Synthetic Sensor Research Articles

The requirements for automation systems based on Boeing 737 MAX crashes

PurposeThis paper aims to show the current situation and additional requirements for the aircraft automation systems based on the lessons learned from the two 737 MAX crashes.Design/methodology/approachIn this study, the Swiss cheese model was used to find the real root causes of the 737 MAX accidents. Then, the results have been compared with the actions taken by the manufacturers and authorities. Based on the comparison, the necessary improvements to prevent such accidents are defined. Regarding the faulty sensor that forms the accidents, a synthetic sensor was developed using an aerodynamic model.FindingsIt has been proven that the safety-critical automation systems should not be designed by relying on a single set of sensor data. Automation levels should be defined in a standard way. Depending on the defined automation level, the system must be designed as either fail-safe or fail-operational system. When designing backup systems, it should be decided by looking at not only whether it has power but also the accuracy of the incoming signals.Practical implicationsAviation certification requirements related to automation systems need to be revised and improved. With this context, it was revealed that the certification processes for automation systems should be re-evaluated and updated by aviation authorities, especially Federal Aviation Administration and European Union Aviation Safety Agency.Originality/valueTask sharing between automation system and pilot based on the classification of automation levels and determining certification requirements accordingly has been brought to the agenda. A synthetic Angle of Attack sensor was developed by using an aerodynamic model for fault detection and diagnosis.

Probing Lanmodulin's Lanthanide Recognition via Sensitized Luminescence Yields a Platform for Quantification of Terbium in Acid Mine Drainage.

Lanmodulin is the first natural, selective macrochelator for f elements-a protein that binds lanthanides with picomolar affinity at 3 EF hands, motifs that instead bind calcium in most other proteins. Here, we use sensitized terbium luminescence to probe the mechanism of lanthanide recognition by this protein as well as to develop a terbium-specific biosensor that can be applied directly in environmental samples. By incorporating tryptophan residues into specific EF hands, we infer the order of metal binding of these three sites. Despite lanmodulin's remarkable lanthanide binding properties, its coordination of approximately two solvent molecules per site (by luminescence lifetime) and metal dissociation kinetics (koff = 0.02-0.05 s-1, by stopped-flow fluorescence) are revealed to be rather ordinary among EF hands; what sets lanmodulin apart is that metal association is nearly diffusion limited (kon ≈ 109 M-1 s-1). Finally, we show that Trp-substituted lanmodulin can quantify 3 ppb (18 nM) terbium directly in acid mine drainage at pH 3.2 in the presence of a 100-fold excess of other rare earths and a 100 000-fold excess of other metals using a plate reader. These studies not only yield insight into lanmodulin's mechanism of lanthanide recognition and the structures of its metal binding sites but also show that this protein's unique combination of affinity and selectivity outperforms synthetic luminescence-based sensors, opening the door to rapid and inexpensive methods for selective sensing of individual lanthanides in the environment and in-line monitoring in industrial operations.

A Flexible Baseline Measuring System Based on Optics for Airborne DPOS.

Three-dimensional imaging for multi-node interferometric synthetic aperture radar (InSAR) or multi-task imaging sensors has become the prevailing trend in the field of aerial remote sensing, which requires multi-node motion information to carry out the motion compensation. A distributed position and orientation system (DPOS) can provide multi-node motion information for InSAR by transfer alignment technology. However, due to wing deformation, the relative spatial relationship between the nodes will change, which will lead to lower accuracy of the transfer alignment. As a result, the flexible baseline between the nodes affects the interferometric phase error compensation and further deteriorates the imaging quality. This paper proposes a flexible baseline measuring system based on optics, which achieves non-connect measurement and overcomes the problem that it is difficult to build an accurate wing deformation model. An accuracy test was conducted in the laboratory, and results showed that the measurement accuracy of the baseline under static and dynamic conditions was less than 0.3 mm and 0.67 mm, respectively.

VIoLET

Internet of Things (IoT) deployments have been growing manifold, encompassing sensors, networks, edge, fog, and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose a virtual environment for validating Internet of Things at large scales (VIoLET), an emulator for defining and launching large-scale IoT deployments within cloud VMs. It allows users to declaratively specify container-based compute resources that match the performance of native IoT compute devices using Docker. These can be inter-connected by complex topologies on which bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation as well. We also incorporate models for CPU resource dynamism, and for failure and recovery of the underlying devices. We offer a detailed comparison of VIoLET’s compute and network performance between the virtual and physical deployments, evaluate its scaling with deployments with up to 1, 000 devices and 4, 000 device-cores, and validate its ability to model resource dynamism. Our extensive experiments show that the performance of the virtual IoT environment accurately matches the expected behavior, with deviations levels within what is seen in actual physical devices. It also scales to 1, 000s of devices and at a modest cloud computing costs of under 0.15% of the actual hardware cost, per hour of use, with minimal management effort. This IoT emulation environment fills an essential gap between IoT simulators and real deployments.

Experimental Analysis of Neural Approaches for Synthetic Angle-of-Attack Estimation

Synthetic sensors enable flight data estimation without devoted physical sensors. Within modern digital avionics, synthetic sensors can be implemented and used for several purposes such as analytical redundancy or monitoring functions. The angle of attack, measured at air data system level, can be estimated using synthetic sensors exploiting several solutions, e.g., model-based, data-driven, and model-free state observers. In the class of data-driven observers, multilayer perceptron neural networks are widely used to approximate the input-output mapping angle-of-attack function. Dealing with experimental flight test data, the multilayer perceptron can provide reliable estimation even though some issues can arise from noisy, sparse, and unbalanced training domain. An alternative is offered by regularization networks, such as radial basis function, to cope with training domain based on real flight data. The present work’s objective is to evaluate performances of a single-layer feed-forward generalized radial basis function network for AoA estimation trained with a sequential algorithm. The proposed analysis is performed comparing results obtained using a multilayer perceptron network adopting the same training and validation data.

Highly sensitive self-healable strain biosensors based on robust transparent conductive nanocellulose nanocomposites: Relationship between percolated network and sensing mechanism

The conventional skin sensor detection of human physiological signals can be an effective method for disease diagnosis and health monitoring, but the poor biocompatibility, low sensitivity and complex design largely limit their applications. Developing natural nanofiller-reinforced composites as strain biosensors is an appealing solution to reduce environmental impacts and overcome technical bottleneck. Herein, a versatile nature skin-inspired composite film as flexible strain biosensor was developed based on cellulose nanocrystals-polyaniline (CNC-PANI) composites by utilizing their percolated conductive network in polyvinyl alcohol (PVA) matrix. The composite electronic skin showed robust mechanical strength (50.62 MPa) and high sensitivity (Gauge Factor = 11.467) with easy water-induced self-healing abilities. Moreover, we investigated the functioning mechanism of percolated network and the sensory behavior determined by CNC nanocomposite alignment. The percolation threshold of CNC-polyaniline (PANI) was determined at 4.278% and 5% CNC-PANI composite film shows the best overall sensing property. It was also discovered that the sensitivity of this type of conductive-filler electronic skin can be divided into two separate regions at different strain range due to its percolated network. With films prepared by dry casting and dip coating, the alignment of CNC-PANI also contributes to this unique change in electrical property. Generally, our results demonstrated the mechanism and tunability of conductive nanofiller-based composite strain biosensors as a potential alternative to commercial synthetic sensors.

Wearable materials with embedded synthetic biology sensors for biomolecule detection.

Integrating synthetic biology into wearables could expand opportunities for noninvasive monitoring of physiological status, disease states and exposure to pathogens or toxins. However, the operation of synthetic circuits generally requires the presence of living, engineered bacteria, which has limited their application in wearables. Here we report lightweight, flexible substrates and textiles functionalized with freeze-dried, cell-free synthetic circuits, including CRISPR-based tools, that detect metabolites, chemicals and pathogen nucleic acid signatures. The wearable devices are activated upon rehydration from aqueous exposure events and report the presence of specific molecular targets by colorimetric changes or via an optical fiber network that detects fluorescent and luminescent outputs. The detection limits for nucleic acids rival current laboratory methods such as quantitative PCR. We demonstrate the development of a face mask with a lyophilized CRISPR sensor for wearable, noninvasive detection of SARS-CoV-2 at room temperature within 90 min, requiring no user intervention other than the press of a button.

ASSESSMENT OF FLOODED AREAS CAUSED BY A DAM BREAK (SARDOBA DAM, UZBEKISTAN)

Abstract. Although dams are very useful engineering structures, they can have extremely harmful consequences if they fail. One example of these failures occurred in Sardoba Reservoir (Uzbekistan). On May 1, 2020, a part of an earthfill dam failed along the Sardoba Reservoir, and a large region with settlements and agricultural areas in Uzbekistan and Kazakhstan was flooded. Accurate mapping and monitoring of the flooded areas are crucial for the damage assessment and the mitigation efforts. Satellite Earth Observation datasets can serve for these purposes due to their greater availability with high spatial and temporal resolutions. However, the optical sensors have limitations for data acquisition due to the atmospheric conditions, particularly the cloud cover, which often severely affects the image usability when floods occur. The synthetic aperture radar sensors provide valuable information under all weather conditions, but their interpretation is relatively difficult. Therefore, a data fusion methodology is proposed here for the integrated use of Sentinel-1 and Sentinel-2 datasets using a set of features obtained from both. Four different feature combinations were evaluated using the random forest classifier. The pre-processing steps for the feature extraction are explained in detail and the results are discussed here. The proposed algorithm exhibits very high classification accuracy for the flooded areas and flooded vegetation classes. The method can be employed for the flash flood mapping at regional scale. In addition, the damage assessment especially for agricultural areas in the region is very important for accounting the economic losses and the resilience purposes.

Yeast Synthetic Minimal Biosensors for Evaluating Protein Production.

The unfolded protein response (UPR) is a highly conserved cellular response in eukaryotic cells to counteract endoplasmic reticulum (ER) stress, typically triggered by unfolded protein accumulation. In addition to its relevance to human diseases like cancer, the induction of the UPR has a significant impact on the recombinant protein production in eukaryotic cell factories, including the industrial workhorseSaccharomyces cerevisiae. Being able to accurately detect and measure this ER stress response in single cells, enables the rapid optimization of protein production conditions and high-throughput strain selection strategies. Current methodologies to monitor the UPR in S. cerevisiae are often temporally and spatially removed from the cultivation stage or lack updated systematic evaluation. To this end, we constructed and systematically evaluated a series of high-throughput UPR sensors by different designs, incorporating either yeast native UPR promoters or novel synthetic minimal UPR promoters. The native promoters of DER1 and ERO1 were identified to have suitable UPR biosensor properties and served as an expression level guide for orthogonal sensor benchmarking. Our best synthetic minimal sensor is only 98 bp in length, has minimal homology to other native yeast sequences and displayed superior sensor characteristics. The synthetic minimal UPR sensor was able to accurately distinguish between cells expressing different heterologous proteins and between the different secretion levels of the same protein. This work demonstrated the potential of synthetic UPR biosensors as high-throughput tools to predict the protein production capacity of strains, interrogate protein properties hampering their secretion, and guide rational engineering strategies for optimal heterologous protein production.

All-Optical Electrophysiology in hiPSC-Derived Neurons With Synthetic Voltage Sensors.

Voltage imaging and “all-optical electrophysiology” in human induced pluripotent stem cell (hiPSC)-derived neurons have opened unprecedented opportunities for high-throughput phenotyping of activity in neurons possessing unique genetic backgrounds of individual patients. While prior all-optical electrophysiology studies relied on genetically encoded voltage indicators, here, we demonstrate an alternative protocol using a synthetic voltage sensor and genetically encoded optogenetic actuator that generate robust and reproducible results. We demonstrate the functionality of this method by measuring spontaneous and evoked activity in three independent hiPSC-derived neuronal cell lines with distinct genetic backgrounds.

Modeling and Reproducing Textile Sensor Noise: Implications for Textile-Compatible Signal Processing Algorithms.

Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). Signal processing algorithms that can extract ECG signals out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The synthetic textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 trials (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs.

State Estimation for HALE UAVs With Deep-Learning-Aided Virtual AOA/SSA Sensors for Analytical Redundancy

High-altitudelong-endurance (HALE) unmanned aerial vehicles (UAVs) are employed in a variety of fields because of their ability to fly for a long time at high altitudes, even in the stratosphere. Two paramount concerns exist: enhancing their safety during long-term flight and reducing their weight as much as possible to increase their energy efficiency based on analytical redundancy approaches. In this letter, a novel deep-learning-aided navigation filter is proposed, which consists of two parts: an end-to-end mapping-based synthetic sensor measurement model that utilizes long short-term memory (LSTM) networks to estimate the angle of attack (AOA) and sideslip angle (SSA) and an unscented Kalman filter for state estimation. Our proposed method can not only reduce the weight of HALE UAVs but also ensure their safety by means of an analytical redundancy approach. In contrast to conventional approaches, our LSTM-based method achieves better estimation by virtue of its nonlinear mapping capability.

Visual pH Sensors: From a Chemical Perspective to New Bioengineered Materials.

Many human activities and cellular functions depend upon precise pH values, and pH monitoring is considered a fundamental task. Colorimetric and fluorescence sensors for pH measurements are chemical and biochemical tools able to sense protons and produce a visible signal. These pH sensors are gaining widespread attention as non-destructive tools, visible to the human eye, that are capable of a real-time and in-situ response. Optical “visual” sensors are expanding researchers’ interests in many chemical contexts and are routinely used for biological, environmental, and medical applications. In this review we provide an overview of trending colorimetric, fluorescent, or dual-mode responsive visual pH sensors. These sensors include molecular synthetic organic sensors, metal organic frameworks (MOF), engineered sensing nanomaterials, and bioengineered sensors. We review different typological chemical entities of visual pH sensors, three-dimensional structures, and signaling mechanisms for pH sensing and applications; developed in the past five years. The progression of this review from simple organic molecules to biological macromolecules seeks to benefit beginners and scientists embarking on a project of pH sensing development, who needs background information and a quick update on advances in the field. Lessons learned from these tools will aid pH determination projects and provide new ways of thinking for cell bioimaging or other cutting-edge in vivo applications.

Control and Validation of an Autonomous Drag Racing Vehicle

<div class="section abstract"><div class="htmlview paragraph">This paper studies the implementation and validation of control algorithms for an autonomous drag racing vehicle. The previously developed modeling equations are first implemented with control in a realistic simulation environment complete with synthetic sensor data and decision-making algorithms. The controller is then transformed into an embedded on-board processing unit for on-vehicle testing. Camera, lidar, and radar sensor data are investigated and algorithms are created to provide information from physical sensors rather than synthetic data. The control related to actuation of the steering, brake, throttle, and shifting systems are further discussed, along with human-vehicle interaction in terms of handoff and emergency takeovers. The control algorithms are then validated on the research vehicle. This is demonstrated by completing a fully autonomous quarter-mile drag race, complete with camera detection for the staging sequence and MPC trajectory following. Two additional safety scenarios are presented and controlled: starting the race off center and fishtailing.</div></div>

Safety Analysis of a Certifiable Air Data System Based on Synthetic Sensors for Flow Angle Estimation

This work deals with the safety analysis of an air data system (ADS) partially based on synthetic sensors. The ADS is designed for the small aircraft transportation (SAT) community and is suitable for future unmanned aerial vehicles and urban air mobility applications. The ADS’s main innovation is based on estimation of the flow angles (angle-of-attack and angle-of-sideslip) using synthetic sensors instead of classical vanes (or sensors), whereas pressure and temperature are directly measured with Pitot and temperature probes. As the air data system is a safety-critical system, safety analyses are performed and the results are compared with the safety objectives required by the aircraft integrator. The present paper introduces the common aeronautical procedures for system safety assessment applied to a safety critical system partially based on synthetic sensors. The mean time between failures of ADS’s sub-parts are estimated on a statistical basis in order to evaluate the failure rate of the ADS’s functions. The proposed safety analysis is also useful in identifying the most critical air data system parts and sub-parts. Possible technological gaps to be filled to achieve the airworthiness safety objectives with nonredundant architectures are also identified.

Design of C band antenna downlink system for monitoring Sentinel 1A signals on ground surface

The C band microwave signals are widely used for Earth observation in the field of remote sensing by using synthetic aperture radar sensor. The coverage of Indian regions by Sentinel 1A SAR sensor in interoferometric wide swath mode, provide an opportunity to research and conduct studies in remote sensing areas from urban planning to disaster management applications. The sensor acquired images undergo calibration test to confirm the quality requirements. The foremost significant step to ensure the availability of the signal of Sentinel 1A on the ground surface is by designing a antenna system which tracks and monitor the signal strength. In this context, the following stages are significant in signal monitoring system: satellite downlink budget calculation to ensure minimum signal strength, designing and configuring antenna system to receive the signals and measurement system such as spectrum analyser to monitor the signal strength. The paper presents the mathematical analysis of designing a down link for receiving Sentinel-1A SAR signals. An antenna subsystem is implemented which includes the design of the parabola antenna with horn feed and the simulation of the same using Matlab platform to confirm antenna parameters such as gain, directivity, VSWR and beam-width which meets the practical requirements

Multivalent molecular tension probes as anisotropic mechanosensors: concept and simulation

Cells use protein-based mechanosensors to measure the physical properties of their surroundings. Synthetic tension sensors made of proteins, DNA, and other molecular building blocks have recently emerged as tools to visualize and perturb the mechanics of these mechanosensors. While almost all synthetic tension sensors are designed to exhibit orientation-independent force responses, recent work has shown that biological mechanosensors often function in a manner that is highly dependent on force orientation. Accordingly, the design of synthetic mechanosensors with orientation-dependent force responses can provide a means to study the role of orientation in mechanosensation. Furthermore, the process of designing anisotropic force responses may yield insight into the physical basis for orientation-dependence in biological mechanosensors. Here, we propose a DNA-based molecular tension sensor design wherein multivalency is used to create an orientation-dependent force response. We apply chemomechanical modeling to show that multivalency can be used to create synthetic mechanosensors with force response thresholds that vary by tens of pN with respect to force orientation.

A Sensor Simulation Framework for Training and Testing Robots and Autonomous Vehicles

Abstract Computer simulation can be a useful tool when designing robots expected to operate independently in unstructured environments. In this context, one needs to simulate the dynamics of the robot’s mechanical system, the environment in which the robot operates, and the sensors which facilitate the robot’s perception of the environment. Herein, we focus on the sensing simulation task by presenting a virtual sensing framework built alongside an open-source, multi-physics simulation platform called Chrono. This framework supports camera, lidar, GPS, and IMU simulation. We discuss their modeling as well as the noise and distortion implemented to increase the realism of the synthetic sensor data. We close with two examples that show the sensing simulation framework at work: one pertains to a reduced scale autonomous vehicle and the second is related to a vehicle driven in a digital replica of a Madison neighborhood.

Performance of the diamond-based beam-loss monitor system of Belle II

We designed, constructed and have been operating a system based on single-crystal synthetic diamond sensors, to monitor the beam losses at the interaction region of the SuperKEKB asymmetric-energy electron–positron collider. The system records the radiation dose-rates in positions close to the inner detectors of the Belle II experiment, and protects both the detector and accelerator components against destructive beam losses, by participating in the beam-abort system. It also provides complementary information for the dedicated studies of beam-related backgrounds. We describe the performance of the system during the commissioning of the accelerator and during the first physics data taking.

Collective Sensitivity of Artificial Hair Sensors to Flow Direction

We study the collective response of synthetic hair sensors to changes in the airflow direction. Specifically, we examine whether an array of isotropic sensors, which are unable to detect the direction of an incident flow when operating in isolation, can together sense not only the magnitude but also the orientation of a freestream flow passing over and through them. To conduct our inquiry, we consider a fundamental scenario involving a boundary-layer flow past a flat plate decorated with a array of sensors. Numerical modeling is used to simulate the interaction between the fluid flow and flexible hair sensors. We find that, indeed, it is feasible to achieve collective directional sensitivity by engineering the arrangement of the sensors. We also propose a supervised machine learning approach capable of deducing the direction of the freestream flow from the deformation signals outputted by individual sensors in the array.