Sensor Setup Research Articles

Gold Nanobipyramids as LPSR Sensing Materials for Glyphosate Detection: Surface Density and Aspect Ratio Effect

The governing of localized surface plasmon resonance (LSPR)-based sensor performance is derived with consideration of shape, surface density, and aspect ratio of sensing material, as well as the refractive index of the medium. Dependencies of the surface density and aspect ratio of gold nanobipyramids (GNBPs) used as sensing materials in self-customized LSPR-based sensor setup for the detection of herbicides namely glyphosate are studied in this work. In this work, the GNBPs with a surface density from 5.21% ± 0.44% to 91.46% ± 3.32% and an aspect ratio from 2.00 ± 0.02 to 2.76 ± 0.05 were fabricated. Experimentally, the result shows a linear relationship between the mean surface density and aspect ratio of GNBPs toward the sensitivity of the LSPR-based sensor for both transverse surface plasmon resonance (t-SPR) and longitudinal surface plasmon resonance (l-SPR) resonance peaks. The optimum result for sensitivity was carried out by a sample with a surface density of 80.02% ± 0.86% and an aspect ratio of 2.58 ± 0.04. Furthermore, this sample also exhibits very clear response recovery behavior and stable repeatability up to five cycles with variances of 0.011% and 0.022%, respectively. The findings are useful to enhance the performance of LSPR-based sensor through process conditions that are able to control the morphology (surface density and aspect ratio) of GNBPs to improve the sensitivity of LSPR-based sensor.

Simultaneous measurement of pressure and temperature in a supersonic ejector using FBG sensors

In this work, we have demonstrated the use of fiber Bragg grating (FBG) sensors for simultaneous measurement of wall static pressure and temperature in a supersonic ejector. Supersonic ejectors are ground-based high-speed aerodynamic test facilities characterized by harsh conditions, such as high pressure and temperature gradients. An FBG-based sensor setup was developed consisting of a pressure measuring bare FBG and a specially designed pressure-insensitive FBG temperature probe that can be mounted on the wall of the supersonic ejector. The FBG temperature probe was used for temperature measurement as well as temperature compensation of the pressure measuring FBG sensor. Wall static pressure measurements in the supersonic ejector were carried out at different tank pressures and Mach number flows. The FBG pressure measurements were validated with those of standard piezoresistive-based sensor measurements. Both responses were found to match closely, with FBG sensors having a faster response time and higher pressure resolution. Fluid structure interaction simulation was carried out in Comsol Multiphysics to understand the interaction of high-speed turbulent flow with FBG sensor. The FBG strain profile due to flow-induced stress and its dependence on flow pressure was studied. A detailed analysis of the effect of preceding fiber length on FBG pressure measurement was carried out. FBG sensors, due to their miniature size, ability to withstand harsh environments and multi-parameter sensing capability, can be used in ground-based aerodynamic test facilities with minimal intrusion into the flow.

Online and Predictive Warning System for Forced Lane Changes Using Risk Maps

The survival analysis of driving trajectories allows for holistic evaluations of car-related risks caused by collisions or curvy roads. This analysis has advantages over common Time-To-X indicators, such as its predictive and probabilistic nature. However, so far, the theoretical risks have not been demonstrated in real-world environments. In this paper, we therefore present Risk Maps (RM) for online warning support in situations with forced lane changes, due to the end of roads. For this purpose, we first unify sensor data in a Relational Local Dynamic Map (R-LDM). RM is afterwards able to be run in real-time and efficiently probes a range of situations in order to determine risk-minimizing behaviors. Hereby, we focus on the improvement of uncertainty-awareness and transparency of the system. Risk, utility and comfort costs are included in a single formula and are intuitively visualized to the driver. In the conducted experiments, a low-cost sensor setup with a GNSS receiver for localization and multiple cameras for object detection are leveraged. The final system is successfully applied on two-lane roads and recommends lane change advices, which are separated in gap and no-gap indications. These results are promising and present an important step towards interpretable safety.

Position estimation in indoor using networked GNSS sensors and a range-azimuth sensor

The global navigation satellite systems (GNSS) receivers suffer from determining an accurate position estimate in the indoor region due to non-line of sight (Non-LOS) conditions. This paper proposes a novel method of position estimation within the indoor region by using distributed GNSS sensors and a range-azimuth sensor setup. All the GNSS sensors are connected to a central node; the inaccurate position estimates evolved from the Kalman filter (KF) framework are transmitted to a central node as primary data. The deviation between the inaccurate position estimate and the GNSS sensor’s actual position is the positional deviation (PD) vector, which needs to be estimated. In the same indoor region, a range-azimuth sensor is also deployed. It estimates the GNSS sensor’s physical location in its local coordinates, and these estimates are being transmitted to a central node as secondary data. Further, by using the primary and secondary data, we formulated a PD vector compensation followed by a sequential fusion (SF) framework to derive the precise locations of both GNSS sensors and the range-azimuth sensor by recursively estimating the PD vector. Finally, the Cramer–Rao lower bound (CRLB) for the proposed framework is derived. The simulation results are quantified with the root mean square error (RMSE) and CRLB.

Laser interference 3-D sensor with line-shaped beam based multipoint measurements using cylindrical lens

Laser Doppler distance sensors enable an absolute shape measurement of rotating objects for instance for monitoring the turning process of workpieces by a simultaneous velocity and distance measurement. However, the sensors allow only single-point measurement, which makes a 3-D measurement time-consuming and limits the sensor application. Therefore, this article presents a novel sensor setup for simultaneous multipoint measurements. Cylindrical lenses are employed to produce expanded, line-shaped laser beams and thereby increase the number of measuring points in a single measurement. A line camera based detection method is proposed for high-speed full field detection, lateral resolution increase and measurement uncertainty reduction. As a result, the proposed sensor system enables simultaneous velocity, distance and thus 3-D absolute shape measurements at a rotating object, with a simultaneous measurement of up to 480 points and a total measurement uncertainty below 1 μm. Moreover, the lateral resolution can be increased by the order of one magnitude to 4.2 μm.

Mid-IR antiresonant hollow-core fiber based chirped laser dispersion spectroscopy of ethane with parts per trillion sensitivity

Rapid development of a new type of air-core guiding fibers, the so-called Antiresonant Hollow-Core Fibers (ARHCFs), have brought a wide range of new application areas of the optical fiber technology. One of them is the laser spectroscopy of gases, where the interaction path length between the gas molecules and the laser beam is one of the most important factors defining the detection capability of the sensors. Due to the ability of efficient light guiding in an empty core, ARHCFs can be filled with the target gas and form long and low-volume absorption cells in the sensor, delivering the interaction distance required by the user and application. However, successful application of the gas-filled ARHCFs necessitates combining several factors, i.e. low-loss guidance and optimized structure of the ARHCF, proper sensor setup, and an efficient gas sensing technique. In this work, we report for the first time the development of a Chirped Laser Dispersion Spectroscopy (CLaDS) sensor of ethane (C2H6) aided with a 30 m long ARHCF-based absorption cell. Thanks to this unique configuration with an extended laser-gas molecules interaction path, the sensor reached a superb detection capability of ∼15 parts-per-trillion by volume (pptv) for a 25-s integration time with a noise equivalent absorption (NEA) coefficient of 3.5 × 10−10 cm−1. The sensitivity of the sensor beats the performance of the reported up to date detectors utilizing mid-IR hollow-core fibers (HCFs).

Alpine Skiing Activity Recognition Using Smartphone’s IMUs

Many studies on alpine skiing are limited to a few gates or collected data in controlled conditions. In contrast, it is more functional to have a sensor setup and a fast algorithm that can work in any situation, collect data, and distinguish alpine skiing activities for further analysis. This study aims to detect alpine skiing activities via smartphone inertial measurement units (IMU) in an unsupervised manner that is feasible for daily use. Data of full skiing sessions from novice to expert skiers were collected in varied conditions using smartphone IMU. The recorded data is preprocessed and analyzed using unsupervised algorithms to distinguish skiing activities from the other possible activities during a day of skiing. We employed a windowing strategy to extract features from different combinations of window size and sliding rate. To reduce the dimensionality of extracted features, we used Principal Component Analysis. Three unsupervised techniques were examined and compared: KMeans, Ward’s methods, and Gaussian Mixture Model. The results show that unsupervised learning can detect alpine skiing activities accurately independent of skiers’ skill level in any condition. Among the studied methods and settings, the best model had 99.25% accuracy.

Design-related reassessment of structures integrating Bayesian updating of model safety factors

In the semi-probabilistic approach of structural design, the partial safety factors are defined by considering some degree of uncertainties to actions and resistance, associated with the parameters’ stochastic nature. However, uncertainties for individual structures can be better examined by incorporating measurement data provided by sensors from an installed health monitoring scheme. In this context, the current study proposes an approach to revise the partial safety factor for existing structures on the action side, γE by integrating Bayesian model updating. A simple numerical example of a beam-like structure with artificially generated measurement data is used such that the influence of different sensor setups and data uncertainties on revising the safety factors can be investigated. It is revealed that the health monitoring system can reassess the current capacity reserve of the structure by updating the design safety factors, resulting in a better life cycle assessment of structures. The outcome is furthermore verified by analysing a real life small railway steel bridge ensuring the applicability of the proposed method to practical applications.

LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM

Loop closure detection is an essential component of simultaneous localization and mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several deep learning approaches have been proposed to address this task; however, their performance has been subpar compared to handcrafted techniques, especially while dealing with reverse loops. In this article, we introduce the novel loop closure detection network (LCDNet) that effectively detects loop closures in light detection and ranging (LiDAR) point clouds by simultaneously identifying previously visited places and estimating the six degrees of freedom relative transformation between the current scan and the map. LCDNet is composed of a shared encoder, a place recognition head that extracts global descriptors, and a relative pose head that estimates the transformation between two point clouds. We introduce a novel relative pose head based on the unbalanced optimal transport theory that we implement in a differentiable manner to allow for end-to-end training. Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art loop closure detection and point cloud registration techniques by a large margin, especially while dealing with reverse loops. Moreover, we integrate our proposed loop closure detection approach into a LiDAR SLAM library to provide a complete mapping system and demonstrate the generalization ability using different sensor setup in an unseen city.

Map-based registration of multi-LiDAR infrastructure sensor setups for real-time applications

The use of infrastructure sensors has proven to be an effective method for recording large traffic datasets. In order to achieve a high data quality for such datasets, an accurate extrinsic calibration of the sensors is essential. While previous approaches are not fast enough or do not fit the use-case of infrastructure sensing, we present a method that is capable of registering LiDAR sensors to a digital map in real-time. We use a mobile infrastructure sensor setup consisting of two measurement units, which we position at different locations with real road traffic. Each measurement unit is equipped with two LiDARs at a mounting height of eight meters. Experiments have shown that our method can perform continuous registration of up to four 128-layer LiDARs. With a voxel size of 0.05 meters used, our method achieves a root-mean-square error (RMSE) of 0.13 meters at an average processing time of 0.05 seconds per frame.

LiDAR-based detection, tracking, and property estimation: A contemporary review

Object detection, Person tracking, and Person property estimation (PPE) are identical innovation areas trying to improve their accuracy in different parameters to fit various real applications. For many years, so much research has been done in these fields. Many scientists also used many more techniques and algorithms. But most of the innovations were deeply based on image-based analysis, where cameras were the critical components of data acquisition. Over the years, new technologies arrived, and different types of research are happening. Rather than cameras, some other sensors, like infrared, depth cameras, and very recently LiDAR sensors, are used to estimate person properties, track them, as well as to detect them. Especially, height, age, gender, region, etc., parameters can be measured as person property. Eventually, 3D object detection by LiDAR will be a state-of-the-art research field with the advent of autonomous driving initiations. We studied many articles and found enthusiastic outcomes with these sensor setups to understand contemporary technology and its efficacy. We categorized these research articles into video camera-based studies and other sensor-based studies. So many surveys have been done on video-based analysis, even with deep learning techniques. Another sensor-based research is very recent, and we do not get enough study on it. We thought to summarize these studies in a survey article, especially LiDAR-based analysis. This article covered most of the recent possible sensor-based studies of detection, person tracking and property estimation except cameras (all, RGB, RGB-D, etc.) based learning.

Quartz-Enhanced Photothermal Spectroscopy-Based Methane Detection in an Anti-Resonant Hollow-Core Fiber.

In this paper, the combination of using an anti-resonant hollow-core fiber (ARHCF), working as a gas absorption cell, and an inexpensive, commercially available watch quartz tuning fork (QTF), acting as a detector in the quartz-enhanced photothermal spectroscopy (QEPTS) sensor configuration is demonstrated. The proof-of-concept experiment involved the detection of methane (CH4) at 1651 nm (6057 cm−1). The advantage of the high QTF Q-factor combined with a specially designed low-noise amplifier and additional wavelength modulation spectroscopy with the second harmonic (2f-WMS) method of signal analysis, resulted in achieving a normalized noise-equivalent absorption (NNEA) at the level of 1.34 × 10−10 and 2.04 × 10−11 W cm−1 Hz−1/2 for 1 and 100 s of integration time, respectively. Results obtained in that relatively non-complex sensor setup show great potential for further development of cost-optimized and miniaturized gas detectors, taking advantage of the combination of ARHCF-based absorption cells and QTF-aided spectroscopic signal retrieval methods.

Identification of Individually Altered Gait Behavior Using an Unobtrusive IMU Sensor Setup.

Gait behavior is considered an important indicator for the assessment of the general health status and provides a diagnostic observation for neuro-degenerative and musculo-skeletal diseases. Individual changes in gait behavior often reflect a deterioration of the current health status in a general sense and therefore provide significant information for clinicians and care-givers. In this work, we have used an unobtrusive sensor setup comprising three inertial measurement units (IMUs) located at the wrist, the chest and the thigh to obtain an objective measure of the human locomotion. We conducted a clinical trial in a movement laboratory environment to obtain a database of gait data at different walking speeds and conditions. The aging-simulation suit GERT was used to deteriorate the individual gait behavior during the experiments. Treadmill walking trials were used to train different classifiers to discriminate normal walking from GERT-affected walking patterns. Level-ground walking trials were used to validate the previously generated classifiers. A five-fold cross validation during the training process yielded overall F1-scores between 0.965 and 0.986. The validation tests showed promising results with prediction accuracies of more than 80%. Clinical relevance- The clinical relevance of this contri-bution can be considered two-fold. First we demonstrate the possibility of an unobtrusive monitoring system to iden-tify individual deterioration of gait behavior. Second we also validate the use of aging-simulation suits to introduce individual changes of gait patterns in healthy subjects to create a database of simulated yet realistic gait impairments associated with aging.

Using Synthesized IMU Data to Train a Long-Short Term Memory-based Neural Network for Unobtrusive Gait Analysis with a Sparse Sensor Setup.

In this work, we evaluated the possibility to use synthesized IMU data for training a deep neural network to generate a more complex, full-body description of the human gait in terms of joint angle trajectories from a sparse sensor setup. In this context, a sparse sensor setup consists of a few sensors attached to human body segments in an unobtrusive manner to possibly provide a monitoring system in an everyday life scenario. Since the relation between the input IMU data and the output joint angle trajectories is highly non-linear, neural networks appear to provide an optimal framework to formulate a mapping description. Especially with respect to periodic signals, recurrent neural networks (RNNs) have gained importance in the recent years. In this work, we have used a special type of RNNs that can be implemented by using long-short term memory (LSTM) cells, which have shown promising results when being applied to sequential data. The artificial training data was generated by a simulative human gait model and virtually attached sensor devices. The trained network was subsequently validated by a dataset that was recorded from a treadmill walking trial using a motion capturing system and an IMU sensor system. The qualitative comparison already shows promising results, however, this study can only be considered to provide preliminary results in this area. Clinical Relevance- This approach has the potential to be applied in the remote assessment of gait behavior during everyday life environments using an unobtrusive sensor net-work. In particular for monitoring older people suffering from an increased fall risk or any significant gait impairments this work is of possible interest.

Technical note: Analyzing river network dynamics and the active length–discharge relationship using water presence sensors

Abstract. Despite the importance of temporary streams for the provision of key ecosystem services, their experimental monitoring remains challenging because of the practical difficulties in performing accurate high-frequency surveys of the flowing portion of river networks. In this study, about 30 electrical resistance (ER) sensors were deployed in a high relief 2.6 km2 catchment of the Italian Alps to monitor the spatio-temporal dynamics of the active river network during 2 months in the late fall of 2019. The setup of the ER sensors was customized to make them more flexible for the deployment in the field and more accurate under low flow conditions. Available ER data were compared to field-based estimates of the nodes' persistency (i.e., a proxy for the probability to observe water flowing over a given node) and then used to generate a sequence of maps representing the active reaches of the stream network with a sub-daily temporal resolution. This allowed a proper estimate of the joint variations of active river network length (L) and catchment discharge (Q) during the entire study period. Our analysis revealed a high cross-correlation between the statistics of individual ER signals and the flow persistencies of the cross-sections where the sensors were placed. The observed spatial and temporal dynamics of the actively flowing channels also highlighted the diversity of the hydrological behavior of distinct zones of the study catchment, which was attributed to the heterogeneity in catchment geology and stream-bed composition. Our work emphasizes the potential of ER sensors for analyzing spatio-temporal dynamics of active channels in temporary streams, discussing the major limitations of this type of technology emerging from the specific application presented herein.

Magnetic-based detection of muscular contraction for controlling hand prosthesis

Muscle contraction information from the residual upper-limb of an amputee can generate intention-based control to prosthetic devices. There are several approaches such as electromyography (EMG), force myography (FMG), and mechanomyography (MMG) that can directly or indirectly measure muscular contractions. However, these techniques suffer from the problems like ambient noise, motion artifacts, inappropriate contact with the skin, inaccurate output, and high cost of measurement. In this work, a novel magnetic-based Hall myography (HMG) technique is introduced that can precisely measure the muscular contractions from the subject's upper-limb. The sensor setup consists of a magnetic coupler and Hall element assembly separated by a mechanical spring. Any contraction from the muscle produces gradual movement of the magnet towards the hall element. This change in displacement is sensed by the hall element and is then translated to a 0–5 V linear output using appropriate preprocessing hardware and software. The sensor performance was evaluated in terms of sensitivity, repeatability, hysteresis, and frequency response. In detecting muscle contractions from the forearm of subjects, the sensor showed highly correlated output (r > 0.94, p-value<0.0001) with the EMG sensor. Moreover, the sensor showed better signal stability and similar dynamic behavior as compared to the EMG sensor. Further, the utility of the sensor was tested for controlling hand prosthesis. Subjects wearing the sensor were able to control the flexion of prosthetic hand fingers proportionally according to their intensity of muscle contraction. With several advantages, Hall myography can replace the EMG technique for prosthesis control application.

Dataset collection from a SubT environment

This article presents a dataset collected from the subterranean (SubT) environment with a current state-of-the-art sensors required for autonomous navigation. The dataset includes sensor measurements collected with RGB, RGB-D, event-based and thermal cameras, 2D and 3D lidars, inertial measurement unit (IMU), and ultra wideband (UWB) positioning systems which are mounted on the mobile robot. The overall sensor setup will be referred further in the article as a data collection platform. The dataset contains synchronized raw data measurements from all the sensors in the robot operating system (ROS) message format and video feeds collected with action and 360 cameras. A detailed description of the sensors embedded into the data collection platform and a data collection process are introduced. The collected dataset is aimed for evaluating navigation, localization and mapping algorithms in SubT environments. This article is accompanied with the public release of all collected datasets from the SubT environment. Link: Dataset

Detection of Acetoin and Diacetyl by a Tobacco Mosaic Virus-Assisted Field-Effect Biosensor

Acetoin and diacetyl have a major impact on the flavor of alcoholic beverages such as wine or beer. Therefore, their measurement is important during the fermentation process. Until now, gas chromatographic techniques have typically been applied; however, these require expensive laboratory equipment and trained staff, and do not allow for online monitoring. In this work, a capacitive electrolyte–insulator–semiconductor sensor modified with tobacco mosaic virus (TMV) particles as enzyme nanocarriers for the detection of acetoin and diacetyl is presented. The enzyme acetoin reductase from Alkalihalobacillus clausii DSM 8716T is immobilized via biotin–streptavidin affinity, binding to the surface of the TMV particles. The TMV-assisted biosensor is electrochemically characterized by means of leakage–current, capacitance–voltage, and constant capacitance measurements. In this paper, the novel biosensor is studied regarding its sensitivity and long-term stability in buffer solution. Moreover, the TMV-assisted capacitive field-effect sensor is applied for the detection of diacetyl for the first time. The measurement of acetoin and diacetyl with the same sensor setup is demonstrated. Finally, the successive detection of acetoin and diacetyl in buffer and in diluted beer is studied by tuning the sensitivity of the biosensor using the pH value of the measurement solution.

Exploring the evapotranspirative cooling effect of a green façade

• External wall behind the green layers were up to 7°C cooler than shade sails. • The gap temperatures behind green façade were always cooler than ambient air. • The gap temperatures behind green façade were cooler than gap behind shade sail. • The contribution of the evapotranspirative to overall gap cooling was to be 35%. Green façades are one of many innovative solutions widely applied to buildings to reduce energy consumption. Green façades can mitigate urban heat through evapotranspiration and shading; however, the relative contribution of interception of solar radiation (shade cooling) versus transpiration (evapotranspirative cooling) is not well understood. This study investigates the cooling provided by different façades, including green façades and shade sails, to quantify the relative impacts of evapotranspiration and shading. The temperature of the ambient air, gaps (areas behind the façades), and external walls behind the façades were measured and compared during different weather conditions. According to the results, during hot sunny days, the external wall temperatures behind the green façade were up to 7°C cooler than those behind the shade sail. Furthermore, the temperatures behind the green façade and shade sail were always cooler than the ambient air, by up to 11 and 6.8°C, respectively, indicating that the evapotranspirative cooling contributed 25-35% of the overall gap cooling induced by the green façades. The thermal benefits provided by the green façade indicated that they could contribute effectively to the sustainability of the building design and constitute an effective nature-based solution to city-scale urban heat and building energy use. The setup of temperature sensors (yellow circles) relative to the external wall and the a) green façade, d) shade sale and g) control – without façade. Diurnal temperature changes from the ambient air to the external wall behind the green façades (b: north-facing; c: west-facing), shade sails (e: north-facing; f: west-facing), and without façades (h: north-facing; i: west-facing) under sunny conditions. Note the difference in temperature scale of the north (b, e and h) and west facing façades (c, f and i). .

Remote State Estimation With Smart Sensors Over Markov Fading Channels

We consider a fundamental remote state estimation problem of discrete-time linear time-invariant (LTI) systems. A smart sensor forwards its local state estimate to a remote estimator over a time-correlated multistate Markov fading channel, where the packet drop probability is time-varying and depends on the current fading channel state. We establish a necessary and sufficient condition for mean-square stability of the remote estimation error covariance in terms of the state transition matrix of the LTI system, the packet drop probabilities in different channel states, and the transition probability matrix of the Markov channel states. To derive this result, we propose a novel estimation-cycle based approach and provide new elementwise bounds of matrix powers. The stability condition is verified by numerical results and is shown more effective than existing sufficient conditions in the literature. We observe that the stability region in terms of the packet drop probabilities in different channel states can either be convex or nonconvex depending on the transition probability matrix of the Markov channel states. Our numerical results suggest that the stability conditions for remote estimation may coincide for setups with a smart sensor and with a conventional one (which sends raw measurements to the remote estimator) though the smart sensor setup achieves a better estimation performance.