SearcharxivSearch

arXiv subjects

Tithi Saha

Publications and source records attributed to Tithi Saha.

2 recordsLinked to original sources

2D GaSe-Based Single-Pixel Spectrometer via Electro-Optical Barrier Co-Modulation

Driven by the growing demand for miniaturized spectrometers for in-situ analysis, and point-of-care diagnostics, conventional spectrometers are often constrained by bulky architectures and pathlength-limited spectral resolution. Achieving high-resolution, single-pixel computational spectrometers is therefore critical for the realization of compact, on-chip systems. Here, we report a single-pixel spectrometer enabled by a single 2D material; few-layer GaSe-based photodetector, in which the Schottky barrier height modulation, governed jointly by applied bias and optical excitation, provides an efficient mechanism for spectral encoding without the need for bulky dispersive elements. The device exhibits a high peak-wavelength accuracy of ~0.78 nm across a broad operational bandwidth (300-700 nm) within a compact footprint of ~100 um^2 and resolves closely spaced spectral features with separations down to ~5 nm. The device operates at low bias (+/- 4V) with an ultralow dark current density ~0.3 pA/um^2 at 4V bias. These results establish a simple, scalable route toward compact, cost-effective spectroscopic systems for on-chip spectral sensing and portable hyperspectral imaging applications.

physics.optics

Current State in Privacy-Preserving Text Preprocessing for Domain-Agnostic NLP

Privacy is a fundamental human right. Data privacy is protected by different regulations, such as GDPR. However, modern large language models require a huge amount of data to learn linguistic variations, and the data often contains private information. Research has shown that it is possible to extract private information from such language models. Thus, anonymizing such private and sensitive information is of utmost importance. While complete anonymization may not be possible, a number of different pre-processing approaches exist for masking or pseudonymizing private information in textual data. This report focuses on a few of such approaches for domain-agnostic NLP tasks.

cs.CL