Results 131 to 140 of about 8,004 (293)
STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORK
Jixie qiangdu, 2018 Adaptive time frequency analysis based on local mean decomposition and nonlinear quantization ability of sample entropy,combined with radial basis function（ RBF） neural network.
WANG MingYue +3 more
doaj
On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification
Advanced Electronic Materials, EarlyView.ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels +4 more
wiley +1 more source
Sample Adaptive Product Quantization for Memoryless Noisy Channels
, 2002 Channel optimized vector quantization (COVQ), as a joint source-channel coding scheme, has proven to perform well in compressing a source and making the resulting quantizer codebook robust to channel noise.
Zahir Raza
core
Advanced Intelligent Discovery, EarlyView.Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj +2 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Deep learning‐based denoising models are applied to DNA data storage systems to enhance error reduction and data fidelity. By integrating DnCNN with DNA sequence encoding methods, the study demonstrates significant improvements in image quality and correction of substitution errors, revealing a promising path toward robust and efficient DNA‐based ...
Seongjun Seo +5 more
wiley +1 more source
Kernelizing Vector Quantization Algorithms
, 2009 International audienceThe kernel trick is a well known approach allowing to implicitly cast a linear method into a nonlinear one by replacing any dot product by a kernel function. However few vector quantization algorithms have been kernelized.
Geist, Matthieu +2 more
core
Generalized Products and Semiclassical Quantization
The Journal of Geometric AnalysisDiscussion of resolution ...
openaire +2 more sources
Advanced Intelligent Discovery, EarlyView.Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto +3 more
wiley +1 more source
Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining
Advanced Intelligent Systems, EarlyView.Calibration‐free electromyography motor intent decoding is enabled through large‐scale supervised pretraining across heterogeneous datasets. A Spatially Aware Feature‐learning Transformer processes variable channel counts and electrode geometries, allowing transfer across users and recording setups. On a held‐out benchmark, fine‐tuned cross‐user models
Alexander E. Olsson +3 more
wiley +1 more source
Product Quantization for Efficient Similarity Search
Analysis of efficient state-of-the-art approximate nearest neighbors search techniques based on product quantization. Experiments to test the efficiency and accuracy of approximate nearest neighbors search techniques in different application ...
ESPOSITO, ROBERTO
core