2026-09-19T22:00:29Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/72838 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

process variations stochastic systems Statistical inference with unreliable binary observations text Singer, Andrew C. Corey, Ryan 2015-01-21T19:48:44Z 2015-01-21T19:48:44Z 2014-12 2015-01-21 2014-12 We describe a novel statistical inference approach to data conversion for mixed-signal interfaces. We propose a data conversion architecture in which a signal is observed by a set of sensors with uncertain parameters, such as highly scaled comparator circuits in an analog-to-digital converter. These sensor outputs are not used to form a quantized representation of the signal but are used directly to make decisions in statistical inference problems such as parameter estimation, classification, and signal detection. We derive a mathematical model of this system and apply information-theoretic tools to describe the achievable performance of such a converter in information processing systems. In particular, we find asymptotic expressions for Fisher information and Kullback-Leibler divergence in terms of the design parameters and the sensor statistics. Simulations of parameter estimation, classification, and symbol detection systems show that these architectures can achieve strong performance even when the devices have significant process variations. We also discuss practical system design and implementation issues including sensor calibration and propose a lower-complexity suboptimal estimation architecture. The analytical and simulation results suggest that it is both possible and practical to build information processing systems using unreliable mixed-signal components. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-12-10T22:54:12Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Corey_Ryan.pdf: 567208 bytes, checksum: 58dd5fc267b73e4644237d0870a42a80 (MD5) Made available in DSpace on 2015-01-21T19:48:44Z (GMT). No. of bitstreams: 1 Ryan_Corey.pdf: 567208 bytes, checksum: 58dd5fc267b73e4644237d0870a42a80 (MD5) http://hdl.handle.net/2142/72838 en Copyright 2014 Ryan M. Corey Analog-to-digital conversion detection and estimation Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS