Combinatorial Channel Signature Modulation for Wireless ad-hoc Networks

In this paper we introduce a novel modulation and multiplexing method which facilitates highly efficient and simultaneous communication between multiple terminals in wireless ad-hoc networks. We term this method Combinatorial Channel Signature Modulation (CCSM). The CCSM method is particularly efficient in situations where communicating nodes operate in highly time dispersive environments. This is all achieved with a minimal MAC layer overhead, since all users are allowed to transmit and receive at the same time/frequency (full simultaneous duplex). The CCSM method has its roots in sparse modelling and the receiver is based on compressive sampling techniques. Towards this end, we develop a new low complexity algorithm termed Group Subspace Pursuit. Our analysis suggests that CCSM at least doubles the throughput when compared to the state-of-the art.Comment: 6 pages, 7 figures, to appear in IEEE International Conference on Communications ICC 201

A novel approach for the hardware implementation of a PPMC statistical data compressor

This thesis aims to understand how to design high-performance compression algorithms suitable for hardware implementation and to provide hardware support for an efficient compression algorithm. Lossless data compression techniques have been developed to exploit the available bandwidth of applications in data communications and computer systems by reducing the amount of data they transmit or store. As the amount of data to handle is ever increasing, traditional methods for compressing data become· insufficient. To overcome this problem, more powerful methods have been developed. Among those are the so-called statistical data compression methods that compress data based on their statistics. However, their high complexity and space requirements have prevented their hardware implementation and the full exploitation of their potential benefits. This thesis looks into the feasibility of the hardware implementation of one of these statistical data compression methods by exploring the potential for reorganising and restructuring the method for hardware implementation and investigating ways of achieving efficient and effective designs to achieve an efficient and cost-effective algorithm. [Continues.

Compressed materialised views of semi-structured data

Query performance issues over semi-structured data have led to the emergence of materialised XML views as a means of restricting the data structure processed by a query. However preserving the conventional representation of such views remains a significant limiting factor especially in the context of mobile devices where processing power, memory usage and bandwidth are significant factors. To explore the concept of a compressed materialised view, we extend our earlier work on structural XML compression to produce a combination of structural summarisation and data compression techniques. These techniques provide a basis for efficiently dealing with both structural queries and valuebased predicates. We evaluate the effectiveness of such a scheme, presenting results and performance measures that show advantages of using such structures

Gbit/second lossless data compression hardware

This thesis investigates how to improve the performance of lossless data compression hardware as a tool to reduce the cost per bit stored in a computer system or transmitted over a communication network. Lossless data compression allows the exact reconstruction of the original data after decompression. Its deployment in some high-bandwidth applications has been hampered due to performance limitations in the compressing hardware that needs to match the performance of the original system to avoid becoming a bottleneck. Advancing the area of lossless data compression hardware, hence, offers a valid motivation with the potential of doubling the performance of the system that incorporates it with minimum investment. This work starts by presenting an analysis of current compression methods with the objective of identifying the factors that limit performance and also the factors that increase it. [Continues.

The Big Picture: Using Desktop Imagery for Detection of Insider Threats

The insider threat is one of the most difficult problems in information security. Prior research addresses its detection by using machine learning techniques to profile user behavior. User behavior is represented as low level system events, which do not provide sufficient contextual information about the user\u27s intentions, and lead to high error rates. Our system uses video of a user\u27s sessions as the representation of their behavior, and detects moments during which they perform sensitive tasks. Analysis of the video is accomplished using OCR, scene detection algorithms, and basic text classification. The system outputs the results to a web interface, and our results show that using desktop imagery is a viable alternative to using system calls for insider threat detection

Accelerated Probabilistic Learning Concept for Mining Heterogeneous Earth Observation Images

We present an accelerated probabilistic learning concept and its prototype implementation for mining heterogeneous Earth observation images, e.g., multispectral images, synthetic aperture radar (SAR) images, image time series, or geographical information systems (GIS) maps. The system prototype combines, at pixel level, the unsupervised clustering results of different features, extracted from heterogeneous satellite images and geographical information resources, with user-defined semantic annotations in order to calculate the posterior probabilities that allow the final probabilistic searches. The system is able to learn different semantic labels based on a newly developed Bayesian networks algorithm and allows different probabilistic retrieval methods of all semantically related images with only a few user interactions. The new algorithm reduces the computational cost, overperforming existing conventional systems, under certain conditions, by several orders of magnitude. The achieved speed-up allows the introduction of new feature models improving the learning capabilities of knowledge-driven image information mining systems and opening them to Big Data environment

GRAPH BASESD WORD SENSE DISAMBIGUATION FOR CLINICAL ABBREVIATIONS USING APACHE SPARK

Identification of the correct sense for an ambiguous word is one of the major challenges for language processing in all domains. Word Sense Disambiguation is the task of identifying the correct sense of an ambiguous word by referencing the surrounding context of the word. Similar to the narrative documents, clinical documents suffer from ambiguity issues that impact automatic extraction of correct sense from the document. In this project, we propose a graph-based solution based on an algorithm originally implemented by Osmar R. Zaine et al. for word sense disambiguation specifically focusing on clinical text. The algorithm makes use of proposed UMLS Metathesaurus as its source of knowledge. As an enhancement to the existing implementation of the algorithm, this project uses Apache Spark - A Big Data Technology for cluster based distributed processing and performance optimization