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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
Description
This dissertation presents a novel algorithm for recovering missing values of co-evolving time series with partial embedded network information. The idea is to connect two sources of data through a shared low dimensional latent space. The proposed algorithm, named NetDyna, is an Expectation-Maximization algorithm, and uses the Kalman filter and matrix factorization approaches to infer the missing values both in the time series and embedded network. The experimental results on real datasets, including a Motes dataset and a Motion Capture dataset, show that (1) NetDyna outperforms other state-of-the-art algorithms, especially with partially observed network information; (2) its computational complexity scales linearly with the time duration of time series; and (3) the algorithm recovers the embedded network in addition to missing time series values.
This dissertation also studies a load balancing algorithm, the so called power-of-two-choices(Po2), for many-server systems (with N servers) and focuses on the convergence of stationary distribution of Po2 in the both light and heavy traffic regimes to the solution of mean-field system. The framework of Stein’s method and state space collapse (SSC) are used to analyze both regimes.
In both regimes, the thesis first uses the argument of state space collapse to show that the probability of the state being far from the mean-field solution is small enough. By a simple Markov inequality, it is able to show that the probability is indeed very small with a proper choice of parameters.
Then, for the state space close to the solution of mean-field model, the thesis uses Stein’s method to show that the stochastic system is close to a linear mean-field model. By characterizing the generator difference, it is able to characterize the dominant terms in both regimes. Note that for heavy traffic case, the lower and upper bound analysis of a tridiagonal matrix, which arises from the linear mean-field model, is needed. From the dominant term, it allows to calculate the coefficient of the convergence rate.
In the end, comparisons between the theoretical predictions and numerical simulations are presented.
This dissertation also studies a load balancing algorithm, the so called power-of-two-choices(Po2), for many-server systems (with N servers) and focuses on the convergence of stationary distribution of Po2 in the both light and heavy traffic regimes to the solution of mean-field system. The framework of Stein’s method and state space collapse (SSC) are used to analyze both regimes.
In both regimes, the thesis first uses the argument of state space collapse to show that the probability of the state being far from the mean-field solution is small enough. By a simple Markov inequality, it is able to show that the probability is indeed very small with a proper choice of parameters.
Then, for the state space close to the solution of mean-field model, the thesis uses Stein’s method to show that the stochastic system is close to a linear mean-field model. By characterizing the generator difference, it is able to characterize the dominant terms in both regimes. Note that for heavy traffic case, the lower and upper bound analysis of a tridiagonal matrix, which arises from the linear mean-field model, is needed. From the dominant term, it allows to calculate the coefficient of the convergence rate.
In the end, comparisons between the theoretical predictions and numerical simulations are presented.
ContributorsHairi, FNU (Author) / Ying, Lei (Thesis advisor) / Wang, Weina (Committee member) / Zhang, Junshan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2020
Description
The manufacturing process for electronic systems involves many players, from chip/board design and fabrication to firmware design and installation.
In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk.
Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities.
It is becoming increasingly necessary to trust but verify the received devices both at production and in the field.
Unauthorized hardware or firmware modifications, known as Trojans,
can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices.
Since Trojans may be triggered in the field at an unknown instance,
it is essential to detect their presence at run-time.
However, it isn't easy to run sophisticated detection algorithms on these devices
due to limited computational power and energy, and in some cases, lack of accessibility.
Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain.
In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory.
When the device enters the test mode, a predefined test application is run on the device
repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes.
The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios.
In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk.
Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities.
It is becoming increasingly necessary to trust but verify the received devices both at production and in the field.
Unauthorized hardware or firmware modifications, known as Trojans,
can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices.
Since Trojans may be triggered in the field at an unknown instance,
it is essential to detect their presence at run-time.
However, it isn't easy to run sophisticated detection algorithms on these devices
due to limited computational power and energy, and in some cases, lack of accessibility.
Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain.
In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory.
When the device enters the test mode, a predefined test application is run on the device
repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes.
The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios.
ContributorsKarabacak, Fatih (Author) / Ozev, Sule (Thesis advisor) / Ogras, Umit Y. (Thesis advisor) / Christen, Jennifer Blain (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2020
Description
Advanced and mature computer simulation methods exist in fluid dynamics, elec-
tromagnetics, semiconductors, chemical transport, and even chemical and material
electronic structure. However, few general or accurate methods have been developed
for quantum photonic devices. Here, a novel approach utilizing phase-space quantum
mechanics is developed to model photon transport in ring resonators, a form of en-
tangled pair source. The key features the model needs to illustrate are the emergence
of non-classicality and entanglement between photons due to nonlinear effects in the
ring. The quantum trajectory method is subsequently demonstrated on a sequence
of elementary models and multiple aspects of the ring resonator itself.
tromagnetics, semiconductors, chemical transport, and even chemical and material
electronic structure. However, few general or accurate methods have been developed
for quantum photonic devices. Here, a novel approach utilizing phase-space quantum
mechanics is developed to model photon transport in ring resonators, a form of en-
tangled pair source. The key features the model needs to illustrate are the emergence
of non-classicality and entanglement between photons due to nonlinear effects in the
ring. The quantum trajectory method is subsequently demonstrated on a sequence
of elementary models and multiple aspects of the ring resonator itself.
ContributorsWelland, Ian Matthew (Author) / Ferry, David K. (Thesis advisor) / Goodnick, Stephen (Thesis advisor) / Zhao, Yuji (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2020
Description
A mathematical approach was developed to evaluate the resilience of coupled power-water networks using a variant of contingency analysis adapted from electric transmission studies. In particular, the “what if” scenarios explored in power systems research were extended and applied for coupled power-water network research by evaluating how stressors and failures in the water network can propagate across system boundaries and into the electric network. Reduction in power system contingency reserves was the metric for determining violation of N-1 contingency reliability. Geospatial considerations were included using high-resolution, publicly available Geographic Information System data on infrastructure in the Phoenix Metropolitan Area that was used to generate a power network with 599 transmission lines and total generation capacity of 18.98 GW and a water network with 2,624 water network lines and capacity to serve up to 1.72M GPM of surface water. The steady-state model incorporated operating requirements for the power network—e.g., contingency reserves—and the water network—e.g., pressure ranges—while seeking to meet electric load and water demand. Interconnections developed between the infrastructures demonstrated how alternations to the system state and/or configuration of one network affect the other network, with results demonstrated through co-simulation of the power network and water network using OpenDSS and EPANET, respectively. Results indicate four key findings that help operators understand the interdependent behavior of the coupled power-water network: (i) two water failure scenarios (water flowing out of Waddell dam and CAP canal flowing west of Waddell dam) are critical to power-water network N-1 contingency reliability above 60% power system loading and at 100% water system demand, (ii) fast-starting natural gas generating units are necessary to maintain N-1 contingency reliability below 60% power system loading, (iii) Coolidge Station was the power plant to most frequently undergo a reduction in reserves amongst the water failure scenarios that cause a violation of N-1 reliability, (iv) power network vulnerability to water network failures was non-linear because it depends on the generating units that are dispatched, which can vary as line thermal limits or unit generation capacities are reached.
ContributorsGorman, Brandon (Author) / Johnson, Nathan G (Thesis advisor) / Seager, Thomas P (Committee member) / Chester, Mikhail V (Committee member) / Arizona State University (Publisher)
Created2020
Description
Photonic integrated circuit (PIC) in the visible spectrum opens up new opportunities for frequency metrology, neurophotonics, and quantum technologies. Group III nitride (III-N) compound semiconductor is a new emerging material platform for PIC in visible spectrum. The ultra-wide bandgap of aluminum nitride (AlN) allows broadband transparency. The high quantum efficiency of indium gallium nitride (InGaN) quantum well is the major enabler for solid-state lighting and provides the opportunities for active photonic integration. Additionally, the two-dimensional electron gas induced by spontaneous and polarization charges within III-N materials exhibit large electron mobility, which is promising for the development of high frequency transistors. Moreover, the noncentrosymmetric crystalline structure gives nonzero second order susceptibility, beneficial for the application of second harmonic generation and entangled photon generation in nonlinear and quantum optical technologies. Despite the promising features of III-N materials, the investigations on the III-N based PICs are still primitive, mainly due to the difficulties in material growth and the lack of knowledge on fundamental material parameters. In this work, firstly, the fundamental nonlinear optical properties of III-N materials will be characterized. Then, the fabrication process flow of III-N materials will be established. Thirdly, the waveguide performance will be theoretically and experimentally evaluated. At last, the supercontinuum generation from visible to infrared will be demonstrated by utilizing soliton dynamics in high order guided modes. The outcome from this work paves the way towards fully integrated optical comb in UV and visible spectrum.
ContributorsChen, Hong (Author) / Zhao, Yuji (Thesis advisor) / Yao, Yu (Committee member) / Wang, Liping (Committee member) / Ning, Cun-Zheng (Committee member) / Arizona State University (Publisher)
Created2020
Description
This dissertation studies the scheduling in two stochastic networks, a co-located wireless network and an outpatient healthcare network, both of which have a cyclic planning horizon and a deadline-related performance metric.
For the co-located wireless network, a time-slotted system is considered. A cycle of planning horizon is called a frame, which consists of a fixed number of time slots. The size of the frame is determined by the upper-layer applications. Packets with deadlines arrive at the beginning of each frame and will be discarded if missing their deadlines, which are in the same frame. Each link of the network is associated with a quality of service constraint and an average transmit power constraint. For this system, a MaxWeight-type problem for which the solutions achieve the throughput optimality is formulated. Since the computational complexity of solving the MaxWeight-type problem with exhaustive search is exponential even for a single-link system, a greedy algorithm with complexity O(nlog(n)) is proposed, which is also throughput optimal.
The outpatient healthcare network is modeled as a discrete-time queueing network, in which patients receive diagnosis and treatment planning that involves collaboration between multiple service stations. For each patient, only the root (first) appointment can be scheduled as the following appointments evolve stochastically. The cyclic planing horizon is a week. The root appointment is optimized to maximize the proportion of patients that can complete their care by a class-dependent deadline. In the optimization algorithm, the sojourn time of patients in the healthcare network is approximated with a doubly-stochastic phase-type distribution. To address the computational intractability, a mean-field model with convergence guarantees is proposed. A linear programming-based policy improvement framework is developed, which can approximately solve the original large-scale stochastic optimization in queueing networks of realistic sizes.
For the co-located wireless network, a time-slotted system is considered. A cycle of planning horizon is called a frame, which consists of a fixed number of time slots. The size of the frame is determined by the upper-layer applications. Packets with deadlines arrive at the beginning of each frame and will be discarded if missing their deadlines, which are in the same frame. Each link of the network is associated with a quality of service constraint and an average transmit power constraint. For this system, a MaxWeight-type problem for which the solutions achieve the throughput optimality is formulated. Since the computational complexity of solving the MaxWeight-type problem with exhaustive search is exponential even for a single-link system, a greedy algorithm with complexity O(nlog(n)) is proposed, which is also throughput optimal.
The outpatient healthcare network is modeled as a discrete-time queueing network, in which patients receive diagnosis and treatment planning that involves collaboration between multiple service stations. For each patient, only the root (first) appointment can be scheduled as the following appointments evolve stochastically. The cyclic planing horizon is a week. The root appointment is optimized to maximize the proportion of patients that can complete their care by a class-dependent deadline. In the optimization algorithm, the sojourn time of patients in the healthcare network is approximated with a doubly-stochastic phase-type distribution. To address the computational intractability, a mean-field model with convergence guarantees is proposed. A linear programming-based policy improvement framework is developed, which can approximately solve the original large-scale stochastic optimization in queueing networks of realistic sizes.
ContributorsLiu, Yiqiu (Author) / Ying, Lei (Thesis advisor) / Shi, Pengyi (Committee member) / Wang, Weina (Committee member) / Zhang, Junshan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2020