Adaptive indoor positioning system based on locating globally deployed WiFi signal sources

Recent trends in data driven applications have encouraged expanding location awareness to indoors. Various attributes driven by location data indoors require large scale deployment that could expand beyond specific venue to a city, country or even global coverage. Social media, assets or personnel tracking, marketing or advertising are examples of applications that heavily utilise location attributes. Various solutions suggest triangulation between WiFi access points to obtain location attribution indoors imitating the GPS accurate estimation through satellites constellations. However, locating signal sources deep indoors introduces various challenges that cannot be addressed via the traditional war-driving or war-walking methods. This research sets out to address the problem of locating WiFi signal sources deep indoors in unsupervised deployment, without previous training or calibration. To achieve this, we developed a grid approach to mitigate for none line of site (NLoS) conditions by clustering signal readings into multi-hypothesis Gaussians distributions. We have also employed hypothesis testing classification to estimate signal attenuation through unknown layouts to remove dependencies on indoor maps availability. Furthermore, we introduced novel methods for locating signal sources deep indoors and presented the concept of WiFi access point (WAP) temporal profiles as an adaptive radio-map with global coverage. Nevertheless, the primary contribution of this research appears in utilisation of data streaming, creation and maintenance of self-organising networks of WAPs through an adaptive deployment of mass-spring relaxation algorithm. In addition, complementary database utilisation components such as error estimation, position estimation and expanding to 3D have been discussed. To justify the outcome of this research, we present results for testing the proposed system on large scale dataset covering various indoor environments in different parts of the world. Finally, we propose scalable indoor positioning system based on received signal strength (RSSI) measurements of WiFi access points to resolve the indoor positioning challenge. To enable the adoption of the proposed solution to global scale, we deployed a piece of software on multitude of smartphone devices to collect data occasionally without the context of venue, environment or custom hardware. To conclude, this thesis provides learning for novel adaptive crowd-sourcing system that automatically deals with tolerance of imprecise data when locating signal sources

Novel techniques for acoustic emission monitoring of fatigue fractures in landing gear.

Acoustic Emission (AE) is a technique for performing Non-Destructive Evaluation (NDE) of structures, whereby ultrasonic transducers are placed upon the surface of the structure in order to detect ultrasound resulting from damage-related events within the structure. Unlike many other NDE techniques, AE is an entirely passive process: the transducers operate in receive mode only. Advances in the sophistication of AE equipment and computer hardware in general mean that it is now possible to perform AE tests involving many sensors over a large structure whilst recording and storing every waveform received at every sensor. There is much interest in using this data to perform detailed analysis of structural integrity, particularly because AE testing is entirely non-invasive and can be largely automated. There are many hurdles to be overcome before AE can be routinely used in such a situation: the quantity of data is huge, and it is of a format which is unusual to most engineers - AE signals comprise short, transient, 'burst' like signals. A significant portion of this thesis is given to considering what to do with all the data, and how it may be understood. The thesis focusses on the development of a data processing method, and its application to landing gear certification tests. The methodology is designed to be generic, in that. it could find application in the on-line monitoring of a variety of engineering structures in the aerospace, automotive and civil-engineering sectors

Features extraction using random matrix theory.

Representing the complex data in a concise and accurate way is a special stage in data mining methodology. Redundant and noisy data affects generalization power of any classification algorithm, undermines the results of any clustering algorithm and finally encumbers the monitoring of large dynamic systems. This work provides several efficient approaches to all aforementioned sides of the analysis. We established, that notable difference can be made, if the results from the theory of ensembles of random matrices are employed. Particularly important result of our study is a discovered family of methods based on projecting the data set on different subsets of the correlation spectrum. Generally, we start with traditional correlation matrix of a given data set. We perform singular value decomposition, and establish boundaries between essential and unimportant eigen-components of the spectrum. Then, depending on the nature of the problem at hand we either use former or later part for the projection purpose. Projecting the spectrum of interest is a common technique in linear and non-linear spectral methods such as Principal Component Analysis, Independent Component Analysis and Kernel Principal Component Analysis. Usually the part of the spectrum to project is defined by the amount of variance of overall data or feature space in non-linear case. The applicability of these spectral methods is limited by the assumption that larger variance has important dynamics, i.e. if the data has a high signal-to-noise ratio. If it is true, projection of principal components targets two problems in data mining, reduction in the number of features and selection of more important features. Our methodology does not make an assumption of high signal-to-noise ratio, instead, using the rigorous instruments of Random Matrix Theory (RNIT) it identifies the presence of noise and establishes its boundaries. The knowledge of the structure of the spectrum gives us possibility to make more insightful projections. For instance, in the application to router network traffic, the reconstruction error procedure for anomaly detection is based on the projection of noisy part of the spectrum. Whereas, in bioinformatics application of clustering the different types of leukemia, implicit denoising of the correlation matrix is achieved by decomposing the spectrum to random and non-random parts. For temporal high dimensional data, spectrum and eigenvectors of its correlation matrix is another representation of the data. Thus, eigenvalues, components of the eigenvectors, inverse participation ratio of eigenvector components and other operators of eigen analysis are spectral features of dynamic system. In our work we proposed to extract spectral features using the RMT. We demonstrated that with extracted spectral features we can monitor the changing dynamics of network traffic. Experimenting with the delayed correlation matrices of network traffic and extracting its spectral features, we visualized the delayed processes in the system. We demonstrated in our work that broad range of applications in feature extraction can benefit from the novel RMT based approach to the spectral representation of the data

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

A STUDY ON RECEIVED SIGNAL STRENGTH BASED INDOOR LOCALIZATION

Ph.DDOCTOR OF PHILOSOPH

Intraflagellar transport particle size scales inversely with flagellar length: revisiting the balance-point length control model

Chlamydomonas reinhardtii IFT particle trains, important for flagella maintenance and assembly, are observed to decrease in size as a function of cilia length

Inhibitory neurons exhibit high controlling ability in the cortical microconnectome

脳が安定して活動を続けられるメカニズムの一端を解明 --新皮質で、抑制性細胞は他細胞を制御しやすいトポロジカルな位置取りをする--. 京都大学プレスリリース. 2021-04-09.The brain is a network system in which excitatory and inhibitory neurons keep activity balanced in the highly non-random connectivity pattern of the microconnectome. It is well known that the relative percentage of inhibitory neurons is much smaller than excitatory neurons in the cortex. So, in general, how inhibitory neurons can keep the balance with the surrounding excitatory neurons is an important question. There is much accumulated knowledge about this fundamental question. This study quantitatively evaluated the relatively higher functional contribution of inhibitory neurons in terms of not only properties of individual neurons, such as firing rate, but also in terms of topological mechanisms and controlling ability on other excitatory neurons. We combined simultaneous electrical recording (~2.5 hours) of ~1000 neurons in vitro, and quantitative evaluation of neuronal interactions including excitatory-inhibitory categorization. This study accurately defined recording brain anatomical targets, such as brain regions and cortical layers, by inter-referring MRI and immunostaining recordings. The interaction networks enabled us to quantify topological influence of individual neurons, in terms of controlling ability to other neurons. Especially, the result indicated that highly influential inhibitory neurons show higher controlling ability of other neurons than excitatory neurons, and are relatively often distributed in deeper layers of the cortex. Furthermore, the neurons having high controlling ability are more effectively limited in number than central nodes of k-cores, and these neurons also participate in more clustered motifs. In summary, this study suggested that the high controlling ability of inhibitory neurons is a key mechanism to keep balance with a large number of other excitatory neurons beyond simple higher firing rate. Application of the selection method of limited important neurons would be also applicable for the ability to effectively and selectively stimulate E/I imbalanced disease states

Development of a model for smart card based access control in multi-user, multi-resource, multi-level access systems

The primary focus of this research is an examination of the issues involved in the granting of access in an environment characterised by multiple users, multiple resources and multiple levels of access permission. Increasing levels of complexity in automotive systems provides opportunities for improving the integration and efficiency of the services provided to the operator. The vehicle lease / hire environment provided a basis for evaluating conditional access to distributed, mobile assets where the principal medium for operating in this environment is the Smart Card. The application of Smart Cards to existing vehicle management systems requires control of access to motor vehicles, control of vehicle operating parameters and secure storage of operating information. The issues addressed include examination of the characteristics of the operating environment, development of a model and design, simulation and evaluation of a multiple application Smart Card. The functions provided by the card include identification and authentication, secure hash and encryption functions which may be applied, in general, to a wide range of access problems. Evaluation of the algorithms implemented indicate that the Smart Card design may be provably secure under single use conditions and conditionally secure under multiple use conditions. The simulation of the card design provided data to support further research and shows the design is practical and able to be implemented on current Smart Card types