Browsing by Subject "Signal processing"

Now showing 1 - 6 of 6
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    Adaptive filter design for sparse signal estimation.
    (2011-12) Yang, Jie
    Recently, sparse signal estimation has become an increasingly important research area in signal processing due to its wide range of applications. Efficient adaptive algorithms have been developed for estimation of various sparse signals, and the approaches developed are usually application-specific. In this dissertation, we investigate the algorithm and system design for sparse signal estimation of several applications of practical interest, specifically echo cancellation, compressive sensing, and power amplifier pre-distortion. For echo cancellation, different approaches are considered to find the optimal solution. A series of algorithms are proposed to improve the performance and reduce the cost. Specifically, we describe novel adaptive tap algorithms with selective update criteria, a μ-law proportionate technique incorporated with efficient memorized proportionate Affine Projection Algorithms, and a new class of proportionate algorithms with gradient-controlled individual step sizes which can be implemented either in the time domain or the frequency domain. For compressive sensing algorithms with the l0 norm constraint, a sparse LMS algorithm with segment zero attractors is introduced. It can achieve significant convergence and error performance improvements while providing reduced computational cost, especially for large sparse systems with colored inputs. Such filters can also be combined with cascade or multistage realizations, thereby yielding even more efficient implementations. We also describe new results for the non-linear signal estimation problem in power amplifier (PA) pre-distortion with dynamic nonlinearities, where the signal can be represented using a Volterra series with sparse coefficients. An efficient solution using a power-indexed look-up table (LUT) based digital pre-distortion (DPD) is proposed to address the current challenge of poor dynamic performance of a PA operating with wideband signals. Experimental results obtained using a 2 GHz power amplifier driven by a 2-carrier WCDMA signal demonstrate very robust and stable performance for the PA in dynamic environments.
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    Aggregating VMT within Predefined Geographic Zones by Cellular Assignment: A Non-GPS-Based Approach to Mileage- Based Road Use Charging
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-08) Davis, Brian; Donath, Max
    Currently, most of the costs associated with operating and maintaining the roadway infrastructure are paid for by revenue collected from the motor fuel use tax. As fuel efficiency and the use of alternative fuel vehicles increases, alternatives to this funding method must be considered. One such alternative is to assess mileage based user fees (MBUF) based on the vehicle miles traveled (VMT) aggregated within the predetermined geographic areas, or travel zones, in which the VMT is generated. Most of the systems capable of this use Global Positioning Systems (GPS). However, GPS has issues with public perception, commonly associated with unwanted monitoring or tracking and is thus considered an invasion of privacy. The method proposed here utilizes cellular assignment, which is capable of determining a vehicle’s current travel zone, but is incapable of determining a vehicle’s precise location, thus better preserving user privacy. This is accomplished with a k-nearest neighbors (KNN) machine learning algorithm focused on the boundary of such travel zones. The work described here focuses on the design and evaluation of algorithms and methods that when combined, would enable such a system. The primary experiment performed evaluates the accuracy of the algorithm at sample boundaries in and around the commercial business district of Minneapolis, Minnesota. The results show that with the training data available, the algorithm can correctly detect when a vehicle crosses a boundary to within ±2 city blocks, or roughly ±200 meters, and is thus capable of assigning the VMT to the appropriate zone. The findings imply that a cellular-based VMT system may successfully aggregate VMT by predetermined geographic travel zones without infringing on the drivers’ privacy.
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    Low-power architectures for signal processing and classification systems.
    (2012-07) Ayinala, Manohar
    Digital signal processing and classification algorithms play a crucial role in modern day biomedical monitoring systems. Fortunately, emerging sensors and stimulators as well as specialized networking technologies have enabled biomedical devices to advance to new frontiers. Deep-brain stimulators, for instance, offer unprecedented modalities for delivering therapy to patients affected by neurological conditions, ranging from Parkinson’s disease to epilepsy; out-patient monitoring networks raise the possibility of comprehensive yet cost-scalable healthcare delivery over large populations with increasingly diverse disease states. The central need, as these systems advance towards intelligent, closed-loop operation, is the ability to detect specific physiological states of interest from signals that are available through sensors. A key challenge in closed-loop biomedical systems is the ability to detect complex physiological states from the patient data within a constrained power budget. Signal processing and data-driven machine learning techniques are major enablers for modeling and detection of such states. However, the computational power scales with the complexity of models required. This thesis considers the VLSI implementation of basic signal processing techniques such as fast Fourier transform (FFT), power spectral density (PSD) computation. Reconfigurable architectures for classification algorithms including support vector machines (SVM) and Adaboost are also presented. The proposed architectures improve performance and reduce area/power consumption. First, we present a novel methodology to design parallel pipelined FFT architectures using folding transformation and register minimization techniques. Novel parallelpipelined architectures for the computation of complex valued fast Fourier transform are derived. The proposed architectures overcome prior bottlenecks and achieve full hardware utilization. The operating frequency of the proposed architecture can be decreased which in turn reduces the power consumption. This significantly reduces power at same speed or increases speed at same power consumption level. The power consumption can be reduced up to 37% in 2-parallel architectures. Further, we propose a novel approach to develop pipelined fast Fourier transform (FFT) architectures for realvalued signals. Novel 2-parallel and 4-parallel architectures are presented for radix-23 and radix-24 algorithms. The proposed radix-23 and radix-24 architectures lead to low hardware complexity compared to a prior RFFT architecture. We propose an efficient architecture for memory-based in-place FFT/IFFT computation. A conflict-free memory addressing scheme is proposed to ensure the continuous operation of the FFT processor. The proposed architecture requires fewer computation cycles along with the low hardware cost compared to prior work. We then present a low-complexity algorithm and architecture to compute power spectral density (PSD) using the Welch method. The complexity reduction comes at the cost of slight performance loss in accuracy due to the approximation used for the implementation of the fractional delay filter. The performance loss is 6-8% using fractional delay filter with 2-3 multipliers. A novel architecture is presented based on the proposed algorithm which consumes 33% less energy compared to the original method. We propose a low-energy reconfigurable architecture for support vector machines (SVMs) based on approximate computing by exploiting the inherent error resilience in the computation. We present two design optimizations, fixed-width multiply-add and non-uniform look-up table (LUT) for exponent function to minimize power consumption and hardware complexity while retaining the classification performance. The proposed design consumes 31% less energy on average compared to a conventional design. Finally, we present a novel low-complexity patient-specific algorithm for seizure prediction using spectral power features. The proposed algorithm achieves a sensitivity of 94.375% for a total of 71 seizure events with a low false alarm rate of 0.13 per hour and 6.5% of time spent in false alarms using an average of 5 features for the Freiburg database. The low computational complexity of the proposed algorithm makes it suitable for an implantable device.
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    Noninvasive imaging of three-dimensional ventricular electrical activity
    (2012-08) Han, Chengzong
    Noninvasive imaging of cardiac electrical activity is of great importance and can facilitate basic cardiovascular research and clinical diagnosis and management of various malignant cardiac arrhythmias. This dissertation research is aimed to investigate a novel physical-model-based 3-dimensional cardiac electrical imaging (3DCEI) approach. The 3DCEI approach is developed by mathematically combining high-density body surface electrocardiograms (ECGs) with the anatomical information. Computer simulation study and animal experiments were conducted to rigorously evaluate the performance of 3DCEI. The simulation results demonstrate that 3DCEI can localize the origin of activation and image the activation sequence throughout the three-dimensional ventricular myocardium. The performance of 3DCEI was also experimentally and rigorously evaluated through well-controlled animal validation studies in both the small animal model (rabbit) and large animal model (canine), with the aid of simultaneous intramural recordings from intra-cardiac mapping using plunge-needle electrodes inserted in the ventricular myocardium. The clinical relevance of 3DCEI was further demonstrated by investigating 3DCEI in cardiac arrhythmias from animal models with experimentally-induced cardiovascular diseases. The consistent agreement between the non-invasively imaged activation sequences and its directly measured counterparts in both the rabbit heart and canine heart implies that 3DCEI is feasible in reconstructing the spatial patterns of ventricular activation sequences, localizing the arrhythmogenic foci, and imaging dynamically changing arrhythmia on a beat-to-beat basis. The promising results presented in this dissertation study suggest that this cardiac electrical imaging approach may provide an important alternative for non-invasively imaging cardiac electrical activity throughout ventricular myocardium and may potentially become an important tool to facilitate clinical diagnosis and treatments of malignant ventricular arrhythmias.
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    Structured sparse models with applications
    (2012-10) Sprechmann, Pablo G.
    Sparse models assume minimal prior knowledge about the data, asserting that the signal has many coefficients close or equal to zero when represented in a given domain. From a data modeling point of view, sparsity can be seen as a form of regularization, that is, as a device to restrict or control the set of coefficient values which are allowed in the model to produce an estimate of the data. In this way, flexibility of the model (that is, the ability of a model to fit given data) is reduced, and robustness is gained by ruling out unrealistic estimates of the coefficients. Implicitly, standard sparse models give the same relevance to all of the very large number of subsets of sparse nonzero coefficients (a number which grows exponentially with the number of atoms in the dictionary). This assumption can be easily proved false in many practical cases. Signals have in general a richer underlying structure that is merely disregarded by the model. In many situations, standard sparse models represent a very good trade off between model simplicity and accuracy. However, many practical situations could greatly benefit from exploiting the structure present in the data, potentially for interpretability purposes, improve performance and faster processing. The main goal of this thesis is to explore different ways of including data structure into sparse models and to evaluate them in real image and signal processing applications. The main directions of research are: (i) extending sparse models through imposing structure in the sparsity patterns of non-zero coefficients in order to stabilize the estimation and account for valuable prior knowledge of the signals; (ii) analyzing how this impacts in challenging real applications where the problem of estimating the model coefficients is very ill-posed. As a fundamental example, the problem of monaural source separation will be extensively evaluated throughout the thesis; (iii) studying ways of exploiting the underlying structure of the data in order to speed up the coding process. One of the most important challenges in sparse modeling is the relatively high computational complexity of the inference algorithms, which is of critical importance when dealing with very large scale (modern-size) applications as well as real-time processing.
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    System study of two dimensional magnetic recording system
    (2014-08) Wang, Yao
    Two-dimensional magnetic recording (TDMR) has been proposed as a promising approach for ultra-high densities towards 10 Tbits/in2. How to effectively write and detect the data to reach such a high density is a challenge. For read back and detection process, a novel system design for sensing very high density magnetic recording data is investigated. The rotated single head (RSH) with oversampled signals, minimum mean squared error equalizers and pattern-dependent noise prediction detector has been proposed. The bit error rate (BER) can be decreased by a factor of 5 compared to a normally oriented head array, increasing user bit density to 10.04 Tbits/in2 with conventional media. Simulation indicates that a rotated head achieves a density gain of 1.7x (single head) or 2.1x (array) over a normally oriented single head (NSH) at a target BER of 10-3 with sampling period of 2nm. The study indicates that the significant improvement in performance of the rotated head compared to the normally oriented head can be attributed to the larger amplitude of its dibit response and the reduced overlap between conditional probability density functions. The proposed reader has been also applied to bit patterned magnetic recording: it has more than 20 dB gain compared to a normally oriented head array for reading back at 10 Tbits/in2. For the writing process, micromagnetic writing on 8nm grains and readback with various readers has also been studied. For a conventional writer recording a pseudo-random binary sequence with 2 Tbits/in2 channel bit density, user densities of 1.52 Tbits/in2 and 1.09 Tbits/in2 can be achieved with a RSH and a NSH, respectively, using oversampling signal processing. Simulation indicates that a RSH with multiple scans and 2D equalization provides better resistance to a skew angle of 15° than NSH. An optimized shingled writer is proposed; simulation indicates that a RSH and rotated head array can reach a user areal density of 3.76 and 4.52 Tbits/in2 for 2 grains per channel bit, which is close to the predicted maximum user areal density (4.66 Tbits/in2) for this grain size obtained with an ideal writer and reader.