Results 91 to 100 of about 21,021 (331)
Advanced Intelligent Systems, EarlyView.Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee +7 more
wiley +1 more source
Parameter Estimation of Convolutional and Helical Interleavers in a Noisy Environment
IEEE Access, 2017 Forward error correction (FEC) codes followed by an interleaver play a significant role in improving the error performance of the digital systems by counteracting random and burst errors.
Swaminathan Ramabadran +3 more
doaj +1 more source
Advanced Intelligent Systems, EarlyView.A data‐efficient artificial intelligence‐assisted framework, which integrates experimental data with machine learning, is developed for the design of bimodal networked dielectric elastomers (DEs) as advanced artificial muscles. It adopts neural networks to predict DEs’ mechanical properties and support vector machines to classify electromechanical ...
Ofoq Normahmedov +8 more
wiley +1 more source
New Code Matched Interleaver for Turbo Codes with Short Frames
Advances in Electrical and Computer Engineering, 2010 Turbo codes are a parallel concatenation of two or more convolutional codes, separated by interleavers, therefore their performance is not influenced just by the constituent encoders, but also by the interleaver.
LAZAR, G. A. +3 more
doaj +1 more source
IEEE Access, 2019 Most multimedia source encoders produce scalable bit streams that are vulnerable to noisy channel effects. Unequal error protection (UEP) designs are attractive solutions for such scalable bit streams.
Marwa Mhamdi +3 more
doaj +1 more source
A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks
Advanced Intelligent Systems, EarlyView.This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan +6 more
wiley +1 more source
Further Results on Quadratic Permutation Polynomial-Based Interleavers for Turbo Codes [PDF]
, 2011 An interleaver is a critical component for the channel coding performance of turbo codes. Algebraic constructions are of particular interest because they admit analytical designs and simple, practical hardware implementation.
Eirik Rosnes, Senior Member
core
Bit‐interleaved space‐time‐frequency coded modulation with general linear precoding [PDF]
, 2007 Tao Huang, Chaowei Yuan, Changlu Sun
openalex +1 more source
A Background Calibration for Joint Mismatch in the OFDM System With Time-Interleaved ADC [PDF]
, 2022 Haoyang Shen, Deepu John, Barry Cardiff
openalex +1 more source