Results 221 to 230 of about 24,566 (328)

A Periodic Error Correction Method for Terahertz Coded‐Aperture Imaging

Advanced Intelligent Systems, EarlyView.
A physics‐constrained unsupervised learning framework with periodic error correction is proposed for terahertz coded‐aperture imaging. The method improves reconstruction robustness against noise and model mismatch, while reducing reliance on large‐scale labeled data.
Hongxu Wu, Jiangtao Ji, Chenxi Liu, Fei Yang, Shuang Peng, Haiying Lu, Junwei Wu, Xiaojian Fu, Huifeng Ma   +8 more
wiley   +1 more source

A hybrid multi-node QKD-ECC architecture for securing IoT networks. [PDF]

Sci Rep
Chaturvedi R, Sahu D, Singh BP, Prakash S, Yang T, Rathore RS, Cengiz K, Ivković N.   +7 more
europepmc   +1 more source

A Novel Semi‐Automated Pipeline for Optimizing 3D‐Printed Drug Formulations

Advanced Intelligent Systems, EarlyView.
A novel, semi‐automated deep learning and differential evolution pipeline designed for generating medicines for 3D‐printed medicines is introduced. This pipeline successfully generates a diverse range of drug formulations with an 80% printing success rate and 92% accuracy in predicting printing parameters.
Youssef Abdalla, Martin Ferianc, Haya Alfassam, Atheer Awad, Ruochen Qiao, Miguel Rodrigues, Mine Orlu, Abdul W. Basit, David Shorthouse   +8 more
wiley   +1 more source

Forensics System for Internet of Vehicles Based on Post-Quantum Blockchain. [PDF]

Sensors (Basel)
Zhang Z, Cao Z, Wang Y.
europepmc   +1 more source

Highly Efficient Classification of Time‐Series Based on Resistive Switching Cluster‐Assembled Materials

Advanced Intelligent Systems, EarlyView.
Cluster‐assembled nanocomposite devices are employed for the classification of neuronal traces, without any preprocessing of the input time‐series or prior training of the nonlinear devices. The classification method relies on the statistical analysis of the device's output time‐series, achieving higher classification accuracy compared to more energy ...
Filippo Profumo, Francesca Borghi, Tommaso Angì, Marta Maschietto, Stefano Vassanelli, Paolo Milani   +5 more
wiley   +1 more source

Loss control-based QKD with noisy devices. [PDF]

Sci Rep
Pastushenko V, Kodukhov A, Shindin A, Zemlyanov V, Pflitsch M, Vinokur V.   +5 more
europepmc   +1 more source

Configurable Kernel Map Implementation in Memristor Crossbar for Convolution Neural Network

Advanced Intelligent Systems, EarlyView.
A configurable kernel map implementation using a memristor crossbar array is presented. The crossbar array area can be configured based on the number of read cycles per inference, which directly affects the inference speed. The algorithm underlying this scheme is described, and convolutional neural network operations are experimentally validated using ...
Gyeonghae Kim, Sangwook Youn, Jinwoo Park, Hyungjin Kim   +3 more
wiley   +1 more source

Mainstream Artificial Intelligence Technologies in Contemporary Ophthalmology

Advanced Intelligent Systems, EarlyView.
This review explores the latest artificial intelligence (AI) technologies in ophthalmology, focusing on four key data types: medical imaging, electronic health records, robotic‐assisted surgery, and genomics. It examines the structural features, use cases, clinical goals, and evaluation metrics of various AI algorithms, while also introducing emerging ...
Shiqi Yin, Lei Wang, Shanjun Wu, Jiewei Jiang, Wei Qiang, Yangyang Wang, Shihong Wang, Yi Shao, Wei Chen, Zhongwen Li   +9 more
wiley   +1 more source

Quantum-resilient and adaptive multi-region data aggregation for IoMT using zero-knowledge proofs and edge intelligence. [PDF]

Sci Rep
Othman SB, Kumar G.
europepmc   +1 more source

Adapting Image‐Based Models for 1D Data via Spider Plot Transformation and Transfer Learning

Advanced Intelligent Systems, EarlyView.
A novel method enables the use of pretrained image‐based neural networks for complex 1D data, including Raman and mid‐infrared spectra, electrocardiograms, and mass spectrometry. 2D spider plots with false‐color fill enable transfer lerning, therefore enhancing data augmentation and model explainability across diverse spectral and time series datasets.
Azadeh Mokari, Oleg Ryabchykov, Thomas Bocklitz   +2 more
wiley   +1 more source