Design and Implementation of Google Cloud Framework for Monitoring Water Distribution Networks

With urbanization and growing human population, water demand is constantly on the rise. Due to limited water resources, providing access to fresh potable water to the rising needs is challenging. Water distribution systems are the main arteries that supply fresh water to all the house-holds, offices and industries. Various factors such as excessive water pressure, aging or environmental disturbances, e.g., from road traffic, can all contribute to damage of water distribution pipelines and can result in leaks in water distribution networks (WDN). This could in turn result in financial loss and could pose additional challenges in providing potable water to the entire community, sometimes even leading to contaminant intrusion. Traditional leak detection methods such as visual inspections can detect leaks; however, this method is reactive in nature and can result in potentially losing large amounts of water before intervention strategies can be employed. On the other hand, hardware-based inspection techniques can accurately detect leaks, but are labor intensive, time consuming, expensive and effective only for short distances. Some existing software techniques are less expensive; however, their effectiveness depends on the accuracy of data collected and operating conditions. Modern existing leak detection techniques based on Internet of Things (IoT)—consisting of data collection sensor sub system, internet connectivity and a decision making sub system—alleviate many issues associated with hardware and software methods, however they are considered to scale poorly and face security issues, fault tolerance issues, inter-operability issues, insufficient storage and processing abilities to store and process large quantities of real time data captured by the sensor sub systems. As a potential solution to these issues, this thesis deals with the application of a cloud-based leak detection system within the overarching concept of IoT. A detailed design and implementation of Google Cloud Platform (GCP) which can provide scalable, secure data processing system to analyze both real-time and batch data collected from IoT devices monitoring a WDN is presented. To circumvent the issue of access to a live WDN, the proposed system uses emulators, python Hyper Text Transport Protocol (HTTP) client running on a computer and a python HTTP client running on an IoT device (Raspberry Pi 3) to simulate live streams of acoustic pressure data from hydrophone sensors. Since the data itself was collected from a live WDN, the decision-making subsystem mimics results expected from live WDN data. The data ingestion layer on GCP incorporates two types of authentication: OAuth2.0 authentication and Application Program Interface (API) key authentication along with other GCP components using service account features to ensure end-to-end secure data processing. Decision support sub-system includes simple, yet powerful algorithm, namely the one class support vector machine (OCSVM) with non-linear radial basis function (RBF) kernel. It is shown in this thesis that GCP provides a scalable and fault tolerant infrastructure at every stage of data life cycle such as data ingestion, storage, processing and results visualization. The implementation in this thesis demonstrates the applicability of the leak detection IoT framework and the concept of a cloud based IoT solution for leak detection in WDN, which is the first demonstration of its kind to the author’s knowledge

First results from the HAYSTAC axion search

The axion is a well-motivated cold dark matter (CDM) candidate first postulated to explain the absence of $CP$ violation in the strong interactions. CDM axions may be detected via their resonant conversion into photons in a "haloscope" detector: a tunable high-$Q$ microwave cavity maintained at cryogenic temperature, immersed a strong magnetic field, and coupled to a low-noise receiver. This dissertation reports on the design, commissioning, and first operation of the Haloscope at Yale Sensitive to Axion CDM (HAYSTAC), a new detector designed to search for CDM axions with masses above $20$ $\mu\mathrm{eV}$. I also describe the analysis procedure developed to derive limits on axion CDM from the first HAYSTAC data run, which excluded axion models with two-photon coupling $g_{a\gamma\gamma} \gtrsim 2\times10^{-14}$ $\mathrm{GeV}^{-1}$, a factor of 2.3 above the benchmark KSVZ model, over the mass range $23.55 < m_a < 24.0$ $\mu\mathrm{eV}$. This result represents two important achievements. First, it demonstrates cosmologically relevant sensitivity an order of magnitude higher in mass than any existing direct limits. Second, by incorporating a dilution refrigerator and Josephson parametric amplifier, HAYSTAC has demonstrated total noise approaching the standard quantum limit for the first time in a haloscope axion search.Comment: Ph.D. thesis. 346 pages, 58 figures. A few typos corrected relative to the version submitted to ProQues

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Computational Methods For The Diagnosis of Rheumatoid Arthritis With Diffuse Optical Tomography

Diffuse optical tomography (DOT) is an imaging technique where near infrared (NIR) photons are used to probe biological tissue. DOT allows for the recovery of three-dimensional maps of tissue optical properties, such as tissue absorption and scattering coefficients. The application of DOT as a tool to aid in the diagnosis of rheumatoid arthritis (RA) is explored in this work. Algorithms for improving the image reconstruction process and for enhancing the clinical value of DOT images are presented in detail. The clinical data considered in this work consists of 99 fingers from subjects with RA and 120 fingers from healthy subjects. DOT scans of the proximal interphalangeal (PIP) joint of each finger is performed with modulation frequencies of 0, 300, and 600 MHz. A computer-aided diagnosis (CAD) framework for extracting heuristic features from DOT images and a method for using these same features to classify each joint as affected or not affected by RA is presented. The framework is applied to the clinical data and results are discussed in detail. Then, an algorithm for recovering the optical properties of biological media using the simplified spherical harmonics (SPN) light propagation model is presented. The computational performance of the algorithm is analyzed and reported. Finally, the SPN reconstruction algorithm is applied to clinical data of subjects with RA and the resulting images are analyzed with the CAD framework. As the first part of the CAD framework, heuristic image features are extracted from the absorption and the scattering coefficient images using multiple compression and dimensionality reduction techniques. Overall, 594 features are extracted from the images of each joint. Then, machine-learning techniques are used to evaluate the ability to discriminate between images of joints with RA and images of healthy joints. An evolution-strategy optimization algorithm is developed to evaluate the classification strength of each feature and to find the multidimensional feature combination that results in optimal classification accuracy. Classification is performed with k-nearest neighbors (KNN), linear (LDA) and quadratic discriminate analysis (QDA), self-organizing maps (SOM), or support vector machines (SVM). Classification accuracy is evaluated based on diagnostic sensitivity and specificity values. Strong evidence is presented that suggest there are clear differences between the tissue optical parameters of joints with RA and joints without RA. It is first shown that data obtained at 600 MHz leads to better classification results than data obtained at 300 and 0 MHz. Analysis of each extracted feature shows that DOT images of subjects with RA are statistically different (p < 0.05) from images of subjects without RA for over 90% of the features. Evidence shows that subjects with RA that do not have detectable signs of erosion, effusion, or synovitis (i.e. asymptomatic subjects) in MRI and US images have optical profiles similar to subjects who do have signs of erosion, effusion, or synovitis; furthermore, both of these cohorts differ from healthy controls subjects. This shows that it may be possible to accurately identify asymptomatic subjects with DOT scans. In contrast, these subjects remain difficult to identify from MRI and US images. The implications of these results are profound, as they suggest it may be possible to identify RA with DOT at an earlier stage compared to standard imaging techniques. Results from the feature-selection algorithm show that the SVM algorithm (with a third order polynomial kernel) achieves 100.0% sensitivity and 97.8% specificity. Lower bounds for these results (at 95.0% confidence level) are 96.4% and 93.8%, respectively. Image features most predictive of RA are from the spatial variation of optical properties and the absolute range in feature values. The optimal classifiers are low dimensional combinations (< 7 features). Robust cross- validation is performed to ensure the generalization of these classification results. The SPN -based reconstruction algorithm uses a reduced-Hessian sequential quadratic programming (rSQP) PDE-constrained optimization approach to maximize computational efficiency. The complex-valued forward model, or frequency domain SPN equations (N = 1, 3), is discretized using the finite-volume method and solved on unstructured computational grids using the restarted GMRES algorithm. The image reconstruction algorithm is presented in detail and its performance benchmarked against the ERT algorithm. The algorithm is subsequently used to recover the absorption and scattering coefficient images of joints scanned in the RA clinical study. While the SPN model is inherently less accurate than the ERT model, it is nevertheless shown that the images obtained with the SP3-based reconstruction algorithm are sufficiently accurate and allow for the diagnosis of RA at clinically relevant sensitivity [87.9% (78.1%, 100.0%)] and specificity [92.9% (84.6%, 100.0%)] values (the 95.0% confidence interval is specified in brackets). In contrast to results obtained with the SP3 model, the images generated with the SP1 algorithm yield significantly lower sensitivity [66.7% (46.6%, 100.0%)] and specificity [81.0% (64.8%, 100.0%)] values. While some numerical accuracy is sacrificed by selecting the SP3 model over the ERT model, the superior computational performance of the SP3 algorithm allows for computation of the absorption and the scattering coefficient images in under 15 minutes and requires less than 200 MB of RAM per finger (compared to the over 180 minutes and over 6 GB of RAM needed by the ERT-based algorithm). Overall, results indicate that the SP3-based reconstruction algorithm provides computational advantages over the ERT-based algorithm without sacrificing significant classification accuracy. In contrast, the SP1 model provides computational advantages compared to the ERT at the expense of classification accuracy. This indicates that the frequency-domain SP3 model is an ideal light propagation model for use in DOT scanning of finger joints with RA. Altogether, the results presented in this dissertation underscore the high potential for DOT to become a clinically useful diagnostic tool. The algorithms and framework developed as part of this dissertation can be directly used on future data to help further validate the hypotheses presented in this work and to further establish DOT imaging as a valuable diagnostic tool