Data Quality Management in Large-Scale Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are cross-domain, multi-model, advance information systems that play a significant role in many large-scale infrastructure sectors of smart cities public services such as traffic control, smart transportation control, and environmental and noise monitoring systems. Such systems, typically, involve a substantial number of sensor nodes and other devices that stream and exchange data in real-time and usually are deployed in uncontrolled, broad environments. Thus, unexpected measurements may occur due to several internal and external factors, including noise, communication errors, and hardware failures, which may compromise these systems quality of data and raise serious concerns related to safety, reliability, performance, and security. In all cases, these unexpected measurements need to be carefully interpreted and managed based on domain knowledge and computational models. Therefore, in this research, data quality challenges were investigated, and a comprehensive, proof of concept, data quality management system was developed to tackle unaddressed data quality challenges in large-scale CPSs. The data quality management system was designed to address data quality challenges associated with detecting: sensor nodes measurement errors, sensor nodes hardware failures, and mismatches in sensor nodes spatial and temporal contextual attributes. Detecting sensor nodes measurement errors associated with the primary data quality dimensions of accuracy, timeliness, completeness, and consistency in large-scale CPSs were investigated using predictive and anomaly analysis models via utilising statistical and machine-learning techniques. Time-series clustering techniques were investigated as a feasible mean for detecting long-segmental outliers as an indicator of sensor nodes’ continuous halting and incipient hardware failures. Furthermore, the quality of the spatial and temporal contextual attributes of sensor nodes observations was investigated using timestamp analysis techniques. The different components of the data quality management system were tested and calibrated using benchmark time-series collected from a high-quality, temperature sensor network deployed at the University of East London. Furthermore, the effectiveness of the proposed data quality management system was evaluated using a real-world, large-scale environmental monitoring network consisting of more than 200 temperature sensor nodes distributed around London. The data quality management system achieved high accuracy detection rate using LSTM predictive analysis technique and anomaly detection associated with DBSCAN. It successfully identified timeliness and completeness errors in sensor nodes’ measurements using periodicity analysis combined with a rule engine. It achieved up to 100% accuracy in detecting potentially failed sensor nodes using the characteristic-based time-series clustering technique when applied to two days or longer time-series window. Timestamp analysis was adopted effectively for evaluating the quality of temporal and spatial contextual attributes of sensor nodes observations, but only within CPS applications in which using gateway modules is possible

MRI images series segmentation using the geodesic deformable model

Image segmentation is regarded as the most crucial medical imaging process because it extracts the region of interest and thus helps upgrade medical diagnosis. The Geodesic deformable model is a curve that deforms within digital images to extract object shapes. The Geodesic deformable model has been used very successfully in the process of single image segmentation but it fails to segment a series of images. In this research, the Geodesic deformable model is developed to overcome its limitation by controlling the speed of the curve deformation. The developed model is implemented on several series of MRI images that include tumors of varying shape complexity. Experimental results show that the developed model performed very well and successfully segmented the series of MRI images which outperform the baseline model

Master of Science

thesisCampylobacteriosis is a foodborne and waterborne zoonotic gastrointestinal illness and the most common cause of acute gastroenteritis worldwide. In the United States Campylobacter infections are second only to Salmonella as the most common cause of gastroenteritis, accounting for an estimated 2.4 million symptomatic infections annually. It is estimated that the total cost of foodborne illness in the United States is $152 billion of which$18.8 billion is attributed to Campylobacter. Diagnosis can be challenging because the organism is difficult to isolate, grow, and identify. Clinical manifestation of Campylobacter is indistinguishable from other enteric pathogens; (Salmonella, Shigella, Yersinia, Clostridium difficile, and E. coli 0157:H7 and other enterohemorrhagic E. coli) therefore, a presumptive diagnosis cannot be made putting them at risk for untreated infection. There are a growing number of diagnostic methods available for detection and/or isolation of Campylobacter species from stool, but there is currently no national or state public health testing guidelines. Eight assays were evaluated for performance in the detection of Campylobacter species in stool. The assays are comprised of four culture medias (CVA, CSM, Cefex, and mCCDA); three EIA/ELISA kits (ImmunoCard STAT! Campy, Premier Campy and ProSpecT Campy); and one molecular method (FilmArray GI panel). The FilmArray GI panel due to its ability to detect viable and nonviable organism was used as the gold standard. To verify the gold standard was accurate all positive FilmArray samples were analyzed by DNA sequencing. The sensitivity and specificity, respectively, of each assay are as follows: CVA 87.8%, 100%; CSM 87.8%, 100%; Cefex 87.8%, 100%; mCCDA 78.0%, 100%; ImmunoCard STAT! Campy 31.7%, 65.2%; Premier Campy 80.5%, 26.1%; and the ProSpecT Campy 75.6%, 82.6%. In contrast the FilmArray produces a sensitivity and specificity of 100% when compared to culture. Furthermore the FilmArray GI panel takes the least amount of time to produce a result, 1 hour compared to 48-72 hours for culture. In conclusion, the FilmArray GI panel is the most sensitive, specific, rapid, cost effective, and objective method for the detection of Campylobacter species in stool. Molecular assays such as the FilmArray GI panel should replace traditional culture techniques in the microbiology lab

Influence of Laser Irradiation Times on Properties of Porous Silicon

Porous silicon (P-Si) has been produced in this work by photoelectrochemical (PEC) etching process. The irradiation has been achieved using diode laser of (2 W) power and 810 nm wavelength. The influence of various irradiation times on the properties of P-Si material such as P-Si layer thickness, surface aspect, pore diameter and the thickness of walls between pores as well as porosity and etching rate was investigated by depending on the scanning electron micrograph (SEM) technique and gravimetric measurements

Bayesian methods for analysing pesticide contamination with uncertain covariates

Two chemical properties of pesticides are thought to control their environmental fate. These are the adsorption coefficient k(_oc) and soil half-life t(^soil_1/2). This study aims to demonstrate the use of Bayesian methods in exploring whether or not it is possible to discriminate between pesticides that leach from those that do not leach on the basis of their chemical properties, when the monitored values of these properties are uncertain, in the sense that there are a range of values reported for both k(_oc) and t(^soil_1/2) - The study was limited to 43 pesticides extracted from the UK Environment Agency (EA) where complete information was available regarding these pesticides. In addition, analysis of data from a separate study, known as "Gustafson's data”, with a single value reported for k(_oc) and t(^soil_1/2) was used as prior information for the EA data. Bayesian methods to analyse the EA data are proposed in this thesis. These methods use logistic regression with random covariates and prior information derives from (i) available United States Department of Agriculture (USDA) data base values of k(_oc) and t(^soil_1/2) for the covariates and (ii) Gustafson's data for the regression parameters. They are analysed by means of Markov Chain Monte Carlo (MCMC) simulation techniques via the freely available WinBUGS software and R package. These methods have succeeded in providing a complete or a good separation between leaching and non-leaching pesticide

HADES: a Hybrid Anomaly Detection System for Large-Scale Cyber-Physical Systems

Smart cities rely on large-scale heterogeneous distributed systems known as Cyber-Physical Systems (CPS). Information systems based on CPS typically analyse a massive amount of data collected from various data sources that operate under noisy and dynamic conditions. How to determine the quality and reliability of such data is an open research problem that concerns the overall system safety, reliability and security. Our research goal is to tackle the challenge of real-time data quality assessment for large-scale CPS applications with a hybrid anomaly detection system. In this paper we describe the architecture of HADES, our Hybrid Anomaly DEtection System for sensors data monitoring, storage, processing, analysis, and management. Such data will be filtered with correlation-based outlier detection techniques, and then processed by predictive analytics for anomaly detection

AWN-similarity: Towards developing free open-source frameworks for measuring Arabic semantic similarity under Windows / Linux operating systems

Arabic is a highly systematic language where its words exhibit elegant and rigorous logic. The field of Arabic word semantic similarity becomes more challenging due to its higher complexity and subtlety. This research is concerned with investigating the development of free open-source frameworks containing packages to calculate the semantic similarity between two Arabic words or concepts. These packages are known as AWN-ConceptSimilarity and AWN-WordSimilarity. The developed packages implement seven semantic similarity algorithms. One of these algorithms was proposed for Arabic and the rest were proposed for English where successfully adapted to Arabic using an Arabic lexical database, Arabic wordnet. The functionality of the developed packages is validated using two-word similarity benchmarks datasets previously produced for Arabic. The results of the validation process indicate that the developed frameworks represent an important contribution to the Arabic semantic similarity field. Moreover, the developed packages are reliable to use and embed them with Arabic researchers' projects for improving or comparing their methodologies

Reasons for the slow uptake of embodied carbon estimation in the Sri Lankan building sector

Global carbon reduction is not merely a responsibility of environmentally advanced developed countries, but also a responsibility of developing countries regardless of their less impact on global carbon emissions. In recognition of that, Sri Lanka as a developing country has initiated promoting green building construction as one reduction strategy. However, notwithstanding the increasing attention on Embodied Carbon (EC) reduction in the global building sector, they still mostly focus on Operational Carbon (OC) reduction (through improving operational energy). An adequate attention has not yet been given on EC estimation and reduction. Therefore, this study aims to identify the reasons for the slow uptake of EC estimation in the Sri Lankan building sector. To achieve this aim, 16 numbers of global barriers to estimate EC were identified through existing literature. They were then subjected to a pilot survey to identify the significant reasons for the slow uptake of EC estimation in the Sri Lankan building sector. A questionnaire with a three-point Likert scale was used to this end. The collected data were analysed using descriptive statistics. The findings revealed that 11 of 16 challenges/ barriers are highly relevant as reasons for the slow uptake in estimating EC in buildings in Sri Lanka while the other five challenges/ barriers remain as moderately relevant reasons. Further, the findings revealed that there are no low relevant reasons.Eventually, the paper concluded that all the known reasons are significant to the Sri Lankan building sector and it is necessary to address them in order to upturn the attention on EC reduction. Keywords — Embodied carbon emissions, embodied carbon estimation, global carbon reduction, Sri Lankan building secto

Perkembangan Pendidikan Tinggi ISLAM di Indonesia (Studi Kasus STIT AI-Kifayah Ke STAI AI-Kifayah)

Penelitian ini bertujuan untuk memahami evolusi pendidikan tinggi Islam di Indonesia, dengan fokus khusus pada transformasi STIT AI-Kifayah menjadi STAI AI-Kifayah. Metodologi yang digunakan adalah pendekatan deskriptif kualitatif, yang dikombinasikan dengan studi kepustakaan. Data dikumpulkan melalui buku dan sumber lain yang relevan dengan subjek penelitian. Hasil penelitian ini menyoroti sejarah perkembangan pendidikan tinggi Islam di Indonesia, dimulai dari berdirinya ptain pada tahun 1951, diikuti oleh adia pada tahun 1957, IAIN pada tahun 1960, STAIN pada tahun 1997, dan UIN pada tahun 2002. Ditemukan bahwa pendidikan Islam semakin memperhatikan dinamika internal dan eksternal, yang berkontribusi pada peningkatan kualitas dan kuantitas. Contoh spesifik dari evolusi ini adalah perubahan stit AI-Kifayah menjadi STAI AI-Kifayah, yang merupakan bagian dari tren peningkatan dan diversifikasi dalam pendidikan tinggi Islam di Indonesia