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Description
Limited spectral access motivates technologies that adapt to diminishing resources and increasingly cluttered environments. A joint positioning-communications system is designed and implemented on \acf{COTS} hardware. This system enables simultaneous positioning of, and communications between, nodes in a distributed network of base-stations and unmanned aerial systems (UASs). This technology offers extreme ranging precision ($<$ 5 cm) with minimal bandwidth (10 MHz), a secure communications link to protect against cyberattacks, a small form factor that enables integration into numerous platforms, and minimal resource consumption which supports high-density networks. The positioning and communications tasks are performed simultaneously with a single, co-use waveform, which efficiently utilizes limited resources and supports higher user densities. The positioning task uses a cooperative, point-to-point synchronization protocol to estimate the relative position and orientation of all users within the network. The communications task distributes positioning information between users and secures the positioning task against cyberattacks. This high-performance system is enabled by advanced time-of-arrival estimation techniques and a modern phase-accurate distributed coherence synchronization algorithm. This technology may be installed in ground-stations, ground vehicles, unmanned aerial systems, and airborne vehicles, enabling a highly-mobile, re-configurable network with numerous applications.
ContributorsHerschfelt, Andrew (Author) / Bliss, Daniel W (Thesis advisor) / Cochran, Douglas (Committee member) / Richmond, Christ (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2019
Description
Physical activity helps in reducing the risk of many chronic diseases, and plays a key role in maintaining good health of an individual. Just Walk is an intensively adaptive physical activity intervention, which has been designed based on system identification and control engineering principles. The goal of Just Walk is to design interventions that are responsive to an individual's changing needs, and thus encourage the individual to increase the number of steps walked.
Regularization is widely used in the field of machine learning. The goal of this thesis is to see how classical system identification principles in combination with machine learning methods like regularization help towards getting improved model estimates for complex systems. Estimating individual behavioral models using traditional prediction error methods can be done using an order selection. However, this method is can be computationally expensive due to the extensive search performed on a large set of order combination. If order selection is not done properly, it can cause bias (low order) and variance (high order) issues. In such cases regularization plays an important role in addressing the bias-variance trade-off.
One of the most important applications of identifying individual behavioral models is to understand what factors impact most the behavior of the person. Here "factors" can be considered as inputs (designed or environmental) to the participant over the course of the study, and the "behavior" is the step count of the participant under study. This is done by estimating models with different input combinations and then seeing which combinations of inputs (influence behavior most) give the best model estimate (best describe behavior of the person). As a part of this thesis, it is studied how regularized models can give a better estimation of personalized behavioral models, for the Just Walk study, which can further help in designing personalized interventions.
Regularization is widely used in the field of machine learning. The goal of this thesis is to see how classical system identification principles in combination with machine learning methods like regularization help towards getting improved model estimates for complex systems. Estimating individual behavioral models using traditional prediction error methods can be done using an order selection. However, this method is can be computationally expensive due to the extensive search performed on a large set of order combination. If order selection is not done properly, it can cause bias (low order) and variance (high order) issues. In such cases regularization plays an important role in addressing the bias-variance trade-off.
One of the most important applications of identifying individual behavioral models is to understand what factors impact most the behavior of the person. Here "factors" can be considered as inputs (designed or environmental) to the participant over the course of the study, and the "behavior" is the step count of the participant under study. This is done by estimating models with different input combinations and then seeing which combinations of inputs (influence behavior most) give the best model estimate (best describe behavior of the person). As a part of this thesis, it is studied how regularized models can give a better estimation of personalized behavioral models, for the Just Walk study, which can further help in designing personalized interventions.
ContributorsMandal, Tarunima (Author) / Rivera, Daniel E (Thesis advisor) / Si, Jennie (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2020
Description
Flexible conducting materials have been in the forefront of a rapidly transforming electronics industry, focusing on wearable devices for a variety of applications in recent times. Over the past few decades, bulky, rigid devices have been replaced with a surging demand for thin, flexible, light weight, ultra-portable yet high performance electronics. The interconnects available in the market today only satisfy a few of the desirable characteristics, making it necessary to compromise one feature over another. In this thesis, a method to prepare a thin, flexible, and stretchable inter-connect is presented with improved conductivity compared to previous achievements. It satisfies most mechanical and electrical conditions desired in the wearable electronics industry. The conducting composite, prepared with the widely available, low cost silicon-based organic polymer - polydimethylsiloxane (PDMS) and silver (Ag), is sandwiched between two cured PDMS layers. These protective layers improve the mechanical stability of the inter-connect. The structure can be stretched up to 120% of its original length which can further be enhanced to over 250% by cutting it into a serpentine shape without compromising its electrical stability. The inter-connect, around 500 µm thick, can be integrated into thin electronic packaging. The synthesis process of the composite material, along with its electrical and mechanical and properties are presented in detail. Testing methods and results for mechanical and electrical stability are also illustrated over extensive flexing and stretching cycles. The materials put into test, along with conductive silver (Ag) - polydimethylsiloxane (PDMS) composite in a sandwich structure, are copper foils, copper coated polyimide (PI) and aluminum (Al) coated polyethylene terephthalate (PET).
ContributorsNandy, Mayukh (Author) / Yu, Hongbin (Thesis advisor) / Chan, Candace (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2020
Description
Technological advances in low power wearable electronics and energy optimization techniques
make motion energy harvesting a viable energy source. However, it has not been
widely adopted due to bulky energy harvester designs that are uncomfortable to wear. This
work addresses this problem by analyzing the feasibility of powering low wearable power
devices using piezoelectric energy generated at the human knee. We start with a novel
mathematical model for estimating the power generated from human knee joint movements.
This thesis’s major contribution is to analyze the feasibility of human motion energy harvesting
and validating this analytical model using a commercially available piezoelectric
module. To this end, we implemented an experimental setup that replicates a human knee.
Then, we performed experiments at different excitation frequencies and amplitudes with
two commercially available Macro Fiber Composite (MFC) modules. These experimental
results are used to validate the analytical model and predict the energy harvested as a function
of the number of steps taken in a day. The model estimates that 13μWcan be generated
on an average while walking with a 4.8% modeling error. The obtained results show that
piezoelectricity is indeed a viable approach for powering low-power wearable devices.
make motion energy harvesting a viable energy source. However, it has not been
widely adopted due to bulky energy harvester designs that are uncomfortable to wear. This
work addresses this problem by analyzing the feasibility of powering low wearable power
devices using piezoelectric energy generated at the human knee. We start with a novel
mathematical model for estimating the power generated from human knee joint movements.
This thesis’s major contribution is to analyze the feasibility of human motion energy harvesting
and validating this analytical model using a commercially available piezoelectric
module. To this end, we implemented an experimental setup that replicates a human knee.
Then, we performed experiments at different excitation frequencies and amplitudes with
two commercially available Macro Fiber Composite (MFC) modules. These experimental
results are used to validate the analytical model and predict the energy harvested as a function
of the number of steps taken in a day. The model estimates that 13μWcan be generated
on an average while walking with a 4.8% modeling error. The obtained results show that
piezoelectricity is indeed a viable approach for powering low-power wearable devices.
ContributorsBandyopadhyay, Shiva (Author) / Ogras, Umit Y. (Thesis advisor) / Fan, Deliang (Committee member) / Trichopoulos, Georgios (Committee member) / Arizona State University (Publisher)
Created2020
Description
Though a single mode of energy transfer, optical radiation meaningfully interacts with its surrounding environment at over a wide range of physical length scales. For this reason, its reconstruction and measurement are of great importance in remote sensing, as these multi-scale interactions encode a great deal of information about distant objects, surfaces, and physical phenomena. For some remote sensing applications, obtaining a desired quantity of interest does not necessitate the explicit mapping of each point in object space to an image space with lenses or mirrors. Instead, only edge rays or physical boundaries of the sensing instrument are considered, while the spatial intensity distribution of optical energy received from a distant object informs its position, optical characteristics, or physical/chemical state.
Admittedly specialized, the principals and consequences of non-imaging optics are nevertheless applicable to heterogeneous semiconductor integration and automotive light detection and ranging (LiDAR), two important emerging technologies. Indeed, a review of relevant engineering literature finds two under-addressed remote sensing challenges. The semiconductor industry lacks an optical strain metrology with displacement resolution smaller than 100 nanometers capable of measuring strain fields between high-density interconnect lines. Meanwhile, little attention is paid to the per-meter sensing characteristics of scene-illuminating flash LiDAR in the context of automotive applications, despite the technology’s much lower cost. It is here that non-imaging optics offers intriguing instrument design and explanations of observed sensor performance at vastly different length scales.
In this thesis, an effective non-contact technique for mapping nanoscale mechanical strain fields and out-of-plane surface warping via laser diffraction is demonstrated, with application as a novel metrology for next-generation semiconductor packages. Additionally, object detection distance of low-cost automotive flash LiDAR, on the order of tens of meters, is understood though principals of optical energy transfer from the surface of a remote object to an extended multi-segment detector. Such information is of consequence when designing an automotive perception system to recognize various roadway objects in low-light scenarios.
Admittedly specialized, the principals and consequences of non-imaging optics are nevertheless applicable to heterogeneous semiconductor integration and automotive light detection and ranging (LiDAR), two important emerging technologies. Indeed, a review of relevant engineering literature finds two under-addressed remote sensing challenges. The semiconductor industry lacks an optical strain metrology with displacement resolution smaller than 100 nanometers capable of measuring strain fields between high-density interconnect lines. Meanwhile, little attention is paid to the per-meter sensing characteristics of scene-illuminating flash LiDAR in the context of automotive applications, despite the technology’s much lower cost. It is here that non-imaging optics offers intriguing instrument design and explanations of observed sensor performance at vastly different length scales.
In this thesis, an effective non-contact technique for mapping nanoscale mechanical strain fields and out-of-plane surface warping via laser diffraction is demonstrated, with application as a novel metrology for next-generation semiconductor packages. Additionally, object detection distance of low-cost automotive flash LiDAR, on the order of tens of meters, is understood though principals of optical energy transfer from the surface of a remote object to an extended multi-segment detector. Such information is of consequence when designing an automotive perception system to recognize various roadway objects in low-light scenarios.
ContributorsHoughton, Todd Kristopher (Author) / Yu, Hongbin (Thesis advisor) / Jiang, Hanqing (Committee member) / Jayasuriya, Suren (Committee member) / Zhang, Liang (Committee member) / Arizona State University (Publisher)
Created2020
Description
The advances of Deep Learning (DL) achieved recently have successfully demonstrated its great potential of surpassing or close to human-level performance across multiple domains. Consequently, there exists a rising demand to deploy state-of-the-art DL algorithms, e.g., Deep Neural Networks (DNN), in real-world applications to release labors from repetitive work. On the one hand, the impressive performance achieved by the DNN normally accompanies with the drawbacks of intensive memory and power usage due to enormous model size and high computation workload, which significantly hampers their deployment on the resource-limited cyber-physical systems or edge devices. Thus, the urgent demand for enhancing the inference efficiency of DNN has also great research interests across various communities. On the other hand, scientists and engineers still have insufficient knowledge about the principles of DNN which makes it mostly be treated as a black-box. Under such circumstance, DNN is like "the sword of Damocles" where its security or fault-tolerance capability is an essential concern which cannot be circumvented.
Motivated by the aforementioned concerns, this dissertation comprehensively investigates the emerging efficiency and security issues of DNNs, from both software and hardware design perspectives. From the efficiency perspective, as the foundation technique for efficient inference of target DNN, the model compression via quantization is elaborated. In order to maximize the inference performance boost, the deployment of quantized DNN on the revolutionary Computing-in-Memory based neural accelerator is presented in a cross-layer (device/circuit/system) fashion. From the security perspective, the well known adversarial attack is investigated spanning from its original input attack form (aka. Adversarial example generation) to its parameter attack variant.
Motivated by the aforementioned concerns, this dissertation comprehensively investigates the emerging efficiency and security issues of DNNs, from both software and hardware design perspectives. From the efficiency perspective, as the foundation technique for efficient inference of target DNN, the model compression via quantization is elaborated. In order to maximize the inference performance boost, the deployment of quantized DNN on the revolutionary Computing-in-Memory based neural accelerator is presented in a cross-layer (device/circuit/system) fashion. From the security perspective, the well known adversarial attack is investigated spanning from its original input attack form (aka. Adversarial example generation) to its parameter attack variant.
Contributorshe, zhezhi (Author) / Fan, Deliang (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Cao, Yu (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2020
Description
Micro Electro Mechanical Systems (MEMS) based accelerometers are one of the most commonly used sensors out there. They are used in devices such as, airbags, smartphones, airplanes, and many more. Although they are very accurate, they degrade with time or get offset due to some damage. To fix this, they must be calibrated again using physical calibration technique, which is an expensive process to conduct. However, these sensors can also be calibrated infield by applying an on-chip electrical stimulus to the sensor. Electrical stimulus-based calibration could bring the cost of testing and calibration significantly down as compared to factory testing. In this thesis, simulations are presented to formulate a statistical prediction model based on an electrical stimulus. Results from two different approaches of electrical calibration have been discussed. A prediction model with a root mean square error of 1% has been presented in this work. Experiments were conducted on commercially available accelerometers to test the techniques used for simulations.
ContributorsBassi, Ishaan (Author) / Ozev, Sule (Thesis advisor) / Christen, Jennifer Blain (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2020
Description
This Master’s thesis includes the design, integration on-chip, and evaluation of a set of imitation learning (IL)-based scheduling policies: deep neural network (DNN)and decision tree (DT). We first developed IL-based scheduling policies for heterogeneous systems-on-chips (SoCs). Then, we tested these policies using a system-level domain-specific system-on-chip simulation framework [11]. Finally, we transformed them into efficient code using a cloud engine [1] and implemented on a user-space emulation framework [61] on a Unix-based SoC. IL is one area of machine learning (ML) and a useful method to train artificial intelligence (AI) models by imitating the decisions of an expert or Oracle that knows the optimal solution. This thesis's primary focus is to adapt an ML model to work on-chip and optimize the resource allocation for a set of domain-specific wireless and radar systems applications. Evaluation results with four streaming applications from wireless communications and radar domains show how the proposed IL-based scheduler approximates an offline Oracle expert with more than 97% accuracy and 1.20× faster execution time. The models have been implemented as an add-on, making it easy to port to other SoCs.
ContributorsHolt, Conrad Mestres (Author) / Ogras, Umit Y. (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Akoglu, Ali (Committee member) / Arizona State University (Publisher)
Created2020
Description
Respiratory behavior provides effective information to characterize lung functionality, including respiratory rate, respiratory profile, and respiratory volume. Current methods have limited capabilities of continuous characterization of respiratory behavior and are primarily targeting the measurement of respiratory rate, which has relatively less value in clinical application. In this dissertation, a wireless wearable sensor on a paper substrate is developed to continuously characterize respiratory behavior and deliver clinically relevant parameters, contributing to asthma control. Based on the anatomical analysis and experimental results, the optimum site for the wireless wearable sensor is on the midway of the xiphoid process and the costal margin, corresponding to the abdomen-apposed rib cage. At the wearing site, the linear strain change during respiration is measured and converted to lung volume by the wireless wearable sensor utilizing a distance-elapsed ultrasound. An on-board low-power Bluetooth module transmits the temporal lung volume change to a smartphone, where a custom-programmed app computes to show the clinically relevant parameters, such as forced vital capacity (FVC) and forced expiratory volume delivered in the first second (FEV1) and the FEV1/FVC ratio. Enhanced by a simple, yet effective machine-learning algorithm, a system consisting of two wireless wearable sensors accurately extracts respiratory features and classifies the respiratory behavior within four postures among different subjects, demonstrating that the respiratory behaviors are individual- and posture-dependent contributing to monitoring the posture-related respiratory diseases. The continuous and accurate monitoring of respiratory behaviors can track the respiratory disorders and diseases' progression for timely and objective approaches for control and management.
ContributorsChen, Ang (Author) / Cao, Yu (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Allee, David (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
Combining the rapid development of semiconductor technologies, miniaturization of integrated circuits (ICs), and scaling down the device size is trending towards faster, cheaper, and more reliable components for low-power integrated circuits. Most research and development relate to efficiency, structure, materials, and performance. However, the thermal problem is also created and becomes more critical with shrinking device dimensions and increased integration densities, such that it affects the device performance and leads to degradation and damage. At the nanometer scale, the self-heating effect (SHE) is one of the main factors to degrade devices. Therefore, tracking and quantifying the SHE is important for reliability and efficiency issues. In this dissertation, engineers design two identical and closely spaced 40nm gate length silicon-on-insulator (SOI) n-channel metal-oxide-semiconductor-field-effect transistors (NMOSFETs) that share a common source with the same active silicon region. One of the MOSFETs acts as a heater to heat-up the active region, while the other one is a thermometer to evaluate the SHE and local temperature changes. The thermometer provides a method to calibrate the numerical models of self-heating and track the heat flow. Moreover, it also involves a trap-rich SOI wafer technology, in which a trap-rich layer, with higher resistivity and lower thermal conductivity compared to conventional bulk silicon substrates. The trap-rich SOI substrates can reduce the cross-talk and minimize the power consumption to increase the system performance. In particular, it offers a solution to radio frequency integrated circuits (RFICs) which require fast switching and low leakage. In high power amplifier (PA) applications, Watt-level PAs operates at less than 50% efficiency because of temperature limitations. The author uses experimental measurements of the local temperature changes, combined with simulations to examine the heat flow and temperature distribution. The approach may be useful to build a self-test application, because it can quantify the temperature changes by putting one or multiple NMOSFET thermometers around a complementary metal-oxide-semiconductor (CMOS) power amplifier, while only adding minimum die area. It points to ways in which it can optimize the reliability of RFIC applications, which operate under high-temperature or high-power conditions to protect the device before it is overheated or damaged.
ContributorsZhang, Xiong (Author) / Thornton, Trevor TT (Thesis advisor) / Vasileska, Dragica DV (Committee member) / Goryll, Michael MG (Committee member) / Myhajlenko, Stefan SM (Committee member) / Arizona State University (Publisher)
Created2020