Platinum group element mineralization at Musongati (Burundi) : concentration and Pd-Rh distribution in pentlandite

The mafic-ultramafic intrusions of the Karagwe-Ankole belt in Burundi are considered as a new potential source for platinum group elements (PGE). The intrusions have mainly been studied for their PGE potential with regard to PGE concentration, but the mineralogical distribution of PGE has not been examined to the same level. This study focuses on the Pd and Rh distribution in pentlandite of ultramafic rocks of the Musongati layered intrusion. The results are based on whole rock and pentlandite analyses which were incorporated into a mass balance. Palladium proportions in pentlandite vary between 4 and 69%. Rhodium is present in proportions ranging from 1-39% in pentlandite. Other PGE distributions could not be determined in pentlandite due to concentrations below detection limits. The results from this study demonstrate that Pd and Rh are hosted by sulfides since sulfur saturation of the magma occurred early on, perhaps before or simultaneously with the precipitation of silicate minerals. Based on these findings, a preliminary model for the mineralization of PGE in the Musongati intrusion is proposed

A MEMORY EFFICIENT HARDWARE BASED PATTERN MATCHING AND PROTEIN ALIGNMENT SCHEMES FOR HIGHLY COMPLEX DATABASES

Protein sequence alignment to find correlation between different species, or genetic mutations etc. is the most computational intensive task when performing protein comparison. To speed-up the alignment, Systolic Arrays (SAs) have been used. In order to avoid the internal-loop problem which reduces the performance, pipeline interleaving strategy has been presented. This strategy is applied to an SA for Smith Waterman (SW) algorithm which is an alignment algorithm to locally align two proteins. In the proposed system, the above methodology has been extended to implement a memory efficient FPGA-hardware based Network Intrusion Detection System (NIDS) to speed up network processing. The pattern matching in Intrusion Detection Systems (IDS) is done using SNORT to find the pattern of intrusions. A Finite State Machine (FSM) based Processing Elements (PE) unit to achieve minimum number of states for pattern matching and bit wise early intrusion detection to increase the throughput by pipelining is presented

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated