Large Sensor Research Articles

Dust-resistant microthermal mass-flow pitot-tube for fixed-wing drones (UAV)

Drones, commonly referred to as Unmanned Aerial Vehicles (UAVs), have become increasingly important for different applications. For the necessary velocity control, fixed-wing drones typically need a classical Pitot-tube, which consists of several components, including a large sensor which usually has to be calibrated before each flight. The microthermal mass-flow measurement principle enables a smaller sensor design, with less components and no pre-flight calibration. However, the drawback of such sensors is their increased sensitivity to particulate matter contamination. The aim of this paper is to present and test a new additively manufactured Pitot-tube design, which prevents a contamination of the microthermal flow sensor. The results of the particulate matter contamination experiment with the microthermal flow sensor show that the average induced error of 38.3% of the state of the art Pitot-tube design is reduced to 4% for the new additively manufactured Pitot-tube. The results of the flight test show a clear correlation of the differential pressure between the new additively manufactured (AM) Pitot-tube with microthermal flow sensing compared to the standard Pitot-tube design with a membrane sensor. Thus, the new AM Pitot-tube design enables accurate airspeed measurement even in environments with high concentration of particulate matter.

L 1 Norm Regularization Robust Attitude Smoother Using Inertial and Magnetic Sensors

A smoother algorithm to estimate attitude using inertial and magnetic sensors is proposed, where an attitude estimation problem is formulated as an equality constrained convex optimization problem. Existing standard smoothers are local estimators, whose estimation performance critically depends on the initial estimation. This issue is avoided by formulating an attitude problem as a global optimization problem. An l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regression term is also introduced to make the proposed method robust to sensor outliers (such as temporary magnetic disturbance or external acceleration). The optimization problem is solved through two approximation steps. In the first step, a relaxed solution is computed by relaxing equality constraints to inequality constraints. In the second step, equality constraints are recovered by searching neighborhood of a relaxed solution. Due to the approximation, the proposed algorithm is not guaranteed to provide a globally optimal solution. However, a measure of how close the computed solution is to the globally optimal solution is provided. Through simulation and experiments, the proposed method is shown to be robust to large sensor outliers: the sum of Euler angle rms errors (for 729 different simulations) by the proposed method is 2.974 (rad), while the sum by the standard smoother is 11.52 (rad).

Flexible ferroelectric wearable devices for medical applications.

SummaryWearable electronics are becoming increasingly important for medical applications as they have revolutionized the way physiological parameters are monitored. Ferroelectric materials show spontaneous polarization below the Curie temperature, which changes with electric field, temperature, and mechanical deformation. Therefore, they have been widely used in sensor and actuator applications. In addition, these materials can be used for conversion of human-body energy into electricity for powering wearable electronics. In this paper, we review the recent advances in flexible ferroelectric materials for wearable human energy harvesting and sensing. To meet the performance requirements for medical applications, the most suitable materials and manufacturing techniques are reviewed. The approaches used to enhance performance and achieve long-term sustainability and multi-functionality by integrating other active sensing mechanisms (e.g. triboelectric and piezoresistive effects) are discussed. Data processing and transmission as well as the contribution of wearable piezoelectric devices in early disease detection and monitoring vital signs are reviewed.

Highly sensitive and large range strain sensor based on synergetic effects with double conductive layer structures

The increasing demand for wearable electronic devices is promoting the development of highly elastic strain sensors, which can monitor various physical parameters and essential factors for realizing next generation electronics. However, it is still challenging to simultaneously achieve wearable strain sensors with sufficient sensitivity within wide stretching sensing ranges under mechanical stimuli in view of their widespread applications, including electronic skin, smart textiles, as well as healthcare monitoring. Herein, we propose a simple, cost-effective strain sensor based on synergetic conductive networks constituted by double-layer microstructures with multi-walled carbon nanotubes and elastomer. The micro-spinous structures of MWCNTs and the peak-valley microstructures have extended stretching sensing ranges with high sensitivities. The double-layer strain sensor exhibits high gauge factors (GF = 12.3 and 29.4) in the two linear stretching strain ranges (0–50 % and 50–200 %) with a long-term reusability, low-hysteresis, and fast response under dynamic loading. This sensor demonstrates excellent mechanical performance in real-time human motion detecting and distinguishing subtle signals, such as vocalization, muscle motions, as well as fingers or wrists.

QCM nanocomposite gas sensors – Expanding the application of waterborne polymer composites based on graphene nanoribbon

Graphene nanoribbons (GNR) are narrow strips of graphene in one dimensional morphology with extraordinary properties, owing to the large edges that offer numerous interfacial contact area to the polymer phase in the composite materials. Consequently, minor amount of GNR incorporated within polymer matrix, will not only reinforced mechanically and thermally the composite materials, but will significantly expand their application potential, giving added value to the cheap polymeric material. Herein, the quartz crystal microbalance (QCM) gas sensing of the nanocomposites was investigated.In this work we took advantage of the polymerization in dispersed media as green synthesis method to produce for first time waterborne polymer/GNR nanocomposites. The lack of aggregation during structuring of GNRs within the polymer matrix and the established covalent bonding between the polymer and GNRs phases were responsible for the observed strong mechanical and thermal reinforcement of the nanocomposites. By exposure of the nanocomposites to low concentrations of toxic gases (CO, NH3, and N2O in a concentration range of 70–1000 ppm), it was found that the sensors are characterized by a large sensor response in short time, at room temperature and with very good reproducibility in three investigated cycles of gas adsorption and desorption. The excellent performance was attributed to the large functional interface created between the GNRs and the polymer that offer numerous adsorption sites, along with the composite morphology that provide availability of these sites to the gasses. The sensors have shown selectivity towards NH3 rather than N2O and CO, likely, because the former interacted with the composite materials by joint Wan der Waals forces and hydrogen bonding, whereas the last two gasses interact exclusively by the van der Waals interactions.

Studies for low mass, large area monolithic silicon pixel detector modules using the MALTA CMOS pixel chip

The MALTA monolithic silicon pixel sensors have been used to study dicing and thinning of monolithic silicon pixel detectors for large area and low mass modules. Dicing as close as possible to the active circuitry will allow to build modules with very narrow inactive regions between the sensors. Inactive edge regions of less than 5μ m to the electronic circuitry could be achieved for 100μm thick sensors. The MALTA chip (Cardella et al., 2019) also offers the possibility to transfer data and power directly from chip to chip. Tests have been carried out connecting two MALTA chips directly using ultrasonic wedge wire bonding. Results from lab tests show that the data accumulated in one chip can be transferred via the second chip to the readout system, without the need of a flexible circuit to route the signals. The concept of chip to chip data and power transfer to achieve low mass modules has also been studied on prototype wafers using Cu-stud interconnection bridges. First results are presented, outlining technical challenges and possible future steps to achieve a low mass large area monolithic pixel sensor module.

Light Drones for Basic In-Field Phenotyping and Precision Farming Applications: RGB Tools Based on Image Analysis.

Plant phenotyping has garnered major attention in recent years, leading to developing new strategies to measure and assess plant traits of interest. For data acquisition of large fields, devices and sensors are required that deliver detailed and reproducible temporal and spatial information on the cultivated crop. This work proposes the potential use of low-cost light drones for in-field phenotyping applications on cereal crops. The proposed method allows to obtain precise measurements of color and height of the plants for the individual plots. The method is based on a color calibration algorithm (TPS-3D interpolating function) and a 3D ortho image reconstruction. The method has been applied on an experimental field with durum and soft wheat parcels obtaining information on real color (with an error lower than 12/256) and height for each single plot.

Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose

Long term sensors drift is a challenging problem to solve for instruments like an Electronic Nose System (ENS). These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performance of ML algorithms and the models those are trained on drift free data fail to perform on the drifted data. Moreover, the response of an electronic nose system depends on the variable response of the sensors and a delay is expected in reaching a steady state by the sensors. In this paper, these two problems of `sensors long term drift' and `delayed response' are solved simultaneously to propose a robust and fast electronic nose system, with following merits: (i) only initial transient state features are used in the proposed system without waiting for the sensors to reach a steady state, (ii) a modified boxplot approach is used to handle noisy/drifted data points as a preprocessing step before the classification setup, (iii) a heuristic tree classification approach with optimized transient features is proposed, (iv) the proposed approach only relies on adapted ML methods contrary to the traditional approaches like system recalibration or sensors replacement for handling sensors drift, and (v) the proposed ML model does not require any target domain data and uses only the source domain data for learning the classifier, opposed to the other ML solutions available in the existing literature. The proposed method is tested using a large scale gas sensors drift benchmark dataset available freely on UCI Machine Learning repository and is found better than the existing state-of-the art approaches with an overall accuracy of 87.34%.

WITHDRAWN: Deep learning based image processing approaches for image deblurring

Mobile phone imagery has evolved substantially over the past two decades. The camera is one of the primary features of a new cell phone. A lot of research in this field has been done to enhance the quality of the image. A small phone structure limits the camera module mounted in cell phones, which means that there can be no mounted thick lenses or large image sensors on the phones. This affects the volume and the picture quality of the captured light. With less light, phones perform worse than digital Single-Lens Reflex (DSLR) cameras as opposed to image quality tests. Post processing is then used to enhance the quality of the image. Dark conditions and fast movement in mobile imaging are difficult. In dark conditions, longer exposure period collects more light, which can cause movement blurred objects. Motion blur artefact, e.g. when photo to graphing a racing car, and may also be caused by fast moving object at daylight. The movement blur causes sharp information to be lost and thus poor picture quality. A deblur ring is the tool used to eliminate flutter from photographs and make them appear clearer. In the field of signal processing deep learning-based approaches have recently become popular. The results have been promising since profound learning algorithms can learning and model nonlinear and complex connections. Deep learning algorithms were also used for several tasks in image restore work, as was done here.

Wireless Sensor Network Applications in Healthcare and Precision Agriculture

A wireless sensor network is a large sensor hub with a confined power supply that performs limited calculations. Due to the degree of restricted correspondence and the large size of the sensor hub, packets sent through the sensor network are based primarily on multihop data transmission. Current wireless sensor networks are widely used in a range of applications, such as precision agriculture, healthcare, and smart cities. The network covers a wide domain and addresses multiple aspects in agriculture, such as soil moisture, temperature, and humidity. Therefore, issues of precision agriculture at the output of the network are analyzed using a star and mesh topology with TCP as the transmission protocol. The system is equipped with two sensors: Arduino DFRobot for soil moisture and DHT11 for relative temperature and humidity. The experiments are performed using the NS2 simulator, which provides an improved interface to analyze the results. The results showed that the proposed mechanism has good performance and output.

Fast Consensus-Based Time Synchronization Protocol Using Virtual Topology for Wireless Sensor Networks

Slow convergence is a major drawback of average-based consensus time synchronization protocols, particularly in large or sparse wireless sensor networks. The convergence speed can be increased by adding more nodes or increasing the transmission range of nodes, because the network becomes strongly connected. However, these solutions are not always feasible owing to hardware constraints. In this article, a virtual topology-based time synchronization protocol (VTSP) is proposed to address the aforementioned drawback of consensus-based protocols. Notably, VTSP performs the consensus process on a virtual topology that has a stronger algebraic connectivity than a physical one. Therefore, VTSP can significantly accelerate convergence speed without modifying the physical structure of the network. The virtual topology is formed by creating virtual links between each node and its two-hop neighbors. Moreover, two optimization techniques are suggested for reducing data redundancy, which is caused during the creation of the virtual links, in timing messages and for speeding up the convergence by excluding edge nodes from the consensus process. Simulation results demonstrate that VTSP can achieve convergence three times faster than the gradient time synchronization protocol, a well-known consensus-based time synchronization protocol, in various topologies while preserving the same level of accuracy.

Large Area Pressure Sensor for Smart Floor Sensor Applications—An Occupancy Limiting Technology to Combat Social Distancing

With the advent of the recent coronavirus pandemic, it has become crucial to maintain social distancing within public spaces. To achieve this, it is important to keep a count of the number of people entering the premises and maintain low occupancy within it. This article presents a very large area pressure sensor based on nickel sulfide deposited on a paper using facile hydrothermal, and sheet to sheet vacuum filtration technique followed by an encapsulation of the sensor using polydimethylsiloxane for smart floor applications. An android application is also developed for the count of the people and the estimated waiting period for the entry.

ResSCNN: A semantic segmentation method for fast processing of large-scale input

With the development of driver-assistance systems and driverless cars, vehicles are becoming more and more intelligent. However, today’s intelligent vehicles rely more on large sensors to sense the environment, so it is becoming more and more important for vehicles to be able to understand road information, and semantic segmentation has developed greatly. We find that the current mainstream semantic segmentation model is slow and inaccurate in the case of large scale input images. In this paper, we combine the large-scale input Fast-SCNN with the residual module to construct the residual semantic segmentation convolutional neural network (ResSCNN), after the experimental test, when the input scale is 1024 × 2048 px, yielding an accuracy of 68.4% mean intersection over union at 123.1 frames per second on Cityscapes dateset. Our network has achieved better results than the previous mainstream methods.

The effect of barium titanate ceramic loading on the stress relaxation behavior of barium titanate‐silicone elastomer composites

AbstractThe stress relaxation behavior of barium titanate (BTO)‐elastomer (Ecoflex) composites, as used in large strain sensors, is studied using the generalized Maxwell‐Wiechert model. In this article, we examine the stress relaxation behavior of ceramic polymer composites by conducting stress relaxation tests on samples prepared with varying the particle loading by 0, 10, 20, 30, and 40 wt% of 100 and 200 nm BTO ceramic particles embedded in a Ecoflex silicone‐based hyperelastic elastomer. The influence of BTO on the Maxwell‐Wiechert model parameters was studied through the stress relaxation results. While a pristine Ecoflex silicone elastomer is predominantly a hyperelastic material, the addition of BTO made the composite behave as a visco‐hyperelastic material. However, this behavior was shown to have a negligible effect on the electrical sensing performance of the large strain sensor.

Geometry Preserving Sampling Method Based on Spectral Decomposition for Large-Scale Environments.

In the context of 3D mapping, larger and larger point clouds are acquired with lidar sensors. Although pleasing to the eye, dense maps are not necessarily tailored for practical applications. For instance, in a surface inspection scenario, keeping geometric information such as the edges of objects is essential to detect cracks, whereas very dense areas of very little information such as the ground could hinder the main goal of the application. Several strategies exist to address this problem by reducing the number of points. However, they tend to underperform with non-uniform density, large sensor noise, spurious measurements, and large-scale point clouds, which is the case in mobile robotics. This paper presents a novel sampling algorithm based on spectral decomposition analysis to derive local density measures for each geometric primitive. The proposed method, called Spectral Decomposition Filter (SpDF), identifies and preserves geometric information along the topology of point clouds and is able to scale to large environments with a non-uniform density. Finally, qualitative and quantitative experiments verify the feasibility of our method and present a large-scale evaluation of SpDF with other seven point cloud sampling algorithms, in the context of the 3D registration problem using the Iterative Closest Point (ICP) algorithm on real-world datasets. Results show that a compression ratio up to 97 % can be achieved when accepting a registration error within the range accuracy of the sensor, here for large scale environments of less than 2 cm.

Initial tests of large format sensors for the ATLAS ITk strip tracker

For the construction of the Inner Tracker (ITk) as part of the phase-II upgrade programme of the ATLAS detector for the High-Luminosity (HL) LHC, batches of Long Strip (LS) and Short Strip (SS) n+-in-p type micro-strip sensors have been produced by Hamamatsu Photonics and Infineon.The full size sensors measure approximately 98 × 98 mm2 and are designed and engineered for tolerance against the 9.7 × 1014 1 MeV neq/cm2 fluence expected at the HL-LHC, including a safety factor of 1.5. Each sensor has 2 or 4 columns of 1280 individual channels arranged at 75.5 μm horizontal pitch.To ensure the sensors comply with their specifications, a Quality Control (QC) procedure has been implemented, comprising measurements on every individual sensor as well as on a sample basis. Every sensor is subjected to an initial visual inspection, after which the full surface of the sensor is captured with very high resolution by an automated camera setup. Non-contact metrology is performed to obtain the sensor surface profile. Electrical measurements establishing the reverse bias leakage current and depletion voltage are then conducted automatically. Sample sensors from every batch are subjected to 40 h of leakage stability checks in controlled atmosphere, and tests on every channel measuring leakage current, coupling capacitance and bias resistance are done. The recorded results are uploaded to a production database following data quality checks.In this paper, QC test validation data and the compiled results for the first batches of production grade sensors are presented. The QC protocol was validated, and the first production sensors were confirmed to be within specification. The results are compared to those from the previous generation of prototype sensors.

Distributed output feedback consensus control of networked homogeneous systems with large unknown actuator and sensor delays

This paper studies observer-based output feedback consensus control of networked homogeneous systems in the presence of unknown bounded actuator and sensor delays. A transport partial differential equation (PDE) is used to describe the dynamics of the delay state, and then a distributed observer on the basis of the estimate delay state and relative output information is designed to estimate the neighborhood consensus error. Predictor-based consensus protocols are proposed by utilizing the observer state and the estimate delay state. Lyapunov functions are explicitly constructed to analyze system stability, and the delay mismatch robustness of the consensus protocols is proved. Simulation results performed on low-Earth-orbit satellite formation flying are presented to illustrate the effectiveness of the proposed scheme.

Transparent phototransistor with high responsivity, sensitivity, and detectivity from heterojunction metal oxide semiconductors

In this work, the high-performance transparent Al:InZnSnO/InZnO/Al:InZnSnO tri-layer thin-film phototransistors are reported. They show a high field-effect mobility of 40.1 cm2/V·s and an excellent high photoresponsivity of 25 000 A/W, a photosensitivity of 3.3 × 107, a specific detectivity of 4.3 × 1017 cm·Hz1/2·W−1 under the illumination at 460 nm with an intensity of 140 μW/cm2. The persistent photoconductivity inherent in phototransistors made of oxide semiconductors overcome by a pulsed gate bias with 1 μs, which accelerates the recombination of photogenerated holes with electrons. This demonstrates that the transparent photosensor array can be integrated monolithically on the display without appreciable loss of transparency for high functionality such as large area image sensor.

Effects of Particle Size on the NO2 Gas Sensing Properties of NiO Nanoparticle-Decorated SnO2 Nanorods

This study reports the effects of NiO nanoparticle (NP) size on the sensing performance of NiO NP-decorated SnO2 nanorods (NRs). NiO NP-decorated SnO2 NRs were synthesized using a two-step process: 1) thermal evaporation of tin powders in an oxidizing atmosphere based on the vapor-liquid-solid growth mechanism and 2) solvothermal decoration of SnO2 NRs with NiO NPs. X-ray diffraction and transmission electron microscopy analyses revealed that both the SnO2 NRs and the NiO NPs were polycrystalline. Scanning electron microscopy images showed that the diameters of the NRs ranged from 100 to 200 nm and that those of the small and the large NiO NPs ranged from 20 to 30 nm and from 80 to 180 nm, respectively. The small NiO NP-decorated SnO2 NRs showed stronger response to NO2 than did the large NiO NP-decorated SnO2 NRs over the concentration range of 0.5–100 ppm. Decoration of SnO2 NRs with small NiO NPs resulted in enhanced sensing performance whereas decoration of SnO2 NRs with large NiO NPs deteriorated the sensing performance. The superior NO2 gas sensing performance of the small NiO NP-decorated SnO2 NR sensor as compared to that of the large NiO NP-decorated SnO2 NR sensor was attributed to a higher ratio of n-SnO2 to p-NiO and a higher number of p-n heterojunctions for the same volume of NiO in the former than in the latter. In addition, the small NiO NP-decorated SnO2 NR sensors showed selectivity toward NO2 against other competing gases such as SO2, CO2, CO, H2, C7H8 and C6H6.

Knowledge Discovery Using Topological Analysis for Building Sensor Data.

Distributed sensor networks are at the heart of smart buildings, providing greater detail and valuable insights into their energy consumption patterns. The problem is particularly complex for older buildings retrofitted with Building Energy Management Systems (BEMS) where extracting useful knowledge from large sensor data streams without full understanding of the underlying system variables is challenging. This paper presents an application of Q-Analysis, a computationally simple topological approach for summarizing large sensor data sets and revealing useful relationships between different variables. Q-Analysis can be used to extract novel structural features called Q-vectors. The Q-vector magnitude visualizations are shown to be very effective in providing insights on macro behaviors, i.e., building floor behaviors in the present case, which are not evident from the use of unsupervised learning algorithms applied on individual terminal units. It has been shown that the building floors exhibited distinct behaviors that are dependent on the set-point distribution, but independent of the time and season of the year.