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Compressive Power Spectral Analysis
No full textAt the heart of digital signal processing (DSP) are the sampling and quantization processes, which convert analog signals into discrete samples and which are implemented in the form of analog to digital converters (ADCs). In some recent applications, there is an increased demand for DSP applications to process signals having a very wide bandwidth. For such signals, the minimum allowable sampling rate is also very high and this has put a very high demand on the ADCs in terms of power consumption. Recently, the emergence of compressive sampling (CS) has offered a solution that allows us to reconstruct the original signal from samples collected from a sampling device operating at sub-Nyquist rate. The application of CS usually involves applying an additional constraint such as a sparsity constraint on the original signal. However, there are also applications where the signal to deal with has a high bandwidth (and thus sub-Nyquist rate sampling is still important) but where only the second-order statistics (instead of the original signal) are required to be reconstructed. In the latter case, depending on the characteristics of the signals, it might be possible to reconstruct the second-order statistics of the received analog signal from its sub-Nyquist rate samples without applying any additional constraints on the original signals. This idea is the key starting point of this thesis. We first focus on time-domain wide-sense stationary (WSS) signals and introduce a method for reconstructing their power spectrum from their sub-Nyquist rate samples without requiring the signal or the power spectrum to be sparse. Our method is examined both in the time- and frequency-domain and the solution is computed using a simple least-squares (LS) approach, which produces a solution if the rank condition of the resulting system matrix is satisfied. To satisfy this rank condition, two options of sampling design are proposed, one of which is the so-called multi-coset sampling. It is show in this thesis that any of the so-called sparse ruler can produce a multi-coset sampling design that guarantees the full rank condition of the system matrix, and thus the optimal compression is achieved by a minimal sparse ruler. While the approach in the previous paragraph is related to time-domain signals, we could extend the discussion about the power spectrum reconstruction from sub-Nyquist rate samples in the context of the spatial-domain signal, which is defined as a sequence of outputs of the antennas in the antenna array at a particular time instant. Given the compressed spatial domain signals, which are obtained from the output of a uniform linear array (ULA) with some antennas turned off, of particular interest is to reconstruct the angular power spectrum, from which the direction of arrival (DOA) of the sources can generally be located. In this thesis, a method to estimate the angular power spectrum and the DOA of possibly fully correlated sources based on second-order statistics of the compressed spatial-domain signals is proposed by employing a so-called dynamic array which is built upon the so-called underlying ULA. In this method, we present the spatial correlation matrices of the output of the dynamic active antenna arrays at all time slots as a linear function of the spatial correlation matrix of the entire underlying uniform array and we solve for this last correlation matrix using LS. The required theoretical condition to ensure the full column rank condition of the system matrix is formulated and designs are proposed to satisfy this condition. Next, we consider both spatio-angular and time-frequency domains and propose a compressive periodogram reconstruction method as our next contribution. We introduce the multibin model, where the entire band is divided into equal-size bins such that the spectra at two frequencies or angles, whose distance is at least equal to the bin size, are uncorrelated. This model results in a circulant structure in the so-called coset correlation matrix, which enables us to introduce a strong compression. We propose the sampling patterns based on a circular sparse ruler to guarantee the full column rank condition of the system matrix and to allow the LS reconstruction of the periodogram. We also provide a method for the case when the bin size is reduced such that the spectra at two frequencies or angles, whose distance is larger than the bin size, can still be correlated. To combine frequency and DOA processing, we also introduce a compressive two-dimensional (2D) frequency- and angular-domain power spectrum reconstruction for multiple uncorrelated time-domain WSS signals received from different sources by a linear array of antennas. We perform spatial-domain compression by deactivating some antennas in an underlying ULA and time-domain compression by multi-coset sampling. Finally, we propose a compressive cyclic spectrum reconstruction approach for wide-sense cyclostationary (WSCS) signals, where we consider sub-Nyquist rate samples produced by non-uniform sampling. This method is proposed after first observing that the block Toeplitz structure emerges in theWSCS signal correlation matrix. This structure is exploited to solve the WSCS signal correlation matrix by LS. The condition for the system matrix to have full column rank is provided and some possible non-uniform sampling designs to satisfy this full column rank condition are presented. Based on all the works that have been done, we have found that focusing on reconstructing the statistical measure of the received signals has significantly relax the sampling requirements and the constraints on both the statistics and the signals themselves. Hence, we would like to conclude that, for given tasks of applications in hand, we should ask ourselves whether statistical measure reconstruction is sufficient since the answer for this question will likely to determine how we should collect the data from the observed phenomena. This underlines the importance of awareness on what kind of information is necessary and sufficient for the tasks in hand before conducting the sensing/sampling process.MicroelectronicsElectrical Engineering, Mathematics and Computer Scienc
Compressive Power Spectrum Estimation: Further study on Non Uniform sampling and Parametric Approach
No full textIn the recent development of wireless communication several applications, such as spectrum sensing for cognitive radio are only interested in the power spectrum. These applications do not require the reconstruction of the original analog signal. According to the Whittaker-Kotelnikov-Shannon-Nyquist theorem, the sampling rate must be at least twice the maximum frequency present in the signal if we want to recover the signal from its samples. If we estimate the power spectrum directly by using a high-rate analog-to-digital converter, we will find that such high-rate ADCs consume a large amount of power because of its high sampling rate. To reduce the burden on the ADCs, we investigate compressive power spectrum sensing. Since the power spectrum is calculated based on the autocorrelations of the signal, we do not need to recover the signals. This allows a reduction of the sampling rate compared with the Nyquist rate while maintaining perfect power spectrum reconstruction. In this thesis, we study power spectrum estimation of a wide-sense stationary signal. In general, the signal, whose power spectrum is to be estimated, is sampled by multi-coset sampling. The parametric method to estimate the power spectrum of the signal is also evaluated and the study of the performance of non-uniform sampling is explored as well.TelecommunicationsElectrical Engineering, Mathematics and Computer Scienc
An Extensible Toolkit For Real-Time High-Performance Wideband Spectrum Sensing
No full textThis document describes the design process of a software toolkit to perform high-performance wideband spectrum sensing. A prominent application of this is Cognitive Radio, a technique that aims to make more efficient use of the available radio spectrum. An extensive theoretical analysis will be performed. Various non-uniform sampling techniques will be discussed, such as coprime and circular sparse sampling. An algorithm to reconstruct the PSD of sub-Nyquist sampled signals will be developed and a detection algorithm which uses this PSD will be proposed. This analysis will be utilised to implement an extensible software toolkit written in Python. The software architecture and various design patterns that were utilised to structure the toolkit will be described and its quality and performance will be analysed. The hardware used for data acquisition, a USRP N210, will be introduced. The work will be concluded with a conclusion and its discussion.Electrical EngineeringCircuits and SystemsElectrical Engineering, Mathematics and Computer Scienc
On Wavelet Based Spectrum Estimation for Dynamic Spectrum Access
No full textOne of the important functionalities of Dynamic Spectrum Access is spectrum estimation. Accuracy and speed of estimation are the key indicators to select the appropriate spectrum estimation technique. In this thesis work, the possibility of employing wavelet packet decomposition as a basis for a new spectrum estimation approach is investigated. Once the new approach is developed, four types of sources, namely partial band, single tone, multi-tones, and swept tone, are used to investigate the performance of the proposed wavelet based approach. Preliminary comparative analysis between the performance of wavelet based approach with conventional techniques, such as Periodogram and Welch technique has also been conducted. The studies show that the wavelet based approach offers great flexibility, reconfigurability and adaptability. Key to the successful operation of the wavelet based spectrum estimation is the choice of the wavelet used. Commonly known wavelets are not suitable for spectrum estimation because they result in estimates with poor frequency resolution. To alleviate this problem, we design and develop a family of wavelets that are maximally frequency selective in nature as our second contribution in this thesis work. To this end, the design constraints are first enlisted. Then the problem, originally non-convex, is reformulated into a convex optimization problem and solved using Semi Definite Programming (SDP) tools. Through simulation studies the benefits of the newly designed wavelets are demonstrated. The next contribution of this thesis work is to combine the existing wavelet packet multi-carrier modulation (WPMCM) technique with our wavelet based spectrum estimator in order to form a wavelet packet transceiver for a dynamic spectrum access environment. To enable the wavelet packet transceiver cognitive radio (CR) system to co-exist with other Licensed Users (LU), a common spectrum pool is maintained and the WPMCM transmission waveform characteristics are shaped to communicate in the idle time-frequency gaps of the licensed user. This is achieved by dynamically vacating wavelet packet carriers in and near the region of the licensed user spectrum. The spectrum estimation unit is tagged to the WPMCM transceiver structure by exploiting the filter bank infrastructure used for Discrete Wavelet Packet Transform implementation. Thus spectrum analysis is done at no additional cost. In the studies, four types of LUs are employed, namely, partial band, single tone, swept and multiple tone. The simulation results show that in the presence of an LU, the proposed spectrum adaptation method offers significant BER improvements allowing the CR to operate invisibly to the LU.TelecommunicationsElectrical Engineering, Mathematics and Computer Scienc
Cooperative compressive power spectrum estimation in wireless fading channels
No full textThis paper considers multiple wireless sensors that cooperatively estimate the power spectrum of the signals received from several sources. We extend our previous work on cooperative compressive power spectrum estimation to accommodate the scenario where the statistics of the fading channels experienced by different sensors are different. The signals received from the sources are assumed to be time-domain wide-sense stationary processes. Multiple sensors are organized into several groups, where each group estimates a different subset of lags of the temporal correlation. A fusion centre (FC) combines these estimates to obtain the power spectrum. As each sensor group computes correlation estimates only at a subset of lags, the sampling rate per sensor can be less than the Nyquist rate. The conditions required for uniqueness of the least-squares estimator are derived based on our previous results. The sensors are combined into clusters in such a way that all sensors within the same cluster experience approximately the same fading statistics. We find that, as long as the number of sensors of each group is the same across clusters, the resulting power spectrum estimate computed by the FC converges to the power spectrum of the transmitted signal scaled by the averaged fading statistics. In a simulation study, we also investigate the performance of our approach when the aforementioned assumption is not true, i.e., when the number of sensors of each group is not the same across clusters. The simulation study shows degradation in the performance of our approach for this case.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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