Feature extraction, such as spectral occupancy, interferer energy and type, or direction-of-arrival, from wideband radio-frequency (RF) signals finds use in a growing number of applications as it enhances RF transceivers with cognitive abilities and enables parameter tuning of traditional RF chains. In power and cost limited applications, e.g., for sensor nodes in the Internet of Things, wideband RF feature extraction with conventional, Nyquist-rate analog-to-digital converters is infeasible. However, the structure of many RF features (such as signal sparsity) enables the use of compressive sensing (CS) techniques that acquire such signals at sub-Nyquist rates; while such CS-based analog-to-information (A2I) converters have the potential to enable low-cost and energy-efficient wideband RF sensing, they suffer from a variety of real-world limitations, such as noise folding, low sensitivity, aliasing, and limited flexibility. This paper proposes a novel CS-based A2I architecture called non-uniform wavelet sampling. Our solution extracts a carefully-selected subset of wavelet coefficients directly in the RF domain, which mitigates the main issues of existing A2I converter architectures. For multi-band RF signals, we propose a specialized variant called non-uniform wavelet bandpass sampling (NUWBS), which further improves sensitivity and reduces hardware complexity by leveraging the multi-band signal structure. We use simulations to demonstrate that NUWBS approaches the theoretical performance limits of ℓ₁-norm-based sparse signal recovery. We investigate hardware-design aspects and show ASIC measurement results for the wavelet generation stage, which highlight the efficacy of NUWBS for a broad range of RF feature extraction tasks in cost- and power-limited applications.
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Wavelet Tree Parsing with Freeform Lensing
We propose an architecture for adaptive sensing of images by progressively measuring its wavelet coefficients. Our approach, commonly referred to as wavelet tree parsing, adaptively selects the specific wavelet coefficients to be sensed by modeling the children of dominant coefficients to be dominant themselves. A key challenge for practical implementation of this technique is that the wavelet patterns, especially at finer scales, occupy a tiny portion of the field of view and, hence, the resulting measurements have very poor light levels and signal-to-noise ratios (SNR). To address this, we propose a novel imaging architecture that uses a phase-only spatial light modulator as a freeform lens to concentrate a light source and create the wavelet patterns. This ensures that the SNR of measurements remain constant across different spatial scales. Using a lab prototype, we demonstrate successful reconstruction on a wide range of real scenes and show that concentrating illumination enables us to outperform non-adaptive techniques as well as adaptive techniques based on traditional projectors.
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- NSF-PAR ID:
- 10106207
- Date Published:
- Journal Name:
- IEEE International Conference on Computational Photography
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Observed ecological responses to climate change are highly individualistic across species and locations, and understanding the drivers of this variability is essential for management and conservation efforts. While it is clear that differences in exposure, sensitivity, and adaptive capacity all contribute to heterogeneity in climate change vulnerability, predicting these features at macroecological scales remains a critical challenge. We explore multiple drivers of heterogeneous vulnerability across the distributions of 96 vegetation types of the ecologically diverse western US, using data on observed climate trends from 1948 to 2014 to highlight emerging patterns of change. We ask three novel questions about factors potentially shaping vulnerability across the region: (a) How does sensitivity to different climate variables vary geographically and across vegetation classes? (b) How do multivariate climate exposure patterns interact with these sensitivities to shape vulnerability patterns? (c) How different are these vulnerability patterns according to three widely implemented vulnerability paradigms—niche novelty (decline in modeled suitability), temporal novelty (standardized anomaly), and spatial novelty (inbound climate velocity)—each of which uses a distinct frame of reference to quantify climate departure? We propose that considering these three novelty paradigms in combination could help improve our understanding and prediction of heterogeneous climate change responses, and we discuss the distinct climate adaptation strategies connected with different combinations of high and low novelty across the three metrics. Our results reveal a diverse mosaic of climate change vulnerability signatures across the region's plant communities. Each of the above factors contributes strongly to this heterogeneity: climate variable sensitivity exhibits clear patterns across vegetation types, multivariate climate change data reveal highly diverse exposure signatures across locations, and the three novelty paradigms diverge widely in their climate change vulnerability predictions. Together, these results shed light on potential drivers of individualistic climate change responses and may help to inform effective management strategies.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCPHOT.2019.8747327
