12 papers · ranked by Valyu relevance
Yuhang Wang, Weihua Chen, Linjing Song, Zhiping Xu + 6 more
With the rapid growth of data volume in sensor networks, lossy source coding systems achieve high-efficiency data compression with low distortion under limited transmission bandwidth. However, conventional compression algorithms rely on a two-stage framework with high computational complexity and frequently struggle to…
Ibrahim Al-dayel, Muhammad Faisal Nadeem, Yasir Bashir, Ayesha Shabbir + 1 more
We propose an information-theoretic encryption scheme consisting of a four-dimensional chaotic map driver in combination with a prediction model using an LSTM neural net to generate a keystream, which was limited only after passing a test based on the largest Lyapunov exponent (LLE). Our security analysis used a…
Kaan Doğan Erdoğan, Nurettin Doğan, Heena Rathore, Yuchen Jiang + 1 more
One of the major challenges in securing medical image communication systems is the secure and efficient management of cryptographic key material. In this paper, we propose a multi-layer image encryption algorithm that addresses image security while reducing per-image key-storage and transmission overhead under a…
S. Subathra, V. Thanikaiselvan
A new multistage encryption algorithm is proposed by integrating the deep neural network with a new 4D multi-scroll chaotic map to enhance the efficiency and improve the security of image transmission in the open channel. This combined network expands the key space and maintains the secrecy of the key with the…
Rashmi Naveen, Archana Praveen Kumar, Sahana Roshan
Security is the main attribute when dealing with information exchange. Confidential information theft, data loss, and data manipulation are conceivable results of security events. Different forms of data hiding are Cryptography and Steganography. Cryptography converts information into an unreadable form, and…
Ying Chen, Jianrong Bao, Yanhai Shang, Chao Liu + 5 more
To obtain efficient channel codes with high power efficiency at moderate signal-to-noise ratios (SNRs), an efficient high-rate protograph quasi-cyclic (QC) low-density parity-check (LDPC) codes is optimally constructed. By an optimized protograph template, the code framework is firstly produced by the extensions of the…
Authors not listed
This work provides a rigorous theoretical investigation of selective error correction strategies for variational quantum algorithms, with focus on understanding the interplay between error suppression, circuit trainability, and computational resource requirements. We develop a mathematical framework that characterizes…
Authors not listed
Pharmacophores are widely used to describe protein-ligand interactions, and the Grids of Pharmacophore Interaction Fields (GRAIL) method extends this concept by representing binding pockets as interpretable sets of interaction type-specific pharmacophoric maps. In this work, we propose a hybrid framework for binding…
Authors not listed
Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…
Authors not listed
The Hidden Subgroup Problem (HSP) unifies several landmark quantum algorithms, yet systematic exploration of its variants and modern applications has slowed. This paper revives HSP-based algorithm design by examining new group structures with direct relevance to post-quantum cryptography, lattice problems, and…
Authors not listed
This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…