Matching Items (106)
Description
Our eyes never stop moving, even during attempted gaze fixation. Fixational eye movements, which include tremor, drift, and microsaccades, are necessary to prevent retinal image adaptation, but may also result in unstable vision. Fortunately, the nervous system can suppress the retinal displacements induced by fixational eye movements and consequently keep our vision stable. The neural correlates of perceptual suppression during fixational eye movements are controversial. Also, the contribution of retinal versus extraretinal inputs to microsaccade-induced neuronal responses in the primary visual cortex (i.e. area V1) remain unclear. Here I show that V1 neuronal responses to microsaccades are different from those to stimulus motions simulating microsaccades. Responses to microsaccades consist of an initial excitatory component followed by an inhibitory component, which may be attributed to retinal and extraretinal signals, respectively. I also discuss the effects of the fixation target's size and luminance on microsaccade properties. Fixation targets are frequently used in psychophysical and electrophysiological research, and may have uncontrolled influences on experimental results. I found that microsaccade rates and magnitudes change linearly with fixation target size, but not with fixation target luminance. Finally, I present ion a novel variation of the Ouchi-Spillmann illusion, in which fixational eye movements may play a role.
ContributorsNajafian Jazi, Ali (Author) / Buneo, Christopher (Thesis advisor) / Martinez-Conde, Susana (Thesis advisor) / Macknik, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
When surgical resection becomes necessary to alleviate a patient's epileptiform activity, that patient is monitored by video synchronized with electrocorticography (ECoG) to determine the type and location of seizure focus. This provides a unique opportunity for researchers to gather neurophysiological data with high temporal and spatial resolution; these data are assessed prior to surgical resection to ensure the preservation of the patient's quality of life, e.g. avoid the removal of brain tissue required for speech processing. Currently considered the "gold standard" for the mapping of cortex, electrical cortical stimulation (ECS) involves the systematic activation of pairs of electrodes to localize functionally specific brain regions. This method has distinct limitations, which often includes pain experienced by the patient. Even in the best cases, the technique suffers from subjective assessments on the parts of both patients and physicians, and high inter- and intra-observer variability. Recent advances have been made as researchers have reported the localization of language areas through several signal processing methodologies, all necessitating patient participation in a controlled experiment. The development of a quantification tool to localize speech areas in which a patient is engaged in an unconstrained interpersonal conversation would eliminate the dependence of biased patient and reviewer input, as well as unnecessary discomfort to the patient. Post-hoc ECoG data were gathered from five patients with intractable epilepsy while each was engaged in a conversation with family members or clinicians. After the data were separated into different speech conditions, the power of each was compared to baseline to determine statistically significant activated electrodes. The results of several analytical methods are presented here. The algorithms did not yield language-specific areas exclusively, as broad activation of statistically significant electrodes was apparent across cortical areas. For one patient, 15 adjacent contacts along superior temporal gyrus (STG) and posterior part of the temporal lobe were determined language-significant through a controlled experiment. The task involved a patient lying in bed listening to repeated words, and yielded statistically significant activations that aligned with those of clinical evaluation. The results of this study do not support the hypothesis that unconstrained conversation may be used to localize areas required for receptive and productive speech, yet suggests a simple listening task may be an adequate alternative to direct cortical stimulation.
ContributorsLingo VanGilder, Jennapher (Author) / Helms Tillery, Stephen I (Thesis advisor) / Wahnoun, Remy (Thesis advisor) / Buneo, Christopher (Committee member) / Arizona State University (Publisher)
Created2013
Description
Intracortical microstimulation (ICMS) within somatosensory cortex can produce artificial sensations including touch, pressure, and vibration. There is significant interest in using ICMS to provide sensory feedback for a prosthetic limb. In such a system, information recorded from sensors on the prosthetic would be translated into electrical stimulation and delivered directly to the brain, providing feedback about features of objects in contact with the prosthetic. To achieve this goal, multiple simultaneous streams of information will need to be encoded by ICMS in a manner that produces robust, reliable, and discriminable sensations. The first segment of this work focuses on the discriminability of sensations elicited by ICMS within somatosensory cortex. Stimulation on multiple single electrodes and near-simultaneous stimulation across multiple electrodes, driven by a multimodal tactile sensor, were both used in these experiments. A SynTouch BioTac sensor was moved across a flat surface in several directions, and a subset of the sensor's electrode impedance channels were used to drive multichannel ICMS in the somatosensory cortex of a non-human primate. The animal performed a behavioral task during this stimulation to indicate the discriminability of sensations evoked by the electrical stimulation. The animal's responses to ICMS were somewhat inconsistent across experimental sessions but indicated that discriminable sensations were evoked by both single and multichannel ICMS. The factors that affect the discriminability of stimulation-induced sensations are not well understood, in part because the relationship between ICMS and the neural activity it induces is poorly defined. The second component of this work was to develop computational models that describe the populations of neurons likely to be activated by ICMS. Models of several neurons were constructed, and their responses to ICMS were calculated. A three-dimensional cortical model was constructed using these cell models and used to identify the populations of neurons likely to be recruited by ICMS. Stimulation activated neurons in a sparse and discontinuous fashion; additionally, the type, number, and location of neurons likely to be activated by stimulation varied with electrode depth.
ContributorsOverstreet, Cynthia K (Author) / Helms Tillery, Stephen I (Thesis advisor) / Santos, Veronica (Committee member) / Buneo, Christopher (Committee member) / Otto, Kevin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly organized into the following two parts: I) spatio-temporal wind power analysis for wind generation forecast and integration, and II) data mining and information fusion of synchrophasor measurements toward secure power grids. Part I is centered around wind power generation forecast and integration. First, a spatio-temporal analysis approach for short-term wind farm generation forecasting is proposed. Specifically, using extensive measurement data from an actual wind farm, the probability distribution and the level crossing rate of wind farm generation are characterized using tools from graphical learning and time-series analysis. Built on these spatial and temporal characterizations, finite state Markov chain models are developed, and a point forecast of wind farm generation is derived using the Markov chains. Then, multi-timescale scheduling and dispatch with stochastic wind generation and opportunistic demand response is investigated. Part II focuses on incorporating the emerging synchrophasor technology into the security assessment and the post-disturbance fault diagnosis of power systems. First, a data-mining framework is developed for on-line dynamic security assessment by using adaptive ensemble decision tree learning of real-time synchrophasor measurements. Under this framework, novel on-line dynamic security assessment schemes are devised, aiming to handle various factors (including variations of operating conditions, forced system topology change, and loss of critical synchrophasor measurements) that can have significant impact on the performance of conventional data-mining based on-line DSA schemes. Then, in the context of post-disturbance analysis, fault detection and localization of line outage is investigated using a dependency graph approach. It is shown that a dependency graph for voltage phase angles can be built according to the interconnection structure of power system, and line outage events can be detected and localized through networked data fusion of the synchrophasor measurements collected from multiple locations of power grids. Along a more practical avenue, a decentralized networked data fusion scheme is proposed for efficient fault detection and localization.
ContributorsHe, Miao (Author) / Zhang, Junshan (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Hedman, Kory (Committee member) / Si, Jennie (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Humans' ability to perform fine object and tool manipulation is a defining feature of their sensorimotor repertoire. How the central nervous system builds and maintains internal representations of such skilled hand-object interactions has attracted significant attention over the past three decades. Nevertheless, two major gaps exist: a) how digit positions and forces are coordinated during natural manipulation tasks, and b) what mechanisms underlie the formation and retention of internal representations of dexterous manipulation. This dissertation addresses these two questions through five experiments that are based on novel grip devices and experimental protocols. It was found that high-level representation of manipulation tasks can be learned in an effector-independent fashion. Specifically, when challenged by trial-to-trial variability in finger positions or using digits that were not previously engaged in learning the task, subjects could adjust finger forces to compensate for this variability, thus leading to consistent task performance. The results from a follow-up experiment conducted in a virtual reality environment indicate that haptic feedback is sufficient to implement the above coordination between digit position and forces. However, it was also found that the generalizability of a learned manipulation is limited across tasks. Specifically, when subjects learned to manipulate the same object across different contexts that require different motor output, interference was found at the time of switching contexts. Data from additional studies provide evidence for parallel learning processes, which are characterized by different rates of decay and learning. These experiments have provided important insight into the neural mechanisms underlying learning and control of object manipulation. The present findings have potential biomedical applications including brain-machine interfaces, rehabilitation of hand function, and prosthetics.
ContributorsFu, Qiushi (Author) / Santello, Marco (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Santos, Veronica (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2013
Description
Reaching movements are subject to noise in both the planning and execution phases of movement production. Although the effects of these noise sources in estimating and/or controlling endpoint position have been examined in many studies, the independent effects of limb configuration on endpoint variability have been largely ignored. The present study investigated the effects of arm configuration on the interaction between planning noise and execution noise. Subjects performed reaching movements to three targets located in a frontal plane. At the starting position, subjects matched one of two desired arm configuration 'templates' namely "adducted" and "abducted". These arm configurations were obtained by rotations along the shoulder-hand axis, thereby maintaining endpoint position. Visual feedback of the hand was varied from trial to trial, thereby increasing uncertainty in movement planning and execution. It was hypothesized that 1) pattern of endpoint variability would be dependent on arm configuration and 2) that these differences would be most apparent in conditions without visual feedback. It was found that there were differences in endpoint variability between arm configurations in both visual conditions, but these differences were much larger when visual feedback was withheld. The overall results suggest that patterns of endpoint variability are highly dependent on arm configuration, particularly in the absence of visual feedback. This suggests that in the presence of vision, movement planning in 3D space is performed using coordinates that are largely arm configuration independent (i.e. extrinsic coordinates). In contrast, in the absence of vision, movement planning in 3D space reflects a substantial contribution of intrinsic coordinates.
ContributorsLakshmi Narayanan, Kishor (Author) / Buneo, Christopher (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the rapid growth of power systems and the concomitant technological advancements, the goal of achieving smart grids is no longer a vision but a foreseeable reality. Hence, the existing grids are undergoing infrastructural modifications to achieve the diverse characteristics of a smart grid. While there are many subjects associated with the operation of smart grids, this dissertation addresses two important aspects of smart grids: increased penetration of renewable resources, and increased reliance on sensor systems to improve reliability and performance of critical power system components. Present renewable portfolio standards are changing both structural and performance characteristics of power systems by replacing conventional generation with alternate energy resources such as photovoltaic (PV) systems. The present study investigates the impact of increased penetration of PV systems on steady state performance as well as transient stability of a large power system which is a portion of the Western U.S. interconnection. Utility scale and residential rooftop PVs are added to replace a portion of conventional generation resources. While steady state voltages are observed under various PV penetration levels, the impact of reduced inertia on transient stability performance is also examined. The simulation results obtained effectively identify both detrimental and beneficial impacts of increased PV penetration both for steady state stability and transient stability performance. With increased penetration of the renewable energy resources, and with the current loading scenario, more transmission system components such as transformers and circuit breakers are subject to increased stress and overloading. This research work explores the feasibility of increasing system reliability by applying condition monitoring systems to selected circuit breakers and transformers. A very important feature of smart grid technology is that this philosophy decreases maintenance costs by deploying condition monitoring systems that inform the operator of impending failures; or the approach can ameliorate problematic conditions. A method to identify the most critical transformers and circuit breakers with the aid of contingency ranking methods is presented in this study. The work reported in this dissertation parallels an industry sponsored study in which a considerable level of industry input and industry reported concerns are reflected.
ContributorsEftekharnejad, Sara (Author) / Heydt, Gerald (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Si, Jennie (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2012
Description
The combined heat and power (CHP)-based distributed generation (DG) or dis-tributed energy resources (DERs) are mature options available in the present energy mar-ket, considered to be an effective solution to promote energy efficiency. In the urban en-vironment, the electricity, water and natural gas distribution networks are becoming in-creasingly interconnected with the growing penetration of the CHP-based DG. Subse-quently, this emerging interdependence leads to new topics meriting serious consideration: how much of the CHP-based DG can be accommodated and where to locate these DERs, and given preexisting constraints, how to quantify the mutual impacts on operation performances between these urban energy distribution networks and the CHP-based DG. The early research work was conducted to investigate the feasibility and design methods for one residential microgrid system based on existing electricity, water and gas infrastructures of a residential community, mainly focusing on the economic planning. However, this proposed design method cannot determine the optimal DG sizing and sit-ing for a larger test bed with the given information of energy infrastructures. In this con-text, a more systematic as well as generalized approach should be developed to solve these problems. In the later study, the model architecture that integrates urban electricity, water and gas distribution networks, and the CHP-based DG system was developed. The pro-posed approach addressed the challenge of identifying the optimal sizing and siting of the CHP-based DG on these urban energy networks and the mutual impacts on operation per-formances were also quantified. For this study, the overall objective is to maximize the electrical output and recovered thermal output of the CHP-based DG units. The electrici-ty, gas, and water system models were developed individually and coupled by the devel-oped CHP-based DG system model. The resultant integrated system model is used to constrain the DG's electrical output and recovered thermal output, which are affected by multiple factors and thus analyzed in different case studies. The results indicate that the designed typical gas system is capable of supplying sufficient natural gas for the DG normal operation, while the present water system cannot support the complete recovery of the exhaust heat from the DG units.
ContributorsZhang, Xianjun (Author) / Karady, George G. (Thesis advisor) / Ariaratnam, Samuel T. (Committee member) / Holbert, Keith E. (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2013
Description
Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following issues pose significant challenges as brain tissue is both functionally and structurally dynamic: a) time varying mechanical properties of the brain tissue-microelectrode interface due to the hyperelastic, viscoelastic nature of brain tissue b) non-stationarities in the neural signal caused by mechanical and physiological events in the interface and c) the lack of visual feedback of microelectrode position in brain tissue. A closed loop control algorithm is proposed here for autonomous navigation of microelectrodes in brain tissue while optimizing the signal-to-noise ratio of multi-unit neural recordings. The algorithm incorporates a quantitative understanding of constitutive mechanical properties of soft viscoelastic tissue like the brain and is guided by models that predict stresses developed in brain tissue during movement of the microelectrode. An optimal movement strategy is developed that achieves precise positioning of microelectrodes in the brain by minimizing the stresses developed in the surrounding tissue during navigation and maximizing the speed of movement. Results of testing the closed-loop control paradigm in short-term rodent experiments validated that it was possible to achieve a consistently high quality SNR throughout the duration of the experiment. At the systems level, new generation of MEMS actuators for movable microelectrode array are characterized and the MEMS device operation parameters are optimized for improved performance and reliability. Further, recommendations for packaging to minimize the form factor of the implant; design of device mounting and implantation techniques of MEMS microelectrode array to enhance the longevity of the implant are also included in a top-down approach to achieve a reliable brain interface.
ContributorsAnand, Sindhu (Author) / Muthuswamy, Jitendran (Thesis advisor) / Tillery, Stephen H (Committee member) / Buneo, Christopher (Committee member) / Abbas, James (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation considers an integrated approach to system design and controller design based on analyzing limits of system performance. Historically, plant design methodologies have not incorporated control relevant considerations. Such an approach could result in a system that might not meet its specifications (or one that requires a complex control architecture to do so). System and controller designers often go through several iterations in order to converge to an acceptable plant and controller design. The focus of this dissertation is on the design and control an air-breathing hypersonic vehicle using such an integrated system-control design framework. The goal is to reduce the number of system-control design iterations (by explicitly incorporate control considerations in the system design process), as well as to influence the guidance/trajectory specifications for the system. Due to the high computational costs associated with obtaining a dynamic model for each plant configuration considered, approximations to the system dynamics are used in the control design process. By formulating the control design problem using bilinear and polynomial matrix inequalities, several common control and system design constraints can be simultaneously incorporated into a vehicle design optimization. Several design problems are examined to illustrate the effectiveness of this approach (and to compare the computational burden of this methodology against more traditional approaches).
ContributorsSridharan, Srikanth (Author) / Rodriguez, Armando A (Thesis advisor) / Mittelmann, Hans D (Committee member) / Si, Jennie (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2014