Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation

A novel approach to hardware fault tolerance is demonstrated that takes inspiration from the human immune system as a method of fault detection. The human immune system is a remarkable system of interacting cells and organs that protect the body from invasion and maintains reliable operation even in the presence of invading bacteria or viruses. This paper seeks to address the field of electronic hardware fault tolerance from an immunological perspective with the aim of showing how novel methods based upon the operation of the immune system can both complement and create new approaches to the development of fault detection mechanisms for reliable hardware systems. In particular, it is shown that by use of partial matching, as prevalent in biological systems, high fault coverage can be achieved with the added advantage of reducing memory requirements. The development of a generic finite-state-machine immunization procedure is discussed that allows any system that can be represented in such a manner to be "immunized" against the occurrence of faulty operation. This is demonstrated by the creation of an immunized decade counter that can detect the presence of faults in real tim

JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition

This paper proposes a novel algorithm to reassemble an arbitrarily shredded image to its original status. Existing reassembly pipelines commonly consist of a local matching stage and a global compositions stage. In the local stage, a key challenge in fragment reassembly is to reliably compute and identify correct pairwise matching, for which most existing algorithms use handcrafted features, and hence, cannot reliably handle complicated puzzles. We build a deep convolutional neural network to detect the compatibility of a pairwise stitching, and use it to prune computed pairwise matches. To improve the network efficiency and accuracy, we transfer the calculation of CNN to the stitching region and apply a boost training strategy. In the global composition stage, we modify the commonly adopted greedy edge selection strategies to two new loop closure based searching algorithms. Extensive experiments show that our algorithm significantly outperforms existing methods on solving various puzzles, especially those challenging ones with many fragment pieces

Bio-inspired approaches for critical infrastructure protection: Application of clonal selection principle for intrusion detection and FACTS placement

In this research, Clonal Selection, an immune system inspired approach, is utilized along with Evolutionary Algorithms to solve complex engineering problems such as Intrusion Detection and optimization of Flexible AC Transmission System (FACTS) device placement in a power grid. The clonal selection principle increases the strength of good solutions and alters their properties to find better solutions in a problem space. A special class of evolutionary algorithms that utilizes the clonal selection principle to guide its heuristic search process is termed Clonal EA. Clonal EAs can be used to solve complex pattern recognition and function optimization problems, which involve searching an enormous problem space for a solution. Intrusion Detection is modeled, in this research, as a pattern recognition problem wherein efficient detectors are to be designed to detect intrusive behavior. Optimization of FACTS device placement in a power grid is modeled as a function optimization problem wherein optimal placement positions for FACTS devices are to be determined, in order to balance load across power lines. Clonal EAs are designed to implement the solution models. The benefits and limitations of using Clonal EAs to solve the above mentioned problems are discussed and the performance of Clonal EAs is compared with that of traditional evolutionary algorithms and greedy algorithms --Abstract, page iii

Face pose estimation in monocular images

People use orientation of their faces to convey rich, inter-personal information. For example, a person will direct his face to indicate who the intended target of the conversation is. Similarly in a conversation, face orientation is a non-verbal cue to listener when to switch role and start speaking, and a nod indicates that a person has understands, or agrees with, what is being said. Further more, face pose estimation plays an important role in human-computer interaction, virtual reality applications, human behaviour analysis, pose-independent face recognition, driver s vigilance assessment, gaze estimation, etc. Robust face recognition has been a focus of research in computer vision community for more than two decades. Although substantial research has been done and numerous methods have been proposed for face recognition, there remain challenges in this field. One of these is face recognition under varying poses and that is why face pose estimation is still an important research area. In computer vision, face pose estimation is the process of inferring the face orientation from digital imagery. It requires a serious of image processing steps to transform a pixel-based representation of a human face into a high-level concept of direction. An ideal face pose estimator should be invariant to a variety of image-changing factors such as camera distortion, lighting condition, skin colour, projective geometry, facial hairs, facial expressions, presence of accessories like glasses and hats, etc. Face pose estimation has been a focus of research for about two decades and numerous research contributions have been presented in this field. Face pose estimation techniques in literature have still some shortcomings and limitations in terms of accuracy, applicability to monocular images, being autonomous, identity and lighting variations, image resolution variations, range of face motion, computational expense, presence of facial hairs, presence of accessories like glasses and hats, etc. These shortcomings of existing face pose estimation techniques motivated the research work presented in this thesis. The main focus of this research is to design and develop novel face pose estimation algorithms that improve automatic face pose estimation in terms of processing time, computational expense, and invariance to different conditions

An anomaly-based intrusion detection system based on artificial immune system (AIS) techniques

Two of the major approaches to intrusion detection are anomaly-based detection and signature-based detection. Anomaly-based approaches have the potential for detecting zero-day and other new forms of attacks. Despite this capability, anomaly-based approaches are comparatively less widely used when compared to signature-based detection approaches. Higher computational overhead, higher false positive rates, and lower detection rates are the major reasons for the same. This research has tried to mitigate this problem by using techniques from an area called the Artificial Immune Systems (AIS). AIS is a collusion of immunology, computer science and engineering and tries to apply a number of techniques followed by the human immune system in the field of computing. An AIS-based technique called negative selection is used. Existing implementations of negative selection algorithms have a polynomial worst-case run time for classification, resulting in huge computational overhead and limited practicality. This research implements a theoretical concept and achieves linear classification time. The results from the implementation are compared with that of existing Intrusion Detection Systems

A Modular Approach to Lung Nodule Detection from Computed Tomography Images Using Artificial Neural Networks and Content Based Image Representation

Lung cancer is one of the most lethal cancer types. Research in computer aided detection (CAD) and diagnosis for lung cancer aims at providing effective tools to assist physicians in cancer diagnosis and treatment to save lives. In this dissertation, we focus on developing a CAD framework for automated lung cancer nodule detection from 3D lung computed tomography (CT) images. Nodule detection is a challenging task that no machine intelligence can surpass human capability to date. In contrast, human recognition power is limited by vision capacity and may suffer from work overload and fatigue, whereas automated nodule detection systems can complement expert’s efforts to achieve better detection performance. The proposed CAD framework encompasses several desirable properties such as mimicking physicians by means of geometric multi-perspective analysis, computational efficiency, and the most importantly producing high performance in detection accuracy. As the central part of the framework, we develop a novel hierarchical modular decision engine implemented by Artificial Neural Networks. One advantage of this decision engine is that it supports the combination of spatial-level and feature-level information analysis in an efficient way. Our methodology overcomes some of the limitations of current lung nodule detection techniques by combining geometric multi-perspective analysis with global and local feature analysis. The proposed modular decision engine design is flexible to modifications in the decision modules; the engine structure can adopt the modifications without having to re-design the entire system. The engine can easily accommodate multi-learning scheme and parallel implementation so that each information type can be processed (in parallel) by the most adequate learning technique of its own. We have also developed a novel shape representation technique that is invariant under rigid-body transformation and we derived new features based on this shape representation for nodule detection. We implemented a prototype nodule detection system as a demonstration of the proposed framework. Experiments have been conducted to assess the performance of the proposed methodologies using real-world lung CT data. Several performance measures for detection accuracy are used in the assessment. The results show that the decision engine is able to classify patterns efficiently with very good classification performance

The machine abnormal degree detection method based on SVDD and negative selection mechanism

As is well-known, fault samples are essential for the fault diagnosis and anomaly detection, but in most cases, it is difficult to obtain them. The negative selection mechanism of immune system, which can distinguish almost all nonself cells or molecules with only the self cells, gives us an inspiration to solve the problem of anomaly detection with only the normal samples. In this paper, we introduced the Support Vector Data Description (SVDD) and negative selection mechanism to separate the state space of machines into self, non-self and fault space. To estimate the abnormal level of machines, a function that could calculate the abnormal degree was constructed and its sensitivity change according to the change of abnormal degree was also discussed. At last, Iris-Fisher and ball bearing fault data set were used to verify the effectiveness of this method

X-ray Computed Tomography and image-based modelling of plant, root and soil systems, for better understanding of phosphate uptake

A major constraint to crop growth is the poor bioavailability of edaphic nutrients, especially phosphate (P). Improving the nutrient acquisition efficiency of crops is crucial in addressing pressing global food-security issues arising from increasing world population, reduced fertile land and changes in the climate. Despite the undoubted importance of root architecture and root/soil interactions to nutrient uptake, there is a lack of approaches for quantifying plant roots non-invasively at all scales. Mathematical models have allowed our understanding of root and soil interactions to be improved, but are almost invariably reliant on idealised geometries or virtual root growth models. In order to improve phenotyping of advantageous traits for low-P conditions and improve the accuracy of root growth and uptake models, more sophisticated and robust approaches to in vivo root and soil characterisation are needed. Microfocus X-ray Computed Tomography (?-CT) is a methodology that has shown promise for noninvasive imaging of roots and soil at various scales. However, this potential has not been extended to consideration of either very small (rhizosphere scale) or large (mature root system scale) samples. This thesis combines discovery experiments and method development in order to achieve two primary objectives:• The development of more robust, well-described approaches to root and soil ?-CT imaging. Chapters 2 and 3 explore the potential of clinical contrasting methods in root investigation, and show how careful consideration of imaging parameters combined with development of user invariant image-processing protocol can improve measurement of macro-porous volume fraction, a key soil parameter. • Chapter 4 develops an assay for first-time 3D imaging of root hairs in situ within the rhizosphere. The resulting data is used to parameterise an explicit P uptake model at the hair scale, suggesting a different contribution of hairs to uptake than was predicted using idealised geometries. Chapter 5 then extends the paradigm for root hair imaging and model generation, building a robust, modular workflow for investigating P dynamics in the rhizosphere that can accommodate non-optimal soil-water states