This study focuses on the optimization of antireflection coatings (ARCs) to enhance the performance of silicon heterojunction (SHJ) solar cells. SHJ solar cells face a significant challenge in achieving their theoreti...
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We present EgoNeRF, a practical solution to reconstruct large-scale real-world environments for VR assets. Given a few seconds of casually captured 360 video, EgoNeRF can efficiently build neural radiance fields. Moti...
We present EgoNeRF, a practical solution to reconstruct large-scale real-world environments for VR assets. Given a few seconds of casually captured 360 video, EgoNeRF can efficiently build neural radiance fields. Motivated by the recent acceleration of NeRF using feature grids, we adopt spherical coordinate instead of conventional Cartesian coordinate. Cartesian feature grid is inefficient to represent large-scale unbounded scenes because it has a spatially uniform resolution, regardless of distance from viewers. The spherical parameterization better aligns with the rays of egocentric images, and yet enables factorization for performance enhancement. However, the naive spherical grid suffers from singularities at two poles, and also cannot represent unbounded scenes. To avoid singularities near poles, we combine two balanced grids, which results in a quasi-uniform angular grid. We also partition the radial grid exponentially and place an environment map at infinity to represent unbounded scenes. Furthermore, with our resampling technique for grid-based methods, we can increase the number of valid samples to train NeRF volume. We extensively evaluate our method in our newly introduced synthetic and real-world egocentric 360 video datasets, and it consistently achieves state-of-the-art performance.
Autonomous take-off and landing capabilities are crucial in UAV vision-based missions, ensuring adaptive navigation, especially in challenging environments where realtime identification and interaction with a variety ...
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Automatic target recognition (ATR) for 3D synthetic aperture sonar (SAS) imagery is an intrinsic challenge in highly cluttered ocean environments, especially for objects partially or completely buried in the sediment....
Automatic target recognition (ATR) for 3D synthetic aperture sonar (SAS) imagery is an intrinsic challenge in highly cluttered ocean environments, especially for objects partially or completely buried in the sediment. Conventional dynamic range compression (DRC) techniques such as log-compression, which is a type of tone mapping intended to appeal to the human visual system, can further obscure the sonar signatures of these already physically occluded objects and lead to suboptimal downstream ATR performance, particularly for convolutional neural networks (CNNs). In this paper, we present a novel machine learning-based approach for tone mapping sub-bottom SAS imagery as a pre-processing stage in the 3D SAS ATR pipeline. This learned tone mapping function can be jointly optimized with a CNN-based ATR algorithm. We train and validate our method on measured volumetric SAS data captured by the Sediment Volume Search Sonar (SVSS) system.
Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on the reconstruction loss, recent works h...
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Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers ...
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Dengue Hemorrhagic Fever is an acute viral infectious disease caused by the dengue virus. Transmitted through the bite of Aedes Mosquitoes and divided into 4 severity. Severity 1 and 2 are characterized by a decrease ...
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In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained...
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We present a 4-channel 15 kS/s Voltage-to-Time Converter (VTC) analog front-end (AFE) with a $0.49\mu W$ impulse-based galvanic uplink for a peripheral nerve interface. Multiple, low-noise, high-data-rate channels a...
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ISBN:
(数字)9798350387179
ISBN:
(纸本)9798350387186
We present a 4-channel 15 kS/s Voltage-to-Time Converter (VTC) analog front-end (AFE) with a
$0.49\mu W$
impulse-based galvanic uplink for a peripheral nerve interface. Multiple, low-noise, high-data-rate channels are needed to sense compound action potentials and measure their conduction velocity as they propagate down a peripheral nerve. To achieve high energy efficiency for these constraints, the AFE encodes and transmits data with time-domain charge-balanced impulses through an implantable galvanic link. Each channel consists of an integrator with charge-based sampling and amplification for rapid multiplexing. A shared VTC encodes the amplitude-domain outputs of each integrator into differential time-domain impulses. Since the timing can be synchronized with stimulation, this AFE achieves instant artifact recovery after rail-to-rail stimulation events. We designed this AFE in a 180nm CMOS process, and the simulation results show an SNDR of 60dB and noise of
$3.7\mu Vrms$
. Thanks to the new galvanic uplink protocol, this front-end only consumes
$11.28\mu W$
including wireless data transmission for four channels.
We compare stochastic programming and robust optimization decision models for informing the deployment of ad hoc flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane...
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