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Description
The primary goal of this thesis is to evaluate the influence of ethyl vinyl acetate (EVA) and polyolefin elastomer (POE) encapsulant types on the glass-glass (GG) photovoltaic (PV) module reliability. The influence of these two encapsulant types on the reliability of GG modules was compared with baseline glass-polymer backsheet (GB) modules for a benchmarking purpose. Three sets of modules, with four modules in each set, were constructed with two substrates types i.e. glass-glass (GG) and glass- polymer backsheet (GB); and 2 encapsulants types i.e. ethyl vinyl acetate (EVA) and polyolefin elastomer (POE). Each module set was subjected to the following accelerated tests as specified in the International Electrotechnical Commission (IEC) standard and Qualification Plus protocol of NREL: Ultraviolet (UV) 250 kWh/m2; Thermal Cycling (TC) 200 cycles; Damp Heat (DH) 1250 hours. To identify the failure modes and reliability issues of the stressed modules, several module-level non-destructive characterizations were carried out and they include colorimetry, UV-Vis-NIR spectral reflectance, ultraviolet fluorescence (UVF) imaging, electroluminescence (EL) imaging, and infrared (IR) imaging. The above-mentioned characterizations were performed on the front side of the modules both before the stress tests (i.e. pre-stress) and after the stress tests (i.e. post-stress). The UV-250 extended stress results indicated slight changes in the reflectance on the non-cell area of EVA modules probably due to minor adhesion loss at the cell and module edges. From the DH-1250 extended stress tests, significant changes, in both encapsulant types modules, were observed in reflectance and UVF images indicating early stages of delamination. In the case of the TC-200 stress test, practically no changes were observed in all sets of modules. From the above short-term stress tests, it appears although not conclusive at this stage of the analysis, delamination seems to be the only failure mode that could possibly be affecting the module performance, as observed from UV and DH extended stress tests. All these stress tests need to be continued to identify the wear-out failure modes and their impacts on the performance parameters of PV modules.
ContributorsBhaskaran, Rahul (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2020
Description
The origins of carrier mobility (μe) were thoroughly investigated in hydrogenated indium oxide (IO:H) and zinc-tin oxide (ZTO) transparent conducting oxide (TCO) thin films. A carrier transport model was developed for IO:H which studied the effects of ionized impurity scattering, polar optical phonon scattering, and grain boundary scattering. Ionized impurity scattering dominated at temperatures below ~240 K. A reduction in scattering charge Z from +2 to +1 as atomic %H increased from ~3 atomic %H to ~5 atomic %H allowed μe to attain >100 cm^2/Vs at ~5 atomic %H.
In highly hydrogenated IO:H, ne significantly decreased as temperature increased from 5 K to 140 K. To probe this unusual behavior, samples were illuminated, then ne, surface work function (WF), and spatially resolved microscopic current mapping were measured and tracked. Large increases in ne and corresponding decreases in WF were observed---these both exhibited slow reversions toward pre-illumination values over 6-12 days. A hydrogen-related defect was proposed as source of the photoexcitation, while a lattice defect diffusion mechanism causes the extended decay. Both arise from an under-coordination of the In.
An enhancement of μe was observed with increasing amorphous fraction in IO:H. An increase in population of corner- and edge-sharing polyhedra consisting of metal cations and oxygen anions is thought to be the origin. This indicates some measure of medium-range order in the amorphous structure, and gives rise to a general principle dictating μe in TCOs---even amorphous TCOs. Testing this principle resulted in observing an enhancement of μe up to 35 cm^2/Vs in amorphous ZTO (a-ZTO), one of the highest reported a-ZTO μe values (at ne > 10^19 cm^-3) to date. These results highlight the role of local distortions and cation coordination in determining the microscopic origins of carrier generation and transport. In addition, the strong likelihood of under-coordination of one cation species leading to high carrier concentrations is proposed. This diverges from the historical indictment of oxygen vacancies controlling carrier population in crystalline oxides, which by definition cannot occur in amorphous systems, and provides a framework to discuss key structural descriptors in these disordered phase materials.
In highly hydrogenated IO:H, ne significantly decreased as temperature increased from 5 K to 140 K. To probe this unusual behavior, samples were illuminated, then ne, surface work function (WF), and spatially resolved microscopic current mapping were measured and tracked. Large increases in ne and corresponding decreases in WF were observed---these both exhibited slow reversions toward pre-illumination values over 6-12 days. A hydrogen-related defect was proposed as source of the photoexcitation, while a lattice defect diffusion mechanism causes the extended decay. Both arise from an under-coordination of the In.
An enhancement of μe was observed with increasing amorphous fraction in IO:H. An increase in population of corner- and edge-sharing polyhedra consisting of metal cations and oxygen anions is thought to be the origin. This indicates some measure of medium-range order in the amorphous structure, and gives rise to a general principle dictating μe in TCOs---even amorphous TCOs. Testing this principle resulted in observing an enhancement of μe up to 35 cm^2/Vs in amorphous ZTO (a-ZTO), one of the highest reported a-ZTO μe values (at ne > 10^19 cm^-3) to date. These results highlight the role of local distortions and cation coordination in determining the microscopic origins of carrier generation and transport. In addition, the strong likelihood of under-coordination of one cation species leading to high carrier concentrations is proposed. This diverges from the historical indictment of oxygen vacancies controlling carrier population in crystalline oxides, which by definition cannot occur in amorphous systems, and provides a framework to discuss key structural descriptors in these disordered phase materials.
ContributorsHusein, Sebastian S.T. (Author) / Bertoni, Mariana I. (Thesis advisor) / Stuckelberger, Michael (Committee member) / Holman, Zachary C. (Committee member) / Crozier, Peter (Committee member) / Arizona State University (Publisher)
Created2020
Description
In the current photovoltaic (PV) industry, the O&M (operations and maintenance) personnel in the field primarily utilize three approaches to identify the underperforming or defective modules in a string: i) EL (electroluminescence) imaging of all the modules in the string; ii) IR (infrared) thermal imaging of all the modules in the string; and, iii) current-voltage (I-V) curve tracing of all the modules in the string. In the first and second approaches, the EL images are used to detect the modules with broken cells, and the IR images are used to detect the modules with hotspot cells, respectively. These two methods may identify the modules with defective cells only semi-qualitatively, but not accurately and quantitatively. The third method, I-V curve tracing, is a quantitative method to identify the underperforming modules in a string, but it is an extremely time consuming, labor-intensive, and highly ambient conditions dependent method. Since the I-V curves of individual modules in a string are obtained by disconnecting them individually at different irradiance levels, module operating temperatures, angle of incidences (AOI) and air-masses/spectra, all these measured curves are required to be translated to a single reporting condition (SRC) of a single irradiance, single temperature, single AOI and single spectrum. These translations are not only time consuming but are also prone to inaccuracy due to inherent issues in the translation models. Therefore, the current challenges in using the traditional I-V tracers are related to: i) obtaining I-V curves simultaneously of all the modules and substrings in a string at a single irradiance, operating temperature, irradiance spectrum and angle of incidence due to changing weather parameters and sun positions during the measurements, ii) safety of field personnel when disconnecting and reconnecting of cables in high voltage systems (especially field aged connectors), and iii) enormous time and hardship for the test personnel in harsh outdoor climatic conditions. In this thesis work, a non-contact I-V (NCIV) curve tracing tool has been integrated and implemented to address the above mentioned three challenges of the traditional I-V tracers.
This work compares I-V curves obtained using a traditional I-V curve tracer with the I-V curves obtained using a NCIV curve tracer for the string, substring and individual modules of crystalline silicon (c-Si) and cadmium telluride (CdTe) technologies. The NCIV curve tracer equipment used in this study was integrated using three commercially available components: non-contact voltmeters (NCV) with voltage probes to measure the voltages of substrings/modules in a string, a hall sensor to measure the string current and a DAS (data acquisition system) for simultaneous collection of the voltage data obtained from the NCVs and the current data obtained from the hall sensor. This study demonstrates the concept and accuracy of the NCIV curve tracer by comparing the I-V curves obtained using a traditional capacitor-based tracer and the NCIV curve tracer in a three-module string of c-Si modules and of CdTe modules under natural sunlight with uniform light conditions on all the modules in the string and with partially shading one or more of the modules in the string to simulate and quantitatively detect the underperforming module(s) in a string.
This work compares I-V curves obtained using a traditional I-V curve tracer with the I-V curves obtained using a NCIV curve tracer for the string, substring and individual modules of crystalline silicon (c-Si) and cadmium telluride (CdTe) technologies. The NCIV curve tracer equipment used in this study was integrated using three commercially available components: non-contact voltmeters (NCV) with voltage probes to measure the voltages of substrings/modules in a string, a hall sensor to measure the string current and a DAS (data acquisition system) for simultaneous collection of the voltage data obtained from the NCVs and the current data obtained from the hall sensor. This study demonstrates the concept and accuracy of the NCIV curve tracer by comparing the I-V curves obtained using a traditional capacitor-based tracer and the NCIV curve tracer in a three-module string of c-Si modules and of CdTe modules under natural sunlight with uniform light conditions on all the modules in the string and with partially shading one or more of the modules in the string to simulate and quantitatively detect the underperforming module(s) in a string.
ContributorsMurali, Sanjay (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2020
Description
In many biological research studies, including speech analysis, clinical research, and prediction studies, the validity of the study is dependent on the effectiveness of the training data set to represent the target population. For example, in speech analysis, if one is performing emotion classification based on speech, the performance of the classifier is mainly dependent on the number and quality of the training data set. For small sample sizes and unbalanced data, classifiers developed in this context may be focusing on the differences in the training data set rather than emotion (e.g., focusing on gender, age, and dialect).
This thesis evaluates several sampling methods and a non-parametric approach to sample sizes required to minimize the effect of these nuisance variables on classification performance. This work specifically focused on speech analysis applications, and hence the work was done with speech features like Mel-Frequency Cepstral Coefficients (MFCC) and Filter Bank Cepstral Coefficients (FBCC). The non-parametric divergence (D_p divergence) measure was used to study the difference between different sampling schemes (Stratified and Multistage sampling) and the changes due to the sentence types in the sampling set for the process.
This thesis evaluates several sampling methods and a non-parametric approach to sample sizes required to minimize the effect of these nuisance variables on classification performance. This work specifically focused on speech analysis applications, and hence the work was done with speech features like Mel-Frequency Cepstral Coefficients (MFCC) and Filter Bank Cepstral Coefficients (FBCC). The non-parametric divergence (D_p divergence) measure was used to study the difference between different sampling schemes (Stratified and Multistage sampling) and the changes due to the sentence types in the sampling set for the process.
ContributorsMariajohn, Aaquila (Author) / Berisha, Visar (Thesis advisor) / Spanias, Andreas (Committee member) / Liss, Julie (Committee member) / Arizona State University (Publisher)
Created2020
Description
The electric power system (EPS) is an extremely complex system that has operational interdependencies with the water delivery and treatment system (WDTS). The term water-energy nexus is commonly used to describe the critical interdependencies that naturally exist between the EPS and water distribution systems (WDS). Presented in this work is a framework for simulating interactions between these two critical infrastructure systems in short-term and long-term time-scales. This includes appropriate mathematical models for system modeling and for optimizing control of power system operation with consideration of conditions in the WDS. Also presented is a complete methodology for quantifying the resilience of the two interdependent systems.
The key interdependencies between the two systems are the requirements of water for the cooling cycle of traditional thermal power plants as well as electricity for pumping and/or treatment in the WDS. While previous work has considered the dependency of thermoelectric generation on cooling water requirements at a high-level, this work considers the impact from limitations of cooling water into network simulations in both a short-term operational framework as well as in the long-term planning domain.
The work completed to set-up simulations in operational length time-scales was the development of a simulator that adequately models both systems. This simulation engine also facilitates the implementation of control schemes in both systems that take advantage of the knowledge of operating conditions in the other system. Initial steps for including the influence of anticipated water availability and water rights attainability within the combined generation and transmission expansion planning problem is also presented. Lastly, the framework for determining the infrastructural-operational resilience (IOR) of the interdependent systems is formulated.
Adequately modeling and studying the two systems and their interactions is becoming critically important. This importance is illustrated by the possibility of unforeseen natural or man-made events or by the likelihood of load increase in the systems, either of which has the risk of putting extreme stress on the systems beyond that experienced in normal operating conditions. Therefore, this work addresses these concerns with novel modeling and control/policy strategies designed to mitigate the severity of extreme conditions in either system.
The key interdependencies between the two systems are the requirements of water for the cooling cycle of traditional thermal power plants as well as electricity for pumping and/or treatment in the WDS. While previous work has considered the dependency of thermoelectric generation on cooling water requirements at a high-level, this work considers the impact from limitations of cooling water into network simulations in both a short-term operational framework as well as in the long-term planning domain.
The work completed to set-up simulations in operational length time-scales was the development of a simulator that adequately models both systems. This simulation engine also facilitates the implementation of control schemes in both systems that take advantage of the knowledge of operating conditions in the other system. Initial steps for including the influence of anticipated water availability and water rights attainability within the combined generation and transmission expansion planning problem is also presented. Lastly, the framework for determining the infrastructural-operational resilience (IOR) of the interdependent systems is formulated.
Adequately modeling and studying the two systems and their interactions is becoming critically important. This importance is illustrated by the possibility of unforeseen natural or man-made events or by the likelihood of load increase in the systems, either of which has the risk of putting extreme stress on the systems beyond that experienced in normal operating conditions. Therefore, this work addresses these concerns with novel modeling and control/policy strategies designed to mitigate the severity of extreme conditions in either system.
ContributorsZuloaga, Scott (Author) / Vittal, Vijay (Thesis advisor) / Zhang, Junshan (Committee member) / Mays, Larry (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2020
Description
Object detection is an interesting computer vision area that is concerned with the detection of object instances belonging to specific classes of interest as well as the localization of these instances in images and/or videos. Object detection serves as a vital module in many computer vision based applications. This work focuses on the development of object detection methods that exhibit increased robustness to varying illuminations and image quality. In this work, two methods for robust object detection are presented.
In the context of varying illumination, this work focuses on robust generic obstacle detection and collision warning in Advanced Driver Assistance Systems (ADAS) under varying illumination conditions. The highlight of the first method is the ability to detect all obstacles without prior knowledge and detect partially occluded obstacles including the obstacles that have not completely appeared in the frame (truncated obstacles). It is first shown that the angular distortion in the Inverse Perspective Mapping (IPM) domain belonging to obstacle edges varies as a function of their corresponding 2D location in the camera plane. This information is used to generate object proposals. A novel proposal assessment method based on fusing statistical properties from both the IPM image and the camera image to perform robust outlier elimination and false positive reduction is also proposed.
In the context of image quality, this work focuses on robust multiple-class object detection using deep neural networks for images with varying quality. The use of Generative Adversarial Networks (GANs) is proposed in a novel generative framework to generate features that provide robustness for object detection on reduced quality images. The proposed GAN-based Detection of Objects (GAN-DO) framework is not restricted to any particular architecture and can be generalized to several deep neural network (DNN) based architectures. The resulting deep neural network maintains the exact architecture as the selected baseline model without adding to the model parameter complexity or inference speed. Performance results provided using GAN-DO on object detection datasets establish an improved robustness to varying image quality and a higher object detection and classification accuracy compared to the existing approaches.
In the context of varying illumination, this work focuses on robust generic obstacle detection and collision warning in Advanced Driver Assistance Systems (ADAS) under varying illumination conditions. The highlight of the first method is the ability to detect all obstacles without prior knowledge and detect partially occluded obstacles including the obstacles that have not completely appeared in the frame (truncated obstacles). It is first shown that the angular distortion in the Inverse Perspective Mapping (IPM) domain belonging to obstacle edges varies as a function of their corresponding 2D location in the camera plane. This information is used to generate object proposals. A novel proposal assessment method based on fusing statistical properties from both the IPM image and the camera image to perform robust outlier elimination and false positive reduction is also proposed.
In the context of image quality, this work focuses on robust multiple-class object detection using deep neural networks for images with varying quality. The use of Generative Adversarial Networks (GANs) is proposed in a novel generative framework to generate features that provide robustness for object detection on reduced quality images. The proposed GAN-based Detection of Objects (GAN-DO) framework is not restricted to any particular architecture and can be generalized to several deep neural network (DNN) based architectures. The resulting deep neural network maintains the exact architecture as the selected baseline model without adding to the model parameter complexity or inference speed. Performance results provided using GAN-DO on object detection datasets establish an improved robustness to varying image quality and a higher object detection and classification accuracy compared to the existing approaches.
ContributorsPrakash, Charan Dudda (Author) / Karam, Lina (Thesis advisor) / Abousleman, Glen (Committee member) / Jayasuriya, Suren (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2020
Description
The inverse problem in electroencephalography (EEG) is the determination of form and location of neural activity associated to EEG recordings. This determination is of interest in evoked potential experiments where the activity is elicited by an external stimulus. This work investigates three aspects of this problem: the use of forward methods in its solution, the elimination of artifacts that complicate the accurate determination of sources, and the construction of physical models that capture the electrical properties of the human head.
Results from this work aim to increase the accuracy and performance of the inverse solution process.
The inverse problem can be approached by constructing forward solutions where, for a know source, the scalp potentials are determined. This work demonstrates that the use of two variables, the dissipated power and the accumulated charge at interfaces, leads to a new solution method for the forward problem. The accumulated charge satisfies a boundary integral equation. Consideration of dissipated power determines bounds on the range of eigenvalues of the integral operators that appear in this formulation. The new method uses the eigenvalue structure to regularize singular integral operators thus allowing unambiguous solutions to the forward problem.
A major problem in the estimation of properties of neural sources is the presence of artifacts that corrupt EEG recordings. A method is proposed for the determination of inverse solutions that integrates sequential Bayesian estimation with probabilistic data association in order to suppress artifacts before estimating neural activity. This method improves the tracking of neural activity in a dynamic setting in the presence of artifacts.
Solution of the inverse problem requires the use of models of the human head. The electrical properties of biological tissues are best described by frequency dependent complex conductivities. Head models in EEG analysis, however, usually consider head regions as having only constant real conductivities. This work presents a model for tissues as composed of confined electrolytes that predicts complex conductivities for macroscopic measurements. These results indicate ways in which EEG models can be improved.
Results from this work aim to increase the accuracy and performance of the inverse solution process.
The inverse problem can be approached by constructing forward solutions where, for a know source, the scalp potentials are determined. This work demonstrates that the use of two variables, the dissipated power and the accumulated charge at interfaces, leads to a new solution method for the forward problem. The accumulated charge satisfies a boundary integral equation. Consideration of dissipated power determines bounds on the range of eigenvalues of the integral operators that appear in this formulation. The new method uses the eigenvalue structure to regularize singular integral operators thus allowing unambiguous solutions to the forward problem.
A major problem in the estimation of properties of neural sources is the presence of artifacts that corrupt EEG recordings. A method is proposed for the determination of inverse solutions that integrates sequential Bayesian estimation with probabilistic data association in order to suppress artifacts before estimating neural activity. This method improves the tracking of neural activity in a dynamic setting in the presence of artifacts.
Solution of the inverse problem requires the use of models of the human head. The electrical properties of biological tissues are best described by frequency dependent complex conductivities. Head models in EEG analysis, however, usually consider head regions as having only constant real conductivities. This work presents a model for tissues as composed of confined electrolytes that predicts complex conductivities for macroscopic measurements. These results indicate ways in which EEG models can be improved.
ContributorsSolis, Francisco Jr. (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Berisha, Visar (Committee member) / Bliss, Daniel (Committee member) / Moraffah, Bahman (Committee member) / Arizona State University (Publisher)
Created2020
Description
Since the advent of High Impedance Surfaces (HISs) and metasurfaces, researchers
have proposed many low profile antenna configurations. HISs possess in-phase reflection, which reinforces the radiation, and enhances the directivity and matching bandwidth of radiating elements. Most of the proposed dipole and loop element designs that have used HISs as a ground plane, have attained a maximum directivity of 8 dBi. While HISs are more attractive ground planes for low profile antennas, these HISs result in a low directivity as compared to PEC ground planes. Various studies have shown that Perfect Electric Conductor (PEC) ground planes are capable of achieving higher directivity, at the expense of matching efficiency, when the spacing
between the radiating element and the PEC ground plane is less than 0.25 lambda. To establish an efficient ground plane for low profile applications, PEC (Perfect Electric Conductor) and PMC (Perfect Magnetic Conductor) ground planes are examined in the vicinity of electric and magnetic radiating elements. The limitation of the two ground planes, in terms of radiation efficiency and the impedance matching, are discussed. Far-field analytical formulations are derived and the results are compared with full-wave EM simulations performed using the High-Frequency Structure Simulator (HFSS). Based on PEC and PMC designs, two engineered ground planes are proposed.
The designed ground planes depend on two metasurface properties; namely in-phase reflection and excitation of surface waves. Two ground plane geometries are considered. The first one is designed for a circular loop radiating element, which utilizes a
circular HIS ring deployed on a circular ground plane. The integration of the loop element with the circular HIS ground plane enhances the maximum directivity up to 10.5 dB with a 13% fractional bandwidth. The second ground plane is designed for a square loop radiating element. Unlike the first design, rectangular HIS patches are utilized to control the excitation of surface waves in the principal planes. The final design operates from 3.8 to 5 GHz (27% fractional bandwidth) with a stable broadside maximum realized gain up to 11.9 dBi. To verify the proposed designs, a prototype was fabricated and measurements were conducted. A good agreement between simulations and measurements was observed. Furthermore, multiple square ring elements are embedded within the periodic patches to form a surface wave planar antenna array. Linear and circular polarizations are proposed and compared to a conventional square ring array. The implementation of periodic patches results in a better matching bandwidth and higher broadside gain compared to a conventional array.
have proposed many low profile antenna configurations. HISs possess in-phase reflection, which reinforces the radiation, and enhances the directivity and matching bandwidth of radiating elements. Most of the proposed dipole and loop element designs that have used HISs as a ground plane, have attained a maximum directivity of 8 dBi. While HISs are more attractive ground planes for low profile antennas, these HISs result in a low directivity as compared to PEC ground planes. Various studies have shown that Perfect Electric Conductor (PEC) ground planes are capable of achieving higher directivity, at the expense of matching efficiency, when the spacing
between the radiating element and the PEC ground plane is less than 0.25 lambda. To establish an efficient ground plane for low profile applications, PEC (Perfect Electric Conductor) and PMC (Perfect Magnetic Conductor) ground planes are examined in the vicinity of electric and magnetic radiating elements. The limitation of the two ground planes, in terms of radiation efficiency and the impedance matching, are discussed. Far-field analytical formulations are derived and the results are compared with full-wave EM simulations performed using the High-Frequency Structure Simulator (HFSS). Based on PEC and PMC designs, two engineered ground planes are proposed.
The designed ground planes depend on two metasurface properties; namely in-phase reflection and excitation of surface waves. Two ground plane geometries are considered. The first one is designed for a circular loop radiating element, which utilizes a
circular HIS ring deployed on a circular ground plane. The integration of the loop element with the circular HIS ground plane enhances the maximum directivity up to 10.5 dB with a 13% fractional bandwidth. The second ground plane is designed for a square loop radiating element. Unlike the first design, rectangular HIS patches are utilized to control the excitation of surface waves in the principal planes. The final design operates from 3.8 to 5 GHz (27% fractional bandwidth) with a stable broadside maximum realized gain up to 11.9 dBi. To verify the proposed designs, a prototype was fabricated and measurements were conducted. A good agreement between simulations and measurements was observed. Furthermore, multiple square ring elements are embedded within the periodic patches to form a surface wave planar antenna array. Linear and circular polarizations are proposed and compared to a conventional square ring array. The implementation of periodic patches results in a better matching bandwidth and higher broadside gain compared to a conventional array.
ContributorsAlharbi, Mohammed (Author) / Balanis, Constantine A (Thesis advisor) / Aberle, James T (Committee member) / Palais, Joseph (Committee member) / Trichopoulos, Georgios C (Committee member) / Arizona State University (Publisher)
Created2020
Description
The following document describes the hardware implementation and analysis of Temporal Interference Mitigation using High-Level Synthesis. As the problem of spectral congestion becomes more chronic and widespread, Electromagnetic radio frequency (RF) based systems are posing as viable solution to this problem. Among the existing RF methods Cooperation based systems have been a solution to a host of congestion problems. One of the most important elements of RF receiver is the spatially adaptive part of the receiver. Temporal Mitigation is vital technique employed at the receiver for signal recovery and future propagation along the radar chain.
The computationally intensive parts of temporal mitigation are identified and hardware accelerated. The hardware implementation is based on sequential approach with optimizations applied on the individual components for better performance.
An extensive analysis using a range of fixed point data types is performed to find the optimal data type necessary.
Finally a hybrid combination of data types for different components of temporal mitigation is proposed based on results from the above analysis.
The computationally intensive parts of temporal mitigation are identified and hardware accelerated. The hardware implementation is based on sequential approach with optimizations applied on the individual components for better performance.
An extensive analysis using a range of fixed point data types is performed to find the optimal data type necessary.
Finally a hybrid combination of data types for different components of temporal mitigation is proposed based on results from the above analysis.
ContributorsSiddiqui, Saquib Ahmad (Author) / Bliss, Daniel (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Ogras, Umit Y. (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2020
Description
Movement disorders are becoming one of the leading causes of functional disability due to aging populations and extended life expectancy. Diagnosis, treatment, and rehabilitation currently depend on the behavior observed in a clinical environment. After the patient leaves the clinic, there is no standard approach to continuously monitor the patient and report potential problems. Furthermore, self-recording is inconvenient and unreliable. To address these challenges, wearable health monitoring is emerging as an effective way to augment clinical care for movement disorders.
Wearable devices are being used in many health, fitness, and activity monitoring applications. However, their widespread adoption has been hindered by several adaptation and technical challenges. First, conventional rigid devices are uncomfortable to wear for long periods. Second, wearable devices must operate under very low-energy budgets due to their small battery capacities. Small batteries create a need for frequent recharging, which in turn leads users to stop using them. Third, the usefulness of wearable devices must be demonstrated through high impact applications such that users can get value out of them.
This dissertation presents solutions to solving the challenges faced by wearable devices. First, it presents an open-source hardware/software platform for wearable health monitoring. The proposed platform uses flexible hybrid electronics to enable devices that conform to the shape of the user’s body. Second, it proposes an algorithm to enable recharge-free operation of wearable devices that harvest energy from the environment. The proposed solution maximizes the performance of the wearable device under minimum energy constraints. The results of the proposed algorithm are, on average, within 3% of the optimal solution computed offline. Third, a comprehensive framework for human activity recognition (HAR), one of the first steps towards a solution for movement disorders is presented. It starts with an online learning framework for HAR. Experiments on a low power IoT device (TI-CC2650 MCU) with twenty-two users show 95% accuracy in identifying seven activities and their transitions with less than 12.5 mW power consumption. The online learning framework is accompanied by a transfer learning approach for HAR that determines the number of neural network layers to transfer among uses to enable efficient online learning. Next, a technique to co-optimize the accuracy and active time of wearable applications by utilizing multiple design points with different energy-accuracy trade-offs is presented. The proposed technique switches between the design points at runtime to maximize a generalized objective function under tight harvested energy budget constraints. Finally, we present the first ultra-low-energy hardware accelerator that makes it practical to perform HAR on energy harvested from wearable devices. The accelerator consumes 22.4 microjoules per operation using a commercial 65 nm technology. In summary, the solutions presented in this dissertation can enable the wider adoption of wearable devices.
Wearable devices are being used in many health, fitness, and activity monitoring applications. However, their widespread adoption has been hindered by several adaptation and technical challenges. First, conventional rigid devices are uncomfortable to wear for long periods. Second, wearable devices must operate under very low-energy budgets due to their small battery capacities. Small batteries create a need for frequent recharging, which in turn leads users to stop using them. Third, the usefulness of wearable devices must be demonstrated through high impact applications such that users can get value out of them.
This dissertation presents solutions to solving the challenges faced by wearable devices. First, it presents an open-source hardware/software platform for wearable health monitoring. The proposed platform uses flexible hybrid electronics to enable devices that conform to the shape of the user’s body. Second, it proposes an algorithm to enable recharge-free operation of wearable devices that harvest energy from the environment. The proposed solution maximizes the performance of the wearable device under minimum energy constraints. The results of the proposed algorithm are, on average, within 3% of the optimal solution computed offline. Third, a comprehensive framework for human activity recognition (HAR), one of the first steps towards a solution for movement disorders is presented. It starts with an online learning framework for HAR. Experiments on a low power IoT device (TI-CC2650 MCU) with twenty-two users show 95% accuracy in identifying seven activities and their transitions with less than 12.5 mW power consumption. The online learning framework is accompanied by a transfer learning approach for HAR that determines the number of neural network layers to transfer among uses to enable efficient online learning. Next, a technique to co-optimize the accuracy and active time of wearable applications by utilizing multiple design points with different energy-accuracy trade-offs is presented. The proposed technique switches between the design points at runtime to maximize a generalized objective function under tight harvested energy budget constraints. Finally, we present the first ultra-low-energy hardware accelerator that makes it practical to perform HAR on energy harvested from wearable devices. The accelerator consumes 22.4 microjoules per operation using a commercial 65 nm technology. In summary, the solutions presented in this dissertation can enable the wider adoption of wearable devices.
ContributorsBhat, Ganapati (Author) / Ogras, Umit Y. (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Nedić, Angelia (Committee member) / Marculescu, Radu (Committee member) / Arizona State University (Publisher)
Created2020