Future-generation wireless and mobile networks are expected to support a panoply of multimedia services, ranging from voice to video data. There is also a de facto "anytime anywhere" mentality that reliable communications should be ubiquitously guaranteed, irrespective of temporal or geographical constraints. However, the implicit catch is that these specifications should be achieved with only minimal infrastructure expansion or cost increases. In this thesis, various signal processing methodologies conducive to attaining these goals are presented. First, a system model that takes into account the time-varying nature of the mobile environment is developed. To this end, a mathematically tractable basis-expansion model (BEM) of the communication channel, augmented with multiple-state characterization, is proposed. In the context of the developed system model, strategies for enhancing the quality of service (QoS), while maintaining resource efficiency, are then studied. Specifically, dynamic channel tracking, adaptive modulation and coding, interpolation and random sampling, and spatiotemporal processing are examined as enabling solutions. Next, the question of how to appropriately aggregate these disparate methods is recast as a nonlinear constrained optimization problem. This enables the construction of a flexible framework that can accommodate a wide range of applications, to deliver practical network designs. In particular, the developed methods are well-suited for multi-user communication systems, implemented using spread-spectrum and multi-carrier solutions, such as code division multiple access (CDMA) and orthogonal frequency division multiplexing (OFDM). Moreover, privacy and security requirements are increasingly becoming essential aspects of the QoS paradigm in communications. Combined with the advent of novel security technologies, such as biometrics, the conventional communication infrastructure is expected to undergo fundamental modifications to support these new system components and modalities. Therefore, within the same framework for maximizing resource efficiency, several unique signal processing applications in network security using biometrics are also investigated in this thesis. It is shown that a resource allocation approach is equally appropriate, and productive, in delivering efficient and practical key distribution and biometric encryption solutions for secure communications.

Future-generation wireless and mobile networks are expected to support a panoply of multimedia services, ranging from voice to video data. There is also a de facto "anytime anywhere" mentality that reliable communications should be ubiquitously guaranteed, irrespective of temporal or geographical constraints. However, the implicit catch is that these specifications should be achieved with only minimal infrastructure expansion or cost increases. In this thesis, various signal processing methodologies conducive to attaining these goals are presented. First, a system model that takes into account the time-varying nature of the mobile environment is developed. To this end, a mathematically tractable basis-expansion model (BEM) of the communication channel, augmented with multiple-state characterization, is proposed. In the context of the developed system model, strategies for enhancing the quality of service (QoS), while maintaining resource efficiency, are then studied. Specifically, dynamic channel tracking, adaptive modulation and coding, interpolation and random sampling, and spatiotemporal processing are examined as enabling solutions. Next, the question of how to appropriately aggregate these disparate methods is recast as a nonlinear constrained optimization problem. This enables the construction of a flexible framework that can accommodate a wide range of applications, to deliver practical network designs. In particular, the developed methods are well-suited for multi-user communication systems, implemented using spread-spectrum and multi-carrier solutions, such as code division multiple access (CDMA) and orthogonal frequency division multiplexing (OFDM). Moreover, privacy and security requirements are increasingly becoming essential aspects of the QoS paradigm in communications. Combined with the advent of novel security technologies, such as biometrics, the conventional communication infrastructure is expected to undergo fundamental modifications to support these new system components and modalities. Therefore, within the same framework for maximizing resource efficiency, several unique signal processing applications in network security using biometrics are also investigated in this thesis. It is shown that a resource allocation approach is equally appropriate, and productive, in delivering efficient and practical key distribution and biometric encryption solutions for secure communications.

With an increasing emphasis on personal privacy, security, and convenience, the security of identification system is an important issue nowadays. In this thesis, two intelligent identification systems, laser speckle image identification system and laser-based finger biometric system, are proposed to perform superior solutions for identification applications. In laser speckle image identification system, we investigated the characteristics of laser speckle as well as proposed an appropriate algorithm to establish this system. The proposed algorithm is a coarse-to-fine process which identifies laser speckle images systematically. In laser-based finger biometric system, a new biometric approach is described to proceed personal identification using a scanner with a low power laser scans across the surface of the finger and continuously recording the reflected intensity at a fixed position. Experimental results show that the recognition rates of the proposed system are both 100%.

In this thesis, we proposed an innovation ideal that is employment of laser to extract finger feature, and constructed laser speckle recognition systems for this kind of feature. When projecting laser on the object surface, the speckle could be obtained to represent the characteristic of object surface by collecting scattered light. Two measurement of scattered light was adopted. First is laser signal recording the strength of scattered light when laser scan across the finger. The second is laser speckle image which is demonstrated when projecting the laser on the fingerprint and simultaneously collecting the scatter light by CCD. We proposed two recognition systems for laser signal and laser speckle. Besides, the proposed laser speckle fingerprint recognition system combines biometric detection, it can accurately distinguish biometric and non-biometric speckle. Experimental results demonstrate that proposed laser speckle recognition systems are feasible and with excellent ability of identity verification.

Thesis (M.S.)--West Virginia University, 2000. / Title from document title page. Document formatted into pages; contains viii, 94 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 88-89).

Thesis (M.Com.)(Informatics)--University of Pretoria, 2003. / Includes bibliographical references.

No description available.

Biometric identification is a pattern recognition based classification system that recognizes an individual by determining its authenticity using a specific physiological or behavioural characteristic (biometric). In contrast to number of commercially available biometric systems for human recognition in the market today, there is no such a biometric system for plant recognition, even though they have many characteristics that are uniquely identifiable at a species level. The goal of the study was to develop a plant species biometric using both global and local features of leaf images. In recent years, various approaches have been proposed for characterizing leaf images. Most of them were based on a global representation of leaf peripheral with Fourier descriptors, polygonal approximations and centroid-contour distance curve. Global representation of leaf shapes does not provide enough information to characterise species uniquely since different species of plants have similar leaf shapes. Others were based on leaf vein extraction using intensity histograms and trained artificial neural network classifiers. Leaf venation extraction is not always possible since it is not always visible in photographic images. This study proposed a novel approach of leaf identification based on feature hierarchies. First, leaves were sorted by their overall shape using shape signatures. Then this sorted list was pruned based on global and local shape descriptors. The consequent biometric was tested using a corpus of 200 leaves from 40 common New Zealand broadleaf plant species which encompass all categories of local information of leaf peripherals. Two novel shape signatures (full-width to length ratio distribution and half-width to length ratio distribution) were proposed and biometric vectors were constructed using both novel shape signatures, complex-coordinates and centroid-distance for comparison. Retrievals were compared and the biometric vector based on full-width to length ratio distribution was found to be the best classifier. Three types of local information of the leaf peripheral (leaf margin coarseness, stem length to blade length ratio and leaf tip curvature) and the global shape descriptor, leaf compactness, were used to prune the list further. The proposed biometric was able to successfully identify the correct species for 37 test images (out of 40). The proposed biometric identified all the test images (100%) correctly if two species were returned compared to the low recall rates of Wang et al. (2003) (30%, if 10 images were returned) and Ye et al. (2004) (71.4%, if top 5 images were returned). The biometric can be strengthened by adding reference images of new species to the database, or by adding more reference images of existing species when the reference images are not enough to cover the leaf shapes.

With human society becoming increasingly computerised, the use of biometrics to automatically establish the identity of an individual is of great interest in a wide variety of applications. Facial appearance is an appealing biometric, on account of its relatively non-intrusive nature. As such, automated face recognition systems have been the subject of much research in recent years. This dissertation describes the development of a fully automatic face recognition system, and provides an analysis of its performance under various di erent operating conditions, in comparison with results published in prior literature. In addition to giving a detailed description of the mathematical underpinnings of the techniques used by the system, we discuss the practical considerations involved in implementing the described techniques. The system presented here uses the eigenface approach to representing facial features. A number of di erent recognition techniques have been implemented and evaluated. These include a number of variants of the original eigenface technique proposed by Turk and Pentland, as well as a related technique based on the probabilistic approach of Moghaddam et al. Due to the wide range of datasets used to evaluate face recognition systems in the literature, it is di cult to reliably compare the performance of di erent systems. The system described here has been tested with datasets encompassing a wide range of di erent conditions, allowing us to draw conclusions about how the characteristics of the test data a ect the results that are obtained. The performance of this system is comparable to other eigenface-based systems documented in the literature, achieving success rates in the region of 85% for large datasets under controlled conditions. However, performance was observed to degrade signi cantly when testing with more free-form images; in particular, the e ects of ageing on facial appearance were noted to cause problems for the system. This suggests that the matter of ageing is still a fruitful direction for further research. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2012.

Thesis (MScIng)--University of Stellenbosch, 2007. / Bibliography. Also available via the Internet.