Raw Measurements Research Articles

AFLI-Calib: Robust LiDAR-IMU extrinsic self-calibration based on adaptive frame length LiDAR odometry

As an effective complement to common laser scanning systems, the portable laser scanning system can acquire point clouds flexibly and quickly. Calibration between Light detection and ranging (LiDAR) sensors and inertial measurement units (IMU) is the prerequisite for laser scanning systems to obtain high-quality point clouds. Related methods have been proposed in the last two decades, where the global navigation satellite system (GNSS) or high-precision calibration fields are commonly used. However, the extrinsic self-calibration of LiDAR-IMU is challenging, due to the large distortion in single-frame point cloud caused by rapid motion and the position errors of IMU integration which drift quickly. At the same time, the highly dynamic motion patterns of portable devices and the changes in the scanned scene structure are not well considered in existing LiDAR odometry methods. To take better advantage of the characteristics of non-repetitive scanning LiDAR sensor, this paper proposes AFLI-Calib, which utilizes adaptive frame length LiDAR odometry to perform the extrinsic self-calibration of LiDAR-IMU. Unlike LiDAR odometry methods with a fixed frame length, the LiDAR frame length is dynamically adjusted according to the motion state of sensors and the matching stability of scenes. The single-frame point cloud is registered to the map through a linear-based continuous-time model, eliminating the motion distortion correction in advance. For further optimization of trajectory and extrinsic parameters, IMU raw measurements and LiDAR observations are involved in the multi-constraint optimization, through tightly-coupled IMU pre-integration constraints, LiDAR point-to-plane constraints, and prior constraints. The method is fully validated using self-collected calibration data of indoor and outdoor scenes and different motion modes. Experiments show that on the test data, the translation parameter accuracy of the method is 0.041 m, which is 56.3% higher than the state-of-the-art method. The standard deviation is significantly reduced, with translation deviation (0.017 m, 0.024 m, 0.022 m) and rotation deviation (0.17°, 0.25°, 0.15°), which verifies the robustness of our method. The average RMSE of distances to the reference point cloud acquired by the terrestrial laser scanning system (TLS) is 0.042 m, showing a high accuracy calibration result. Comparative experiments with the fixed frame length LiDAR odometry method and classical “correction-then-registration” motion distortion model further verify the superiority and effectiveness of the proposed adaptive frame length LiDAR odometry.

Neural networks for calculation of complex fields from far-field intensities in mesoscopic photonic systems

Mesoscopic photonics is built on the foundations of theories from mesoscopic physics of electron propagation, although optical techniques have enabled major strides in experimental research on the topic. Theoretical techniques calculate relevant parameters using wave functions or electric fields inside a sample, while experiments mostly measure intensities in the far field. Ideally, the theoretically calculated and experimentally measured parameters must be on equal footing. Here, we employ deep neural networks that calculate near-field intensities and, subsequently, real and complex fields, from simulated far-field measurements. A fully connected deep neural network is built for one-dimensional systems, while a convolutional neural network is developed for two-dimensional systems. The accuracy of these networks is consistently above 95%. We reveal the improvement in estimation of transport parameters by the predicted near-field data from raw measurement data.

Correlation-based damage detection method using convolutional neural network for civil infrastructure

We present a novel damage detection method named CorCNN that utilizes one-dimensional convolutional neural networks to detect damage based on observed changes in correlation between measurements. CNN architecture is used in the method to automatically extract important information from raw measurement data. A CNN model is trained in an unsupervised manner, eliminating the need for data labeling. An assessment of structural responses to a 20 m full-scale bridge in healthy and damaged conditions is conducted to validate the method. For the investigated problem, hyperparameters are optimised to find the optimal combination. To detect the presence of damage, residuals derived from the discrepancies between the actual data and prediction are analyzed. Additionally, CorCNN is compared to other machine learning methods, including linear regression, artificial neural networks, and random forests, using the given dataset. According to the results, the CorCNN method outperforms other machine learning models in detecting damage to the structure.

A Data Quality Assessment Approach for High-Precision GNSS Continuously Operating Reference Stations (CORS) with Case Studies in Hong Kong and Canada/USA

Centimeter-level or better positioning accuracy is needed in engineering surveying applications. When employing the Global Navigation Satellite System (GNSS) in engineering surveying, the high-precision real-time kinematic (RTK) positioning method must be used to achieve such positioning accuracy. Currently, precise point positioning (PPP) cannot reliably achieve the positioning accuracy needed for engineering surveying in real time. The high-precision RTK positioning method needs carrier-phase measurements and a reference station/network. Surveyors may not need a GNSS receiver in their organizations/companies to act as the reference station. Continuously Operating Reference Stations (CORS), run by international/national organizations/agencies or private companies such as GNSS receiver manufacturers, let users freely access the raw GNSS measurements or corrections for real-time and post-processing applications. The positioning accuracy of the GNSS rover is affected by the data quality of the reference stations, including virtual reference stations (VRS). The International GNSS Service (IGS) currently provides the number of cycle slips and the L1 and L2 average pseudorange multipath errors per station daily. The US National Geodetic Survey (NGS) provides daily station coordinate residuals. Carrier-phase data quality of the CORS stations is not provided by their organizations/agencies. Nowadays, many CORS stations track multi-GNSS satellites. This paper proposes a multi-GNSS and multi-frequency data quality assessment approach for CORS stations with a focus on carrier-phase data quality. The proposed approach is demonstrated with case studies on IGS/CORS networks in Hong Kong and Canada/USA. In other words, a strategy to obtain non-linear combined carrier-phase multipath errors and noise is proposed in this work. The data quality of a CORS station depends on the site environment, monument type and height, and GNSS receiver/antenna. An account of the data quality at some selected stations is given; the main focus of the paper is on the proposed data quality assessment approach.

PD30-03 IMPORTANCE OF COGNITIVE LOAD FOR AUTOMATED ERROR DETECTION IN ROBOT-ASSISTED SURGERY EDUCATION

You have accessJournal of UrologyCME1 Apr 2023PD30-03 IMPORTANCE OF COGNITIVE LOAD FOR AUTOMATED ERROR DETECTION IN ROBOT-ASSISTED SURGERY EDUCATION Rafal Kocielnik, Jasper A. Laca, Elyssa Y. Wong, Timothy N. Chu, Sidney I. Roberts, Sandra Marshall, Anima Anandkumar, and Andrew J. Hung Rafal KocielnikRafal Kocielnik More articles by this author , Jasper A. LacaJasper A. Laca More articles by this author , Elyssa Y. WongElyssa Y. Wong More articles by this author , Timothy N. ChuTimothy N. Chu More articles by this author , Sidney I. RobertsSidney I. Roberts More articles by this author , Sandra MarshallSandra Marshall More articles by this author , Anima AnandkumarAnima Anandkumar More articles by this author , and Andrew J. HungAndrew J. Hung More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003316.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Cognitive Load (CL) can affect surgeon performance and the ability of training surgeons to receive feedback. We investigate the value of CL for detecting surgical errors in robot-assisted surgery (RAS) with a machine learning (ML)-based model. METHODS: We use 2 datasets (DS1, DS2) where 24 and 44 surgeons, respectively, performed a simulated dry-lab tissue dissection task on a live daVinci Xi surgical robot. The data contains video and per-second Index of Cognitive Activity (ICA), derived from pupillary change tracked via Tobii Eyetrackers. DS1 includes surgeons with low, medium, and high experience (by hours of RAS) as well as annotated tissue Tear (n=70) and Puncture (n=62) errors. DS2 contains data from low experience surgeons only and annotations for Tears (n=445) and Punctures (n=527). We extract 5 sec of video and varying history of ICA (from 5 to 60 sec) before and around human-labeled errors (positive label). We randomly sample the remaining data (negative label). We train variations of ML architecture consisting of video processing (pretrained Resnet18) and/or ICA processing (LSTM). We train the model on 3 different 80/20 train-test between-participant random splits. RESULTS: When including surgeons of mixed experience levels (DS1), ICA alone (AUC=64.28) is competitive with video alone (AUC=62.59) in detecting surgical errors (Figure 1A). However, the use of ICA in combination with video improves AUC in both datasets with gains of 5.55 (DS1) and 3.24 (DS2). History of ICA up to 20-30 seconds seems beneficial for error detection, after which point there is no further gain (Figure 1B). This is likely attributed to gradual build-up in CL leading to an error, and low-frequency of ICA measurements (1/sec), which may hinder extracting useful information from short time windows. The challenge of extracting meaningful information seems supported by Figure 1C, where providing processed ICA is beneficial (AUC=62.95) on top of raw ICA measurements (AUC=58.88). CONCLUSIONS: A surgeon’s CL in the context of surgical errors in RAS education is important. We show how CL is particularly valuable when a dataset includes mixed-experience surgeons. We also show the importance of ICA history length and pre-processing. Further work will explore better methods of extracting valuable features from CL data and its importance in surgery. Source of Funding: This material is based upon work supported by the National Science Foundation under Grant # 2030859 to the Computing Research Association for the CIFellows Project. Additionally, research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA273031. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e831 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Rafal Kocielnik More articles by this author Jasper A. Laca More articles by this author Elyssa Y. Wong More articles by this author Timothy N. Chu More articles by this author Sidney I. Roberts More articles by this author Sandra Marshall More articles by this author Anima Anandkumar More articles by this author Andrew J. Hung More articles by this author Expand All Advertisement PDF downloadLoading ...

A Novel GNSS Fault Detection and Exclusion Method for Cooperative Positioning System

Global Navigation Satellite System (GNSS)-based cooperative positioning is highly degraded in dense urban areas due to non-line-of-sight (NLOS) and multipath effects. Multiple simultaneous faulty GNSS measurements can result in traditional fault detection and exclusion (FDE) methods finding a consistent but erroneous group of satellites. This paper describes a novel distributed FDE method based on cooperative reliability message (CRM), which is suitable for cooperative positioning system in connected vehicles. The idea is to take into account not only the consistency check on raw GNSS measurements, but also the prior knowledge about the quality of GNSS measurements offered by nearby cooperators so as to improve the ability to exclude multiple GNSS faults. The line-of-sight (LOS) satellites identified by cooperators are stored in CRMs, which will be shared to the host vehicle to generate the candidates for the set of LOS satellites observed locally. Based on the candidates, a bottom-up searching strategy is designed to find the healthy satellites and isolate the faulty ones precisely. Several experiments involving four moving vehicles were conducted in dense urban areas. Results indicate the superiority of CRM-based method in excluding multiple faults over existing methods.

Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

This paper is concerned with the problem of finite-horizon energy-to-peak state estimation for a class of networked linear time-varying systems. Due to the inherent vulnerability of network-based communication, the measurement signals transmitted over a communication network might be intercepted by potential eavesdroppers. To avoid information leakage, by resorting to an artificial-noise-assisted method, we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term. A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption scheme. The purpose of the addressed problem is to design an encryption-decryption scheme and a state estimator such that: 1) the desired secrecy capacity is ensured; and 2) the required finite-horizon <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2}-l_{\infty}$</tex> performance is achieved. Sufficient conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state estimator. Finally, simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

Laboratory determination of gravimetric correction factors for real-time area measurements of electronic cigarette aerosols: Part 2

The popularity and widespread of electronic cigarettes (ECIGs) have warranted a necessity for monitoring different populations directly or indirectly exposed to particulate matter (PM) from ECIGs. To accurately estimate exposure to aerosols from ECIGs, it is important to correctly measure the concentration of aerosols. Real-time aerosol monitors are usually based on optics that provides biased measurements that are adjusted using filter correction factors. In addition, low-cost aerosol sensors have become popular that allow widespread of these sensors and can estimate ECIG aerosol exposure after calibration. Therefore, this study determined the filter correction factors for three aerosol monitors (MiniWRAS, pDR, SidePak) based on exposure to six ECIG brands and two particle sizes (PM1 and PM2.5). In addition, the GeoAir2 low-cost monitor was evaluated compared to filter corrected MiniWRAS measurements for these exposures. The study also evaluated the real-time measurements and Mass Median Diameter (MMD) for each ECIG brand. The correction factors varied between aerosol monitors, ECIG brands and particle sizes, with ranges for PM2.5 that span between 1.85 and 2.42 (MiniWRAS), 0.50 and 0.58 (pDR) and 0.24 and 0.28 (SidePak). The GeoAir2 underestimated the reference measurements but had a high correlation (r = 0.90 - 0.94), showing that the monitor can be effectively calibrated to provide good estimates. The real-time average PM2.5 measurements differed between aerosol instruments with concentrations that ranged between 1,398 and 3,706 µg/m3 (MiniWRAS), 5,479 and 16,825 µg/m3 (pDR), and 9,783 and 29,808 µg/m3 (SidePak). Finally, the MMD values spanned between 0.45 and 0.80 µm based on the MiniWRAS measurements. The differences between the raw real-time measurements for the aerosol monitors show the necessity for developing filter correction factors to accurately measure ECIG exposure.

In vivo transepidermal water loss: Validation of a new multi-sensor open chamber water evaporation system Tewameter TM Hex.

Instrumentation technology for transepidermal water loss measurements has not been substantially modified since its introduction by Nilsson in 1977. Recent progress in sensor development allowed a new sensor arrangement using a matrix of 30 sensors. Raw measurement values are processed with spatial statistical analysis. We aimed to compare the new, multi-sensor probe (Tewameter TMHex) with the established Tewameter TM300 probe and to gain reference data for the new parameters of transepidermal energy loss and water vapor concentration on skin. Baseline measurements and repeated measurements on the volar forearm and assessment on eight different anatomical locations were performed on 24 healthy volunteers (both gender) with the TMHex and the TM300. A significant correlation (p<0.001; R-coefficient=0.9) between TMHex and the TM300 with a low coefficient of variance (CV) 11% for TMHex and 19% for TM300, could be assessed. The CV ranged between 7% (right inner upper arm) and 14% (palms). Average transepidermal heat loss ranged from 12W/m2 on the lower leg to 38.8W/m2 on the palm. The correlation between TMHex and TM300 along with the robustness of the measurements with the TMHex shows that the new probe for assessment of epidermal barrier function is comparable to the TM300. In most conditions, TMHex provides more accurate measurements than TM 300. New parameters open the field to studying skin's water and energy balance.

Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20

In recent years, numerous smartphones have been equipped with global navigation satellite system (GNSS) technology, enabling individuals to utilize their own devices for positioning and navigation purposes. In 2016, with the launch of a mobile app by Google, namely GnssLogger, smartphone users with Android version 7 or higher were able to record raw GNSS measurements (i.e., pseudorange, carrier phase, Doppler, and carrier-to-noise density ratio (C/N0)). Since then, enhancing the accuracy and efficiency of smartphone positioning has become an interesting area of research. Precise point positioning (PPP) is a powerful method providing precise real-time positioning of a single receiver, and it can be applied to smartphone observations as well. Achieving high-precision PPP requires selecting appropriate functional and stochastic models. In this study, we investigate the development of more reliable stochastic models for smartphone GNSS observations. The least-square variance component estimation (LS-VCE) method is applied to double-difference (DD) pseudorange and carrier phase observations from two Samsung S20s to obtain appropriate variances for GPS and GLONASS. According to the results, there is no significant correlation between the pseudorange and carrier phase observations of GPS and GLONASS on the L1 frequency. Furthermore, the quality of GLONASS carrier phase observations is comparable to that of GPS. The model's performance is then assessed with respect to single-frequency precise point positioning (SF-PPP) using a dataset collected in kinematic mode from a Samsung S20 smartphone. A significant improvement of 25.1% and 32.7% on the root-mean-square (RMS) of horizontal positioning and the 50th percentile error, respectively, was achieved when employing the obtained stochastic model.

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This article introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-processing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human-in-the-loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers being deployed, resulting in a significantly higher probability of intercept (POI). The architecture of the monitoring system is described in detail in this article, including an analysis of the energy consumption and optimization. Controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches. The ML results show that accurate and reliable detection and classification are possible with COTS hardware.

Quality of Stormwater Infrastructure Systems in Vulnerable Communities: Three Case Studies from Texas.

A properly functioning local stormwater drainage system is essential for mitigating flood risks. This study evaluates the quality of roadside drainage channels in three underserved communities in Texas: the Sunnyside neighborhood in Houston (Harris County), a neighborhood in the City of Rockport (Aransas County), and the Hoehn colonia (Hidalgo County). These communities have a history of flooding, are highly socially vulnerable, and rely on roadside ditches as their principal stormwater drainage system for runoff control. Mobile lidar (Light Detection and Ranging) measurements were collected for 6.09 miles of roadside channels in these communities. The raw lidar measurements were processed to evaluate drainage conditions based on the channel's geometric properties, hydraulic capacity, and level of service. The assessment results are linked to a Geographic Information System (GIS) tool for enhanced visualization. Finally, the paper provides insights regarding the quality of stormwater infrastructure in the study communities and discusses their practical implications.

Methods and system for recording human physiological signals from implantable leads during spinal cord stimulation.

This article presents a method-including hardware configuration, sampling rate, filtering settings, and other data analysis techniques-to measure evoked compound action potentials (ECAPs) during spinal cord stimulation (SCS) in humans with externalized percutaneous electrodes. The goal is to provide a robust and standardized protocol for measuring ECAPs on the non-stimulation contacts and to demonstrate how measured signals depend on hardware and processing decisions. Two participants were implanted with percutaneous leads for the treatment of chronic pain with externalized leads during a trial period for stimulation and recording. The leads were connected to a Neuralynx ATLAS system allowing us to simultaneously stimulate and record through selected electrodes. We examined different hardware settings, such as online filters and sampling rate, as well as processing techniques, such as stimulation artifact removal and offline filters, and measured the effects on the ECAPs metrics: the first negative peak (N1) time and peak-valley amplitude. For accurate measurements of ECAPs, the hardware sampling rate should be least at 8 kHz and should use a high pass filter with a low cutoff frequency, such as 0.1 Hz, to eliminate baseline drift and saturation (railing). Stimulation artifact removal can use a double exponential or a second-order polynomial. The polynomial fit is 6.4 times faster on average in computation time than the double exponential, while the resulting ECAPs' N1 time and peak-valley amplitude are similar between the two. If the baseline raw measurement drifts with stimulation, a median filter with a 100-ms window or a high pass filter with an 80-Hz cutoff frequency preserves the ECAPs. This work is the first comprehensive analysis of hardware and processing variations on the observed ECAPs from SCS leads. It sets recommendations to properly record and process ECAPs from the non-stimulation contacts on the implantable leads.

Smartphone-Based Indoor Localization via Network Learning With Fusion of FTM/RSSI Measurements

This letter proposes a deep neural network (DNN)-based indoor localization approach that leverages WiFi Fine Timing Measurement (FTM) and Received Signal Strength Indicator (RSSI) as environment features to provide accurate location estimation. Our method uses DNN with raw FTM and RSSI measurements for self-learning and produces enhanced ranging information in the presence of measurement noise. Experimental data was obtained from real-world settings using commercial off-the-shelf devices in two different indoor office environments. The proposed solution was evaluated regarding the localization Mean Squared Error, demonstrating remarkable accuracy and outperforming state-of-the-art methods.

Methodology of Airborne Gravimetry Surveying and Strapdown Gravimeter Data Processing

The paper presents a methodology of preparing and conducting airborne gravimetry survey using an inertial measuring unit (IMU) or strapdown inertial navigation system as airborne gravimeter. We also discuss the key aspects of developing appropriate postprocessing algorithms and software and their application to gravimeter raw data processing. Solving the strapdown inertial airborne gravimetry problem, we use readings of the IMU sensors (accelerometers and gyroscopes) as primary information. The second equally important source of information is raw (pseudorange, Doppler pseudorange rate, and carrier phase) measurements from the GNSS receivers on board the aircraft and on the ground. The developed methodology, algorithms, and practical recommendations presented in the paper are based on the authors’ long-term experience in airborne gravimetry.

Characterisation and position-aiding performance analysis of Doppler observation from android smartphones

AbstractIt has been acknowledged that the Doppler is beneficial to the GNSS positioning of smartphones. However, analysis of Doppler precision on smartphones is insufficient. In this paper, we focus on the characteristic analysis of the raw Doppler measurement from Android smartphones. A comprehensive investigation of the Doppler was conducted. The results illustrate that the availability of Doppler is stable and higher than that of carrier measurements, which means that the Doppler-smoothed code (DSC) method is more effective. However, there is a constant bias between the Doppler and the code rate in Xiaomi MI8, which indicates that extra processing of the DSC method is necessary for this phone. Additionally, it is demonstrated that the relationship between the Doppler and C/N0 can be expressed as an exponential function, and the fitting parameters are provided. The numerical experiment in car-borne and hand-held scenes was conducted for evaluating the performance of the Doppler-aided positioning algorithm. For positioning, the improvement reaches 37 ⋅ 69%/37 ⋅ 14%/26 ⋅ 61% in the east, north and up components, respectively, after applying the Doppler aiding. For velocity estimation, the improvement reaches 29 ⋅ 62%/39 ⋅ 63%/29 ⋅ 37% in the three components, respectively.

Quantitative assessment of adulteration of coconut oil using transmittance multispectral imaging.

Economical to a fault, coconut oil is a commodity related to fraudulent activities such as oil adulteration for undue profits. Unfortunately, the conventional methods used in the detection of adulteration and toxicants are laborious, destructive, and time-consuming. Hence, it is imperative to engineer a non-destructive and rapid screening test with sufficient accuracy. To that end, the proposed work has an in-house developed imaging system hardware and a method to estimate relevant quality parameters from multispectral imagery. Multispectral images of adulterated coconut oil were analyzed through a cascade of statistical algorithms: Fisher Discriminant Analysis and Bhattacharyya distance respectively. In this work, a functional relationship was developed for the estimation of adulteration level that recorded an R2 of 0.9876 for the training samples and an MSE of 0.0029 for the testing samples. Besides, the proposed imaging system offers flexibility on post-processing of raw measurements as the algorithm is designed to operate from raw multispectral images. In addition, the developed imaging system is economical in its capacity to estimate the adulteration of coconut oil with remarkable accuracy considering the low cost of production. Moreover, the proposed work validates the use of multispectral imagery as an initial screening technique instead of expensive spectroscopy methods.

Detection of data-driven blind cyber-attacks on smart grid: A deep learning approach

An immense challenge to future smart cities is providing resilience against cyber-attacks on critical infrastructures like the smart grid. Data-driven cyber-attack strategies like the false data injection attack (FDIA) can modify the states of the grid, hence posing a critical scenario. With an accurate knowledge of measurement subspace, this work demonstrates an effective blind FDIA formulation strategy. The current trend in the identification of such attacks is generally confined to their presence detection within the measurements, while their intrusion points remain concealed. To alleviate this concern, this study promotes an effective execution of novel robust, nonlinear deep learning models which can not only effectually identify the presence but also the exact locations of intrusions of blind attacks in real-time while working concurrently with the conventional bad data detector, thus furnishing a cost-effective approach. Moreover, such neural network models demonstrate a multilabel classification strategy by capturing the inconsistency with co-occurrence dependency of the attack vectors introduced into the raw measurements. Furthermore, these deep learning structures prove to be model-free, hence attacks can be determined without any statistical knowledge of the grid. The performance of the proposed framework is evaluated utilizing the standard IEEE test bench undertaking diverse noise and attack scenarios.

Raw bioelectrical impedance measurements are not different between White and Black adults when matched for sex, age, BMI, and other physical characteristics

Raw bioelectrical impedance measurements are often used as a prognosticator of health status because of their association with disease states and malnutrition. Although studies consistently show the effect of physical characteristics on bioelectrical impedance, few investigations describe the effect of race, particularly for Black adults, and many bioelectrical impedance standards were produced from primarily White adults almost 2 decades ago. Therefore, this study sought to evaluate the racial differences in bioelectrical impedance measurements using bioimpedance spectroscopy between non-Hispanic White and non-Hispanic Black adults matched for age, sex, and body mass index. We hypothesized that Black adults would have a lower phase angle from higher resistance and lower reactance compared with White adults. One hundred non-Hispanic White (n=50) and non-Hispanic Black (n=50) males (n=34) and females (n=66) matched for sex, age, and body mass index completed this cross-sectional study. Participants underwent several anthropometric assessments, including height, weight, waist circumference, hip circumference, bioimpedance spectroscopy, and dual-energy X-ray absorptiometry. Bioelectrical impedance measures of resistance, reactance, phase angle, and impedance were all collected at frequencies of 5, 50, and 250 kHz and bioelectrical impedance vector analysis was performed using 50-kHz data. There were no significant differences for any anthropometric variable between Black and White participants in the total sample or by sex groups. In addition, there were no significant racial differences for any bioelectrical impedance assessment, including bioelectrical impedance vector analysis. Differences in bioelectrical impedance are likely not a function of race between Black and White adults and concerns regarding its utility should not be based on this characteristic.

Innovative Fusion Strategy for MEMS Redundant-IMU Exploiting Custom 3D Components.

In recent years, the overall performances of inertial Micro-Electro Mechanical Sensors (MEMSs) exhibited substantial improvements to values very close or similar to so-called tactical-grade sensors. However, due to their high costs, numerous researchers are currently focusing on the performance enhancement of cheap consumer-grade MEMS inertial sensors for all those applications (as an example, small unmanned aerial vehicles, UAVs), where cost effectiveness is a relevant request; the use of redundancy proves to be a feasible method for this purpose. In this regard, the authors propose, hereinafter, a suitable strategy aimed at fusing raw measurements provided by multiple inertial sensors mounted on a 3D-printed structure. In particular, accelerations and angular rates measured by the sensors are averaged according to weights associated with the results of an Allan variance approach; the lower the noise figure of the sensors, the greater their weight on the final averaged values. On the other hand, possible effects on the measurements due to the use of a 3D structure in reinforced ONYX (a material capable of providing better mechanical specifications for avionic applications with respect to other solutions for additive manufacturing) were evaluated. The performance of a prototype implementing the considered strategy is compared with that of a tactical-grade inertial measurement unit in stationary conditions, exhibiting differences as low as 0.3 degrees in heading measurements. Moreover, the reinforced ONYX structure does not significantly affect the measured values in terms of both thermal and magnetic field while assuring better mechanical characteristics with respect to other 3D printing materials, thanks to a tensile strength of about 250 MPa and a specific stacking sequence of continuous fibers. Finally, a test conducted on an actual UAV highlights performance very close to that of a reference unit, with root-mean-square error in heading measurements as low as 0.3 degrees in observation intervals up to 140 s.