Detection of Freezing of Gait Using Template-Matching-Based Approaches

Every year, injuries associated with fall incidences cause lots of human suffering and assets loss for Parkinson’s disease (PD) patients. Thereinto, freezing of gait (FOG), which is one of the most common symptoms of PD, is quite responsible for most incidents. Although lots of researches have been done on characterized analysis and detection methods of FOG, large room for improvement still exists in the high accuracy and high efficiency examination of FOG. In view of the above requirements, this paper presents a template-matching-based improved subsequence Dynamic Time Warping (IsDTW) method, and experimental tests were carried out on typical open source datasets. Results show that, compared with traditional template-matching and statistical learning methods, proposed IsDTW not only embodies higher experimental accuracy (92%) but also has a significant runtime efficiency. By contrast, IsDTW is far more available in real-time practice applications

An approach for real world data modelling with the 3D terrestrial laser scanner for built environment

Capturing and modelling 3D information of the built environment is a big challenge. A number of techniques and technologies are now in use. These include EDM, GPS, and photogrammetric application, remote sensing and traditional building surveying applications. However, use of these technologies cannot be practical and efficient in regard to time, cost and accuracy. Furthermore, a multi disciplinary knowledge base, created from the studies and research about the regeneration aspects is fundamental: historical, architectural, archeologically, environmental, social, economic, etc. In order to have an adequate diagnosis of regeneration, it is necessary to describe buildings and surroundings by means of documentation and plans. However, at this point in time the foregoing is considerably far removed from the real situation, since more often than not it is extremely difficult to obtain full documentation and cartography, of an acceptable quality, since the material, constructive pathologies and systems are often insufficient or deficient (flat that simply reflects levels, isolated photographs,..). Sometimes the information in reality exists, but this fact is not known, or it is not easily accessible, leading to the unnecessary duplication of efforts and resources. In this paper, we discussed 3D laser scanning technology, which can acquire high density point data in an accurate, fast way. Besides, the scanner can digitize all the 3D information concerned with a real world object such as buildings, trees and terrain down to millimetre detail Therefore, it can provide benefits for refurbishment process in regeneration in the Built Environment and it can be the potential solution to overcome the challenges above. The paper introduce an approach for scanning buildings, processing the point cloud raw data, and a modelling approach for CAD extraction and building objects classification by a pattern matching approach in IFC (Industry Foundation Classes) format. The approach presented in this paper from an undertaken research can lead to parametric design and Building Information Modelling (BIM) for existing structures. Two case studies are introduced to demonstrate the use of laser scanner technology in the Built Environment. These case studies are the Jactin House Building in East Manchester and the Peel building in the campus of University Salford. Through these case studies, while use of laser scanners are explained, the integration of it with various technologies and systems are also explored for professionals in Built Environmen

Robust Real-Time Recognition of Action Sequences Using a Multi-Camera Network

Real-time identification of human activities in urban environments is increasingly becoming important in the context of public safety and national security. Distributed camera networks that provide multiple views of a scene are ideally suited for real-time action recognition. However, deployments of multi-camera based real-time action recognition systems have thus far been inhibited because of several practical issues and restrictive assumptions that are typically made such as the knowledge of a subjects orientation with respect to the cameras, the duration of each action and the conformation of a network deployment during the testing phase to that of a training deployment. In reality, action recognition involves classification of continuously streaming data from multiple views which consists of an interleaved sequence of various human actions. While there has been extensive research on machine learning techniques for action recognition from a single view, the issues arising in the fusion of data from multiple views for reliable action recognition have not received as much attention. In this thesis, I have developed a fusion framework for human action recognition using a multi-camera network that addresses these practical issues of unknown subject orientation, unknown view configurations, action interleaving and variable duration actions.;The proposed framework consists of two components: (1) a score-fusion technique that utilizes underlying view-specific supervised learning classifiers to classify an action within a given set of frames and (2) a sliding window technique that is used to parse a sequence of frames into multiple actions. The use of a score-fusion technique as opposed to a feature-level fusion of data from multiple views allows us to robustly classify actions even when camera configurations are arbitrary and different from training phase and at the same time reduces the required network bandwidth for data transmission permitting wireless deployments. Moreover, the proposed framework is independent of the underlying classifier that is used to generate scores for each action snippet and thus offers more flexibility compared to sequential approaches like Hidden Markov Models. The amount of training and parameterization is also significantly lower compared to HMM-based approaches. This Real-Time recognition system has been tested on 4 classifiers which are Linear Discriminant Analysis, Multinomial Naive Bayes, Logistic Regression and Support Vector Machines. Over 90% accuracy has been achieved by this system in Real-Time recognizing variable duration actions performed by the subject. The performance of the system is also shown to be robust to camera failures

An Investigation and Application of Biology and Bioinformatics for Activity Recognition

Activity recognition in a smart home context is inherently difficult due to the variable nature of human activities and tracking artifacts introduced by video-based tracking systems. This thesis addresses the activity recognition problem via introducing a biologically-inspired chemotactic approach and bioinformatics-inspired sequence alignment techniques to recognise spatial activities. The approaches are demonstrated in real world conditions to improve robustness and recognise activities in the presence of innate activity variability and tracking noise

Study of ligand-based virtual screening tools in computer-aided drug design

Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested in silico with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates. One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes. In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.Siirretty Doriast

Bio : A Mulrimodal biometric authentication system for person identification and verification

Not availabl

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book