arXiv subjects

Yu-Tang Chang

Publications and source records attributed to Yu-Tang Chang.

4 recordsLinked to original sources

arXiv2025

Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges

Hyperspectral imaging (HSI) analysis faces computational bottlenecks due to massive data volumes that exceed available memory. While foundation models pre-trained on large remote sensing datasets show promise, their learned representations often fail to transfer to domain-specific applications like close-range agricultural monitoring where spectral signatures, spatial scales, and semantic targets differ fundamentally. This report presents Deep Global Clustering (DGC), a conceptual framework for memory-efficient HSI segmentation that learns global clustering structure from local patch observations without pre-training. DGC operates on small patches with overlapping regions to enforce consistency, enabling training in under 30 minutes on consumer hardware while maintaining constant memory usage. On a leaf disease dataset, DGC achieves background-tissue separation (mean IoU 0.925) and demonstrates unsupervised disease detection through navigable semantic granularity. However, the framework suffers from optimization instability rooted in multi-objective loss balancing: meaningful representations emerge rapidly but degrade due to cluster over-merging in feature space. We position this work as intellectual scaffolding - the design philosophy has merit, but stable implementation requires principled approaches to dynamic loss balancing. Code and data are available at https://github.com/b05611038/HSI_global_clustering.

cs.CV↗

arXiv2025

SparseEB-gMCR: A Generative Solver for Extreme Sparse Components with Application to Contamination Removal in GC-MS

Analytical chemistry instruments provide physically meaningful signals for elucidating analyte composition and play important roles in material, biological, and food analysis. These instruments are valued for strong alignment with physical principles, enabling compound identification through pattern matching with chemical libraries. More reliable instruments generate sufficiently sparse signals for direct interpretation. Generative multivariate curve resolution (gMCR) and its energy-based solver (EB-gMCR) offer powerful tools for decomposing mixed signals suitable for chemical data analysis. However, extreme signal sparsity from instruments such as GC-MS or 1H-NMR can impair EB-gMCR decomposability. To address this, a fixed EB-select module inheriting EB-gMCR's design was introduced for handling extreme sparse components. Combined with minor adjustments to energy optimization, this led to SparseEB-gMCR. In synthetic datasets, SparseEB-gMCR exhibited comparable decomposability and graceful scalability to dense-component EB-gMCR. The sparse variant was applied to real GC-MS chromatograms for unsupervised contamination removal. Analysis showed siloxane-related pollution signals were effectively eliminated, improving compound identification reliability. Results demonstrate that SparseEB-gMCR preserves the decomposability and self-determining component capability of EB-gMCR while extending adaptability to sparse and irregular chemical data. With this sparse extension, the EB-gMCR family becomes applicable to wider ranges of real-world chemical datasets, providing a general mathematical framework for signal unmixing and contamination elimination in analytical chemistry.

cs.CE↗

arXiv2025

EB-gMCR: Energy-Based Generative Modeling for Signal Unmixing and Multivariate Curve Resolution

Signal unmixing analysis decomposes data into basic patterns and is widely applied in chemical and biological research. Multivariate curve resolution (MCR), a branch of signal unmixing, separates mixed signals into components (base patterns) and their concentrations (intensity), playing a key role in understanding composition. Classical MCR is typically framed as matrix factorization (MF) and requires a user-specified number of components, usually unknown in real data. Once data or component number increases, the scalability of these MCR approaches face significant challenges. This study reformulates MCR as a data generative process (gMCR), and introduces an Energy-Based solver, EB-gMCR, that automatically discovers the smallest component set and their concentrations for reconstructing the mixed signals faithfully. On synthetic benchmarks with up to 256 components, EB-gMCR attains high reconstruction fidelity and recovers the component count within 5% at 20dB noise and near-exact at 30dB. On two public spectral datasets, it identifies the correct component count and improves component separation over MF-based MCR approaches (NMF variants, ICA, MCR-ALS). EB-gMCR is a general solver for fixed-pattern signal unmixing (components remain invariant across mixtures). Domain priors (non-negativity, nonlinear mixing) enter as plug-in modules, enabling adaptation to new instruments or domains without altering the core selection learning step. The source code is available at https://github.com/b05611038/ebgmcr_solver.

cs.LG↗

arXiv2013

Creating high density ensembles of nitrogen-vacancy centers in nitrogen-rich type Ib nanodiamonds

This work explores the possibility of increasing the density of negatively charged nitrogen-vacancy centers [NV-] in nanodiamonds using nitrogen-rich type Ib diamond powders as the starting materials. The nanodiamonds (10 - 100 nm in diameters) were prepared by ball-milling of microdiamonds, in which the density of neutral and automatically dispersed nitrogen atoms [N0] was measured by diffuse reflectance infrared Fourier transform spectroscopy (DRIFT). A systematic measurement for the fluorescence intensities and lifetimes of the crushed monocrystalline diamonds as a function of [N0] indicated that the [NV-] increases nearly linearly with [N0] at 100 - 200 ppm. The trend, however, failed to continue for nanodiamonds with higher [N0] (up to 390 ppm) but poorer crystallinity. We attribute the result to a combined effect of fluorescence quenching as well as the lower conversion efficiency of vacancies to NV- due to the presence of more impurities and defects in these as-grown diamond crystallites. The principles and practice of fabricating brighter and smaller fluorescent nanodiamonds (FNDs) are discussed.

cond-mat.mtrl-sci↗