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- Creators: Barrett, The Honors College
- Resource Type: Text
- Status: Published
Description
Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.
ContributorsHirte, Amanda (Author) / Kosut, Oliver (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems. First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance. We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking. For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate compressive sensing with PF to estimate neural activity. Simulation results show that both sensor scheduling and compressive sensing based methods achieve comparable tracking performance with significantly reduced number of sensors.
ContributorsMiao, Lifeng (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Zhang, Junshan (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Doppler radar can be used to measure respiration and heart rate without contact and through obstacles. In this work, a Doppler radar architecture at 2.4 GHz and a new signal processing algorithm to estimate the respiration and heart rate are presented. The received signal is dominated by the transceiver noise, LO phase noise and clutter which reduces the signal-to-noise ratio of the desired signal. The proposed architecture and algorithm are used to mitigate these issues and obtain an accurate estimate of the heart and respiration rate. Quadrature low-IF transceiver architecture is adopted to resolve null point problem as well as avoid 1/f noise and DC offset due to mixer-LO coupling. Adaptive clutter cancellation algorithm is used to enhance receiver sensitivity coupled with a novel Pattern Search in Noise Subspace (PSNS) algorithm is used to estimate respiration and heart rate. PSNS is a modified MUSIC algorithm which uses the phase noise to enhance Doppler shift detection. A prototype system was implemented using off-the-shelf TI and RFMD transceiver and tests were conduct with eight individuals. The measured results shows accurate estimate of the cardio pulmonary signals in low-SNR conditions and have been tested up to a distance of 6 meters.
ContributorsKhunti, Hitesh Devshi (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Bliss, Daniel (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can potentially provide pre-symptomatic diagnosis for infectious diseases or detection of biological threats. Currently, traditional bioinformatics tools, such as data mining classification algorithms, are used to process the large amount of peptide microarray data. However, these methods generally require training data and do not adapt to changing immune conditions or additional patient information. This work proposes advanced processing techniques to improve the classification and identification of single and multiple underlying immune response states embedded in immunosignatures, making it possible to detect both known and previously unknown diseases or biothreat agents. Novel adaptive learning methodologies for un- supervised and semi-supervised clustering integrated with immunosignature feature extraction approaches are proposed. The techniques are based on extracting novel stochastic features from microarray binding intensities and use Dirichlet process Gaussian mixture models to adaptively cluster the immunosignatures in the feature space. This learning-while-clustering approach allows continuous discovery of antibody activity by adaptively detecting new disease states, with limited a priori disease or patient information. A beta process factor analysis model to determine underlying patient immune responses is also proposed to further improve the adaptive clustering performance by formatting new relationships between patients and antibody activity. In order to extend the clustering methods for diagnosing multiple states in a patient, the adaptive hierarchical Dirichlet process is integrated with modified beta process factor analysis latent feature modeling to identify relationships between patients and infectious agents. The use of Bayesian nonparametric adaptive learning techniques allows for further clustering if additional patient data is received. Significant improvements in feature identification and immune response clustering are demonstrated using samples from patients with different diseases.
ContributorsMalin, Anna (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Lacroix, Zoé (Committee member) / Arizona State University (Publisher)
Created2013
Description
The world of a hearing impaired person is much different than that of somebody capable of discerning different frequencies and magnitudes of sound waves via their ears. This is especially true when hearing impaired people play video games. In most video games, surround sound is fed through some sort of digital output to headphones or speakers. Based on this information, the gamer can discern where a particular stimulus is coming from and whether or not that is a threat to their wellbeing within the virtual world. People with reliable hearing have a distinct advantage over hearing impaired people in the fact that they can gather information not just from what is in front of them, but from every angle relative to the way they're facing. The purpose of this project was to find a way to even the playing field, so that a person hard of hearing could also receive the sensory feedback that any other person would get while playing video games To do this, visual surround sound was created. This is a system that takes a surround sound input, and illuminates LEDs around the periphery of glasses based on the direction, frequency and amplitude of the audio wave. This provides the user with crucial information on the whereabouts of different elements within the game. In this paper, the research and development of Visual Surround Sound is discussed along with its viability in regards to a deaf person's ability to learn the technology, and decipher the visual cues.
ContributorsKadi, Danyal (Co-author) / Burrell, Nathaneal (Co-author) / Butler, Kristi (Co-author) / Wright, Gavin (Co-author) / Kosut, Oliver (Thesis director) / Bliss, Daniel (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2015-05
Description
The purpose of the solar powered quadcopter is to join together the growing technologies of photovoltaics and quadcopters, creating a single unified device where the technologies harmonize to produce a new product with abilities beyond those of a traditional battery powered drone. Specifically, the goal is to take the battery-only flight time of a quadcopter loaded with a solar array and increase that flight time by 33% with additional power provided by solar cells. The major concepts explored throughout this project are quadcopter functionality and capability and solar cell power production. In order to combine these technologies, the solar power and quadcopter components were developed and analyzed individually before connecting the solar array to the quadcopter circuit and testing the design as a whole. Several solar copter models were initially developed, resulting in multiple unique quadcopter and solar cell array designs which underwent preliminary testing before settling on a finalized design which proved to be the most effective and underwent final timed flight tests. Results of these tests are showing that the technologies complement each other as anticipated and highlight promising results for future development in this area, in particular the development of a drone running on solar power alone. Applications for a product such as this are very promising in many fields, including the industries of power, defense, consumer goods and services, entertainment, marketing, and medical. Also, becoming a more popular device for UAV hobbyists, such developments would be very appealing for leisure flying and personal photography purposes as well.
ContributorsMartin, Heather Catrina (Author) / Bowden, Stuart (Thesis director) / Aberle, James (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
The apparent phenomenon of the human eye retaining images for fractions of a second after the light source has gone is known as Persistence of Vision. While its causes are not fully understood, it can be taken advantage of in order to create illusions which trick the mind into perceiving something which, in actuality, is very different from what the mind portrays. It has motivated many creative engineering technologies in the past and is the core for how we perceive motion in movies and animations. This project applies the persistence of vision concept to a lesser explored medium; the wheel of a moving bicycle. The motion of the wheel, along with intelligent control of discrete LEDs, create vibrant illusions of solid lines and shapes. These shapes make up the image to be displayed on the bike wheel. The rotation of the bike wheel can be compensated for in order to produce a standing image (or images) of the user's choosing. This thesis details how the mechanism for conducting the individual LEDs was created in order to produce a device which is capable of delivering colorful, standing images of the user's choosing.
ContributorsSaltwick, Ian Mark (Author) / Goryll, Michael (Thesis director) / Kozicki, Michael (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This project was centered around designing a processor model (using the C programming language) based on the Coldfire computer architecture that will run on third party software known as Open Virtual Platforms. The end goal is to have a fully functional processor that can run Coldfire instructions and utilize peripheral devices in the same way as the hardware used in the embedded systems lab at ASU. This project would cut down the substantial amount of time students spend commuting to the lab. Having the processor directly at their disposal would also encourage them to spend more time outside of class learning the hardware and familiarizing themselves with development on an embedded micro-controller. The model will be accurate, fast and reliable. These aspects will be achieved through rigorous unit testing and use of the OVP platform which provides instruction accurate simulations at hundreds of MIPS (million instructions per second) for the specified model. The end product was able to accurately simulate a subset of the Coldfire instructions at very high rates.
ContributorsDunning, David Connor (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-12
Description
Lighting Audio is a team of senior electrical engineering students at the Arizona State University mentored by Director Emeritus Professor Ronald Roedel and 2nd Committee Member George Karady attempting to prove the feasibility of a consumer grade plasma arc speaker. The plasma arc speaker is a project that explores the use of high voltage arcs to produce audible sound amplification. The goal of the project is to prove feasibility that a consumer grade plasma arc speaker could exist in the marketplace. The inherent challenge was producing audio amplification that could compete with current loudspeakers all while ensuring user safety from the hazards of high voltage and current shock, electromagnetic damage, and ozone from the plasma arc. The project has thus far covered the process of design conception to realization of a prototype device. The operation of the plasma arc speaker is based on the high voltage plasma arc created between two electrodes. The plasma arc rapidly heats and cools the surrounding air creating changes in air pressure which vibrate the air. These pockets of pressurized air are heard as sound. The circuit incorporates a flyback transformer responsible for creating the high voltage necessary for arcing.
ContributorsNandan, Rahul S (Author) / Roedel, Ronald (Thesis director) / Huffman, James (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2014-05
Description
Lighting Audio is a team of senior electrical engineering students at the Arizona State University mentored by Director Emeritus Professor Ronald Roedel and 2nd Committee Member George Karady attempting to prove the feasibility of a consumer grade plasma arc speaker. The plasma arc speaker is a project that explores the use of high voltage arcs to produce audible sound amplification. The goal of the project is to prove feasibility that a consumer grade plasma arc speaker could exist in the marketplace. The inherent challenge was producing audio amplification that could compete with current loudspeakers all while ensuring user safety from the hazards of high voltage and current shock, electromagnetic damage, and ozone from the plasma arc. The project has thus far covered the process of design conception to realization of a prototype device. The operation of the plasma arc speaker is based on the high voltage plasma arc created between two electrodes. The plasma arc rapidly heats and cools the surrounding air creating changes in air pressure which vibrate the air. These pockets of pressurized air are heard as sound. The circuit incorporates a flyback transformer responsible for creating the high voltage necessary for arcing.
ContributorsNandan, Rahul S (Author) / Roedel, Ronald (Thesis director) / Huffman, James (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2014-05