SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements.

Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea-land segmentation.

Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring.

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring.

Accurate Detection and Localization of Water Pipe Leaks through Model-Based TDR Inversion.

The problem of water scarcity affects many areas of the world due to water mismanagement and overconsumption and, more recently, to climate change. Monitoring the integrity of distribution systems is, therefore, increasingly important to avoid the waste of clean water. This paper presents a new signal processing technique for enhancing the performance of the methodology of leak detection in water distribution pipes based on time domain reflectometry (TDR). The new technique is based on a particular kind of TDR inversion (spatial TDR) based on a "gray-box" lumped parameter model of the system. The model does not include, e.g., radiative phenomena, non-TEM (transverse electromagnetic) modes etc. but is capable of reproducing accurately the complicated reflectograms obtained by a TDR leak detection system assuming a proper profile of capacitance per unit length along the sensing element. Even more importantly, the model is identified using only the reflectograms taken by the system with very little prior information about the system components. The developed technique is able to estimate with good accuracy, from reflectograms with unclear or ambiguous interpretation, the position and the extension of a region where water is located. The measurement is obtained without prior electromagnetic characterization of the TDR system components and without the need of modeling or quantifying a number of electromagnetic effects typical of on-site measurements.

Split Ring Resonator Network and Diffused Sensing Element Embedded in a Concrete Beam for Structural Health Monitoring.

The aim of this work is to propose two different and integrated sensors for the structural health monitoring of concrete beams. In particular, a diffused sensing element and a split ring resonator network are presented. The first sensor is able to detect the variations in the dielectric properties of the concrete along the whole beam length, for a diffuse monitoring both during the important concrete curing phase and also for the entire life cycle of the concrete beams. The resonators instead work punctually, in their surroundings, allowing an accurate evaluation of the permittivity both during the drying phase and after. This allows the continuous monitoring of any presence of water both inside the concrete beam and at points that can be critical, in the case of beams in dams, bridges or in any case subject to a strong presence of water which could lead to deterioration, or worse, cause serious accidents. Moreover, the punctual sensors are able to detect the presence of cracks in the structure and to localize them.

Detection and Characterization of Multiple Discontinuities in Cables with Time-Domain Reflectometry and Convolutional Neural Networks.

In this paper, a convolutional neural network for the detection and characterization of impedance discontinuity points in cables is presented. The neural network analyzes time-domain reflectometry signals and produces a set of estimated discontinuity points, each of them characterized by a class describing the type of discontinuity, a position, and a value quantifying the entity of the impedance discontinuity. The neural network was trained using a great number of simulated signals, obtained with a transmission line simulator. The transmission line model used in simulations was calibrated using data obtained from stepped-frequency waveform reflectometry measurements, following a novel procedure presented in the paper. After the training process, the neural network model was tested on both simulated signals and measured signals, and its detection and accuracy performances were assessed. In experimental tests, where the discontinuity points were capacitive faults, the proposed method was able to correctly identify 100% of the discontinuity points, and to estimate their position and entity with a root-mean-squared error of 13 cm and 14 pF, respectively.

Assessing exclusionary power of a paternity test involving a pair of alleged grandparents.

BACKGROUND: The power of a genetic test battery to exclude a pair of individuals as grandparents is an important consideration for parentage testing laboratories. However, a reliable method to calculate such a statistic with short-tandem repeat (STR) genetic markers has not been presented. STUDY DESIGN AND METHODS: Two formulae describing the random grandparents not excluded (RGPNE) statistic at a single genetic locus were derived: RGPNE = a(4 - 6a + 4a(2)- a(3)) when the paternal obligate allele (POA) is defined and RGPNE = 2[(a + b)(2 - a - b)][1 - (a + b)(2 - a - b)] + [(a + b)(2 - a - b)] when the POA is ambiguous. A minimum number of genetic markers required to yield cumulative RGPNE values of not greater than 0.01 was calculated with weighted average allele frequencies of the CODIS STR loci. RGPNE data for actual grandparentage cases are also presented to empirically examine the exclusionary power of routine casework. RESULTS: A comparison of RGPNE and random man not excluded (RMNE) values demonstrates the increased difficulty involved in excluding two individuals as grandparents compared to excluding a single alleged parent. A minimum of 12 STR markers is necessary to achieve RGPNE values of not greater than 0.01 when the mother is tested; more than 25 markers are required without the mother. Cumulative RGPNE values for each of 22 nonexclusionary grandparentage cases were not more than 0.01 but were significantly weaker when calculated without data from the mother. CONCLUSION: Calculation of the RGPNE provides a simple means to help minimize the potential of false inclusions in grandparentage analyses. This study also underscores the importance of testing the mother when examining the parents of an unavailable alleged father (AF).

Identification and characterization of variant alleles at CODIS STR loci.

Short tandem repeat (STR) profiles from 32,671 individuals generated by the ABI Profiler Plus and Cofiler systems were screened for variant alleles not represented within manufacturer-provided allelic ladders. A total of 85 distinct variants were identified at 12 of the 13 CODIS loci, most of which involve a truncated tetranucleotide repeat unit. Twelve novel alleles, identified at D3S1358, FGA, D18S51, D5S818, D7S820 and TPOX, were confirmed by nucleotide sequence analysis and include both insertions and deletions involving the repeat units themselves as well as DNA flanking the repeat regions. Population genetic data were collected for all variants and frequencies range from 0.0003 (many single observations) to 0.0042 (D7S820 '10.3' in North American Hispanics). In total, the variant alleles identified in this study are carried by 1.6% of the estimated 1 million individuals tested annually in the U.S. for the purposes of parentage resolution. A paternity case involving a recombination event of paternal origin is presented and demonstrates how variant alleles can significantly strengthen the genetic evidence in troublesome cases. In such instances, increased costs and turnaround time associated with additional testing may be eliminated.