A technique is demonstrated for extending the dynamic range of 1-bit analog-to-digital converters (ADCs) that sample at the maximum rate using a sinusoid reference r (t) = A(r) cos(2pif(r)t). The ADC has a detection limit B = piA(r)delta/Delta, where 2delta(s) is the base-clock period, and Delta = sampling interval = 1/2f(r) greater than or equal to 2delta. Optimal sampling is achieved at Delta = 2delta, but with large quantization errors found in the sampled representation of the input signal s(t). Dithering with noise n(sigma)(t) of appropriate variance sigma(2) is utilized to measure a subthreshold s(t) where \s(t)\ < B for all t. Both uniform white noise (UWN) and Gaussian white noise (GWN) are utilized. With UWN dithering at sigma = B, we could reduce the errors to levels that are produced by an equivalent q-bit amplitude-sampling (bipolar) ADC by observing the dithered signal s(t) + n(sigma)(t) over a time duration of T[(0.116/vertical bar V vertical bar)(2(q)-1)](0.995), where T is the sampling period, and +/- V are the ADC supply voltages. With GWN dithering at sigma = 0.5B, the duration required is T[(0.109/V)(2(q-1))](0.996).
机构:
Beihang Univ, Beijing, Peoples R ChinaBeihang Univ, Beijing, Peoples R China
Gu, Jiaxin
Zhao, Junhe
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Beihang Univ, Beijing, Peoples R ChinaBeihang Univ, Beijing, Peoples R China
Zhao, Junhe
Jiang, Xiaolong
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机构:
Beihang Univ, Beijing, Peoples R China
Baidu Res, Inst Deep Learning, Beijing, Peoples R China
Natl Engn Lab Deep Learning Technol & Applicat, Beijing, Peoples R ChinaBeihang Univ, Beijing, Peoples R China
Jiang, Xiaolong
Zhang, Baochang
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Beihang Univ, Beijing, Peoples R ChinaBeihang Univ, Beijing, Peoples R China
Zhang, Baochang
Liu, Jianzhuang
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机构:
Huawei Noahs Ark Lab, Beijing, Peoples R ChinaBeihang Univ, Beijing, Peoples R China
Liu, Jianzhuang
Guo, Guodong
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机构:
Baidu Res, Inst Deep Learning, Beijing, Peoples R China
Natl Engn Lab Deep Learning Technol & Applicat, Beijing, Peoples R ChinaBeihang Univ, Beijing, Peoples R China
Guo, Guodong
Ji, Rongrong
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机构:
Xiamen Univ, Sch Informat Sci & Engn, Fujian, Peoples R China
Peng Cheng Lab, Shenzhen, Peoples R ChinaBeihang Univ, Beijing, Peoples R China
Ji, Rongrong
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019),
2019,
: 4908
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4916