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ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / While great strides have been made in recent years by government agencies in deploying proactive network security tools, the federal government as a whole desires to continue to press the state of the art in protecting its IT infrastructure. To this end, the US Department of Homeland Security (DHS) has created the Continuous Diagnostic and Mitigation (CDM) program [1] (also known as Continuous Monitoring, CM). It strives to establish a technology framework whereby agency federal government IT networks can be continuously monitored for threats and vulnerabilities, providing an analysis and correlation capability that will enable entities to better evaluate risk. It also defines a hierarchical dash-boarding capability that facilitates both aggregation and communication of each agency's network health status into abstracted levels of summary so the federal system as a whole can be better evaluate their IT security posture. Going forward, these technologies will dramatically impact all government agencies, the Department of Defense (DOD), and commercial entities.

Cutting operations are important and commonly used operations in the field of manufacturing. Automated machining is today commonly used in CNC-machines. One common drawback with automated machining is that the tool condition is challenging to predict which leads to a conservative tool replacement times. This leads to a low utilisation of the tool economical lifetime and an unnecessary high number of tool replacements. Methods for indirect continuous monitoring of the tool wear exist but usually require retrofitting of external sensors that can be both costly and also interrupt the machine operation due to the additional wiring. It is therefore of interest to investigate the possibility to use the, often high resolution, sensors already fitted in a CNC-machine to extract valuable data that can indirectly give an estimation of the tool condition. This thesis work has, with attention to the X-, Y- and Z-position sensors, resulted in development of algorithms that show relations between tool wear and data acquired from these sensors. The algorithms operate in the frequency domain to determine changes in the dynamic response over the time of tool degradation.

Accelerated warming of high latitude systems of the northern hemisphere is expected to cause significant changes to the hydro-ecology of Arctic lakes. To record comprehensive and meaningful baseline hydrological, limnological, and ecological conditions to which future change can be compared, all available environmental information generated on Noell Lake, NWT was compiled and synthesized. Data included: physical and geographical characteristics (bathymetric and drainage basin attributes); general regional climatology; water quality (nutrients, major anions/cations, dissolved oxygen, dissolved organic carbon); biological composition (fish community, macrophyte, phytoplankton, epiphyton and epipelon surveys) and seasonal patterns in primary productivity (as measured by chlorophyll-a (Chl-a)). A field-monitoring study was conducted from September 2010 to July 2013 assessing the application, reliability, and quality control/quality assurance of a newly developed automated buoy-based Arctic Lake Monitoring System (ALMS). The ALMS continuously measured a range of lake limnological and water quality parameters under both open-water and under-ice conditions. Overall, the ALMS provided a usable, uninterrupted record of changes in measured environmental, hydrological, and limnological parameters in both the epilimnion and hypolimnion. Noell Lake was determined to be spatially homogeneous with respect to the limnological measurements taken and, thus, the data recorded by the instrument arrays were determined to be representative of the lake as a whole. In addition to the measurements made by environmental sensors mounted on the buoy and mooring components, an augmentary array of in-situ sampling campaigns and controlled experiments were conducted to produce a continuous and comprehensive description of daily and seasonal changes to the hydrological and limnological conditions of Noell Lake. The continuous data series confirmed that Noell Lake is dimictic, with mixing events occurring in August and June, and hypoxic oxygen conditions occurring in March. Nutrient limitation experiments revealed that autotrophic productivity in Noell Lake was nitrogen-limited. Compiling data from existing literature involved >700 northern, high-latitude lakes; patterns in temporal and latitudinal changes in Arctic lake primary productivity (as measured by open-water, epilimnion Chl-a) and geochemistry were assessed. The key hypothesis tested was whether Arctic lakes are showing increased primary productivity (i.e., “greening”), through time and by latitude, similar to that documented for Arctic terrestrial systems. In general, significant decreases in lake Chl-a was observed in Arctic and sub-Arctic lakes over a ≈50 year time span. Separation of lakes by latitudinal bands revealed that trends in the lower Arctic region (60.00-69.99 Degrees North) showed a significant decreasing time trend, while high Arctic lakes displayed no trends. Corresponding temporal trends of total phosphorous (TP), total nitrogen (TN), and dissolved organic carbon (DOC) differed depending on the latitude of the lakes. Re-evaluation of the original northern-lake productivity models developed by Flanagan et al. (2003) through the use of the new, independent datasets (>700 lakes) as well as the addition of other environmental variables (DOC, dissolved inorganic carbon, lake depth, conductivity, and ice-cover) showed that the original models were valid and the most parsimonious in predicting variation in algal biomass in northern latitude lakes. Only measures of dissolved nutrients (TP, TN) and latitude are required to predict autotrophic water column productivity. / Graduate

abstract: Respiratory behavior provides effective information to characterize lung functionality, including respiratory rate, respiratory profile, and respiratory volume. Current methods have limited capabilities of continuous characterization of respiratory behavior and are primarily targeting the measurement of respiratory rate, which has relatively less value in clinical application. In this dissertation, a wireless wearable sensor on a paper substrate is developed to continuously characterize respiratory behavior and deliver clinically relevant parameters, contributing to asthma control. Based on the anatomical analysis and experimental results, the optimum site for the wireless wearable sensor is on the midway of the xiphoid process and the costal margin, corresponding to the abdomen-apposed rib cage. At the wearing site, the linear strain change during respiration is measured and converted to lung volume by the wireless wearable sensor utilizing a distance-elapsed ultrasound. An on-board low-power Bluetooth module transmits the temporal lung volume change to a smartphone, where a custom-programmed app computes to show the clinically relevant parameters, such as forced vital capacity (FVC) and forced expiratory volume delivered in the first second (FEV1) and the FEV1/FVC ratio. Enhanced by a simple, yet effective machine-learning algorithm, a system consisting of two wireless wearable sensors accurately extracts respiratory features and classifies the respiratory behavior within four postures among different subjects, demonstrating that the respiratory behaviors are individual- and posture-dependent contributing to monitoring the posture-related respiratory diseases. The continuous and accurate monitoring of respiratory behaviors can track the respiratory disorders and diseases' progression for timely and objective approaches for control and management. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

Diesel particulate matter (DPM) is the solid portion of diesel exhaust. DPM occurs primarily in the submicron range, and poses a number of respiratory and other health hazards including cardiovascular and pulmonary disease. Underground miners typically have the highest DPM exposures compared to other occupations. This is because many mines are characterized by confined work spaces and large diesel equipment fleets. Exposures can be a particularly high hazard in large opening mines where ventilation can be challenging. As such, DPM monitoring is critical to protecting miner health and informing a range of engineering decisions. DPM is primarily composed of two components, elemental carbon (EC) and organic carbon (OC), which are often summed to report total carbon (TC). The ratio of EC to OC, and presence of a number of other minor constituents such as sorbed metals, can vary with many factors such as engine operating conditions, maintenance, fuel types and additives, and the level and type of exhaust after-treatments used. Given its complexity, DPM cannot be measured directly, and either TC or EC are generally used as a surrogate. Currently, the Mining Safety and Health Administration (MSHA) limits personal exposures of underground metal/non-metal miners to 160 µg TC/m3 on an 8-hr time weighted average basis. Compliance is demonstrated by collecting full-shift personal filter samples, which are later analyzed using the NIOSH 5040 Standard Method. For engineering purposes, area samples can also be collected and analyzed. The typical lag time between sample collection and reporting of results is on the order of weeks, and this presents a real problem for identifying and remediating conditions that led to overexposures or high DPM in area samples. The handheld FLIR Airtec monitor was developed to provide real-time DPM data and allow immediate decision making. The monitor works on a laser extinction principle to measure EC, the black component of DPM, as mass accumulates on a filter. The Airtec has proven useful for personal monitoring and short-term DPM surveying. However, capabilities are needed for continuous, long-term monitoring. Continuous DPM monitoring would be highly valuable for applications such as design and operation of ventilation on demand systems, or engineering studies of new ventilation, exhaust treatment or other DPM controls. The work presented in this thesis considers three continuous monitors, two of which are already commercially available: Magee Scientific's AE33 black carbon (BC) Aethalometer and Sunset Laboratory's Semi-Continuous OCEC Field Analyzer. The third monitor, called the Airwatch, is still in development. The AE33 and Airwatch effectively operate on the same principle as the Airtec, but include a self-advancing filter tape to allow autonomous operation over relatively long periods of time. The OCEC field monitor is essentially a field version of the laboratory analyzer used for traditional 5040 Method analysis. The AE33 has been briefly demonstrated in mine environments in a couple of other studies, but further testing is needed. The current prototype of the Airwatch and the OCEC field monitor have never been mine-tested. Two separate studies are reported here. The first is a field study in an underground stone mine that tested the Airwatch prototype and AE33 head-to-head under relatively high DPM conditions. Results demonstrated that both instruments could track general trends, but that further work was needed to identify and resolve issues associated with use of both instruments in high-DPM environments – and with basic design elements of the Airwatch. Additionally, the need to calibrate the monitors' output data to the standard measure of EC (i.e., 5040 Method EC) was made clear. In the second study, laboratory testing was conducted under very controlled conditions to meet this need, and another round of field testing was also done. The second study also included the OCEC field monitor. The laboratory tests yielded data to allow interpretation of the AE33 and Airwatch results with respect to 5040 EC. These tests also shed light on the current range EC concentrations over which these monitors can provide reliable data – which is indeed a primary range of interest for mines. As expected, the OCEC field monitor was shown to produce lab-grade results across a wide range of concentrations. The field testing in the second study demonstrated that all three monitors could operate autonomously in a mine environment over extended periods of time (i.e., weeks to months). Overall, it can be concluded that the AE33 and OCEC field monitor represent off-the-shelf options for DPM monitoring in mines, and the Airwatch might be another option if fully developed in the future. Selection of a particular monitoring tool should include careful consideration of specific factors including data quality needs, conditions in the intended monitoring location(s), and general user friendliness of the monitor. / Master of Science

As the impacts of human activities increase sediment transport by aquatic systems the need to accurately quantify this transport becomes paramount. Turbidity is recognized as an effective tool for monitoring suspended sediments in aquatic systems, and with recent technological advances turbidity can be measured in-situ remotely, continuously, and at much finer temporal scales than was previously possible. Although turbidity provides an improved method for estimation of suspended-sediment concentration (SSC), compared to traditional discharge-based methods, there is still significant variability in turbidity-based SSC estimates and in sediment loadings calculated from those estimates. The purpose of this study was to improve the turbidity-based estimation of SSC. Working at two monitoring sites on the Roanoke River in southwestern Virginia, stage, turbidity, and other water-quality parameters and were monitored with in-situ instrumentation, suspended sediments were sampled manually during elevated turbidity events; those samples were analyzed for SSC and for physical properties; rainfall was quantified by geologic source area. The study identified physical properties of the suspended-sediment samples that contribute to SSC-estimation variance and hydrologic variables that contribute to variance in those physical properties. Results indicated that the inclusion of any of the measured physical properties, which included grain-size distributions, specific surface-area, and organic carbon, in turbidity-based SSC estimation models reduces unexplained variance. Further, the use of hydrologic variables, which were measured remotely and on the same temporal scale as turbidity, to represent these physical properties, resulted in a model which was equally as capable of predicting SSC. A square-root transformed turbidity-based SSC estimation model developed for the Roanoke River at Route 117 monitoring station, which included a water level variable, provided 63% less unexplained variance in SSC estimations and 50% narrower 95% prediction intervals for an annual loading estimate, when compared to a simple linear regression using a logarithmic transformation of the response and regressor (turbidity). Unexplained variance and prediction interval width were also reduced using this approach at a second monitoring site, Roanoke River at Thirteenth Street Bridge; the log-based transformation of SSC and regressors was found to be most appropriate at this monitoring station. Furthermore, this study demonstrated the potential for a single model, generated from a pooled set of data from the two monitoring sites, to estimate SSC with less variance than a model generated only from data collected at this single site. When applied at suitable locations, the use of this pooled model approach could provide many benefits to monitoring programs, such as developing SSC-estimation models for multiple sites which individually do not have enough data to generate a robust model or extending the model to monitoring sites between those for which the model was developed and significantly reducing sampling costs for intensive monitoring programs. / Master of Science

<p>Stalno praćenje rada softvera je neophodno da bi se utvrdilo da li softver po&scaron;tuje zadate nivoe kvaliteta. Na osnovu sakupljenih podataka, moguće je da se predvidi i dalje pona&scaron;anje aplikacije i da se izvr&scaron;i izbor daljih&nbsp;akcija da bi se održao zahtevani nivo. Tema ove disertacije je razvoj sistema za kontinualno praćenje performansi softvera, kao i razvoj modela za predviđanje performansi softvera. Za implementaciju sistema potrebljena je JEE tehnologija, ali je sistem razvijen tako da može da se primeni i za praćenje softvera razvijenog za druge platforme. Sistem je modelovan tako minimalno utiče na performanse sistema softvera koji se prati. Linearna regresija je upotrebljena za modelovanje zavisnosti performansi od okruženja u kom se softver izvr&scaron;ava. Sistem je upotrebljen za praćenje izabrane JEE aplikacije.</p> / <p>Continuous monitoring of software is necessary to determine whether the software performs within required service perfomance levels. Based on collected&nbsp;data, it is possible to predict the future performance of applications and to plan further actions in order to maintain the required service levels. The theme of this dissertation is the development of systems for continuous performance monitoring software, as well as the development of models for predicting the performance of software. To implement the system was used JEE technologies, but the system was developed so that it can be used for tracking software developed for other platforms. The system is modeled as a minimum impact on system performance software that is monitored. Linear regression was used for modeling the dependence of the performance environment in which the software is running. The system was used to monitor selected JEE applications.</p>

A dependência tecnológica das atividades e dos processos de negócios no mundo corporativo impulsionou o desenvolvimento de novas técnicas de auditoria para apurar possíveis falhas sistêmicas, que pudessem afetar os controles internos das companhias. Sendo assim, a necessidade de automação dos testes de auditoria motivou a elaboração do conceito e a implantação de projetos de auditoria contínua de dados no ambiente empresarial. Esta tese avalia os principais aspectos críticos para a construção da auditoria contínua de dados, considerando o uso da tecnologia da informação e das técnicas de auditoria com auxílio do computador. A partir da fundamentação teórica formularam-se quatro premissas básicas, as quais derivaram oito proposições e onze aspectos críticos, os quais foram colocados em prova de conceito, por meio de estudo de casos múltiplos no cenário empresarial brasileiro. A pesquisa afirma que os aspectos críticos para a construção da auditoria contínua de dados, originários da fundamentação teórica e corroborados pelo resultado do estudo de casos múltiplos, servirão como direcionadores para a inicialização de projetos de auditoria contínua, bem como para sua reestruturação. / The technology dependence of activities and business processes in corporate world driving the development of new audit techniques to investigate possible systemic failures, that could affect the internal controls of companies. Thus, the need for automation of auditing tests led to the development of the concept and implementation of projects for continuous auditing of data in the enterprise environment. This thesis evaluates the major critical issues for the construction of continuous auditing of data, considering the use of information technology and the computer assisted audit techniques. From the theoretical foundation formulated four basic premises, which were derived eight propositions and eleven critical aspects, which were placed in a proof of concept, through multiple case study in the Brazilian business scenario. The research argues that the critical aspects for the construction of continuous auditing of data, originated in theoretical foundation and corroborated by the results of multiple case study, will serve as drivers for the initialization of continuous auditing projects as well as for its restructuring.

Chemometrics is a discipline of chemistry which uses mathematical and statistical tools to help in the extraction of chemical information from measured data. With the assistance of chemometric methods, infrared (IR) spectroscopy has become a widely applied quantitative analysis tool. This dissertation explores two challenging applications of IR spectroscopy facilitated by chemometric methods: (1) passive Fourier transform (FT) remote sensing and (2) process monitoring by near-infrared (NIR) spectroscopy. Passive FT-IR remote sensing offers a measurement method to detect gaseous species in the outdoor environment. Two major obstacles limit the application of this method in quantitative analysis: (1) the effect of both temperature and concentration on the measured spectral intensities and (2) the difficulty and cost of collecting reference data for use in calibration. To address these problems, a quantitative analysis protocol was designed based on the use of a radiance model to develop synthetic calibration data. The synthetic data served as the input to partial least-squares (PLS) regression in order to construct models for use in estimating ethanol and methanol concentrations. The methodology was tested with both laboratory and field remote sensing data. Near-infrared spectroscopy has attracted significant interest in process monitoring because of the simplicity in sample preparation and the compatibility with aqueous solutions. For use in process monitoring, the need exists for robust calibrations. A challenge in the NIR region is that weak, broad and highly overlapped spectral bands make it difficult to extract useful chemical information from measured spectra. In this case, signal processing methods can be helpful in removing unwanted signals and thereby uncovering useful information. When applying signal processing as a spectral preprocessing tool and regression analysis for building a quantitative calibration model, optimizing the parameters that specify the details of the methods is crucial. In this research, particle swarm optimization, a population-based optimization method was applied. Digital filtering and wavelet processing methods were evaluated for their utility as spectral preprocessing tools. Both a pump-controlled flowing system and bioreactor runs involving the yeast, Pichia pastoris, were studied in this work. In investigating the bioreactor runs, insufficient reference data resulted in difficulties in employing the PLS calibration method. Instead, the augmented classical least-squares modeling technique was applied since it requires only pure-component or composite spectra of the analyte and background matrix rather than a large set of mixture samples of known analyte concentration.