The Uncertainty Assessment by the Monte Carlo Analysis of NDVI Measurements Based on Multispectral UAV Imagery.

This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry-clear, humid-hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh).

From Pixels to Precision: A Survey of Monocular Visual Odometry in Digital Twin Applications.

This survey provides a comprehensive overview of traditional techniques and deep learning-based methodologies for monocular visual odometry (VO), with a focus on displacement measurement applications. This paper outlines the fundamental concepts and general procedures for VO implementation, including feature detection, tracking, motion estimation, triangulation, and trajectory estimation. This paper also explores the research challenges inherent in VO implementation, including scale estimation and ground plane considerations. The scientific literature is rife with diverse methodologies aiming to overcome these challenges, particularly focusing on the problem of accurate scale estimation. This issue has been typically addressed through the reliance on knowledge regarding the height of the camera from the ground plane and the evaluation of feature movements on that plane. Alternatively, some approaches have utilized additional tools, such as LiDAR or depth sensors. This survey of approaches concludes with a discussion of future research challenges and opportunities in the field of monocular visual odometry.

Over the Limits of Traditional Sampling: Advantages and Issues of AICs for Measurement Instrumentation.

Data acquisition systems have shown the need of wideband spectrum monitoring for many years. This paper describes and discusses a recently proposed architecture aimed at acquiring efficiently wideband signals, named the Analog-to-Information Converter (AIC). AIC framework and working principle implementing the sub-Nyquist sampling are analyzed in general terms. Attention is specifically focused on the idea of exploiting the condition of the signals that, despite their large bandwidth, have a small information content in the frequency domain. However, as clarified in the paper, employing AICs in measurement instrumentation necessarily entails their characterization, through the analysis of their building blocks and the corresponding non-idealities, in order to improve the signal reconstruction.

Sensors and Measurements for UAV Safety: An Overview.

Unmanned aerial vehicles' (UAVs) safety has gained great research interest due to the increase in the number of UAVs in circulation and their applications, which has inevitably also led to an increase in the number of accidents in which these vehicles are involved. The paper presents a classification of UAV safety solutions that can be found in the scientific literature, putting in evidence the fundamental and critical role of sensors and measurements in the field. Proposals from research on each proposed class concerning flight test procedures, in-flight solutions including soft propeller use, fault and damage detection, collision avoidance and safe landing, as well as ground solution including testing and injury and damage quantification measurements are discussed.

ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals.

This paper presents an innovative method for multiple lead electrocardiogram (ECG) monitoring based on Compressed Sensing (CS). The proposed method extends to multiple leads signals, a dynamic Compressed Sensing method, that were previously developed on a single lead. The dynamic sensing method makes use of a sensing matrix in which its elements are dynamically obtained from the signal to be compressed. In this method, for the application to multiple leads, it is proposed to use a single sensing matrix for which its elements are obtained from a combination of multiple leads. The proposed method is evaluated on a wide set of signals and acquired on healthy subjects and on subjects affected by different pathologies, such as myocardial infarction, cardiomyopathy, and bundle branch block. The experimental results demonstrated that the proposed method can be adopted for a Compression Ratio (CR) up to 10, without compromising signal quality. In particular, for CR= 10, it exhibits a percentage of root-mean-squared difference average among a wide set of ECG signals lower than 3%.

A Dictionary Optimization Method for Reconstruction of ECG Signals after Compressed Sensing.

This paper presents a new approach for the optimization of a dictionary used in ECG signal compression and reconstruction systems, based on Compressed Sensing (CS). Alternatively to fully data driven methods, which learn the dictionary from the training data, the proposed approach uses an over complete wavelet dictionary, which is then reduced by means of a training phase. Moreover, the alignment of the frames according to the position of the R-peak is proposed, such that the dictionary optimization can exploit the different scaling features of the ECG waves. Therefore, at first, a training phase is performed in order to optimize the overcomplete dictionary matrix by reducing its number of columns. Then, the optimized matrix is used in combination with a dynamic sensing matrix to compress and reconstruct the ECG waveform. In this paper, the mathematical formulation of the patient-specific optimization is presented and three optimization algorithms have been evaluated. For each of them, an experimental tuning of the convergence parameter is carried out, in order to ensure that the algorithm can work in its most suitable conditions. The performance of each considered algorithm is evaluated by assessing the Percentage of Root-mean-squared Difference (PRD) and compared with the state of the art techniques. The obtained experimental results demonstrate that: (i) the utilization of an optimized dictionary matrix allows a better performance to be reached in the reconstruction quality of the ECG signals when compared with other methods, (ii) the regularization parameters of the optimization algorithms should be properly tuned to achieve the best reconstruction results, and (iii) the Multiple Orthogonal Matching Pursuit (M-OMP) algorithm is the better suited algorithm among those examined.

Sensors and Measurements for Unmanned Systems: An Overview.

The advance of technology has enabled the development of unmanned systems/vehicles used in the air, on the ground or on/in the water. The application range for these systems is continuously increasing, and unmanned platforms continue to be the subject of numerous studies and research contributions. This paper deals with the role of sensors and measurements in ensuring that unmanned systems work properly, meet the requirements of the target application, provide and increase their navigation capabilities, and suitably monitor and gain information on several physical quantities in the environment around them. Unmanned system types and the critical environmental factors affecting their performance are discussed. The measurements that these kinds of vehicles can carry out are presented and discussed, while also describing the most frequently used on-board sensor technologies, as well as their advantages and limitations. The paper provides some examples of sensor specifications related to some current applications, as well as describing the recent research contributions in the field.

The IEEE TC-10 Standards: Update 2019.

There is a global need to standardize the terms and the test and computational methods that are used to describe and/or measure the parameters that characterize and define the performance of devices that generate signals and subsequently measure and analyze the waveforms acquired of those signals. This standardization is essential for accurate, reproducible, reliable, and communicable characterization of the performance of these devices, which supports technology and product advancement, product comparison and performance tracking, and device calibration and traceability. Users of the devices need to unambiguously specify the device performance required for particular applications. Manufacturers need to unambiguously state the performance of their devices (e.g., instruments, components, etc.). Metrology facilities need to perform calibrations with well-defined methods to produce reliable data expressed in clear terms. Measurement instruments need to acquire data with well-defined methods and present their results clearly. Technical Committee 10 (TC-10), the Waveform Generation, Measurement, and Analysis Committee of the IEEE Instrumentation and Measurement (I&M) Society, develops documentary standards to address these needs. The TC-10 comprises an international group of electronics engineers, mathematicians, professors and physicists with representatives from national metrology laboratories, national science laboratories, component manufacturers, the test instrumentation industry, academia, and end users. The published standards developed and maintained by the TC10 include: IEEE Std 181-2011, "Standard on Transitions, Pulses, and Related Waveforms;" IEEE Std 1057-2017, "Standard for Digitizing Waveform Recorders;" IEEE Std 1241-2010, "Standard for Terminology and Test Methods for Analog-to-Digital Converters;" IEEE Std 1658-2011, "Standard for Terminology and Test Methods for Digital-to-Analog Converters;" and the IEEE Std 1696-2013, "Standard for Terminology and Test Methods for Circuit Probes." In development is the IEEE Draft Std. P2414 "Draft Standard for Jitter and Phase Noise." The status of these standards are described herein.

Measuring the magnetic axis alignment during solenoids working.

A method for monitoring the misalignment of the magnetic axis in solenoids is proposed. This method requires only a few measurements of the magnetic field at fixed positions inside the magnet aperture, and thus overcomes the main drawback of sturdy moving mechanics of other Hall sensor-based methods. Conversely to state-of-the-art axis determination, the proposed method can be applied also during magnet operations, when the axis region and almost the whole remaining magnet aperture are not accessible. Moreover, only a few measurements of the magnetic field at fixed positions inside the magnet aperture are required: thus a slow process such as the mapping of the whole aperture of a magnet by means of moving stages is not necessary. The mathematical formulation of the method is explained, and a case study on a model of a multi-layer solenoid is presented. For this case study, the uncertainty is assessed and the optimal placement of the Hall transducers is derived.

Early-stage fault isolation based on frequency response fitted by small-size samples for cryogenic cold compressors with active magnetic bearings.

A model-based method for fault detection and early-stage isolation, applicable when unfaulty conditions can be identified only by a reduced number of trials (even only one), is presented. The basic idea is to model analytically the uncertainty of the unfaulty frequency response and express the fault condition in terms of the noise power variance. A preliminary fault isolation is carried out by sensitivity analysis in order to identify the most influencing model parameters and assess their influence on the estimated noise. Then, during maintenance tests, the noise power is checked to detect the faulty condition. This technique is conceived to check the quality of a critical component in an experimental installation (fault detection and early-stage isolation), as well as to detect its faulty dynamic behaviors over a long horizon maintenance test campaign (condition monitoring). The method was applied to four cold compressors with active magnetic bearings at CERN by proving to be able to detect an actual faulty condition in one of such compressors.