Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

Multi–Dimensional Wireless Signal Identification Based on Support Vector Machines

ABSTRACT: Radio air interface identification provides necessary information for dynamically and efficiently exploiting the wireless radio frequency spectrum. In this study, a general machine learning framework is proposed for Global System for Mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), and Long Term Evolution (LTE) signal identification by utilizing the outputs of the spectral correlation function (SCF), fast Fourier Transform (FFT), auto-correlation function (ACF), and power spectral density (PSD) as the training inputs for the support vector machines (SVMs). In order to show the robustness and practicality of the proposed method, the performance of the classifier is investigated with respect to different fading channels by using simulation data. Various over-the-air real-world measurements are taken to show that wireless signals can be successfully distinguished from each other without any prior information while accounting for a comprehensive set of parameters such as different kernel types, number of in-phase/quadrature (I/Q) samples, training set size, or signal-to-noise ratio (SNR) values. Furthermore, the performance of the proposed classifier is compared to the existing well-known deep learning (DL) networks. The comparative performance of the proposed method is also quantified by classification confusion matrices and Precision/Recall/F-1-scores. It is shown that the investigated system can be also utilized for spectrum sensing and its performance is also compared with that of cyclostationary feature detection spectrum sensing

Modelling surface topography from reflected light.

This thesis is concerned with the use of the modulus of the Fourier spectrum to characterise object features and also to reconstruct object surfaces in the complete absence of phase information. In general, a phaseless synthesis is completely meaningless and many characteristic features of the object are obliterated when the modulus of the spectral components is inverse Fourier transformed with zero phase. However, the outcome is different when the object possesses some form of regularity and repetition in its characteristics. In such circumstances, the utilisation of both the modulus and the intensity of the spatial spectrum can reveal information regarding the characteristic features of the object surface. The first part of this research has utilised the intensity of the spectral components as a means of surface feature characterisation in the study of a machined surface. Two separate approaches were adopted for assessing the zero-phase images. Both the optically recorded Fourier spectrum and the computer simulated Fourier spectrum were used to extract surface related parameters in the zero-phase synthesis. Although merely a characterisation, the zero-phase synthesis of the spectral components revealed periodic behaviour very similar to that present in the original surface. The presence of such cyclic components was confirmed by their presence in travelling microscope images and in scanning electron microscope images of the surface. Additionally, a novel approach has been adopted to recover finer periodicities on the surface. The scale sensitivity of the frequency domain fosters an exceptional means through which digital magnification can be performed with the added advantage that it is accompanied by enhanced resolution. Magnification realised through spatial frequency data is by far superior to any spatial domain magnification. However, there are limitations to this approach. The second part of this research has been centred around the possible use of a non-iteratively based approach for extracting the unknown phases from the modulus of the Fourier spectrum and thus retrieving the 3-D geometrical structure of the unknown object surface as opposed to characterising its profile. The logarithmic Hilbert transform is one such approach which allows a non-iterative means of extracting unknown phases from the modulus of the Fourier spectrum. However, the technique is only successful for object surfaces which are well-behaved and display well-behaved spectral characteristics governed by continuity. For real object surfaces where structure, definition and repetition governs the characteristics, the spectrum is not well behaved. The spectrum is populated by maxi ma, minima and many isolated regions which are occupied by colonies of zeros disrupting the continuity. A new and unique approach has been devised by the author to reform the spectral behaviour of real object surfaces without affecting the fidelity that it conveys. The resultant information enables phase extraction to be achieved through the logarithmic Hilbert transform. It is possible to reform the spread of spectral behaviour to cultivate better continuity amongst its spectral components through an object scale change. The combination of the logarithmic Hilbert transform and the Fourier scaling principle has led to a new approach for extracting the unknown phases for real object structures which would otherwise have been impossible to perform through the use of Hilbert transformation alone. The validity of the technique has been demonstrated in a series of simulations conducted on one-dimensional objects as well as the two-dimensional object specimens. The limitations of the approach, improvements and the feasibility for practical implementation are ail issues which have been addressed

Blind Numerology Identification for Mixed Numerologies

5G New Radio (NR) introduces new flexibility that different numerologies can be selected to meet the requirements of a wide variety of services. For this new structure, blind numerology identification can increase system efficiency. Therefore, we propose a blind identification method for mixed numerologies. An autocorrelation method is applied in the time domain by correlating the cyclic prefix (CP) signal of the candidate numerology in the received composite signal for numerology type identification. Then, the location of each numerology in the frequency domain is identified by the variance difference in the power spectral density (PSD) of the subbands, on which different numerologies are occupied. The simulation results are obtained under additive white Gaussian noise (AWGN) and frequencyselective channels. The obtained results show that the proposed method has a robust identification accuracy and a satisfactory BER performance as compared to the non-blind identification approach in the conventional mixed-numerology system.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

AFIT School of Engineering Contributions to Air Force Research and Technology. Calendar Year 1971

This report contains abstracts of Master of Science theses and Doctoral Dissertations completed during the 1971 calendar year at the School of Engineering, Air Force Institute of Technology

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies