9 papers
Adaptive Non-Uniform Sampling of Bandlimited Signals via Algorithm-Encoder Co-Design
Kaluguri Yashaswini, Anshu Arora, Satish Mulleti
We propose an adaptive non-uniform sampling framework for bandlimited signals based on an algorithm-encoder co-design perspective. By revisiting the convergence analysis of iterati…
Tunable Domain Adaptation Using Unfolding
Snehaa Reddy, Jayaprakash Katual, Satish Mulleti
Machine learning models often struggle to generalize across domains with varying data distributions, such as differing noise levels, leading to degraded performance. Traditional st…
Two-Dimensional Tomographic Reconstruction From Projections With Unknown Angles and Unknown Spatial Shifts
Shreyas Jayant Grampurohit, Satish Mulleti, Ajit Rajwade
In parallel beam computed tomography (CT), an object is reconstructed from a series of projections taken at different angles. However, in some industrial and biomedical imaging app…
A Non-Uniform Quantization Framework for Time-Encoding Machines
Kaluguri Yashaswini, Anshu Arora, Satish Mulleti
Time encoding machines (TEMs) provide an event-driven alternative to classical uniform sampling, enabling power-efficient representations without a global clock. While prior work a…
Verifiable Deep Quantitative Group Testing
Shreyas Jayant Grampurohit, Satish Mulleti, Ajit Rajwade
We present a neural network-based framework for solving the quantitative group testing (QGT) problem that achieves both high decoding accuracy and structural verifiability. In QGT,…
Linear-Bias Time Encoding for Low-Rate Quantized Representation of Bandlimited Signals
Anshu Arora, Kaluguri Yashaswini, Satish Mulleti
Integrate-and-fire time encoding machines (IF-TEMs) provide an efficient framework for asynchronous sampling of bandlimited signals through discrete firing times. However, conventi…