Acoustic noise removal by combining wiener and wavelet filtering techniques

This thesis investigates the application of Wiener filtering and wavelet techniques for the removal of noise from underwater acoustic signals. Both FIR and IIR Wiener filters are applied in separate methods which involve the filtering of wavelet coefficients which have been produced through a discrete wavelet decomposition of the acoustic signal. The effectiveness of the noise removal methods is evaluated by applying them to simulated data. The combined Wiener wavelet filtering methods are compared to traditional denoising techniques which include Wiener filtering and wavelet thresholding methodshttp://www.archive.org/details/acousticnoiserem00fornLieutenant Commander, United States NavyApproved for public release; distribution is unlimited

Task-Driven Dictionary Learning

Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.Comment: final draft post-refereein

On Kernel Selection of Multivariate Local Polynomial Modelling and its Application to Image Smoothing and Reconstruction

This paper studies the problem of adaptive kernel selection for multivariate local polynomial regression (LPR) and its application to smoothing and reconstruction of noisy images. In multivariate LPR, the multidimensional signals are modeled locally by a polynomial using least-squares (LS) criterion with a kernel controlled by a certain bandwidth matrix. Based on the traditional intersection confidence intervals (ICI) method, a new refined ICI (RICI) adaptive scale selector for symmetric kernel is developed to achieve a better bias-variance tradeoff. The method is further extended to steering kernel with local orientation to adapt better to local characteristics of multidimensional signals. The resulting multivariate LPR method called the steering-kernel-based LPR with refined ICI method (SK-LPR-RICI) is applied to the smoothing and reconstruction problems in noisy images. Simulation results show that the proposed SK-LPR-RICI method has a better PSNR and visual performance than conventional LPR-based methods in image processing. © 2010 The Author(s).published_or_final_versio

Biorthogonality in lapped transforms : a study in high-quality audio compression

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 76-82).by Shiufun Cheung.Ph.D

Advanced Telecommunications and Signal Processing Program

Contains an introduction and reports on twelve research projects.AT&T FellowshipAdvanced Telecommunications Research ProgramINTEL FellowshipU.S. Navy - Office of Naval Research NDSEG Graduate FellowshipMaryland Procurement Office Contract MDA904-93-C-418

The design and implementation of a wideband digital radio receiver

Historically radio has been implemented using largely analogue circuitry. Improvements in mixed signal and digital signal processing technology are rapidly leading towards a largely digital approach, with down-conversion and filtering moving to the digital signal processing domain. Advantages of this technology include increased performance and functionality, as well as reduced cost. Wideband receivers place the heaviest demands on both mixed signal and digital signal processing technology, requiring high spurious free dynamic range (SFDR) and signal processing bandwidths. This dissertation investigates the extent to which current digital technology is able to meet these demands and compete with the proven architectures of analogue receivers. A scalable generalised digital radio receiver capable of operating in the HF and VHF bands was designed, implemented and tested, yielding instantaneous bandwidths in excess of 10 MHz with a spurious-free dynamic range exceeding 80 decibels below carrier (dBc). The results achieved reflect favourably on the digital receiver architecture. While the necessity for minimal analogue circuitry will possibly always exist, digital radio architectures are currently able to compete with analogue counterparts. The digital receiver is simple to manufacture, based on the use of largely commercial off-the-shelf (COTS) components, and exhibits extreme flexibility and high performance when compared with comparably priced analogue receivers

Collaborative spectrum sensing in cognitive radio networks

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated by government agencies. With the explosive emergence of wireless applications, the demands for the RF spectrum are constantly increasing. On the other hand, it has been reported that localised temporal and geographic spectrum utilisation efficiency is extremely low. Cognitive radio is an innovative technology designed to improve spectrum utilisation by exploiting those spectrum opportunities. This ability is dependent upon spectrum sensing, which is one of most critical components in a cognitive radio system. A significant challenge is to sense the whole RF spectrum at a particular physical location in a short observation time. Otherwise, performance degrades with longer observation times since the lagging response to spectrum holes implies low spectrum utilisation efficiency. Hence, developing an efficient wideband spectrum sensing technique is prime important. In this thesis, a multirate asynchronous sub-Nyquist sampling (MASS) system that employs multiple low-rate analog-to-digital converters (ADCs) is developed that implements wideband spectrum sensing. The key features of the MASS system are, 1) low implementation complexity, 2) energy-efficiency for sharing spectrum sensing data, and 3) robustness against the lack of time synchronisation. The conditions under which recovery of the full spectrum is unique are presented using compressive sensing (CS) analysis. The MASS system is applied to both centralised and distributed cognitive radio networks. When the spectra of the cognitive radio nodes have a common spectral support, using one low-rate ADC in each cognitive radio node can successfully recover the full spectrum. This is obtained by applying a hybrid matching pursuit (HMP) algorithm - a synthesis of distributed compressive sensing simultaneous orthogonal matching pursuit (DCS-SOMP) and compressive sampling matching pursuit (CoSaMP). Moreover, a multirate spectrum detection (MSD) system is introduced to detect the primary users from a small number of measurements without ever reconstructing the full spectrum. To achieve a better detection performance, a data fusion strategy is developed for combining sensing data from all cognitive radio nodes. Theoretical bounds on detection performance are derived for distributed cognitive radio nodes suffering from additive white Gaussian noise (AWGN), Rayleigh fading, and log-normal fading channels. In conclusion, MASS and MSD both have a low implementation complexity, high energy efficiency, good data compression capability, and are applicable to distributed cognitive radio networks