Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

This thesis presents a new testbed for Automatic Incident Detection (AID) systems that uses real-time traffic video and data feeds from the Ministry of Transportation, Ontario (MTO) COMPASS Advanced Traffic Management System (ATMS). This new testbed, termed the AID Comparison and Analysis Tool (AID CAAT), consists largely of a data warehouse storing a significant amount of traffic video, the corresponding traffic data and an accurate log of incident start/end times. An evaluation was conducted whereby the AID CAAT was used to calibrate, and then analyze the performance of four AID systems: California Algorithm 8, McMaster Algorithm, the Genetic Adaptive Incident Detection (GAID) Algorithm and the Citilog - VisioPAD. The traditional measures of effectiveness (MOE) were initially used for this evaluation: detection rate (DR), false alarm rate (FAR), and mean time to detection (MTTD). However, an in-depth analysis of the test results (facilitated by the AID CAAT) revealed the need for two additional MOEs: False Normal Rate and Nuisance Rate. The justification and sample calculations for these new MOEs are also provided. This evaluation shows the considerable advantages of the AID CAAT, and also suggests the strengths and weaknesses of the AID systems tested. / Thesis / Master of Applied Science (MASc)

Botnets are a global problem, and effective botnet detection requires cooperation of large Internet Service Providers, allowing near global visibility of traffic that can be exploited to detect them. The global visibility comes with huge challenges, especially in the amount of data that has to be analysed. To handle such large volumes of data, a robust and effective detection method is the need of the hour and it must rely primarily on a reduced or abstracted form of data such as a graph of hosts, with the presence of an edge between two hosts if there is any data communication between them. Such an abstraction would be easy to construct and store, as very little of the packet needs to be looked at. Structured P2P command and control have been shown to be robust against targeted and random node failures, thus are ideal mechanisms for botmasters to organize and command their botnets effectively. Thus this thesis develops a scalable, efficient and robust algorithm for the detection of structured P2P botnets in large traffic graphs. It draws from the advances in the state of the art in Community Detection, which aim to partition a graph into dense communities. Popular Community Detection Algorithms with low theoretical time complexities such as Label Propagation, Infomap and Louvain Method have been implemented and compared on large LFR benchmark graphs to study their efficiency. Louvain method is found to be capable of handling graphs of millions of vertices and billions of edges. This thesis analyses the performance of this method with two objective functions, Modularity and Stability and found that neither of them are robust and general. In order to overcome the limitations of these objective functions, a third objective function proposed in the literature is considered. This objective function has previously been used in the case of Protein Interaction Networks successfully, and used in this thesis to detect structured P2P botnets for the first time. Further, the differences in the topological properties - assortativity and density, of structured P2P botnet communities and benign communities are discussed. In order to exploit these differences, a novel measure based on mean regular degree is proposed, which captures both the assortativity and the density of a graph and its properties are studied. This thesis proposes a robust and efficient algorithm that combines the use of greedy community detection and community filtering using the proposed measure mean regular degree. The proposed algorithm is tested extensively on a large number of datasets and found to be comparable in performance in most cases to an existing botnet detection algorithm called BotGrep and found to be significantly faster.

Oral feeding competency is a primary requirement for preterm infant hospital release. Currently there is no widely accepted method to objectively measure oral feeding. Feeding consists primarily of the integration of three individual feeding events: sucking, breathing, and swallowing, and the objective of feeding coordination is to minimize aspiration. The purpose of this work was to quantify the infant feeding process from signals obtained during bottle feeding and ultimately develop a measure of feeding coordination. Sucking was measured using a pressure transducer embedded within a modified silicone bottle block. Breathing was measured using a thermistor embedded within nasal cannula, and swallowing was measured through the use of several different piezoelectric sensors. In addition to feeding signals, electrocardiogram (ECG) signals were obtained as an indicator of overall infant behavioral state during feeding. Event detection algorithms for the individual feeding signals were developed and validated, then used for the development of a measurement of feeding coordination. The final suck event detection algorithm was the result of an iterative process that depended on the validity of the signal model. As the model adapted to better represent the data, the accuracy and specificity of the algorithm improved. For the breath signal, however, the primary barrier to effective event detection was significant baseline drift. The frequency components of the baseline drift overlapped significantly with the breath event frequency components, so a time domain solution was developed. Several methods were tested, and it was found that the acceleration vector of the signal provided the most robust representation of the underlying breath signal while minimizing baseline drift. Swallow signal event detection was not possible due to a lack of available data resulting from problems with the consistency of the obtained signal. A robust method was developed for the batch processing of heart rate variability analysis. Finally a method of coordination analysis was developed based on the event detection algorithm outputs. Coordination was measured by determining the percentage of feeding time that consisted of overlapping suck and breath activity.

<p>Quick and accurate segmentation and object detection of the biomedical image is the starting point of most disease analysis and understanding of biological processes in medical research. It will enhance drug development and advance medical treatment, especially in cancer-related diseases. However, identifying the objects in the CT or MRI images and labeling them usually takes time even for an experienced person. Currently, there is no automatic detection technique for nucleus identification, pneumonia detection, and fetus brain segmentation. Fortunately, as the successful application of artificial intelligence (AI) in image processing, many challenging tasks are easily solved with deep convolutional neural networks. In light of this, in this thesis, the deep learning based object detection and segmentation methods were implemented to perform the nucleus segmentation, lung segmentation, pneumonia detection, and fetus brain segmentation. The semantic segmentation is achieved by the customized U-Net model, and the instance localization is achieved by Faster R-CNN. The reason we choose U-Net is that such a network can be trained end-to-end, which means the architecture of this network is very simple, straightforward and fast to train. Besides, for this project, the availability of the dataset is limited, which makes U-Net a more suitable choice. We also implemented the Faster R-CNN to achieve the object localization. Finally, we evaluated the performance of the two models and further compared the pros and cons of them. The preliminary results show that deep learning based technique outperforms all existing traditional segmentation algorithms. </p>

A tecnologia de radar e comunicação em banda ultralarga (UWB) tem sido investigada intensamente nas últimas décadas. Esta tecnologia emprega pulsos de curta duração, que resultam em um espalhamento de energia em uma faixa ampla de frequência, fazendo com que, em transmissão de dados, o sinal apareça como ruído branco, dificultando a sua interceptação. Uma das aplicações mais promissoras da tecnologia UWB é na implementação de radares para detecção de pessoas, no monitoramento de ambientes, no uso em missões de resgate em desmoronamento de construções e na detecção de pessoas soterradas. Entretanto, diversos são os desafios para a construção de radares UWB: geração e recepção do pulso UWB, antenas, processamento, análise e visualização dos dados. Este trabalho propõe uma nova antena para aplicações de radar para visualização através da parede, e realiza a sua caracterização usando algoritmos de detecção de pessoas. Para a caracterização, o trabalho apresenta um cenário experimental que utiliza um sistema de caracterização de antena UWB. A antena proposta consiste em modificações na estrutura de uma antena antipodal, de forma a se obter alto ganho e estabilidade nos diagramas de irradiação, requisitos importantes para esse tipo de aplicação. Os resultados obtidos experimentalmente mostram que a antena proposta apresenta diagrama de irradiação direcional e principalmente alto ganho, característica importante para as aplicações de detecção. Processando-se os dados obtidos experimentalmente com algoritmos de detecção clássicos e adaptativo, verificou-se que a antena cumpre os requisitos da aplicação de detecção de pessoas. / Ultra-Wideband (UWB) radar and communication systems have been widely studied in the last decades. This technology employs very short duration pulses, resulting in energy spreaded in a very wide frequency band. In data communication applications, the signal appears as a white noise, making difficult the interception. One of the most promising applications of this technology is in radar implementation for people detection, in environment monitoring, rescue missions etc. The challenges to UWB radar implementation area diverse: pulse generation, pulse transmission and reception, antennas, and data processing, analysis and visualization. This work proposes a new antenna for through the wall (TTW) UWB radar. The antenna is characterized using people detection algorithms. A scenario, composed by a UWB antenna characterization system is presented. The antenna is a antipodal antenna modified in order to obtain high gain and stability in the irradiation diagram. Using people detection algorithms, it was verified full attendance to application requirements.

A tecnologia de radar e comunicação em banda ultralarga (UWB) tem sido investigada intensamente nas últimas décadas. Esta tecnologia emprega pulsos de curta duração, que resultam em um espalhamento de energia em uma faixa ampla de frequência, fazendo com que, em transmissão de dados, o sinal apareça como ruído branco, dificultando a sua interceptação. Uma das aplicações mais promissoras da tecnologia UWB é na implementação de radares para detecção de pessoas, no monitoramento de ambientes, no uso em missões de resgate em desmoronamento de construções e na detecção de pessoas soterradas. Entretanto, diversos são os desafios para a construção de radares UWB: geração e recepção do pulso UWB, antenas, processamento, análise e visualização dos dados. Este trabalho propõe uma nova antena para aplicações de radar para visualização através da parede, e realiza a sua caracterização usando algoritmos de detecção de pessoas. Para a caracterização, o trabalho apresenta um cenário experimental que utiliza um sistema de caracterização de antena UWB. A antena proposta consiste em modificações na estrutura de uma antena antipodal, de forma a se obter alto ganho e estabilidade nos diagramas de irradiação, requisitos importantes para esse tipo de aplicação. Os resultados obtidos experimentalmente mostram que a antena proposta apresenta diagrama de irradiação direcional e principalmente alto ganho, característica importante para as aplicações de detecção. Processando-se os dados obtidos experimentalmente com algoritmos de detecção clássicos e adaptativo, verificou-se que a antena cumpre os requisitos da aplicação de detecção de pessoas. / Ultra-Wideband (UWB) radar and communication systems have been widely studied in the last decades. This technology employs very short duration pulses, resulting in energy spreaded in a very wide frequency band. In data communication applications, the signal appears as a white noise, making difficult the interception. One of the most promising applications of this technology is in radar implementation for people detection, in environment monitoring, rescue missions etc. The challenges to UWB radar implementation area diverse: pulse generation, pulse transmission and reception, antennas, and data processing, analysis and visualization. This work proposes a new antenna for through the wall (TTW) UWB radar. The antenna is characterized using people detection algorithms. A scenario, composed by a UWB antenna characterization system is presented. The antenna is a antipodal antenna modified in order to obtain high gain and stability in the irradiation diagram. Using people detection algorithms, it was verified full attendance to application requirements.

Dans le contexte des réseaux complexes, cette thèse s’inscrit dans deux axes : (1) Méthodologiede la détection de communautés et (2) Evaluation de la qualité des algorithmes de détection de communautés. Dans le premier axe, nous nous intéressons en particulier aux approches fondées sur les Leaders (sommets autour desquels s’agrègent les communautés). Premièrement, nous proposons un enrichissement de la méthodologie LICOD qui permet d’évaluer les différentes stratégies des algorithmes fondés sur les leaders, en intégrant différentes mesures dans toutes les étapes de l’algorithme. Deuxièmement, nous proposons une extension de LICOD, appelée it-LICOD. Cette extension introduit une étape d’auto-validation de l’ensemble des leaders. Les résultats expérimentaux de it-LICOD sur les réseaux réels et artificiels sont bons par rapport à LICOD et compétitifs par rapport aux autres méthodes. Troisièmement, nous proposons une mesure de centralité semi-locale, appelée TopoCent, pour remédier au problème de la non-pertinence des mesures locales et de la complexité de calcul élevée des mesures globales. Nous montrons expérimentalement que LICOD est souvent plus performant avec TopoCent qu’avec les autres mesures de centralité. Dans le deuxième axe, nous proposons deux méthodes orientées-tâche, CLE et PLE, afin d’évaluer les algorithmes de détection de communautés. Nous supposons que la qualité de la solution des algorithmes peut être estimée en les confrontant à d’autres tâches que la détection de communautés en elle-même. Dans la méthode CLE nous utilisons comme tâche la classification non-supervisée et les algorithmes sont évalués sur des graphes générés à partir des jeux de données numériques. On bénéficie dans ce cas de la disponibilité de la vérité de terrain (les regroupements) de plusieurs jeux de données numériques. En ce qui concerne la méthode PLE, la qualité des algorithmes est mesurée par rapport à leurs contributions dans une tâche de prévision de liens. L’expérimentation des méthodes CLE et PLE donne de nouveaux éclairages sur les performances des algorithmes de détection de communautés / In this thesis we focus, on one hand, on community detection in complex networks, and on the other hand, on the evaluation of community detection algorithms. In the first axis, we are particularly interested in Leaders driven community detection algorithms. First, we propose an enrichment of LICOD : a framework for building different leaders-driven algorithms. We instantiate different implementations of the provided hotspots. Second, we propose an extension of LICOD, we call it-LICOD. This extension introduces a self-validation step of all identified leaders. Experimental results of it-LICOD on real and artificial networks show that it outperform the initial LICOD approach. Obtained results are also competitive with those of other state-of-the art methods. Thirdly, we propose a semi-local centrality measure, called TopoCent, that address the problem of the irrelevance of local measures and high computational complexity of globalmeasures. We experimentally show that LICOD is often more efficient with TopoCent than with the other classical centrality measures. In the second axis, we propose two task-based community evaluation methods : CLE and PLE. We examine he hypothesis that the quality of community detection algorithms can be estimated by comparing obtained results in the context of other relevent tasks. The CLE approach, we use a data clustering task as a reference. The PLE method apply a link prediction task. We show that the experimentation of CLE and PLE methods gives new insights into the performance of community detection algorithms.

The subject of this thesis is the construction of one-way communication device. This device consists of the transmitter and the receiver, both of which are connected to its respective computer via USB interface. This device allows sending UTF-8 characters or files from one computer to another. Both computers are running a graphical user interface program. The core of a transmitter is a digital signal modulating laser diode. The receiver is made of photovoltaic panel with a resonance circuit and an amplifier. Communication between the electronics and the computer is driven by microcontrollers. Received messages are verified with one of multiple error detection algorithms, which can be selected by user in the utility program.