Results 61 to 70 of about 2,157,914 (307)

Explicit Compression Degradation Estimations for Low‐Sampling Single‐Pixel Imaging using Hadamard Basis

Advanced Science, EarlyView.
This study proposes a degradation estimation technique to explicitly describe compressive sampling for low‐sampling Hadamard single‐pixel imaging. Blur kernels in explicit degradation models are estimated by the self‐supervised learning method without labeled data and implicit priors.
Haoyu Zhang, Jie Cao, Chang Zhou, Haifeng Yao, Qun Hao   +4 more
wiley   +1 more source

A Fourier-analytic Approach to Counting Partial Hadamard Matrices

, 2010
In this paper, we study a family of lattice walks which are related to the Hadamard conjecture. There is a bijection between paths of these walks which originate and terminate at the origin and equivalence classes of partial Hadamard matrices. Therefore,
de Launey, Warwick, Levin, David A.
core   +1 more source

Tunneling‐Controlled Fusion of Short‐ and Long‐Term Memory in SiO2/HfO2‐Based Neuromorphic Device for Time‐Series Prediction

Advanced Science, EarlyView.
Based on trap‐assisted tunneling, the devices can fuse STM/LTM, where the low switching energy of 1 pJ and stable low‐power retention (0.2 % loss ratio and 3.05 × 10−11 W) is achieved. Training in a long short‐term memory network it allows to analysis time‐series data and then makes precise long‐term predictions with an error ratio of 4.465 ...
Chengdong Yang, Lihua Xu, Linlin Su, Yue Wu   +3 more
wiley   +1 more source

Design and Implementation of Sequency-Ordered Fast Walsh-Hadamard Transform (FWHT) in WCDMA

Tikrit Journal of Engineering Sciences, 2013
This paper presents the implementation of the Fast Walsh Hadamard Transform (FWHT) in sequence ordered method, in order to minimize the interference due to two users trying to transmit at the same time, and an attempt has made to simulate them.
Safaa M. Z. Al-Ubaidi
doaj   +1 more source

Inferring Gene Regulatory Networks From Single‐Cell RNA Sequencing Data by Dual‐Role Graph Contrastive Learning

Advanced Science, EarlyView.
RegGAIN is a novel and powerful deep learning framework for inferring gene regulatory networks (GRNs) from single‐cell RNA sequencing data. By integrating self‐supervised contrastive learning with dual‐role gene representations, it consistently outperforms existing methods in both accuracy and robustness.
Qiyuan Guan, Jiating Yu, Jieyi Pan, Fan Yuan, Jiadong Ji, Rusong Zhao, Zhi‐Ping Liu, Bingqiang Liu, Ling‐Yun Wu, Duanchen Sun   +9 more
wiley   +1 more source

Metaheuristics in the Optimization of Cryptographic Boolean Functions

Entropy, 2020
Generating Boolean Functions (BFs) with high nonlinearity is a complex task that is usually addresses through algebraic constructions. Metaheuristics have also been applied extensively to this task. However, metaheuristics have not been able to attain so
Isaac López-López, Guillermo Sosa-Gómez, Carlos Segura, Diego Oliva, Omar Rojas   +4 more
doaj   +1 more source

Orthogonal Transform Multiplexing with Memoryless Nonlinearity: a Possible Alternative to Traditional Coded-Modulation Schemes

, 2017
In this paper, we propose a novel joint coding-modulation technique based on serial concatenation of orthogonal linear transform, such as discrete Fourier transform (DFT) or Walsh-Hadamard transform (WHT), with memoryless nonlinearity.
Zhidkov, Sergey V.
core   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Visual Quality Improvement of Watermarked Image Based on Singular Value Decomposition Using Walsh Hadamard Transform [PDF]

, 2023
Aris Marjuni, Ahmad Zainul Fanani, Oky Dwi Nurhayati   +2 more
openalex   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source