MonoSLAM: Real-time single camera SLAM

Published versio

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Improving Wearable Activity Recognition via Fusion of Multiple Equally-Sized Data Subwindows

Ponencia de la conferencia "15th International Work-Conference on Artificial Neural Networks, IWANN 2019; Gran Canaria; Spain; 12 June 2019 through 14 June 2019"The automatic recognition of physical activities typically involves various signal processing and machine learning steps used to transform raw sensor data into activity labels. One crucial step has to do with the segmentation or windowing of the sensor data stream, as it has clear implications on the eventual accuracy level of the activity recogniser. While prior studies have proposed specific window sizes to generally achieve good recognition results, in this work we explore the potential of fusing multiple equally-sized subwindows to improve such recognition capabilities. We tested our approach for eight different subwindow sizes on a widely-used activity recognition dataset. The results show that the recognition performance can be increased up to 15% when using the fusion of equally-sized subwindows compared to using a classical single window

A Multifaceted Approach to Covert Attention Brain-Computer Interfaces

Over the last years, brain-computer interfaces (BCIs) have shown their value for assistive technology and neurorehabilitation. Recently, a BCI-approach for the rehabilitation of hemispatial neglect has been proposed on the basis of covert visuospatial attention (CVSA). CVSA is an internal action which can be described as shifting one's attention to the visual periphery without moving the actual point of gaze. Such attention shifts induce a lateralization in parietooccipital blood flow and oscillations in the so-called alpha band (8-14 Hz), which can be detected via electroencephalography (EEG), magnetoencephalography (MEG) or functional magnetic resonance imaging (fMRI). Previous studies have proven the technical feasibility of using CVSA as a control signal for BCIs, but unfortunately, these BCIs could not provide every subject with sufficient control. The aim of this thesis was to investigate the possibility of amplifying the weak lateralization patterns in the alpha band - the main reason behind insufficient CVSA BCI performance. To this end, I have explored three different approaches that could lead to better performing and more inclusive CVSA BCI systems. The first approach illuminated the changes in the behavior and brain patterns by closing the loop between subject and system with continuous real-time feedback at the instructed locus of attention. I could observe that even short (20 minutes) stretches of real-time feedback have an effect on behavioral correlates of attention, even when the changes observed in the EEG remained less conclusive. The second approach attempted to complement the information extracted fromthe EEG signal with another sensing modality that could provide additional information about the state of CVSA. For this reason, I firstly combined functional functional near-infrared spectroscopy (fNIRS) with EEG measurements. The results showed that, while the EEG was able to pick up the expected lateralization in the alpha band, the fNIRS was not able to reliably image changes in blood circulation in the parietooccipital cortex. Secondly, I successfully combined data from the EEG with measures of pupil size changes, induced by a high illumination contrast between the covertly attended target regions, which resulted in an improved BCI decoding performance. The third approach examined the option of using noninvasive electrical brain stimulation to boost the power of the alpha band oscillations and therefore render the lateralization pattern in the alpha band more visible compared to the background activity. However, I could not observe any impact of the stimulation on the ongoing alpha band power, and thus results of the subsequent effect on the lateralization remain inconclusive. Overall, these studies helped to further understand CVSA and lay out a useful basis for further exploration of the connection between behavior and alpha power oscillations in CVSA tasks, as well as for potential directions to improve CVSA-based BCIs

Activity related biometrics for person authentication

One of the major challenges in human-machine interaction has always been the development of such techniques that are able to provide accurate human recognition, so as to other either personalized services or to protect critical infrastructures from unauthorized access. To this direction, a series of well stated and efficient methods have been proposed mainly based on biometric characteristics of the user. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the systems but also the intrusiveness of the collecting methods. The current thesis deals with the investigation of novel, activity-related biometric traits and their potential for multiple and unobtrusive authentication based on the spatiotemporal analysis of human activities. In particular, it starts with an extensive bibliography review regarding the most important works in the area of biometrics, exhibiting and justifying in parallel the transition that is performed from the classic biometrics to the new concept of behavioural biometrics. Based on previous works related to the human physiology and human motion and motivated by the intuitive assumption that different body types and different characters would produce distinguishable, and thus, valuable for biometric verification, activity-related traits, a new type of biometrics, the so-called prehension biometrics (i.e. the combined movement of reaching, grasping activities), is introduced and thoroughly studied herein. The analysis is performed via the so-called Activity hyper-Surfaces that form a dynamic movement-related manifold for the extraction of a series of behavioural features. Thereafter, the focus is laid on the extraction of continuous soft biometric features and their efficient combination with state-of-the-art biometric approaches towards increased authentication performance and enhanced security in template storage via Soft biometric Keys. In this context, a novel and generic probabilistic framework is proposed that produces an enhanced matching probability based on the modelling of the systematic error induced during the estimation of the aforementioned soft biometrics and the efficient clustering of the soft biometric feature space. Next, an extensive experimental evaluation of the proposed methodologies follows that effectively illustrates the increased authentication potential of the prehension-related biometrics and the significant advances in the recognition performance by the probabilistic framework. In particular, the prehension biometrics related biometrics is applied on several databases of ~100 different subjects in total performing a great variety of movements. The carried out experiments simulate both episodic and multiple authentication scenarios, while contextual parameters, (i.e. the ergonomic-based quality factors of the human body) are also taken into account. Furthermore, the probabilistic framework for augmenting biometric recognition via soft biometrics is applied on top of two state-of-art biometric systems, i.e. a gait recognition (> 100 subjects)- and a 3D face recognition-based one (~55 subjects), exhibiting significant advances to their performance. The thesis is concluded with an in-depth discussion summarizing the major achievements of the current work, as well as some possible drawbacks and other open issues of the proposed approaches that could be addressed in future works.Open Acces

Technology 2002: The Third National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2002 Conference and Exposition, December 1-3, 1992, Baltimore, MD. Volume 2 features 60 papers presented during 30 concurrent sessions

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks