Matching Items (60)
Filtering by
- All Subjects: Electrical Engineering
Description
Video denoising has been an important task in many multimedia and computer vision applications. Recent developments in the matrix completion theory and emergence of new numerical methods which can efficiently solve the matrix completion problem have paved the way for exploration of new techniques for some classical image processing tasks. Recent literature shows that many computer vision and image processing problems can be solved by using the matrix completion theory. This thesis explores the application of matrix completion in video denoising. A state-of-the-art video denoising algorithm in which the denoising task is modeled as a matrix completion problem is chosen for detailed study. The contribution of this thesis lies in both providing extensive analysis to bridge the gap in existing literature on matrix completion frame work for video denoising and also in proposing some novel techniques to improve the performance of the chosen denoising algorithm. The chosen algorithm is implemented for thorough analysis. Experiments and discussions are presented to enable better understanding of the problem. Instability shown by the algorithm at some parameter values in a particular case of low levels of pure Gaussian noise is identified. Artifacts introduced in such cases are analyzed. A novel way of grouping structurally-relevant patches is proposed to improve the algorithm. Experiments show that this technique is useful, especially in videos containing high amounts of motion. Based on the observation that matrix completion is not suitable for denoising patches containing relatively low amount of image details, a framework is designed to separate patches corresponding to low structured regions from a noisy image. Experiments are conducted by not subjecting such patches to matrix completion, instead denoising such patches in a different way. The resulting improvement in performance suggests that denoising low structured patches does not require a complex method like matrix completion and in fact it is counter-productive to subject such patches to matrix completion. These results also indicate the inherent limitation of matrix completion to deal with cases in which noise dominates the structural properties of an image. A novel method for introducing priorities to the ranked patches in matrix completion is also presented. Results showed that this method yields improved performance in general. It is observed that the artifacts in presence of low levels of pure Gaussian noise appear differently after introducing priorities to the patches and the artifacts occur at a wider range of parameter values. Results and discussion suggesting future ways to explore this problem are also presented.
ContributorsMaguluri, Hima Bindu (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Claveau, Claude (Committee member) / Arizona State University (Publisher)
Created2013
Description
Power management plays a very important role in the current electronics industry. Battery powered and handheld applications require novel power management techniques to extend the battery life. Most systems have multiple voltage regulators to provide power sources to the different circuit blocks and/or sub-systems. Some of these voltage regulators are low dropout regulators (LDOs) which typically require output capacitors in the range of 1's to 10's of µF. The necessity of output capacitors occupies valuable board space and can add additional integrated circuit (IC) pin count. A high IC pin count can restrict LDOs for system-on-chip (SoC) solutions. The presented research gives the user an option with regard to the external capacitor; the output capacitor can range from 0 - 1µF for a stable response. In general, the larger the output capacitor, the better the transient response. Because the output capacitor requirement is such a wide range, the LDO presented here is ideal for any application, whether it be for a SoC solution or stand-alone LDO that desires a filtering capacitor for optimal transient performance. The LDO architecture and compensation scheme provide a stable output response from 1mA to 200mA with output capacitors in the range of 0 - 1µF. A 2.5V, 200mA any-cap LDO was fabricated in a proprietary 1.5µm BiCMOS process, consuming 200µA of ground pin current (at 1mA load) with a dropout voltage of 250mV. Experimental results show that the proposed any-cap LDO exceeds transient performance and output capacitor requirements compared to previously published work. The architecture also has excellent line and load regulation and less sensitive to process variation. Therefore, the presented any-cap LDO is ideal for any application with a maximum supply rail of 5V.
ContributorsTopp, Matthew (Author) / Bakkaloglu, Bertan (Thesis advisor) / Thornton, Trevor (Committee member) / Ozev, Sule (Committee member) / Arizona State University (Publisher)
Created2012
Description
Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the "smart grid," an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated. The algorithm is found to perform well on many types of faults commonly occurring in PV arrays. Among several types of detection algorithms considered, only the MCD shows high performance on both types of faults.
ContributorsBraun, Henry (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this thesis, we consider the problem of fast and efficient indexing techniques for time sequences which evolve on manifold-valued spaces. Using manifolds is a convenient way to work with complex features that often do not live in Euclidean spaces. However, computing standard notions of geodesic distance, mean etc. can get very involved due to the underlying non-linearity associated with the space. As a result a complex task such as manifold sequence matching would require very large number of computations making it hard to use in practice. We believe that one can device smart approximation algorithms for several classes of such problems which take into account the geometry of the manifold and maintain the favorable properties of the exact approach. This problem has several applications in areas of human activity discovery and recognition, where several features and representations are naturally studied in a non-Euclidean setting. We propose a novel solution to the problem of indexing manifold-valued sequences by proposing an intrinsic approach to map sequences to a symbolic representation. This is shown to enable the deployment of fast and accurate algorithms for activity recognition, motif discovery, and anomaly detection. Toward this end, we present generalizations of key concepts of piece-wise aggregation and symbolic approximation for the case of non-Euclidean manifolds. Experiments show that one can replace expensive geodesic computations with much faster symbolic computations with little loss of accuracy in activity recognition and discovery applications. The proposed methods are ideally suited for real-time systems and resource constrained scenarios.
ContributorsAnirudh, Rushil (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2012
Description
The high cut-off frequency of deep sub-micron CMOS technologies has enabled the integration of radio frequency (RF) transceivers with digital circuits. However, the challenging point is the integration of RF power amplifiers, mainly due to the low breakdown voltage of CMOS transistors. Silicon-on-insulator (SOI) metal semiconductor field effect transistors (MESFETs) have been introduced to remedy the limited headroom concern in CMOS technologies. The MESFETs presented in this thesis have been fabricated on different SOI-CMOS processes without making any change to the standard fabrication steps and offer 2-30 times higher breakdown voltage than the MOSFETs on the same process. This thesis explains the design steps of high efficiency and wideband RF transmitters using the proposed SOI-CMOS compatible MESFETs. This task involves DC and RF characterization of MESFET devices, along with providing a compact Spice model for simulation purposes. This thesis presents the design of several SOI-MESFET RF power amplifiers operating at 433, 900 and 1800 MHz with ~40% bandwidth. Measurement results show a peak power added efficiency (PAE) of 55% and a peak output power of 22.5 dBm. The RF-PAs were designed to operate in Class-AB mode to minimize the linearity degradation. Class-AB power amplifiers lead to poor power added efficiency, especially when fed with signals with high peak to average power ratio (PAPR) such as wideband code division multiple access (W-CDMA). Polar transmitters have been introduced to improve the efficiency of RF-PAs at backed-off powers. A MESFET based envelope tracking (ET) polar transmitter was designed and measured. A low drop-out voltage regulator (LDO) was used as the supply modulator of this polar transmitter. MESFETs are depletion mode devices; therefore, they can be configured in a source follower configuration to have better stability and higher bandwidth that MOSFET based LDOs. Measurement results show 350 MHz bandwidth while driving a 10 pF capacitive load. A novel polar transmitter is introduced in this thesis to alleviate some of the limitations associated with polar transmitters. The proposed architecture uses the backgate terminal of a partially depleted transistor on SOI process, which relaxes the bandwidth and efficiency requirements of the envelope amplifier in a polar transmitter. The measurement results of the proposed transmitter demonstrate more than three times PAE improvement at 6-dB backed-off output power, compared to the traditional RF transmitters.
ContributorsGhajar, Mohammad Reza (Author) / Thornton, Trevor (Thesis advisor) / Aberle, James T., 1961- (Committee member) / Bakkaloglu, Bertan (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2012
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 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.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
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 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.
ContributorsKulkarni, Kuldeep Sharad (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
Every engineer is responsible for completing a capstone project as a culmination of accredited university learning to demonstrate technical knowledge and enhance interpersonal skills, like teamwork, communication, time management, and problem solving. This project, with three or four engineers working together in a group, emphasizes not only the importance of technical skills acquired through laboratory procedures and coursework, but the significance of soft skills as one transitions from a university to a professional workplace; it also enhances the understanding of an engineer's obligation to ethically improve society by harnessing technical knowledge to bring about change. The CC2541 Smart SensorTag is a device manufactured by Texas Instruments that focuses on the use of wireless sensors to create low energy applications, or apps; it is equipped with Bluetooth Smart, which enables it to communicate wirelessly with similar devices like smart phones and computers, assisting greatly in app development. The device contains six built-in sensors, which can be utilized to track and log personal data in real-time; these sensors include a gyroscope, accelerometer, humidifier, thermometer, barometer, and magnetometer. By combining the data obtained through the sensors with the ability to communicate wirelessly, the SensorTag can be used to develop apps in multiple fields, including fitness, recreation, health, safety, and more. Team SensorTag chose to focus on health and safety issues to complete its capstone project, creating applications intended for use by senior citizens who live alone or in assisted care homes. Using the SensorTag's ability to track multiple local variables, the team worked to collect data that verified the accuracy and quality of the sensors through repeated experimental trials. Once the sensors were tested, the team developed applications accessible via smart phones or computers to trigger an alarm and send an alert via vibration, e-mail, or Tweet if the SensorTag detects a fall. The fall detection service utilizes the accelerometer and gyroscope sensors with the hope that such a system will prevent severe injuries among the elderly, allow them to function more independently, and improve their quality of life, which is the obligation of engineers to better through their work.
ContributorsMartin, Katherine Julia (Author) / Thornton, Trevor (Thesis director) / Goryll, Michael (Committee member) / Electrical Engineering Program (Contributor) / School of Film, Dance and Theatre (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
This thesis presents a gas sensor readout IC for amperometric and conductometric electrochemical sensors. The Analog Front-End (AFE) readout circuit enables tracking long term exposure to hazardous gas fumes in diesel and gasoline equipments, which may be correlated to diseases. Thus, the detection and discrimination of gases using microelectronic gas sensor system is required. This thesis describes the research, development, implementation and test of a small and portable based prototype platform for chemical gas sensors to enable a low-power and low noise gas detection system. The AFE reads out the outputs of eight conductometric sensor array and eight amperometric sensor arrays. The IC consists of a low noise potentiostat, and associated 9bit current-steering DAC for sensor stimulus, followed by the first order nested chopped £U£G ADC. The conductometric sensor uses a current driven approach for extracting conductance of the sensor depending on gas concentration. The amperometric sensor uses a potentiostat to apply constant voltage to the sensors and an I/V converter to measure current out of the sensor. The core area for the AFE is 2.65x0.95 mm2. The proposed system achieves 91 dB SNR at 1.32 mW quiescent power consumption per channel. With digital offset storage and nested chopping, the readout chain achieves 500 fÝV input referred offset.
ContributorsKim, Hyun-Tae (Author) / Bakkaloglu, Bertan (Thesis advisor) / Vermeire, Bert (Committee member) / Spanias, Andreas (Committee member) / Thornton, Trevor (Committee member) / Arizona State University (Publisher)
Created2011
Description
Graphene, a one atomic thick planar sheet of carbon atoms, has a zero gap band structure with a linear dispersion relation. This unique property makes graphene a favorite for physicists and engineers, who are trying to understand the mechanism of charge transport in graphene and using it as channel material for field effect transistor (FET) beyond silicon. Therefore, an in-depth exploring of these electrical properties of graphene is urgent, which is the purpose of this dissertation. In this dissertation, the charge transport and quantum capacitance of graphene were studied. Firstly, the transport properties of back-gated graphene transistor covering by high dielectric medium were systematically studied. The gate efficiency increased by up to two orders of magnitude in the presence of a high top dielectric medium, but the mobility did not change significantly. The results strongly suggested that the previously reported top dielectric medium-induced charge transport properties of graphene FETs were possibly due to the increase of gate capacitance, rather than enhancement of carrier mobility. Secondly, a direct measurement of quantum capacitance of graphene was performed. The quantum capacitance displayed a non-zero minimum at the Dirac point and a linear increase on both sides of the minimum with relatively small slopes. The findings - which were not predicted by theory for ideal graphene - suggested that scattering from charged impurities also influences the quantum capacitance. The capacitances in aqueous solutions at different ionic concentrations were also measured, which strongly suggested that the longstanding puzzle about the interfacial capacitance in carbon-based electrodes had a quantum origin. Finally, the transport and quantum capacitance of epitaxial graphene were studied simultaneously, the quantum capacitance of epitaxial graphene was extracted, which was similar to that of exfoliated graphene near the Dirac Point, but exhibited a large sub-linear behavior at high carrier density. The self-consistent theory was found to provide a reasonable description of the transport data of the epitaxial graphene device, but a more complete theory was needed to explain both the transport and quantum capacitance data.
ContributorsXia, Jilin (Author) / Tao, N.J. (Thesis advisor) / Ferry, David (Committee member) / Thornton, Trevor (Committee member) / Tsui, Raymond (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2010