Results 201 to 210 of about 7,364 (234)

Decoding Alzheimer's genetic risk through intercellular communication in the human brain: Lessons from Clusterin. [PDF]

Curr Opin Neurobiol
Lish AM, Young-Pearse TL.
europepmc   +1 more source

Dynamic coding network for robust fruit detection in low-visibility agricultural scenes. [PDF]

Front Plant Sci
Lu H, Jin T, Wan C, Sun S, Zhou X, Wang F.   +5 more
europepmc   +1 more source

Decoding of columnar-level organization across cortical depth using BOLD- and CBV-fMRI at 7 T. [PDF]

Imaging Neurosci (Camb)
Haenelt D, Chaimow D, Schmidt ME, Nasr S, Weiskopf N, Trampel R.   +5 more
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Critical scaling of novelty in the cortex. [PDF]

Nat Commun
Ribeiro TL, Vakili A, Gifford B, Siddiqui R, Sinfuego V, Pajevic S, Plenz D.   +6 more
europepmc   +1 more source

Ill-posed analysis of time-domain deconvolution identification of ice load on the shell structures of ice-going vessels. [PDF]

Sci Rep
Kong S, Lei J.
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Iterative decoder architectures

IEEE Communications Magazine, 2003
Implementation constraints on iterative decoders applying message-passing algorithms are investigated. Serial implementations similar to traditional microprocessor datapaths are compared against architectures with multiple processing elements that exploit the inherent parallelism in the decoding algorithm. Turbo codes and low-density parity check codes,
null Engling Yeo, B. Nikolic, V. Anantharam   +2 more
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Deriving the PCC iterative decoder from the SCC iterative decoder

2010 6th International Symposium on Turbo Codes & Iterative Information Processing, 2010
In this paper, we show that the iterative decoder for parallel concatenated codes can be derived from the iterative decoder for serially concatenated codes. In order to show this, we use the observation that any parallel concatenated code (PCC) may be regarded as a serially concatenated code (SCC) with certain code constraints satisfied.
Anders Nilsson, Tor M. Aulin
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Iterative decoding beyond belief propagation

2010 Information Theory and Applications Workshop (ITA), 2010
At the heart of modern coding theory lies the fact that low-density parity-check (LDPC) codes can be efficiently decoded by belief propagation (BP). The BP is an inference algorithm which operates on a graphical model of a code, and lends itself to low-complexity and high-speed implementations, making it the algorithm of choice in many applications. It
Planjery, Shiva, Chilappagari, Shashi Kiran, Vasic, Bane, Declercq, David, Danjean, Ludovic   +4 more
openaire   +1 more source