Node-Pore Coded Coincidence Correction: Coulter Counters, Code Design, and Sparse Deconvolution

Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution

Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution by Michael Kellman Master of Science in Electrical Engineering and Computer Science University of California, Berkeley Professor Michael Lustig, Chair We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and a...

Multiterminal Source Code Design Based on Slepian-Wolf coded quantization∗

Multiterminal (MT) source coding refers to separate lossy encoding and joint decoding of multiple correlated sources. This paper presents two practical MT coding schemes under the same general framework of Slepian-Wolf coded quantization (SWCQ) for both direct and indirect quadratic Gaussian MT source coding problems with two encoders. The first asymmetric SWCQ scheme relies on quantization and...

Code, coding and coded perspectives

McLuhan (1962) argued that ways of thought are a function of the technologies used to communicate ideas. Together Eisenstein (1980) and Crosby (1997) would argue the ability to reproduce precise tables of numbers and diagrams enabled the scientific revolution. All three works focus on print. Code will prove as significant as print. Code is a terse, simple, explicit description of action and int...

Coded Sparse Matrix Multiplication

In a large-scale and distributed matrix multiplication problem C = AB, where C ∈ Rr×t, the coded computation plays an important role to effectively deal with “stragglers” (distributed computations that may get delayed due to few slow or faulty processors). However, existing coded schemes could destroy the significant sparsity that exists in large-scale machine learning problems, and could resul...

Modified Sparse Multichannel Blind Deconvolution