Unleashing Dynamic Range and Resolution in Unlimited Sensing Framework via Novel Hardware
Not on the map. Why?
Abstract (arXiv)
Conventional digitization based on the Shannon-Nyquist method, implemented via analog-to-digital converters (ADCs), faces fundamental limitations. High-dynamic-range (HDR) signals often get clipped or saturated in practice. Given a fixed bit budget, one must choose between minimizing quantization noise or accommodating HDR inputs. The Unlimited Sensing Framework (USF) eliminates saturation by incorporating nonlinear folding in analog hardware, resulting in modulo signals. Quantizing or digitizing modulo signals enables low quantization noise as the modulo representation maps HDR signals into low-dynamic-range (LDR) samples. In the context of USF, the core innovation of this paper is a novel, low-cost, integrator-based efficient modulo ADC hardware implementation that imposes no restrictions on folding rates, enabling significantly HDR capture. The feasibility of this design is demonstrated by hardware experiments showcasing clear advantages across different quantitative performance metrics. These include capturing HDR signals with a 60-fold increase in dynamic range, achieving up to 5 Effective Number of Bits (ENOBs), and improving Signal-to-Noise and Distortion (SINAD) by 30 dB.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 26 Oct 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 20251-Bit Unlimited Sampling Beyond Fourier Domain: Low-Resolution Sampling of Quantization Noise
- 2023Modulation For Modulo: A Sampling-Efficient High-Dynamic Range ADC ↗
- 2025FPGA-Enabled Modulo ADC with x100 Dynamic-Range Expansion: Hardware Design and Performance Evaluation ↗
- 2025Practical Modulo Sampling: Mitigating High-Frequency Components
- 2023HDR Imaging With One-Bit Quantization
- 2025Modulo Sampling: Performance Guarantees in The Presence of Quantization
- 2024Compressed Sensing Based Residual Recovery Algorithms and Hardware for Modulo Sampling
- 2024Achieving Robustness in Blind Modulo Analog-to-Digital Conversion ↗