Unsupervised Anomaly Detection of High Dimensional Data with Low Dimensional Embedded Manifold

Anomaly detection techniques are supposed to identify anomalies from loads of seemingly homogeneous data and being able to do so can lead us to timely, pivotal and actionable decisions, saving us from potential human, financial and informational loss. In anomaly detection, an often encountered situation is the absence of prior knowledge about the nature of anomalies. Such circumstances advocate for ‘unsupervised’ learning-based anomaly detection techniques. Compared to its ‘supervised’ counterpart, which possesses the luxury to utilize a labeled training dataset containing both normal and anomalous samples, unsupervised problems are far more difficult. Moreover, high dimensional streaming data from tons of interconnected sensors present in modern day industries makes the task more challenging. To carry out an investigative effort to address these challenges is the overarching theme of this dissertation. In this dissertation, the fundamental issue of similarity measure among observations, which is a central piece in any anomaly detection techniques, is reassessed. Manifold hypotheses suggests the possibility of low dimensional manifold structure embedded in high dimensional data. In the presence of such structured space, traditional similarity measures fail to measure the true intrinsic similarity. In light of this revelation, reevaluating the notion of similarity measure seems more pressing rather than providing incremental improvements over any of the existing techniques. A graph theoretic similarity measure is proposed to differentiate and thus identify the anomalies from normal observations. Specifically, the minimum spanning tree (MST), a graph-based approach is proposed to approximate the similarities among data points in the presence of high dimensional structured space. It can track the structure of the embedded manifold better than the existing measures and help to distinguish the anomalies from normal observations. This dissertation investigates further three different aspects of the anomaly detection problem and develops three sets of solution approaches with all of them revolving around the newly proposed MST based similarity measure. In the first part of the dissertation, a local MST (LoMST) based anomaly detection approach is proposed to detect anomalies using the data in the original space. A two-step procedure is developed to detect both cluster and point anomalies. The next two sets of methods are proposed in the subsequent two parts of the dissertation, for anomaly detection in reduced data space. In the second part of the dissertation, a neighborhood structure assisted version of the nonnegative matrix factorization approach (NS-NMF) is proposed. To detect anomalies, it uses the neighborhood information captured by a sparse MST similarity matrix along with the original attribute information. To meet the industry demands, the online version of both LoMST and NS-NMF is also developed for real-time anomaly detection. In the last part of the dissertation, a graph regularized autoencoder is proposed which uses an MST regularizer in addition to the original loss function and is thus capable of maintaining the local invariance property. All of the approaches proposed in the dissertation are tested on 20 benchmark datasets and one real-life hydropower dataset. When compared with the state of art approaches, all three approaches produce statistically significant better outcomes. “Industry 4.0” is a reality now and it calls for anomaly detection techniques capable of processing a large amount of high dimensional data generated in real-time. The proposed MST based similarity measure followed by the individual techniques developed in this dissertation are equipped to tackle each of these issues and provide an effective and reliable real-time anomaly identification platform

Cardinality Enhancement of SAC-OCDMA Systems Using New Diagonal Double Weight Code

Optical code division multiple access (OCDMA) provides  another dimension to multiple access systems,  in which each user is assigned a unique code. This allows  each subscriber  to simultaneously access the medium without any contention. However, simultaneous access of multiple users introduce multiple access interference  (MAI)  which primarily deteriorates  the performance of OCDMA systems.  This paper proposes a new code called diagonal double weight (DDW) code to elevate the performance and cardinality of spectral amplitude coding  (SAC) OCDMA  systems. Performance of our proposed code is evaluated using comprehensive analytical  analysis  followed by simulation analysis. Examination  of bit error rate shows that DDW code along with  single photodiode detection  technique  provides efficient performance, with added benefits of simplified design, large cardinality and ease of implementation

Design of heavy-duty pavements incorporating a granular interlayer

Pavements have been used to transport goods and people from one place to another over the years. Pavement construction technologies have been continuously evolving starting from compacted ground to brick roads and modern hot mix asphalt roads capable of speed, safety, durability and comfort. The advancing construction technologies have led to heavy-duty pavements (Inverted Pavements) in South Africa to accomodate heavy loads in warm climates. Inverted Pavements are types of pavement in which the layer arrangement is different from that of a conventional flexible pavement. In an inverted pavement, the unbound granular base layer is constructed just under the hot mix asphalt surfacing and another granular layer modified with cement is constructed underneath it. This current project involves designing inverted pavements based on laboratory testing. The project is divided into different phases which are summarised here. As a low stiffness unbound layer is constructed in a sandwich between stiffer layers, its performance becomes one of the determining factors relating to the pavement’s resistance to failure. Therefore, extensive laboratory testing has been performed in the current project to study unbound material performance under different influencing factors including moisture, fines content, number of load applications and stress conditions. The results show that the selected unbound granular material (UGM) gradations based on South African base specifications performed significantly well under these influencing factors. The resilient and permanent deformation performance of unbound granular materials is presented in detail in this thesis. The second phase of the project involved testing different inverted pavement combinations in the laboratory to establish the effect of layer thickness on performance under given loading and environmental conditions. Permanent deformation and reflective cracking were the criteria to assess the pavement performance under load. Different laboratory testing matrices were developed for permanent deformation and reflective cracking. The testing was performed at different temperatures under the same load to evaluate the pavement’s resistance to both failure criteria in a wheel tracking device. A conventional mould was used for evaluating permanent deformation while a new mould was designed and manufactured for studying crack initiation and propagation. The results showed that the different thickness combinations within an inverted pavement performed differently with some of the thickness variations affecting the performance significantly. The third phase of the project involved the prediction of permanent deformation and crack propagation in inverted pavements. Different techniques were combined to develop a methodology to predict the permanent deformation in a full-depth inverted pavement. The predicted permanent deformation values were compared with the measured ones and very close correspondance was observed. Similarly, crack initiation and propagation were also predicted by developing a mechanics-based methodology calculating the forces and hence bending in asphalt surfacing leading to calculation of tensile strains. The calculated strains were used to develop a fatigue characteristic. The methodology developed was applied to a real pavement and promising results were obtained

Power Allocation for Conventional and Buffer-Aided Link Adaptive Relaying Systems with Energy Harvesting Nodes

Energy harvesting (EH) nodes can play an important role in cooperative communication systems which do not have a continuous power supply. In this paper, we consider the optimization of conventional and buffer-aided link adaptive EH relaying systems, where an EH source communicates with the destination via an EH decode-and-forward relay. In conventional relaying, source and relay transmit signals in consecutive time slots whereas in buffer-aided link adaptive relaying, the state of the source-relay and relay-destination channels determines whether the source or the relay is selected for transmission. Our objective is to maximize the system throughput over a finite number of transmission time slots for both relaying protocols. In case of conventional relaying, we propose an offline and several online joint source and relay transmit power allocation schemes. For offline power allocation, we formulate an optimization problem which can be solved optimally. For the online case, we propose a dynamic programming (DP) approach to compute the optimal online transmit power. To alleviate the complexity inherent to DP, we also propose several suboptimal online power allocation schemes. For buffer-aided link adaptive relaying, we show that the joint offline optimization of the source and relay transmit powers along with the link selection results in a mixed integer non-linear program which we solve optimally using the spatial branch-and-bound method. We also propose an efficient online power allocation scheme and a naive online power allocation scheme for buffer-aided link adaptive relaying. Our results show that link adaptive relaying provides performance improvement over conventional relaying at the expense of a higher computational complexity.Comment: Submitted to IEEE Transactions on Wireless Communication

The Predictability of International Mutual Funds

The predictability of the US-based international mutual fund returns has received renewed consideration in recent academic studies. This dissertation extends recent research by exploring the 2,479 daily return observations covering the period from January 4, 1993 to October 31, 2002 for all categories of international mutual funds. This exploration splits the sample, uses the initial sub-sample to investigate return patterns of international mutual funds and develops trading rules based on the predictable return patterns, and tests those rules on the holdout sample. The empirical findings suggest that smart investors may earn higher riskadjusted returns by following daily dynamic trading strategies. The excess returns earned by investors are statistically and economically significant, irrespective of load or no-load mutual funds and even in the presence of various exchange restrictions and regulations

Do Public Expenditure and Macroeconomic Uncertainty Matter to Private Investment? Evidence from Pakistan

This study examines the role of macroeconomic uncertainty and public expenditure in determining private fixed investment in Pakistan. It is found that individual series are nonstationary. There is a long-run relationship between private fixed investment, public consumption expenditure, public development expenditure, and market activities. It is revealed that public development expenditure stimulates private investment, whereas public consumption expenditure is detrimental to private investment. The preferred dynamic private fixed investment function confirms that in the short run, public development expenditure enhances private investment. Moreover, macroeconomic instability and uncertainty depresses private investment in Pakistan

The Predictability of International Mutual Funds

The predictability of the US-based international mutual fund returns has received renewed consideration in recent academic studies. This dissertation extends recent research by exploring the 2,479 daily return observations covering the period from January 4, 1993 to October 31, 2002 for all categories of international mutual funds. This exploration splits the sample, uses the initial sub-sample to investigate return patterns of international mutual funds and develops trading rules based on the predictable return patterns, and tests those rules on the holdout sample. The empirical findings suggest that smart investors may earn higher riskadjusted returns by following daily dynamic trading strategies. The excess returns earned by investors are statistically and economically significant, irrespective of load or no-load mutual funds and even in the presence of various exchange restrictions and regulations