The tremendous ongoing growth of wireless digital communications has raised spectrum shortage and security issues. In particular, the need for new spectrum is the main obstacle in continuing this growth. Recent studies on radio spectrum usage have shown that pre-allocation of spectrum bands to specific wireless communication applications leads to poor utilization of those allocated bands. Therefore, research into new techniques for efficient spectrum utilization is being aggressively pursued by academia, industry, and government. Such research efforts have given birth to two concepts: Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) network. CR is believed to be the key enabling technology for DSA network implementation. CR based DSA (cDSA) networks utilizes white spectrum for its operational frequency bands. White spectrum is the set of frequency bands which are unoccupied temporarily by the users having first rights to the spectrum (called primary users). The main goal of cDSA networks is to access of white spectrum. For proper access, CR nodes must identify the right cDSA network and the absence of primary users before initiating radio transmission. To solve the cDSA network access problem, methods are proposed to design unique second-order cyclic features using Orthogonal Frequency Division Multiplexing (OFDM) pilots. By generating distinct OFDM pilot patterns and measuring spectral correlation characteristics of the cyclostationary OFDM signal, CR nodes can detect and uniquely identify cDSA networks. For this purpose, the second-order cyclic features of OFDM pilots are investigated analytically and through computer simulation. Based on analysis results, a general formula for estimating the dominant cycle frequencies is developed. This general formula is used extensively in cDSA network identification and OFDM signal detection, as well as pilot pattern estimation. CR spectrum awareness capability can be enhanced when it can classify the modulation type of incoming signals at low and varying signal-to-noise ratio. Signal classification allows CR to select a suitable demodulation process at the receiver and to establish a communication link. For this purpose, a threshold-based technique is proposed which utilizes cycle-frequency domain profile for signal detection and feature extraction. Hidden Markov Models (HMMs) are proposed for the signal classifier. The spectrum awareness capability of CR can be undermined by spoofing radio nodes. Automatic identification of malicious or malfunctioning radio signal transmitters is a major concern for CR information assurance. To minimize the threat from spoofing radio devices, radio signal fingerprinting using second-order cyclic features is proposed as an approach for Specific Emitter Identification (SEI). The feasibility of this approach is demonstrated through the identification of IEEE 802.11a/g OFDM signals from different Wireless Local Area Network (WLAN) card manufactures using HMMs. / Ph. D.

Le contrôle non destructif de défauts du type fissures pénétrables ou impénétrables constitue un problème inverse très intéressant parmi ceux de la physique, de l'ingénierie des matériaux et structures, des sciences médicales, etc., et en soi est donc un sujet d'importance sociétale certaine. Le but de cette thèse est de développer des méthodes de reconstruction efficaces afin de les appliquer à une variété de problèmes de fissures. Premièrement, nous proposons un algorithme non-itératif afin de déterminer les extrémités de fissures conductrices, algorithme basé sur une formulation asymptotique appropriée et une méthode d'identification de pôles simples et de résidus d'une fonction méromorphe. Puis un algorithme non-itératif de type MUSIC(MUltiple SIgnal Classification) est considéré afin d'imager une fissure pénétrable ou impénétrable à partir du champ qu'elle diffracte, ce champ pouvant être représenté grâce à une formulation asymptotique rigoureuse. Une technique d'ensembles de niveaux est alors proposé afin de reconstruire une fissure pénétrable, deux fonctions d'ensemble de niveaux étant utilisées pour la décrire puisqu'une méthode traditionnelle d'ensembles de niveaux ne le permet pas de par sa petite épaisseur. Finalement, cette thèse traite de la reconstruction des fissures courtes et étendues avec des conditions limites de Dirichlet. Nous développons alors un algorithme de type MUSIC pour reconstruire les petites fissures et un algorithme d'optimisation pour les fissures longues basé sur la formulation asymptotique. Des simulations numériques nombreuses illustrent les performances des méthodes de reconstruction proposées.

[pt] A técnica de análise de sinais corrompidos por ruído baseada no comportamento de subespaços vetoriais foi tema de alguns trabalhos publicados desde o início da década de 80. Esta nova técnica passou a ter grande importância no processamento de sinais digitais devido a fatores como robustez e precisão.Porém, o maior problema associado a este novo método é o seu elevado custo computacional. Esta característica limitou o emprego da técnica em sistemas - offline - . A preocupação então passou a ser rastrear a variação do comportamento dos subespaços vetoriais de modo eficiente. O objetivo deste rastreamento seria o emprego da técnica em alguns sistemas que operam em tempo real. Este trabalho de tese propõe um novo algoritmo de rastreamento de subespaços vetoriais. O objetivo é apresentar um algoritmo que demonstre um bom desempenho, com relação aos demais já existentes, permitindo eventual aplicação em sistemas que atuem em tempo real. Como contribuição adicional, são apresentadas uma nova análise e caracterização de sistemas que se assemelham aos circulantes, sendo para isto reinterpretada a decomposição de matrizes circulantes. O conjunto de contribuições é aplicado a um novo sistema automático de classificação de sinais comunicação, quanto ao tipo de modulação. / [en] The signal subspace analysis technique, usually applied to signals corrupted by noise, is theme of some papers since the beginning of the 80s decade. This new technique has presented important features, as robustness and precision, and became widely employed in digital signal processing. However, the main problem associated to this new method is the high computational cost. This characteristic has restricted the use of signal subspace analysis to some off-line systems. A possible way to overcome this burden was to track the signal and noise subspace behavior in the time-domain. The main objective of these methods is to allow the signal subspace analysis technique application to real time systems, sometimes at the expense of limiting analysis precision or scope. This work proposes a new subspace tracking procedure. The goal is to describe a new algorithm with good performance (precision-speed), allowing some real time systems applications. A new analysis and characterization of almost circulant systems is introduced by reinterpreting the circulating matrix decomposition scheme. The set of contributions is applied to a new analogue modulation communication signals automatic recognition structure.

The radar transmitter identification problem involves the identification of a specific radar transmitter based on a received pulse. The radar transmitters are of identical make and model. This makes the problem challenging since the differences between radars of identical make and model will be solely due to component tolerances and variation. Radar pulses also vary in time and frequency which means that the problem is non-stationary. Because of this fact, time-frequency representations such as shift-invariant quadratic time-frequency representations (Cohen’s class) and wavelets were used. A model for a radar transmitter was developed. This consisted of an analytical solution to a pulse-forming network and a linear model of an oscillator. Three signal classification algorithms were developed. A signal classifier was developed that used a radially Gaussian Cohen’s class transform. This time-frequency representation was refined to increase the classification accuracy. The classification was performed with a support vector machine classifier. The second signal classifier used a wavelet packet transform to calculate the feature values. The classification was performed using a support vector machine. The third signal classifier also used the wavelet packet transform to calculate the feature values but used a Universum type classifier for classification. This classifier uses signals from the same domain to increase the classification accuracy. The classifiers were compared against each other on a cubic and exponential chirp test problem and the radar transmitter model. The classifier based on the Cohen’s class transform achieved the best classification accuracy. The classifier based on the wavelet packet transform achieved excellent results on an Electroencephalography (EEG) test dataset. The complexity of the wavelet packet classifier is significantly lower than the Cohen’s class classifier. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / unrestricted

The train represents a complex system, where every sub-system has an important role. If a subsystem doesn’t work how it should, the correctness of whole the train can be uncertain. To ensure that system works properly, we should test each sub-system individually and integrate them together in the whole system. Each of these subsystems consists of the different modules with different functionalities what should be tested. Testing of different functionalities often requires a different approach. For some functionalities, it is necessary domain knowledge from the human expert, such as classification of signals in different use cases in Propulsion and Controls (PPC) in Bombardier Transportation. Due to this reason, we need to simulate of using experts knowledge in the certain domain. We are investigating the use of machine learning techniques for solving this cases and creating system what will automatically classify different signals using the previous human knowledge. This case study is conducted in Bombardier Transportation (BT), Västerås in departments Train Control Management System (TCMS) and Propulsion and Controls (PPC), where data is collected, analyzed and evaluated. We proposed a method for solving the oracle problem based on machine learning approach for different for certain use case. Also, we explained different steps what can be used for solving the test oracle problem where signals are part of verdict process

Direction Finding (DF) algorithms are used by the Swedish Defence Research Agency (FOI) in the context of electronic warfare against radio. Parallelizing these algorithms using a Graphics Processing Unit (GPU) might improve performance, and thereby increase military support capabilities. This thesis selects the DF algorithms Correlative Interferometer (CORR), Multiple Signal Classification (MUSIC) and Weighted Subspace Fitting (WSF), and examines to what extent GPU implementation of these algorithms is suitable, by analysing, implementing and evaluating. Firstly, six general criteria for GPU suitability are formulated. Then the three algorithms are analyzed with regard to these criteria, giving that MUSIC and WSF are both 58% suitable, closely followed by CORR on 50% suitability. MUSIC is selected for implementation, and an open source implementation is extended to three versions: a multicore CPU version, a GPU version (with Eigenvalue Decomposition (EVD) and pseudo spectrum calculation performed on the GPU), and a MIXED version (with only pseudo spectrum calculation on the GPU). These versions are then evaluated for angle resolutions between 1° and 0.025°, and CUDA block sizes between 8 and 1024. It is found that the GPU version is faster than the CPU version for angle resolutions above 0.1°, and the largest measured speedup is 1.4 times. The block size has no large impact on the total runtime. In conclusion, the overall results indicate that it is not entirely suitable, yet somewhat beneficial for large angle resolutions, to implement MUSIC using GPU computing.

With the reality that diverse air interfaces and dissimilar access networks coexist, accompanied by the trend that dynamic spectrum access (DSA) is allowed and will be gradually employed, cognition and cooperation form a promising framework to achieve the ideality of seamless ubiquitous connectivity in future communication networks. In this dissertation, the cognitive gateway (CG), conceived as a special cognitive radio (CR) node, is proposed and designed to facilitate universal interoperability among incompatible waveforms. A proof-of-concept prototype is built and tested. Located in places where various communication nodes and diverse access networks coexist, the CG can be easily set up and works like a network server with differentiated service (Diffserv) architecture to provide automatic traffic relaying and link establishment. The author extracts a scalable '“source-CG-destination“ snapshot from the entire network and investigates the key enabling technologies for such a snapshot. The CG features provide universal interoperability, which is enabled by a generic waveform representation format and the reconfigurable software defined radio platform. According to the trend of an all IP-based solution for future communication systems, the term “waveform“ in this dissertation has been defined as a protocol stack specification suite. The author gives a generic waveform representation format based on the five-layer TCP/IP protocol stack architecture. This format can represent the waveforms used by Ethernet, WiFi, cellular system, P25, cognitive radios etc. A significant advantage of CG over other interoperability solutions lies in its autonomy, which is supported by appropriate signaling processes and automatic waveform identification. The service process in a CG is usually initiated by the users who send requests via their own waveforms. These requests are transmitted during the signaling procedures. The complete operating procedure of a CG is depicted as a waveform-oriented cognition loop, which is primarily executed by the waveform identifier, scenario analyzer, central controller, and waveform converter together. The author details the service process initialized by a primary user (e.g. legacy public safety radio) and that initialized by a secondary user (e.g. CR), and describes the signaling procedures between CG and clients for the accomplishment of CG discovery, user registration and un-registration, link establishment, communication resumption, service termination, route discovery, etc. From the waveforms conveyed during the signaling procedures, the waveform identifier extracts the parameters that can be used for a CG to identify the source waveform and the destination waveform. These parameters are called “waveform indicators.“ The author analyzes the four types of waveforms of interest and outlines the waveform indicators for different types of communication initiators. In particular, a multi-layer waveform identifier is designed for a CG to extract the waveform indicators from the signaling messages. For the physical layer signal recognition, a Universal Classification Synchronization (UCS) system has been invented. UCS is conceived as a self-contained system which can detect, classify, synchronize with a received signal and provide all parameters needed for physical layer demodulation without prior information from the transmitter. Currently, it can accommodate the modulations including AM, FM, FSK, MPSK, QAM and OFDM. The design and implementation details of a UCS have been presented. The designed system has been verified by over-the-air (OTA) experiments and its performance has been evaluated by theoretical analysis and software simulation. UCS can be ported to different platforms and can be applied for various scenarios. An underlying assumption for UCS is that the target signal is transmitted continually. However, it is not the case for a CG since the detection objects of a CG are signaling messages. In order to ensure higher recognition accuracy, signaling efficiency, and lower signaling overhead, the author addresses the key issues for signaling scheme design and their dependence on waveform identification strategy. In a CG, waveform transformation (WT) is the last step of the link establishment process. The resources required for transformation of waveform pairs, together with the application priority, constitute the major factors that determine the link control and scheduling scheme in a CG. The author sorts different WT into five categories and describes the details of implementing the four typical types of WT (including physical layer analog – analog gateway, up to link layer digital – digital gateway, up-to-network-layer digital gateway, and Voice over IP (VoIP) – an up to transport layer gateway) in a practical CG prototype. The issues that include resource management and link scheduling have also been addressed. This dissertation presents a CG prototype implemented on the basis of GNU Radio plus multiple USRPs. In particular, the service process of a CG is modeled as a two-stage tandem queue, where the waveform identifier queues at the first stage can be described as M/D/1/1 models and the waveform converter queue at the second stage can be described as G/M/K/K model. Based on these models, the author derives the theoretical block probability and throughput of a CG. Although the “source-CG-destination” snapshot considers only neighboring nodes which are one-hop away from the CG, it is scalable to form larger networks. CG can work in either ad-hoc or infrastructure mode. Utilizing its capabilities, CG nodes can be placed in different network architectures/topologies to provide auxiliary connectivity. Multi-hop cooperative relaying via CGs will be an interesting research topic deserving further investigation. / Ph. D.

The angle of arrival estimation of multiple sources plays a vital role in the field of array signal processing as MIMO systems can be employed at both the transmitter and the receiver end and the system capacity, reliability and throughput can be significantly increased by using array signal processing. Almost all applications require accurate direction of arrival (DOA) estimation to localize the sources of the signals. Another important parameter of localization systems is the array geometry and sensor design which can be application specific and is used to estimate the DOA. In this work, various array geometries and arrival estimation algorithms are studied and then a new scheme for multiple source estimation is proposed and evaluated based on the performance of subspace and non-subspace decomposition methods. The proposed scheme has shown to outperform the conventional Multiple Signal Classification (MUSIC) estimation and Bartlett estimation techniques. The new scheme has a better performance advantage at low and high signal to noise ratio values (SNRs). The research work also studies different array geometries for both single and multiple incident sources and proposes a geometry which is cost effective and efficient for 3, 4, and 5 antenna array elements. This research also considers the shape of the ground plane and its effects on the angle of arrival estimation and in addition it shows how the mutual couplings between the elements effect the overall estimation and how this error can be minimised by using a decoupling matrix. At the end, a novel miniaturised multi element reconfigurable antenna to represent the receiver base station is designed and tested. The antenna radiation patterns in the azimuth angle are almost omni-directional with linear polarisation. The antenna geometry is uniplanar printed logspiral with striplines feeding network and biased components to improve the impedance bandwidth. The antenna provides the benefit of small size, and re-configurability and is very well suited for the asset tracking applications.

No description available.

L’attention visuo-spatiale peut être déployée à différentes localisations dans l’espace indépendamment de la direction du regard, et des études ont montré que les composantes des potentiels reliés aux évènements (PRE) peuvent être un index fiable pour déterminer si celle-ci est déployée dans le champ visuel droit ou gauche. Cependant, la littérature ne permet pas d’affirmer qu’il soit possible d’obtenir une localisation spatiale plus précise du faisceau attentionnel en se basant sur le signal EEG lors d’une fixation centrale. Dans cette étude, nous avons utilisé une tâche d’indiçage de Posner modifiée pour déterminer la précision avec laquelle l’information contenue dans le signal EEG peut nous permettre de suivre l’attention visuelle spatiale endogène lors de séquences de stimulation d’une durée de 200 ms. Nous avons utilisé une machine à vecteur de support (MVS) et une validation croisée pour évaluer la précision du décodage, soit le pourcentage de prédictions correctes sur la localisation spatiale connue de l’attention. Nous verrons que les attributs basés sur les PREs montrent une précision de décodage de la localisation du focus attentionnel significative (57%, p<0.001, niveau de chance à 25%). Les réponses PREs ont également prédit avec succès si l’attention était présente ou non à une localisation particulière, avec une précision de décodage de 79% (p<0.001). Ces résultats seront discutés en termes de leurs implications pour le décodage de l’attention visuelle spatiale, et des directions futures pour la recherche seront proposées. / Visuospatial attention can be deployed to different locations in space independently of ocular fixation, and studies have shown that event-related potential (ERP) components can effectively index whether such covert visuospatial attention is deployed to the left or right visual field. However, it is not clear whether we may obtain a more precise spatial localization of the focus of attention based on the EEG signals during central fixation. In this study, we used a modified Posner cueing task with an endogenous cue to determine the degree to which information in the EEG signal can be used to track visual spatial attention in presentation sequences lasting 200 ms. We used a machine learning classification method to evaluate how well EEG signals discriminate between four different locations of the focus of attention. We then used a multi-class support vector machine (SVM) and a leave-one-out cross-validation framework to evaluate the decoding accuracy (DA). We found that ERP-based features from occipital and parietal regions showed a statistically significant valid prediction of the location of the focus of visuospatial attention (DA = 57%, p < .001, chance-level 25%). The mean distance between the predicted and the true focus of attention was 0.62 letter positions, which represented a mean error of 0.55 degrees of visual angle. In addition, ERP responses also successfully predicted whether spatial attention was allocated or not to a given location with an accuracy of 79% (p < .001). These findings are discussed in terms of their implications for visuospatial attention decoding and future paths for research are proposed.