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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
In the first chapter of the study, wavelet multiresolution analysis (WMRA) is extended to describe inter-phase, cross-scale interactions involving turbulence kinetic energy (TKE) of particle-laden turbulence. Homogeneous isotropic turbulence suspended with inertial particles at the Stokes number of unity is analyzed. Effects of the two-way coupling on spectral TKE transfer are examined. Particle concentration alone does not indicate a definite direction of inter-phase energy transfer. Rather, particle clusters behave as an energy source or sink with similar probabilities. In addition, the joint statistics show thequalitative consistency of the subgrid-scale (SGS) Stokes number in describing the two-way interactions, which should be considered in the SGS modeling of two-way coupled particle-laden turbulence.
In the second chapter, direct numerical simulation (DNS) of viscoelastic turbulent channel flow is conducted and the resulting velocity field is analyzed using the WMRA to identify the drag reduction mechanism by polymer additives. At the friction Reynolds number Re? = 145 and the Weissenberg number Wi = 40, the DNS of a viscoelastic channel flow is performed using the finitely extensible nonlinear elastic model. In-plane WMRA is performed to investigate the modulation of TKE due to interactions between polymer solution and turbulence across different scales. A formulation is proposed to evaluate the effects of polymers on the spectral TKE transfer. Using joint probability analysis, it has been shown that polymers absorb TKE from the near-wall region and store it as elastic energy at ?+ ≲ 20, while they enhance TKE in the log layer.
Ultimately, this study introduces a framework for optimizing large-eddy simulation
(LES) models via WMRA. By employing the spectrally and spatially localized decomposition of wavelets, an optimal balance between resolved inter-scale energy transfer and modeled SGS dissipation is enforced across a range of nominal LES grid widths. This formulation either determines a constant for the SGS model or offers an analytical expression for SGS closure that maximizes spectral energy transfer between resolved and unresolved scales at a specific cutoff scale. This proposed approach is assessed in the context of incompressible HIT. The constant of the one-parameter Smagorinsky closure model is optimized to align with the theoretical predictions.
ContributorsNabavi Bavil, Miralireza (Author) / Kim, Jeonglae JK (Thesis advisor) / Peet, Yulia YP (Committee member) / Kasbaoui, Mohamed Houssem MK (Committee member) / Pathikonda, Gokul GP (Committee member) / Scotti, Alberto AS (Committee member) / Arizona State University (Publisher)
Created2023