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Fast, variable system delay correction for spiral MRI

Description

Magnetic Resonance Imaging using spiral trajectories has many advantages in speed, efficiency in data-acquistion and robustness to motion and flow related artifacts. The increase in sampling speed, however, requires high performance of the gradient system. Hardware inaccuracies from system delays

Magnetic Resonance Imaging using spiral trajectories has many advantages in speed, efficiency in data-acquistion and robustness to motion and flow related artifacts. The increase in sampling speed, however, requires high performance of the gradient system. Hardware inaccuracies from system delays and eddy currents can cause spatial and temporal distortions in the encoding gradient waveforms. This causes sampling discrepancies between the actual and the ideal k-space trajectory. Reconstruction assuming an ideal trajectory can result in shading and blurring artifacts in spiral images. Current methods to estimate such hardware errors require many modifications to the pulse sequence, phantom measurements or specialized hardware. This work presents a new method to estimate time-varying system delays for spiral-based trajectories. It requires a minor modification of a conventional stack-of-spirals sequence and analyzes data collected on three orthogonal cylinders. The method is fast, robust to off-resonance effects, requires no phantom measurements or specialized hardware and estimate variable system delays for the three gradient channels over the data-sampling period. The initial results are presented for acquired phantom and in-vivo data, which show a substantial reduction in the artifacts and improvement in the image quality.

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Date Created
2013

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Clinically relevant classification and retrieval of diabetic retinopathy images

Description

Diabetic retinopathy (DR) is a common cause of blindness occurring due to prolonged presence of diabetes. The risk of developing DR or having the disease progress is increasing over time. Despite advances in diabetes care over the years, DR remains

Diabetic retinopathy (DR) is a common cause of blindness occurring due to prolonged presence of diabetes. The risk of developing DR or having the disease progress is increasing over time. Despite advances in diabetes care over the years, DR remains a vision-threatening complication and one of the leading causes of blindness among American adults. Recent studies have shown that diagnosis based on digital retinal imaging has potential benefits over traditional face-to-face evaluation. Yet there is a dearth of computer-based systems that can match the level of performance achieved by ophthalmologists. This thesis takes a fresh perspective in developing a computer-based system aimed at improving diagnosis of DR images. These images are categorized into three classes according to their severity level. The proposed approach explores effective methods to classify new images and retrieve clinically-relevant images from a database with prior diagnosis information associated with them. Retrieval provides a novel way to utilize the vast knowledge in the archives of previously-diagnosed DR images and thereby improve a clinician's performance while classification can safely reduce the burden on DR screening programs and possibly achieve higher detection accuracy than human experts. To solve the three-class retrieval and classification problem, the approach uses a multi-class multiple-instance medical image retrieval framework that makes use of spectrally tuned color correlogram and steerable Gaussian filter response features. The results show better retrieval and classification performances than prior-art methods and are also observed to be of clinical and visual relevance.

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Date Created
2012

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Image processing using approximate data-path units

Description

In this work, we present approximate adders and multipliers to reduce data-path complexity of specialized hardware for various image processing systems. These approximate circuits have a lower area, latency and power consumption compared to their accurate counterparts and produce fairly

In this work, we present approximate adders and multipliers to reduce data-path complexity of specialized hardware for various image processing systems. These approximate circuits have a lower area, latency and power consumption compared to their accurate counterparts and produce fairly accurate results. We build upon the work on approximate adders and multipliers presented in [23] and [24]. First, we show how choice of algorithm and parallel adder design can be used to implement 2D Discrete Cosine Transform (DCT) algorithm with good performance but low area. Our implementation of the 2D DCT has comparable PSNR performance with respect to the algorithm presented in [23] with ~35-50% reduction in area. Next, we use the approximate 2x2 multiplier presented in [24] to implement parallel approximate multipliers. We demonstrate that if some of the 2x2 multipliers in the design of the parallel multiplier are accurate, the accuracy of the multiplier improves significantly, especially when two large numbers are multiplied. We choose Gaussian FIR Filter and Fast Fourier Transform (FFT) algorithms to illustrate the efficacy of our proposed approximate multiplier. We show that application of the proposed approximate multiplier improves the PSNR performance of 32x32 FFT implementation by 4.7 dB compared to the implementation using the approximate multiplier described in [24]. We also implement a state-of-the-art image enlargement algorithm, namely Segment Adaptive Gradient Angle (SAGA) [29], in hardware. The algorithm is mapped to pipelined hardware blocks and we synthesized the design using 90 nm technology. We show that a 64x64 image can be processed in 496.48 µs when clocked at 100 MHz. The average PSNR performance of our implementation using accurate parallel adders and multipliers is 31.33 dB and that using approximate parallel adders and multipliers is 30.86 dB, when evaluated against the original image. The PSNR performance of both designs is comparable to the performance of the double precision floating point MATLAB implementation of the algorithm.

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Date Created
2013

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Designing m-health modules with sensor interfaces for DSP education

Description

Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices

Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices for real-time data analysis and decision-making in digital signal processing (DSP) applications. Most of the current efforts in DSP education focus on building tools to facilitate understanding of the mathematical principles. However, there is a disconnect between real-world data processing problems and the material presented in a DSP course. Sophisticated mobile interfaces and apps can potentially play a crucial role in providing a hands-on-experience with modern DSP applications to students. In this work, a new paradigm of DSP learning is explored by building an interactive easy-to-use health monitoring application for use in DSP courses. This is motivated by the increasing commercial interest in employing mobile phones for real-time health monitoring tasks. The idea is to exploit the computational abilities of the Android platform to build m-Health modules with sensor interfaces. In particular, appropriate sensing modalities have been identified, and a suite of software functionalities have been developed. Within the existing framework of the AJDSP app, a graphical programming environment, interfaces to on-board and external sensor hardware have also been developed to acquire and process physiological data. The set of sensor signals that can be monitored include electrocardiogram (ECG), photoplethysmogram (PPG), accelerometer signal, and galvanic skin response (GSR). The proposed m-Health modules can be used to estimate parameters such as heart rate, oxygen saturation, step count, and heart rate variability. A set of laboratory exercises have been designed to demonstrate the use of these modules in DSP courses. The app was evaluated through several workshops involving graduate and undergraduate students in signal processing majors at Arizona State University. The usefulness of the software modules in enhancing student understanding of signals, sensors and DSP systems were analyzed. Student opinions about the app and the proposed m-health modules evidenced the merits of integrating tools for mobile sensing and processing in a DSP curriculum, and familiarizing students with challenges in modern data-driven applications.

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Date Created
2013

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Development of Automatic Control Software for a Patient Monitoring Camera System

Description

Electroencephalogram (EEG) used simultaneously with video monitoring can record detailed patient physiology during a seizure to aid diagnosis. However, current patient monitoring systems typically require a patient to stay in view of a fixed camera limiting their freedom of movement.

Electroencephalogram (EEG) used simultaneously with video monitoring can record detailed patient physiology during a seizure to aid diagnosis. However, current patient monitoring systems typically require a patient to stay in view of a fixed camera limiting their freedom of movement. The goal of this project is to design an automatic patient monitoring system with software to track patient movement in order to increase a patient's mobility. This report discusses the impact of an automatic patient monitoring system and the design steps used to create and test a functional prototype.

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2014-05

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Feature extraction from compressive cameras with application to activity recognition

Description

Recent advances in camera architectures and associated mathematical representations now enable compressive acquisition of images and videos at low data-rates. While most computer vision applications of today are composed of conventional cameras, which collect a large amount redundant data and

Recent advances in camera architectures and associated mathematical representations now enable compressive acquisition of images and videos at low data-rates. While most computer vision applications of today are composed of conventional cameras, which collect a large amount redundant data and power hungry embedded systems, which compress the collected data for further processing, compressive cameras offer the advantage of direct acquisition of data in compressed domain and hence readily promise to find applicability in computer vision, particularly in environments hampered by limited communication bandwidths. However, despite the significant progress in theory and methods of compressive sensing, little headway has been made in developing systems for such applications by exploiting the merits of compressive sensing. In such a setting, we consider the problem of activity recognition, which is an important inference problem in many security and surveillance applications. Since all successful activity recognition systems involve detection of human, followed by recognition, a potential fully functioning system motivated by compressive camera would involve the tracking of human, which requires the reconstruction of atleast the initial few frames to detect the human. Once the human is tracked, the recognition part of the system requires only the features to be extracted from the tracked sequences, which can be the reconstructed images or the compressed measurements of such sequences. However, it is desirable in resource constrained environments that these features be extracted from the compressive measurements without reconstruction. Motivated by this, in this thesis, we propose a framework for understanding activities as a non-linear dynamical system, and propose a robust, generalizable feature that can be extracted directly from the compressed measurements without reconstructing the original video frames. The proposed feature is termed recurrence texture and is motivated from recurrence analysis of non-linear dynamical systems. We show that it is possible to obtain discriminative features directly from the compressed stream and show its utility in recognition of activities at very low data rates.

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Date Created
2012

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Camera calibration using adaptive segmentation and ellipse fitting for localizing control points

Description

There is a growing interest for improved high-accuracy camera calibration methods due to the increasing demand for 3D visual media in commercial markets. Camera calibration is used widely in the fields of computer vision, robotics and 3D reconstruction. Camera calibration

There is a growing interest for improved high-accuracy camera calibration methods due to the increasing demand for 3D visual media in commercial markets. Camera calibration is used widely in the fields of computer vision, robotics and 3D reconstruction. Camera calibration is the first step for extracting 3D data from a 2D image. It plays a crucial role in computer vision and 3D reconstruction due to the fact that the accuracy of the reconstruction and 3D coordinate determination relies on the accuracy of the camera calibration to a great extent. This thesis presents a novel camera calibration method using a circular calibration pattern. The disadvantages and issues with existing state-of-the-art methods are discussed and are overcome in this work. The implemented system consists of techniques of local adaptive segmentation, ellipse fitting, projection and optimization. Simulation results are presented to illustrate the performance of the proposed scheme. These results show that the proposed method reduces the error as compared to the state-of-the-art for high-resolution images, and that the proposed scheme is more robust to blur in the imaged calibration pattern.

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Date Created
2012

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Magnetic resonance imaging of the brain: enabling advances in efficient non-cartesian sampling

Description

Magnetic Resonance Imaging (MRI) is limited in speed and resolution by the inherently low Signal to Noise Ratio (SNR) of the underlying signal. Advances in sampling efficiency are required to support future improvements in scan time and resolution. SNR efficiency

Magnetic Resonance Imaging (MRI) is limited in speed and resolution by the inherently low Signal to Noise Ratio (SNR) of the underlying signal. Advances in sampling efficiency are required to support future improvements in scan time and resolution. SNR efficiency is improved by sampling data for a larger proportion of total imaging time. This is challenging as these acquisitions are typically subject to artifacts such as blurring and distortions. The current work proposes a set of tools to help with the creation of different types of SNR efficient scans. An SNR efficient pulse sequence providing diffusion imaging data with full brain coverage and minimal distortion is first introduced. The proposed method acquires single-shot, low resolution image slabs which are then combined to reconstruct the full volume. An iterative deblurring algorithm allowing the lengthening of spiral SPoiled GRadient echo (SPGR) acquisition windows in the presence of rapidly varying off-resonance fields is then presented. Finally, an efficient and practical way of collecting 3D reformatted data is proposed. This method constitutes a good tradeoff between 2D and 3D neuroimaging in terms of scan time and data presentation. These schemes increased the SNR efficiency of currently existing methods and constitute key enablers for the development of SNR efficient MRI.

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Date Created
2011

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Filtering techniques for improving radio-frequency identification machine control

Description

Human operators have difficulty driving cranes quickly, accurately, and safely because of the slow response of heavy crane structures, non-intuitive control interfaces, and payload oscillations. Recently, a novel hand-motion crane control system has been proposed to improve performance by coupling

Human operators have difficulty driving cranes quickly, accurately, and safely because of the slow response of heavy crane structures, non-intuitive control interfaces, and payload oscillations. Recently, a novel hand-motion crane control system has been proposed to improve performance by coupling an intuitive control interface with an element that reduces the complex oscillatory behavior of the payload. Hand-motion control allows operators to drive a crane by simply moving a hand-held radio-frequency tag through the desired path. Real-time location sensors are used to track the movements of the tag and the tag position is used in a feedback control loop to drive the crane. An input shaper is added to eliminate dangerous payload oscillations. However, tag position measurements are corrupted by noise. It is important to understand the noise properties so that appropriate filters can be designed to mitigate the effects of noise and improve tracking accuracy. This work discusses implementing filtering techniques to address the issue of noise in the operating environment. Five different filters are used on experimentally-acquired tag trajectories to reduce noise. The filtered trajectories are then used to drive crane simulations. Filter performance is evaluated with respect to the energy usage of the crane trolley, the settling time of the crane payload oscillations, and the safety corridor of the crane trajectory. The effects of filter window lengths on these parameters are also investigated. An adaptive filtering technique, namely the Kalman filter, adapts to the noise characteristics of the workspace to minimize the tag tracking error and performs better than the other filtering techniques examined.

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Date Created
2012

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Fully passive wireless acquisition of neuropotentials

Description

The ability to monitor electrophysiological signals from the sentient brain is requisite to decipher its enormously complex workings and initiate remedial solutions for the vast amount of neurologically-based disorders. Despite immense advancements in creating a variety of instruments to record

The ability to monitor electrophysiological signals from the sentient brain is requisite to decipher its enormously complex workings and initiate remedial solutions for the vast amount of neurologically-based disorders. Despite immense advancements in creating a variety of instruments to record signals from the brain, the translation of such neurorecording instrumentation to real clinical domains places heavy demands on their safety and reliability, both of which are not entirely portrayed by presently existing implantable recording solutions. In an attempt to lower these barriers, alternative wireless radar backscattering techniques are proposed to render the technical burdens of the implant chip to entirely passive neurorecording processes that transpire in the absence of formal integrated power sources or powering schemes along with any active circuitry. These radar-like wireless backscattering mechanisms are used to conceive of fully passive neurorecording operations of an implantable microsystem. The fully passive device potentially manifests inherent advantages over current wireless implantable and wired recording systems: negligible heat dissipation to reduce risks of brain tissue damage and minimal circuitry for long term reliability as a chronic implant. Fully passive neurorecording operations are realized via intrinsic nonlinear mixing properties of the varactor diode. These mixing and recording operations are directly activated by wirelessly interrogating the fully passive device with a microwave carrier signal. This fundamental carrier signal, acquired by the implant antenna, mixes through the varactor diode along with the internal targeted neuropotential brain signals to produce higher frequency harmonics containing the targeted neuropotential signals. These harmonics are backscattered wirelessly to the external interrogator that retrieves and recovers the original neuropotential brain signal. The passive approach removes the need for internal power sources and may alleviate heat trauma and reliability issues that limit practical implementation of existing implantable neurorecorders.

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Date Created
2014