An Advanced Signal Processing Toolkit for Java applications
The aim of this study is to examine the capability, performance, and relevance of a signal processing toolkit in Java, a programming language for Web-based applications. Due to the simplicity, ease and application use of the toolkit and with the advanced Internet technologies such as Remote Method Invocation (RMI), a spectral estimation applet has been created in the Java environment. This toolkit also provides an interactive and visual approach in understanding the various theoretical concepts of spectral estimation and shows the need to create more application applets to better understand the various concepts of signal and image processing. This study also focuses on creating a Java toolkit for embedded systems, such as Personal Digital Assistants (PDAs), embedded Java board, and supporting integer precision, and utilizing COordinate Rotation DIgital Computer (CORDIC) algorithm, both aimed to provide good performance in resource-limited environments. The results show a feasibility and necessity of developing a standardized Application Programming Interface (API) for the fixed-point signal processing library.
A Two-Level Reconfigurable Cell Array for Digital Signal Processing
Reconfigurable hardware has become an attractive option for implementing digital signal processing, especially in systems that require both high performance and flexibility. This thesis presents a novel two-level reconfigurable architecture targeted toward systems with these requirements. The architecture supports a large orthogonal design space whereby designers can customize the word length, amount of parallelism, number of functional units, and functional unit connectivity to meet the needs of the application. On the upper level, algorithms are mapped onto an array of 4-bit cells and a hierarchical interconnection fabric. The interconnection structure contains a mesh of 4-bit busses for local data transfer, as well as an H-tree for communicating results between functional units. On the lower level, each cell contains a small matrix of elements that collectively implement all necessary operations. The matrix of elements has only two configurations: one optimized for mathematical functions such as multiply-accumulates, and the other optimized for memory operations. The system also contains pipeline latches to maximize clock rate and throughput. Circuit simulations indicate that the architecture achieves a clock frequency of 200 MHz in a modest 0.25-μm CMOS technology. An initial prototype of the reconfigurable cell has been fabricated in 0.5-μm CMOS and tested for functionality. The estimated execution time for a 16-bit, 256-point Fast Fourier Transform shows a speedup ranging from 1.6 to 14 compared to contemporary digital signal processors.
Blind Adaptive Dereverberation of Speech Signals Using a Microphone Array
In this thesis, we present a blind adaptive speech dereverberation method based on the use of a reduced mutually referenced equalizers (RMRE) criterion. The method is based on the idea of the inversion of single-input multiple-output FIR linear systems, and as such requires the use of multiple microphones. However, unlike many traditional microphone array methods, there is no need for a specific array configuration or geometry. The RMRE method finds a subset of equalizers for a given delay in a single step, without the need for the typical channel estimation step. This makes the method practical in terms of implementation and avoids the pitfalls of the more complicated two step dereverberation approach, typical in many inversion methods. Additionally, only the second-order statistics of the signals recorded by the microphones are used, without the need for utilizing higher-order statistics information typically needed when the channsls have a nonminimum phase response, as is the case with room impulse responses. We present simulations and experimental results that demonstrate the applicability of the method when the input is speech, and show that in the noiseless case, perfect dereverberation can be achieved. We also evaluate its performance in the presence of noise, and we present a possible way to modify the proposed RMRE to work for very low SNR values. We also explore the problems when model-order mismatches are present, and demonstrate that the under-modeling of the channel impulse responses order can be combated by increasing the number of microphones. For order over-estimation, we will show that RMRE can handle such errors with no modification.
Automated Accident Detection in Intersections Via Digital Audio Signal Processing
The aim of this thesis is to design a system for automated accident detection in intersections. The input to the system is a three-second audio signal. The system can be operated in two modes: two-class and multi-class. The output of the two-class system is a label of “crash” or “non-crash”. In the multi-class system, the output is the label of “crash” or various non-crash incidents including “pile drive”, “brake”, and “normal-traffic” sounds. The system designed has three main steps in processing the input audio signal. They are: feature extraction, feature optimization and classification. Five different methods of feature extraction are investigated and compared; they are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstrum transform and Mel frequency cepstral transform. Linear discriminant analysis (LDA) is used to optimize the features obtained in the feature extraction stage by linearly combining the features using different weights. Three types of statistical classifiers are investigated and compared: the nearest neighbor, nearest mean, and maximum likelihood methods. Data collected from Jackson, MS and Starkville, MS and the crash signals obtained from Texas Transportation Institute crash test facility are used to train and test the designed system. The results showed that the wavelet based feature extraction method with LDA and maximum likelihood classifier is the optimum design. This wavelet-based system is computationally inexpensive compared to other methods. The system produced classification accuracies of 95% to 100% when the input signal has a signal-to-noise-ratio of at least 0 decibels. These results show that the system is capable of effectively classifying “crash” or “non-crash” on a given input audio signal.
Ignal Enhancement Using Time-Frequency Based Denoising
This thesis investigates and compares time and wavelet-domain denoising techniques where received signals contain broadband noise. We consider how time and wavelet-domain denoising schemes and their combinations compare in the mean squared error sense. This work applies Wiener prediction and Median filtering as they do not require any prior signal knowledge. In the wavelet-domain we use soft or hard thresholding on the detail coefficients. In addition, we explore the effect of these wavelet-domain thresholding techniques on the coefficients associated with cycle-spinning and the newly proposed recursive cycle-spinning scheme. Finally, we note that thresholding does not make an attempt to de-noise coefficients that remain after thresholding; therefore we apply time domain techniques to the remaining detail coefficients from the first level of decomposition in an attempt to de-noise them further prior to reconstruction. This thesis applies and compares these techniques using a mean squared error criterion to identify the best performing in a robust test signal environment. We find that soft thresholding with Stein’s Unbiased Risk Estimate (SURE) thresholding produces the best mean squared error results in each test case and that the addition of Wiener prediction to the first level of decomposition coefficients leads to a slightly enhanced performance. Finally, we illustrate the effects of denoising algorithms on longer data segments.
Efficient Signal Processing Techniques for Future Wireless Communications Systems
Wireless communications systems are evolving to be more diverse in use and more ubiquitous in nature. It is of fundamental importance that we consume the resources available in such systems, i.e., bandwidth and energy, to preserve room for more users and to preserve longevity. Signal processing can greatly help us achieve this. In this thesis we consider improving the utility of resources available in wireless communications systems. The basic obstacle for most wireless communications systems is the multipath channel that causes intersymbol interference. Channel estimation is a crucial step for recovering the transmitted symbols. Moreover, as more devices are equipped with wireless capabilities, the bandwidth becomes scarce and it is important to allow more than one device or more than one user to use the same frequency range or the same channel. However, this introduces multiuser interference, which is again eliminated only if the channel is known. Furthermore, most wireless systems are battery powered, at least at the transmitter end. Hence it is crucial that energy consumption is minimized to preserve the longevity of the system. The contribution of this thesis is three fold: (i) We propose novel bandwidth efficient blind channel estimation algorithms for single input multiple output systems, and for multiuser OFDM systems. The former exploits cyclostationarity inherent in communications signals. The latter exploits the structure introduced to the transmitted signal via precoding. We consider design of such precoders by optimizing performance metrics such as the bit error rate and signal to interference plus noise ratio. (ii) In the multiuser systems case, we propose a novel cooperative OFDM system and show that, when users face significantly different channel conditions, cooperation can improve the performance of all the cooperating users. (iii) We consider energy efficient training based system estimation in large MIMO systems. The goal there is to minimize energy consumption both in transmission of training symbols and in performing computations. We show that by using a divide and conquer strategy in selecting the active set of transmitters and receivers, it is possible to minimize energy consumption without degrading the accuracy of the channel estimate.
Image Analysis Using a Dual-Tree M-Band Wavelet Transform
We propose a 2D generalization to the M-band case of the dual-tree decomposition structure (initially proposed by N. Kingsbury and further investigated by I. Selesnick) based on a Hilbert pair of wavelets. We particularly address (i) the construction of the dual basis and (ii) the resulting directional analysis. We also revisit the necessary pre-processing stage in the M-band case. While several reconstructions are possible because of the redundancy of the representation, we propose a new optimal signal reconstruction technique, which minimizes potential estimation errors. The effectiveness of the proposed M- band decomposition is demonstrated via denoising comparisons on several image types (natural, texture, seismics), with various M-band wavelets and thresholding strategies. Signicant improvements in terms of both overall noise reduction and direction preservation are observed.
Noise covariance properties in Dual-Tree Wavelet Decompositions
Dual-tree wavelet decompositions have recently gained much popularity, mainly due to their ability to provide an accurate directional analysis of images combined with a reduced redundancy. When the decomposition of a random process is performed – which occurs in particular when an additive noise is corrupting the signal to be analyzed – it is useful to characterize the statistical properties of the dual-tree wavelet coefficients of this process. As dual-tree decompositions constitute overcomplete frame expansions, correlation structures are introduced among the coefficients, even when a white noise is analyzed. In this paper, we show that it is possible to provide an accurate description of the covariance properties of the dual-tree coefficients of a wide-sense stationary process. The expressions of the (cross-) covariance sequences of the coefficients are derived in the one and two-dimensional cases. Asymptotic results are also provided, allowing to predict the behaviour of the second-order moments for large lag values or at coarse resolution. In addition, the crosscorrelations between the primal and dual wavelets, which play a primary role in our theoretical analysis, are calculated for a number of classical wavelet families. Simulation results are finally provided to validate these results.
A Nonlinear Stein Based Estimator for Multichannel Image Denoising
The use of multicomponent images has become widespread with the improvement of multisensor systems having increased spatial and spectral resolutions. However, the observed images are often corrupted by an additive Gaussian noise. In this paper, we are interested in multichannel image denoising based on a multiscale representation of the images. A multivariate statistical approach is adopted to take into account both the spatial and the inter-component correlations existing between the different wavelet subbands. More precisely, we propose a new parametric nonlinear estimator which generalizes many reported denoising methods. The derivation of the optimal parameters is achieved by applying Stein’s principle in the multivariate case. Experiments performed on multispectral remote sensing images clearly indicate that our method outperforms conventional wavelet denoising techniques.
Active control of automobile cabin noise with conventional and advanced speakers
Recently much research has focused on the control of enclosed sound fields, particularly in automobiles. Both Active Noise Control (ANC) and Active Structural Acoustic Control (ASAC) techniques are being applied to problems stemming from power train noise and road noise (noise due to the interaction of the tires with the surface of the road). Due to the low frequency characteristics of these noise problems, large acoustic sources are required to obtain efficient control of the sound field. This creates demand in the automobile industry for compact lightweight sources. This work is concerned with the application of active control to power train noise, as well as road noise in the interior cabin of a sport utility vehicle using advanced, compact lightweight piezoelectric acoustic sources. First, a test structure approximately the same size as the automobile was built to study the principles of active noise control in a cavity. A finite element model of the cavity was created in order to optimize the positions of the error sensors and the control sources. Experimental work was performed with the optimized actuator and sensor locations in order to validate the model, and draw conclusions regarding the conditions to obtain global control of the sound field. Second, a broad-band feedforward filtered-X LMS algorithm was used to control power train noise. Preliminary power train noise tests were conducted using arrangements of four microphones and up to four commercially available speakers for control. Attenuation of seven decibel (dB) at the error sensors was measured in the 40-500 Hz frequency band. The dimensions of the zone of quiet generated by the control were measured, and show that noise reductions were obtained for a large volume surrounding the error sensors. Next, advanced speakers were implemented for active control of power train noise. The results obtained with different arrangements of these speakers were very similar to those obtained with the commercially-available speakers. These advanced speakers use piezoelectric devices to induce the displacement of a speaker membrane, which radiates sound. Their lighter weight and compact dimensions are a significant advantage over conventional speakers, for their application in automobile. Third, preliminary results were obtained for active control of road noise. The controller used an optimized set of four reference signals to control the noise at one error sensor using one control source. Two sets of tests were conducted. The first set of tests was performed on a dynamometer, which simulates the effects of the road on the tires. The second set of tests was performed on a rough road. Reduction of two to four decibel of the sound pressure level at the error sensor was obtained between 100 and 200 Hz.
Digital Signal Processing Maths
Modern digital signal processing makes use of a variety of mathematical techniques. These techniques are used to design and understand efficient filters for data processing and control.
HIERARCHICAL MOTION ESTIMATION FOR EMBEDDED OBJECT TRACKING
This paper presents an algorithm developed to provide automatic motion detection and object tracking embedded within intelligent CCTV systems. The algorithm development focuses on techniques which provide an efficient embedded systems implementation with the ability to target both FPGA and DSP devices. During algorithm development constraints on hardware implementation have been fully considered resulting in an algorithm which, when targeted at current FPGA devices, will take full advantage of the DSP resource commonly provided in such devices. The hierarchical structure of the proposed algorithm provides the system with a multi-level motion estimation process allowing low resolution estimation for motion detection and further higher resolution stages for motion estimation. An initial MATLAB prototype has demonstrated this algorithm capable of object motion estimation while compensating for camera motion, allowing a moving object to be tracked by a moving camera.
Hidden Markov Model based recognition of musical pattern in South Indian Classical Music
Automatic recognition of musical patterns plays a crucial part in Musicological and Ethno musicological research and can become an indispensable tool for the search and comparison of music extracts within a large multimedia database. This paper finds an efficient method for recognizing isolated musical patterns in a monophonic environment, using Hidden Markov Model. Each pattern, to be recognized, is converted into a sequence of frequency jumps by means of a fundamental frequency tracking algorithm, followed by a quantizer. The resulting sequence of frequency jumps is presented to the input of the recognizer which use Hidden Markov Model. The main characteristic of Hidden Markov Model is that it utilizes the stochastic information from the musical frame to recognize the pattern. The methodology is tested in the context of South Indian Classical Music, which exhibits certain characteristics that make the classification task harder, when compared with Western musical tradition. Recognition of 100% has been obtained for the six typical music pattern used in practise. South Indian classical instrument, flute is used for the whole experiment.
A DSP-Based Computational Engine For a Brain-Machine Interface
The fields of neurobiology and electrical engineering have come together to pursue an integrated Brain-Machine Interface (BMI). Signal processing methods are used to find mapping algorithms between motor cortex neural firing rate and hand position. This cognitive extension could help patients with quadriplegia regain some independence using a thought-controlled robot arm. Current signal processing methods to achieve realtime neural-to-motor translation involve large, multi-processor systems to produce motor control parameters. Eventually, software running in a portable signal processing system is needed to allow for the patient to have the BMI in a backpack or attached to a wheelchair. This thesis presents a DSP-Based Computational Engine for a Brain-Machine Interface. The development of a DSP Board based on the Texas Instruments TMS320VC33 DSP will be presented, along with implementations of two digital filters and their training methods: 1) FIR trained with Normalized Least Mean Square Adaptive Filter (NLMS) and 2) Recurrent Multi-Layer Perceptron (RMLP) trained with Real-Time Recurrent Learning (RTRL). The requirements of the DSP Board, component selection and integration, and control software are discussed. The DSP implementations of the digital filters are presented, along with performance and timing analysis in real data collected from an Owl Monkey at Duke University. The weights of the FIR-NLMS filter converged similarly on the DSP as they did in MATLAB. Likewise, the weights of the RMLP-RTRL filter converged similarly on the DSP as they did using the Backpropagation Through Time method in NeuroSolutions. The custom DSP Board and two digital algorithms implemented in this thesis create a starting point for an integrated, portable, real-time signal processing solution for a Brain-Machine Interface.
Energy Profiling of DSP Applications, A Case Study of an Intelligent ECG Monitor
Proper balance of power and performance for optimum system organization requires precise profiling of the power consumption of different hardware subsystems as well as software functions. Moreover, power consumption of mobile systems is even more important, since the battery is a large portion of the overall size and weight of the system. Average power consumption is only a crude estimate of power requirements and battery life; a much better estimate can be made using dynamic power consumption. Dynamic power consumption is a function of the execution profile of the given application running on specific hardware platform. In this paper we introduce a new environment for energy profiling of DSP applications. The environment consists of a JTAG emulator, a high-resolution HP 3583A multimeter and a workstation that controls devices and stores the traces. We use Texas Instruments’ Real Time Data Exchange mechanism (RTDXÔ) to generate an execution profile and custom procedures for energy profile data acquisition using GPIB interface. We developed custom procedures to correlate and analyze both energy and execution profiles. The environment allows us to improve the system power consumption through changes in software organization and to measure real battery life for the given hardware, software and battery configuration. As a case study, we present the analysis of a real-time portable ECG monitor implemented using a Texas Instruments TMS320C5410-100 processor board, and a Del Mar PWA ECG Amplifier.
Introduction to Compressed Sensing
Chapter 1 of the book: "Compressed Sensing: Theory and Applications".
Wavelet Denoising for TDR Dynamic Range Improvement
A technique is presented for removing large amounts of noise present in time-domain-reflectometry (TDR) waveforms to increase the dynamic range of TDR waveforms and TDR based s-parameter measurements.
Development of a real time test platform for motor drive algorithms
In this thesis a real time test platform for a permanent magnet synchronous motor is developed. The implemented algorithm is Field Oriented Control (FOC) and it is implemented on a Texas Instruments TMS320F2808 Digital Signal Processor (DSP). The platform is developed in a rapid prototyping approach using Matlab/Simulink and the Real Time Workshop (RTW) packages.With this software the control algorithm and its interface to different DSP modules, such as A/D converter and PWM module, is constructed as a Simulink block scheme. The blocks used come from ordinary Simulink libraries and libraries provided by the RTW packages. From the Simulink block scheme Matlab can auto generate embedded C code adapted for different embedded targets, in this case the 2808 DSP.The developed real time test platform is also a Simulink model, though different from the algorithm model. When the start simulation command is given in the platform model a Graphical User Interface is loaded which lets the user specify motor parameters and certain algorithm parameters. Once the parameters are chosen RTW generates code from the algorithm model, loads it into the DSP and runs the generated program. From the platform model it is possible to set the reference speed of the motor in real time and monitor/log motor parameters such as actual speed and stator currents.
IMPLEMENTATION OF PERIODOGRAM SMOOTHING OF NOISYIMPLEMENTATION OF PERIODOGRAM SMOOTHING OF NOISY SIGNALS USING TMS320C6713 DSK
Periodogram Smoothing is a technique of power spectrum estimation. The discrete Fourier transform of a digital signal simply resolves the frequency components. The algorithm is implemented on Texas Instruments’ TMS320C6713 DSP Starter Kit (DSK). This is a 32-bit floating-point digital signal processor running at 225 MHz. The programs are basically written in the C programming language. However, those sections of code which are time-critical and memory-critical are written in assembly language of C6713. A MATLAB™ graphical user interface is also provided. The MATLAB™ program calls C programs loaded in Code Composer Studio (CCS). The C programs in turn call the assembly programs when required.
