Wave Propagation in Periodic Acoustic Metamaterials: from 1D to 3D

Wave propagation in periodic structures has been studied for centuries; for example, Newton derived the velocity of sound based on a linear lattice. Recently, advanced manufacturing techniques have led to the fabrication of geometrically complex architected materials with acoustic properties unattainable by their constituent materials. Such rationally designed structures are often called acoustic metamaterials and they can be engineered to transmit, block, amplify, or redirect acoustic waves. Subwave-length building blocks, typically periodic (but not necessarily so), can be assembled into effectively continuous materials to manipulate dispersive properties of vibrational waves in ways that differ substantially in conventional media. This thesis investigates rationally designed acoustic metamaterials, ranging from 1D to 3D, and how acoustic wave propagation can be controlled by these artificially structured composite materials for ultrasound-related biomedical applications. I first explore 1D wave propagation in acoustic metamaterials to study the basic mechanics and relevant analysis skills. Bio-inspired helical mechanical metamaterials are designed and their normal modes are investigated. I demonstrate the ability to vary the acoustic properties of the helical metamaterials by perturbing the geometrical structure and mass distribution. By locally adding eccentric and denser elements in the unit cells, I change the moment of inertia of the system and introduce centro-asymmetry. This allows me to control the degree of mode coupling and the width of subwavelength band gaps in the dispersion relation, which are the product of enhanced local resonance hybridization. Then I study 2D wave propagation in microlattice acoustic metamaterials for ultra- sound manipulation. When coupled with pressure waves in the surrounding fluid, the dynamic behavior of microlattices in the long wavelength limit can be explained in the context of Biot’s theory of poroelasticity. I exploit elastoacoustic wave propagation within 3D-printed polymeric microlattices to design a gradient refractive index lens for underwater wave focusing. A modified Luneburg lens index profile adapted for ultrasonic wave lensing is demonstrated via the finite element method and underwater testing, showcasing a computationally efficient poroelasticity-based design approach that enables accelerated design of acoustic wave manipulation devices. Lastly, I show that tailorable 3D wave propagation can be achieved based on the findings from the previous chapters. Functional ultrasound imaging enables sensitive, high-resolution imaging of neural activity in freely behaving animals and human patients. However, the skull acts as an aberrant and absorbing layer for sound waves, leading to most functional ultrasound experiments being conducted after skull removal. A microscale 2-photon polymerization technique is adopted to fabricate a conformal acoustic window with a high stiffness-to-density ratio and sonotransparency. Long-term biocompatibility and lasting signal sensitivity are demonstrated over a long period of time (&#62; 4 months) by conducting ultrasound imaging in mouse models implanted with the metamaterial skull prosthesis.</p

HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera

This paper proposes a novel visual simultaneous localization and mapping (SLAM) system called Hybrid Depth-augmented Panoramic Visual SLAM (HDPV-SLAM), that employs a panoramic camera and a tilted multi-beam LiDAR scanner to generate accurate and metrically-scaled trajectories. RGB-D SLAM was the design basis for HDPV-SLAM, which added depth information to visual features. It aims to solve the two major issues hindering the performance of similar SLAM systems. The first obstacle is the sparseness of LiDAR depth, which makes it difficult to correlate it with the extracted visual features of the RGB image. A deep learning-based depth estimation module for iteratively densifying sparse LiDAR depth was suggested to address this issue. The second issue pertains to the difficulties in depth association caused by a lack of horizontal overlap between the panoramic camera and the tilted LiDAR sensor. To surmount this difficulty, we present a hybrid depth association module that optimally combines depth information estimated by two independent procedures, feature-based triangulation and depth estimation. During a phase of feature tracking, this hybrid depth association module aims to maximize the use of more accurate depth information between the triangulated depth with visual features tracked and the deep learning-based corrected depth. We evaluated the efficacy of HDPV-SLAM using the 18.95 km-long York University and Teledyne Optech (YUTO) MMS dataset. The experimental results demonstrate that the two proposed modules contribute substantially to the performance of HDPV-SLAM, which surpasses that of the state-of-the-art (SOTA) SLAM systems.Comment: 8 pages, 3 figures, To be published in IEEE International Conference on Automation Science and Engineering (CASE) 202

nuQmm: Quantized MatMul for Efficient Inference of Large-Scale Generative Language Models

The recent advance of self-supervised learning associated with the Transformer architecture enables natural language processing (NLP) to exhibit extremely low perplexity. Such powerful models demand ever-increasing model size and, thus, large amounts of computations and memory footprints. In this paper, we propose an efficient inference framework for large-scale generative language models. As the key to reducing model size, we quantize weights by a non-uniform quantization method. Then, quantized matrix multiplications are accelerated by our proposed kernel, called nuQmm, which allows a wide trade-off between compression ratio and accuracy. Our proposed nuQmm reduces the latency of not only each GPU but also the entire inference of large LMs because a high compression ratio (by low-bit quantization) mitigates the minimum required number of GPUs. Assuming 2-bit quantization, we demonstrate that nuQmm can reduce latency to generate each token for OPT-175B (that requires 8 GPUs without nuQmm) by 47.3% using 8 GPUs or by 23.2% using only 2 GPUs.Comment: 15 pages (including 5 pages of References & Appendix), 14 figures, 7 table

Thermoelectric model to characterize carrier transport in organic semiconductors

A model for the Seebeck coefficient in the regime of hopping transport that includes the effects of Gaussian carrier density of states width and carrier localization allows these parameters to be derived independently of the attempt-to-jump rate, which can subsequently be derived from measured electrical conductivity. This model is applied to prototypical small molecular and polymer organic semiconductors to characterize carrier localization, quantify the role of dopants on the hopping transport parameters, and derive the effective dopant ionization fraction and activation energy.clos

Homogeneous bilayer graphene film based flexible transparent conductor

Graphene is considered a promising candidate to replace conventional transparent conductors due to its low opacity, high carrier mobility and flexible structure. Multi-layer graphene or stacked single layer graphenes have been investigated in the past but both have their drawbacks. The uniformity of multi-layer graphene is still questionable, and single layer graphene stacks require many transfer processes to achieve sufficiently low sheet resistance. In this work, bilayer graphene film grown with low pressure chemical vapor deposition was used as a transparent conductor for the first time. The technique was demonstrated to be highly efficient in fabricating a conductive and uniform transparent conductor compared to multi-layer or single layer graphene. Four transfers of bilayer graphene yielded a transparent conducting film with a sheet resistance of 180 {\Omega}_{\square} at a transmittance of 83%. In addition, bilayer graphene films transferred onto plastic substrate showed remarkable robustness against bending, with sheet resistance change less than 15% at 2.14% strain, a 20-fold improvement over commercial indium oxide films.Comment: Published in Nanoscale, Nov. 2011 : http://www.rsc.org/nanoscal