Crypt Edge Detection Using PSO,Label Matrix And BI-Cubic Interpolation For Better Iris Recognition(PSOLB)

Iris identification is an automatic system to recognise an individual in biometric applications.Human iris is an internal organ that can be accessed from external view of the body.Moreover,the structure of the iris is formed in a complete random manner and has unique features such as crypts,furrows,collarets,pupil,freckles, and blotches.In fact, no iris patterns are the same.The iris structure is stable which it means the location of the iris features is permanent at certain point.Nevertheless,the shape of iris features changes slowly due to several factors which include aging,surgery,growth,emotion and dietary habits. Recently,there has been renewed interest in iris features detection.Gabor filter,cross entrophy, upport vector,and canny edge detection are methods which produce iris codes in binary codes representation.However,problems have occurred in iris recognition since low quality iris images are created due to blurriness,indoor or outdoor settings, and camera specifications.Failure was detected in 21% of the intra-class comparisons cases which were taken between intervals of three and six months intervals.However,the mismatch or False Rejection Rate (FRR) in iris recognition is still alarmingly high.Higher FRR also causes the value of Equal Error Rate (EER) to be high.The main reason for high values of FRR and EER is that there are changes in the iris due to the amount of light entering into the iris that changes the size of the unique features in the iris.One of the solutions to this problem is by finding any technique or algorithm to automatically detect the unique features.Therefore a new model is introduced which is called Crypt Edge Detection which combines PSO,Label Matrix,and Bi-Cubic Interpolation for Iris Recognition (PSOLB) to solve the problem of detection in iris features.In this research, the unique feature known as crypts has been chosen due to its accessibility and sustainability.Feature detection is performed using particle swarm optimisation (PSO) as an algorithm to select the best iris texture among the unique iris features by finding the pixel values according to the range of selected features.Meanwhile, label matrix will detect the edge of the crypt and the bi-cubic interpolation technique creates sharp and refined crypt images.In order to evaluate the proposed approach,FAR and FRR are measured using Chinese Academy of Sciences' Institute of Automation (CASIA) database for high quality images.For CASIA version 3 image databases, the crypt feature shows that the result of FRR is 21.83% and FAR is 78.17%.The finding from the experiment indicates that by using the PSOLB,the intersection between FAR and FRR produces the Equal Error Rate (EER) with 0.28%,which indicated that equal error rate is lower than previous value, which is 0.38%.Thus,there are advantages from using PSOLB as it has the ability to adapt with unique iris features and use information in iris template features to determine the user.The outcome of this new approach is to reduce the EER rates since lower EER rates can produce accurate detection of unique features.In conclusion,the contribution of PSOLB brings an innovation to the extraction process in the biometric technology and is beneficial to the communities

Particle Swarm Optimisation Prediction Model for Surface Roughness

Acrylic sheet is a crystal clear (with transparency equal to optical glass), lightweight material having outstanding weather ability, high impact resistance, good chemical resistance, and excellent thermo-formability and machinability. This paper develops the artificial intelligent model using partial swarm optimization (PSO) to predict the optimum surface roughness when cutting acrylic sheets with laser beam cutting (LBC). Response surface method (RSM) was used to minimize the number of experiments. The effect of cutting speed, material thickness, gap of tip and power towards surface roughness were investigated. It was found that the surface roughness is significantly affected by the tip distance followed by the power requirement, cutting speed and material thickness. Surface roughness becomes larger when using low power, tip distance and material thickness. Combination of low cutting speed, high power, tip distance and material distance produce fine surface roughness. Some defects were found in microstructure such as burning, melting and wavy surface. The optimized parameters by PSO are cutting speed (2600 pulse/s), tip distance (9.70 mm), power (95%) and material thickness (9 mm) which produce roughness around 0.0129 µm

A Robust Approach to Optimal Matched Filter Design in Ultrasonic Non-Destructive Evaluation (NDE)

The matched filter was demonstrated to be a powerful yet efficient technique to enhance defect detection and imaging in ultrasonic non-destructive evaluation (NDE) of coarse grain materials, provided that the filter was properly designed and optimized. In the literature, in order to accurately approximate the defect echoes, the design utilized the real excitation signals, which made it time consuming and less straightforward to implement in practice. In this paper, we present a more robust and flexible approach to optimal matched filter design using the simulated excitation signals, and the control parameters are chosen and optimized based on the real scenario of array transducer, transmitter-receiver system response, and the test sample, as a result, the filter response is optimized and depends on the material characteristics. Experiments on industrial samples are conducted and the results confirm the great benefits of the method

Secure and robust multi-constrained QoS aware routing algorithm for VANETs

Secure QoS routing algorithms are a fundamental part of wireless networks that aim to provide services with QoS and security guarantees. In Vehicular Ad hoc Networks (VANETs), vehicles perform routing functions, and at the same time act as end-systems thus routing control messages are transmitted unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on the routing process, and manipulation of the routing control messages. In this paper, we propose a novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ the Ant Colony Optimisation (ACO) technique to compute feasible routes in VANETs subject to multiple QoS constraints determined by the data traffic type. Moreover, we extend the VANET-oriented Evolving Graph (VoEG) model to perform plausibility checks on the exchanged routing control messages among vehicles. Simulation results show that the QoS can be guaranteed while applying security mechanisms to ensure a reliable and robust routing service

Numerical modelling and condition assessment of timber utility poles using stress wave techniques

University of Technology Sydney. Faculty of Engineering and Information Technology.Timber utility poles are traditionally used for electricity and telecommunication distribution and represent a significant part of the infrastructure for electricity distribution and communication networks in Australia and New Zealand. Nearly 7 million timber poles are in service and about $40-$50 million is spent annually on their maintenance and asset management. To prevent the ageing poles from collapse, about 300,000 electricity poles are replaced in the Eastern States of Australia every year. However, up to 80% of the replaced poles are still in a very good condition (Nguyen et al., 2004). Therefore, huge natural resources and money is wasted. Accordingly, a reliable non-destructive evaluation technique is essential for the condition assessment of timber poles/piles to ensure public safety, operational efficiency and to reduce the maintenance cost. Several non-destructive testing (NDT) methods based on stress wave propagation have been used in practice for the condition assessment of timber poles. However, stress wave propagation in timber poles especially with the effect of soil embedment coupled with unknown pole conditions below ground line (such as deterioration, moisture etc.) is complicated, and therefore it hindered the successful application of these NDT methods for damage identification of timber poles. Moreover, some stress wave based NDT methods are often based on over-simplified assumptions and thus fail to deliver reliable results. In the presented study, in order to gain an in-depth understanding of the propagation of stress waves in damaged poles and to develop an effective damage detection method, a solid numerical study of wave behaviour is undertaken and novel wavelet packet energy (WPE) method is investigated for damage identification. Numerical studies utilises finite element (FE) models to track the wave propagation behaviour characteristics considering different boundary conditions, material properties as well as impact and sensing locations. WPE is a sensitive indicator for structural damage and has been used for damage detection in various types of structures. This thesis presents a comprehensive investigation on the novel use of WPE for damage identification in timber utility poles using FE models. The research study comprises several aspects of investigations such as a comparative study between 2D and 3D models, a sensitivity study of mesh density for 2D models, and a study of the novel WPE-based technique for damage classification and detection in timber poles. Support vector machine (SVM) is imported for damage classification and particle swarm optimisation (PSO) is selected to achieve the classification. The results clearly show the effectiveness of the proposed novel WPE based damage identification technique. Damage prediction based on optimisation procedure is also carried out in this thesis. Several numerical models with different damage conditions are created and the damage size is predicted according to optimisation procedure based on information from sample damaged model. Genetic algorithm and artificial fish swarm algorithm are used as optimisation algorithms and the comparative study is conducted based on the prediction results. The influence of damage on the strength of timber utility poles is also studied in this thesis. The damage conditions are changes in diameter, length as well as location. Wind is considered as a main reason to cause the collapse of timber utility poles in this research. Wind load is defined based on Australian standards and the Ausgrid manual, and the corresponding stress is calculated through FE analysis. According to this analysis, it can be found that under specific damage conditions, some small damage may cause collapse; however, for certain conditions, the timber poles can still be safe even when large damage exists. In conclusion, a novel WPE based damage detection method has been successfully developed to address the limitations of existing methods for condition assessment of timber utility poles. The numerical verification has shown the method is effective for identification of the classification and severity of damage

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

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature