Results 121 to 130 of about 3,418 (298)

Reversible Data Hiding Exploiting Variance in Wavelet Coefficients

Journal of Computer Science and Technology, 2011
In this paper, we present a new reversible data hiding scheme that utilizes the wavelet transform and better exploits the large wavelet coefficient variance to achieve high capacity and imperceptible embedding.
Xu-Ren Luo, Te-Lung Yin
doaj  

Reversible data hiding in encrypted images with multi-prediction and adaptive huffman encoding. [PDF]

Sci Rep, 2023
Ren H, Bai GR, Chen TT, Yue Z, Ren RY.
europepmc   +1 more source

Ultra‐High‐Throughput Discovery of Multifunctional Polyphenolic Coatings on Droplet Microarrays

Advanced Materials, EarlyView.
An ultra‐high‐throughput (UHT) combinatorial strategy enables the miniaturized synthesis and screening of ≈30 000 polyamine‐polyphenolic (PaPp) coatings using droplet microarrays (DMA). This approach reveals hundreds of previously unknown fluorescent, redox‐active, and antibacterial materials, including multifunctional, cell‐compatible surfaces ...
Vania Tanda Widyaya, Joaquín E. Urrutia Gómez, Paul Reuß, Alexander Welle, Rolf A. Gattung, Jana Mayer, Peter Krolla, Markus Reischl, Pascal Friederich, Anna A. Popova, Thomas Schwartz, Pavel A. Levkin   +11 more
wiley   +1 more source

A Texture-Aware CNN Predictor for Reversible Data Hiding

Mathematics
Reversible data hiding (RDH) enables the reversible embedding of additional data into cover media, allowing the cover media to be perfectly recovered after extracting the embedded data.
Mohsin Shah, Chang Choi
doaj   +1 more source

A Novel 3D Reversible Data Hiding Scheme Based on Integer-Reversible Krawtchouk Transform for IoMT. [PDF]

Sensors (Basel), 2023
Yamni M, Daoui A, Pławiak P, Mao H, Alfarraj O, El-Latif AAA.   +5 more
europepmc   +1 more source

Deep Learning Inverse Design of Phase‐Change Reconfigurable Terahertz Metadevices for Multidimensional Secure Communication

Advanced Materials, EarlyView.
A deep learning inverse‐design framework is established to create versatile reconfigurable terahertz metadevices. By synergizing deep learning with phase‐change materials, this approach enables on‐demand customization of multidimensional electromagnetic responses.
Yisheng Dong, Xieyu Chen, Aarthy Nagarajan, Yi Yang, Zhihao Wang, Rui Yu, Chuang Zheng, Chunmei Ouyang, Xueqian Zhang, Tun Cao, Ranjan Singh, Zhen Tian   +11 more
wiley   +1 more source

Sub‐Diffraction Nanolithography of Halide Perovskite via Reversible All‐Optical Crystallization–Decomposition

Advanced Materials, EarlyView.
A reversible, all‐optical modulation strategy enables resist‐free, sub‐diffraction nanolithography of halide perovskite films. Leveraging 800 nm fs laser‐induced crystallization and 365 nm laser driven decomposition, reconfigurable patterning achieves feature sizes down to 93.5 nm. This chemistry‐free approach offers dynamic control over optoelectronic
Zhengfen Wan, Xiao Huang, Fangyi Zhang, Qiming Zhang, Min Gu   +4 more
wiley   +1 more source

Reversible Data Hiding in Encrypted Image Using Multiple Data-Hiders Sharing Algorithm. [PDF]

Entropy (Basel), 2023
Weng CY, Yang CH.
europepmc   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

High-Capacity Reversible Data Hiding in Encrypted Images with Flexible Restoration. [PDF]

J Imaging, 2022
Arai E, Imaizumi S.
europepmc   +1 more source