Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Towards hybrid molecular simulations

In many biology, chemistry and physics applications molecular simulations can be used to study material and process properties. The level of detail needed in such simulations depends on the application. In some cases quantum mechanical simulations are indispensable. However, traditional ab-initio methods, usually employing plane waves or a linear combination of atomic orbitals as a basis, are extremely expensive in terms of computational as well as memory requirements. The well-known fact that electronic wave functions vary much more rapidly near the atomic nuclei than in inter-atomic regions calls for a multi-resolution approach, allowing one to use low resolution and to add extra resolution only in those regions where necessary, so limiting the costs. This is provided by an alternative basis formed of wavelets. Using such a wavelet basis, a method has been developed for solving electronic structure problems that has been applied successfully to 2D quantum dots and 3D molecular systems. In other cases, it suffices to use effective potentials to describe the atomic interaction instead of the use of the electronic structure, enabling the simulation of larger systems. Molecular dynamics simulations with such effective potentials have been used for a systematic study of surface wettability influence on particle and heat flow in nanochannels, showing that the effects at the solid-gas interface are crucial for the behavior of the whole nanochannel. Again in other cases even coarse grained models can be used where the average behavior of several atoms is combined into a single particle. Such a model, refraining from as much detail as possible while maintaining realistic behavior, has been developed for lipids and with this model the dynamics of membranes and vesicle formation have been studied in detail. A disadvantage of molecular dynamics simulations with effective potentials is that no reactions are possible. Therefore a new method has been developed, where molecular dynamics is coupled with stochastic reactions. Using this method, both unilamellar and multilamellar vesicle formation, and vesicle growth, bursting, and healing are shown. Still larger systems can be simulated using other methods, like the direct simulation Monte Carlo method. However, as shown for nanochannels, these methods are not always accurate enough. But, exploiting again that the finest level of detail is often only needed in part of the domain, a hybrid method has been developed coupling molecular dynamics, where needed for accuracy, and direct simulation Monte Carlo, where possible in order to speed up the calculation. Further development of such hybrid simulations will further increase molecular simulation’s scientific role

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions

This dissertation is the accumulation of the application of adaptive, empirical learning-based methods in the study and characterization of the El Niño Southern Oscillation. In specific, it focuses on ENSO’s effects on rainfall and drought conditions in two major regions shown to be linked through the strength of the dependence of their climate on ENSO: 1) the southern Pacific Coast of the United States and 2) the Nile River Basin. In these cases, drought and rainfall are tied to deep economic and social factors within the region. The principal aim of this dissertation is to establish, with scientific rigor, an epistemological and foundational justification of adaptive learning models and their utility in the both the modeling and understanding of a wide-reaching climate phenomenon such as ENSO. This dissertation explores a scientific justification for their proven accuracy in prediction and utility as an aide in deriving a deeper understanding of climate phenomenon. In the application of drought forecasting for Southern California, adaptive learning methods were able to forecast the drought severity of the 2015-2016 winter with greater accuracy than established models. Expanding this analysis yields novel ways to analyze and understand the underlying processes driving California drought. The pursuit of adaptive learning as a guiding tool would also lead to the discovery of a significant extractable components of ENSO strength variation, which are used with in the analysis of Nile River Basin precipitation and flow of the Nile River, and in the prediction of Nile River yield to p=0.038. In this dissertation, the duality of modeling and understanding is explored, as well as a discussion on why adaptive learning methods are uniquely suited to the study of climate phenomenon like ENSO in the way that traditional methods lack. The main methods explored are 1) differentiable Programming, as a means of construction of novel self-learning models through which the meaningfulness of parameters arises from emergent phenomenon and 2) empirical decompositions, which are driven by an adaptive rather than rigid component extraction principle, are explored further as both a predictive tool and as a tool for gaining insight and the construction of models

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Signal processing and machine learning methods with applications in EEG-based emotion recognition

Automatic emotion recognition has become increasingly popular, with applications in marketing, advertising, e-learning, entertainment, and more. Currently, the majority of automated emotion recognition is performed using facial expressions, body language, and speech intonation patterns. In recent years, using brain signals has become increasing popular. Being able to understand and analyse brain signals is beneficial in many applications. The goal of this thesis is to develop an effective method for extracting and representing EEG signals associated with human emotions, and to develop a robust classifier using machine learning tools for emotion recognition. The thesis aims to address the common problems related to the EEG-based emotion recognition datasets, including dealing with small sample sizes, low signal-to-noise-ratio and high dimensional data. The contributions of this thesis lie in the proposed subject-dependent and subject-independent EEG-based emotion recognition frameworks. These frameworks are shown to accurately perform two-class classification as well as multi-class classification. In addition, a novel mutual information based signal reduction algorithm is introduced, aiming to increase the accuracy of EEG-based emotion recognition when the duration of the recording due to chosen stimuli is long. Furthermore, Gaussian Process classification is introduced for the purpose of EEG-based emotion recognition. This classifier is combined with the subject-dependent and subject-independent emotion recognition schemes and is shown to increase the accuracy when compared to the previous commonly used classifiers. By using publicly available EEG datasets, the proposed novel frameworks are evaluated and shown to improve the EEG-based emotion recognition when compared against state-of-the-art methods. In addition, different signal processing methods suitable for EEG-based emotion recognition are introduced, explored, and analysed. An in-depth comparison of different feature extraction, feature selection, and classification methods is given using the proposed subject-dependent and subject-independent emotion recognition schemes

Discriminative learning with application to interactive facial image retrieval

The amount of digital images is growing drastically and advanced tools for searching in large image collections are therefore becoming urgently needed. Content-based image retrieval is advantageous for such a task in terms of automatic feature extraction and indexing without human labor and subjectivity in image annotations. The semantic gap between high-level semantics and low-level visual features can be reduced by the relevance feedback technique. However, most existing interactive content-based image retrieval (ICBIR) systems require a substantial amount of human evaluation labor, which leads to the evaluation fatigue problem that heavily restricts the application of ICBIR. In this thesis a solution based on discriminative learning is presented. It extends an existing ICBIR system, PicSOM, towards practical applications. The enhanced ICBIR system allows users to input partial relevance which includes not only relevance extent but also relevance reason. A multi-phase retrieval with partial relevance can adapt to the user's searching intention in a from-coarse-to-fine manner. The retrieval performance can be improved by employing supervised learning as a preprocessing step before unsupervised content-based indexing. In this work, Parzen Discriminant Analysis (PDA) is proposed to extract discriminative components from images. PDA regularizes the Informative Discriminant Analysis (IDA) objective with a greatly accelerated optimization algorithm. Moreover, discriminative Self-Organizing Maps trained with resulting features can easily handle fuzzy categorizations. The proposed techniques have been applied to interactive facial image retrieval. Both a query example and a benchmark simulation study are presented, which indicate that the first image depicting the target subject can be retrieved in a small number of rounds