. This, combined with the large active area, will allow counting protons delivered with high efficiency at the high rates of a clinical beam.

Large measurement-range and low temperature cross-sensitivity optical fiber curvature sensor based on Michelson interferometer

A large-measurement-range fiber curvature sensor based on Michelson interferometer is proposed and investigated. The sensor is fabricated by sandwiching a bitaper between a single-mode fiber (SMF) and a piece of multimode fiber (MMF), and the MMF end face is multiply discharged to form a fiber sphere. The mode interference is accumulated in the MMF for the bitaper could excite high-order modes and couple them with core mode. The sensor can be defined as a cantilever beam structure, in which the fiber sphere serves as a mass block, while the bitaper and MMF act as cantilever beam. The reflection interference spectrum is sensitive to ambient curvature, so a large-range curvature detection is realized. Experimental results show that the sensor has a sensitivity of −0.6888 nm/m−1 and −1.812 dB/m−1 in the curvature range of 0–12.2894 m−1. The maximum temperature sensitivity is 0.0078 nm/°C and 0.007 dB /°C in the temperature range of 30–150 °C, and the maximum temperature cross-sensitivity is −0.0113 m−1/°C, which could be neglected in the application. The sensor has advantages of low cost, easy fabrication, large curvature detection range, and low temperature cross-sensitivity, making it has broad application prospects in the curvature sensing field.

Screen Violence from Settler Colonialism to Cognitive Capitalism

While the HBO show Westworld (2016–present, created by Lisa Joy and Jonathan Nolan) has gained much critical attention for its byzantine plotting and philosophical conundrums, the present discussion focuses instead on the basic premise on which the titular park operates, namely that the algorithms that govern human behavior can be disclosed by studying how human beings behave toward image beings. Under the guise of a tactile experience of a make-believe past, the park attractions clandestinely function as a large behavioral sensor, extracting actionable data from the guests who reveal their inner drives when interacting with the host environment. Taking its cue from the opening titles of the first season, the argument pivots on the master trope of the series: the machine-readable scroll of perforated paper that commands the automated performance of the player piano. This motif is examined through a double-pronged approach that aligns the anthropology of images developed by Hans Belting, which understands the relation between humans and images as the interactions between “hosts” and “guests,” with the archaeology of media and its dominant concern to uncover the prehistory of the automated control systems of the computer age. While Westworld proffers a timely allegory of biopolitical capture along the digital frontier, the show ultimately testifies to the failure to constructively engage with the precarious relation between hosts and guests that to an equal extent defines our contemporary moment. The initial problem raised by Westworld, the ethics of killing virtual beings, thus gives rise to a broader historical inquiry that concerns the inability of human societies to face the past and deal with the images they inherit.

High-speed hydrogen sensor fabricated using a platinum/titanium oxide nanocontact

Titanium dioxide (TiO2) thin films have been extensively studied for use in solid-state hydrogen (H2) gas sensors. In this work, TiOx/platinum (Pt) nanocontacts which constitute a Schottky-diode sensor were developed using shadow evaporation. Large resistance changes and short reaction times were achieved through measurement of the resistance of the contacts. The sensing was quite depended on the thickness of the TiOx layer. The results indicate that the large sensor response over 105 was achieved just neighboring outermost surfaces of TiOx layer. As this sensor can be fabricated at temperatures less than 400 K, it is expected to be applicable to on-tip sensors.

Wearable OPM-MEG: A changing landscape for epilepsy.

Magnetoencephalography with optically pumped magnometers(OPM-MEG) is an emerging and novel, cost-effective wearable system that can simultaneously record neuronal activity with high temporal resolution ("when" neuronal activity occurs) and spatial resolution ("where" neuronal activity occurs). This paper will first outline recent methodological advances in OPM-MEG compared to conventional superconducting quantum interference device (SQUID)-MEG before discussing how OPM-MEG can become a valuable and noninvasive clinical support tool in epilepsy surgery evaluation. Although OPM-MEG and SQUID-MEG share similar data features, OPM-MEG is a wearable design that fits children and adults, and it is also robust to head motion within a magnetically shielded room. This means that OPM-MEG can potentially extend the application of MEG into the neurobiology of severe childhood epilepsies with intellectual disabilities (e.g., epileptic encephalopathies) without sedation. It is worth noting that most OPM-MEG sensors are heated, which may become an issue with large OPM sensor arrays (OPM-MEG currently has fewer sensors than SQUID-MEG). Future implementation of triaxial sensors may alleviate the need for large OPM sensor arrays. OPM-MEG designs allowing both awake and sleep recording are essential for potential long-term epilepsy monitoring.