We present a novel application of 3D Gaussian Splatting (3DGS) for the large-scale archival of thousands of temples in Bali. Our project, the Bali Digital Heritage Initiative, adopts a participatory approach in which local communities collect video footage of these vulnerable temples, which we transform into 3DGS scenes to serve as digital archives. Unlike existing approaches that require uploading curated sets of images, video contribution simplifies the process for the community. We expect this to encourage a high participation rate and increase the number of archived temples. However, these community-sourced videos are often of variable quality and usually aren’t ideal for 3D reconstruction, introducing technical challenges. This paper first presents several key challenges in the development of the 3DGS pipeline in this context, including managing video quality, combining multiple videos, ensuring reliable results from Structure from Motion, and maintaining the visual quality of 3DGS scenes. We then propose and test solutions to many of these challenges, addressing all parts of the reconstruction pipeline. We also introduce a postprocessing stage that cleans up the scene through a series of purpose-built 3DGS filters, which consider attributes of Gaussian primitives, including local density, geometry, and colors. Finally, we discuss future research directions to address unsolved problems and improve the performance of the pipeline.
The safety, performance, and lifespan of lithium-ion (Li-ion) batteries heavily depend on understanding their internal chemo-mechanical state. Conventional battery management systems, which depend on terminal electrical measurements, offer limited insight into the dynamic stress and strain that develop during operation. This paper introduces a non-destructive, in-situ method for monitoring the internal stress state of a commercial Li-ion pouch cell using single-frequency ultrasonic testing. Initially, a relation between stress-strain buildup within the battery and the amplitude of the transmitted ultrasonic signal is established. Then, a 120 kHz ultrasonic signal is transmitted through the cell in a through-transmission setup, and the amplitude of the received signal is analyzed as the main indicator for stress-strain and degradation monitoring. Experimental results over four charge-discharge cycles show a strong correlation between signal amplitudes and the State of Charge, with amplitude generally increasing during charging due to intercalation-induced compressive stress. The technique demonstrates high sensitivity, capturing non-monotonic behaviors attributed to electrode phase transitions. Additionally, a gradual, cycle-by-cycle decrease in overall signal amplitude is observed, providing a direct measure for tracking cumulative mechanical degradation and State of Health. These results demonstrate that ultrasonic signal amplitude is a comprehensive, versatile feature capable of offering real-time insights into the complex acousto-mechanical behavior of Li-ion batteries, offering a promising enhancement for next-generation battery diagnostics.
This paper presents a novel alternative approach to perception for aerial vehicles operating in confined spaces such as ballast tanks. It focuses on designing and implementing a system of small Time-of-Flight (ToF) sensors arranged in an array that could replace conventional, expensive, and bulky 2D or 3D LiDAR systems. Confined spaces often feature narrow, dark, and dusty environments. Here, conventional vision-based solutions fail for autonomous navigation and even basic flight tasks such as obstacle avoidance. Furthermore, carrying conventional LiDAR sensors significantly reduces flight time, especially for small drones(< 2.5 kg). Therefore, this paper proposes a system using VL53L8CX sensors as an optimal alternative, which significantly reduces the weight and computational power. A novel approach is developed to combine 12 of these ToF sensors across two SPI buses. This setup provides a 360 degrees radial field of view within a 4m radius around the aerial vehicle, which is strategically designed to accommodate these sensors. The proposed design is validated and compared against a 2D lidar system for collision prevention in a PX4 SITL simulation of a ballast tank environment. Additionally, the sparsity and accuracy of the generated point cloud are compared with those of 3D LiDAR while flying within a mock ballast tank. This design and study aim to establish a baseline for lightweight, compact, and safe navigation for small drones in confined and featureless environments.
This paper addresses the critical engineering challenge of achieving both high classification accuracy and rapid training convergence in neuromorphic vision systems. We introduce a hybrid Quantum Variational Reservoir-enhanced Multi-Layer Perceptron (QVR-MLP) and benchmark its performance against a classical MLP baseline. Both architectures are trained on an established event-based dataset from a Polarimetric Dynamic Vision Sensor (pDVS) capturing four distinct motion patterns modulated at varying speeds. Our methodology employs a computationally efficient Fast Fourier Transform (FFT) based feature extraction pipeline that processes asynchronous event streams without requiring full-frame reconstruction. The experimental results establish that quantum-enhanced neuromorphic computing is a robust and viable engineering solution for improving performance in resource-constrained neuromorphic vision applications.
Tunable Diode Laser Absorption Spectroscopy (TDLAS) is widely adopted for non-intrusive temperature and gas molar concentration imaging. Classical imaging methods in TDLAS tomography follow a two-stage approach, i.e., reconstructions of absorbance at two transitions and two-line thermometry for imaging of temperature and gas molar concentration. However, the separation of two stages lead to neglect of the physical constraint among these four variables, absorbance at two transitions, temperature and gas molar concentration, which leads to significant error propagation. An imaging model is proposed to mitigate the issue. In this model, a mapping from temperature and gas molar concentration to absorbance at two transitions is pre-established and incorporated into the reconstruction as a constraint. An optimization objective is constructed with projection constraint, mapping induced constraint and local smoothness priors. The four variables are jointly reconstructed in iteration. Numerical simulations and comparison to the typical SART with two-line thermometry method were conducted. In noise-free cases, the proposed method achieved lower image errors, with average reductions of 0.068 in temperature and 0.182 in gas molar concentration. In noisy cases, average image error decreases of 0.064 in temperature and 0.163 in gas molar concentration were yielded by the proposed method across all SNR levels. These demonstrate that the proposed method significantly reduces image errors and artifacts, improves reconstruction accuracy, and enhances robustness to noise.
