Department Publications

Solar photovoltaic (PV) systems have become increasingly popular as a sustainable energy source due to the growing need for renewable energy on a global scale. However, the buildup of dust and dirt on solar panels frequently compromises the effectiveness of these systems. Existing cleaning methods often require manual labor, exhibit low efficiency, and are restricted to either a single cleaning method or operational mode. To address these issues, we designed and implemented a semi-autonomous solar panel cleaning robot capable of both dry and wet cleaning. The robot can autonomously navigate on solar panels with a tilt of up to 20 or be manually controlled using a mobile app, and it operates for approximately 75 minutes on a single charge. This study enhances key aspects of the robotic system, integrating advanced edge detection and transition mechanisms using both ultrasonic sensors and image processing techniques. We implemented a robust localization system based on bus-bar detection for precise navigation, upgraded the water supply mechanism and refined the orientation estimation algorithm.

Material that constitute aggregate depend on their packing efficiency that is defined by the shape distribution of aggregates. Geometrical shape descriptors would not capture morphological aspects of different dimensional scales and frequencies, failing to wholistically numerically represent shape in material prediction models. Fourier shape descriptors are used to analyse single particle morphology, but efficiency to characterize shape of lump samples has not been assessed. This study analyses aggregates milled for different number of revolutions (0, 200, 500 and 1000) in a Los Angeles Abrasion Value instrument, that contrives morphological alterations of different scales. Means of zonal frequency components amplitudes (form, angularity and texture) are statistically different for all classes with more than 99 % confidence. Support Vector Machine and K Nearest Neighbour algorithms classified pairwise …

A four-dimensional light field (LF) captures both textural and geometrical information of a scene in contrast to a two-dimensional image that captures only the textural information of a scene. Post-capture refocusing is an exciting application of LFs enabled by the geometric information captured. Previously proposed LF refocusing methods are mostly limited to the refocusing of single planar or volumetric region of a scene corresponding to a depth range and cannot simultaneously generate in-focus and out-of-focus regions having the same depth range. In this paper, we propose an end-to-end pipeline to simultaneously refocus multiple arbitrary planar or volumetric regions of a dense or a sparse LF. We employ pixel-dependent shifts with the typical shift-and-sum method to refocus an LF. The pixel-dependent shifts enables to refocus each pixel of an LF independently. For sparse LFs, the shift-and-sum method introduces ghosting artifacts due to the spatial undersampling. We employ a deep learning model based on U-Net architecture to almost completely eliminate the ghosting artifacts. The experimental results obtained with several LF datasets confirm the effectiveness of the proposed method. In particular, sparse LFs refocused with the proposed method archive structural similarity index higher than 0.9 despite having only 20% of data compared to dense LFs.

Image-to-point cross-modal learning has emerged to address the scarcity of large-scale 3D datasets in 3D representation learning. However, current methods that leverage 2D data often result in large, slow-to-train models, making them computationally expensive and difficult to deploy in resource-constrained environments. The architecture design of such models is therefore critical, determining their performance, memory footprint, and compute efficiency. The Joint-embedding Predictive Architecture (JEPA) has gained wide popularity in self-supervised learning for its simplicity and efficiency, but has been under-explored in cross-modal settings, partly due to the misconception that masking is intrinsic to JEPA. In this light, we propose CrossJEPA, a simple Cross-modal Joint Embedding Predictive Architecture that harnesses the knowledge of an image foundation model and trains a predictor to infer embeddings of specific rendered 2D views from corresponding 3D point clouds, thereby introducing a JEPA-style pretraining strategy beyond masking. By conditioning the predictor on cross-domain projection information, CrossJEPA purifies the supervision signal from semantics exclusive to the target domain. We further exploit the frozen teacher design with a one-time target embedding caching mechanism, yielding amortized efficiency. CrossJEPA achieves a new state-of-the-art in linear probing on the synthetic ModelNet40 (94.2%) and the real-world ScanObjectNN (88.3%) benchmarks, using only 14.1M pretraining parameters (8.5M in the point encoder), and about 6 pretraining hours on a standard single GPU. These results position CrossJEPA …

Five-dimensional (5-D) light field videos (LFVs) capture spatial, angular, and temporal variations in light rays emanating from scenes. This leads to a significantly large amount of data compared to conventional three-dimensional videos, which capture only spatial and temporal variations in light rays. In this paper, we propose an LFV compression technique using low-complexity 5-D approximate discrete cosine transform (ADCT). To further reduce the computational complexity, our algorithm exploits the partial separability of LFV representations. It applies two-dimensional (2-D) ADCT for sub-aperture images of LFV frames with intra-view and inter-view configurations. Furthermore, we apply one-dimensional ADCT to the temporal dimension. We evaluate the performance of the proposed LFV compression technique using several 5-D ADCT algorithms, and the exact 5-D discrete cosine transform (DCT). The experimental results obtained with LFVs confirm that the proposed LFV compression technique provides a more than 250 times reduction in the data size with near-lossless fidelity with a peak-signal-to-noise ratio greater than 40 dB and structural similarity index greater than 0.9. Furthermore, compared to the exact DCT, our algorithms requires approximately 10 times less computational complexity.

Semi-supervised domain generalization (SSDG) leverages a small fraction of labeled data alongside unlabeled data to enhance model generalization. Most of the existing SSDG methods rely on pseudo-labeling (PL) for unlabeled data, often assuming access to domain labels-a privilege not always available. However, domain shifts introduce domain noise, leading to inconsistent PLs that degrade model performance. Methods derived from FixMatch suffer particularly from lower PL accuracy, reducing the effectiveness of unlabeled data. To address this, we tackle the more challenging domain-label agnostic SSDG, where domain labels for unlabeled data are not available during training. First, we propose a feature modulation strategy that enhances class-discriminative features while suppressing domain-specific information. This modulation shifts features toward Similar Average Representations-a modified version …

Robust feature tracking is a basic requisite for indoor robot navigation. However, indoor scenes mostly contain poorly localizable features. As a result, indoor feature tracking without artificial markers is challenging and remains an attractive problem. We propose to solve this problem by constraining the locations of a large number of non-distinctive features by several planar homographies which are computed using distinctive features. Use of a large number of non-distinctive features within the constraints imposed by planar homographies allows us to gain robustness against the absence of artificial landmarks. Also, the lesser computational cost in estimating these non-distinctive features helps to maintain the efficiency of the proposed method. Our outcome is a robust in door feature tracker. In this dissertation, we will establish that multi-homographic in door feature tracking is feasible with the combination of …

The rapid evolution of Artificial Intelligence (AI) has revolutionized multimedia creation, particularly through Text-to-Image (T2I) diffusion models, which synthesize images from textual descriptions with impressive capabilities. Building on this, enhancing video GIF generation has become a promising frontier. However, the field of Text-to-Video (T2V) GIFs remains underexplored, with limited research addressing this specific application. This synthesis method expands creative expression and finds utility in education, marketing, and entertainment. The lack of focused work highlights both novelty and opportunity for impactful contributions. This study focuses on optimizing T2V GIF generation in terms of computational and parameter efficiency. The resulting model maintains a parameter count below 1 billion, enabling faster training, reduced inference time, lower memory usage, and compatibility with low-end hardware. Inspired …

With the recent advancement of Large Language Models (LLMs), environmental perception methods, and frameworks for simplifying robot functionality implementation, robotic receptionists present an exciting opportunity to minimize humanrelated errors. However, most existing humanoid robots are costly and overqualified for such tasks, as they are primarily designed for research applications. To address this, we introduce a standalone, offline, cost-effective smart mobile robot receptionist built on an edge device that efficiently performs a predefined set of tasks while utilizing minimal hardware and computational resources. Our robot receptionist can engage in smooth conversations, recognize and interact with both new and returning users, perform hand gestures, provide navigation assistance through verbal and physical guidance, and autonomously navigate while avoiding static and dynamic obstacles …