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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Electrochemical Detection of Lactate and Glucose with Enzyme-Free and Dual-Sensing Using Nickel-Oxide Nanoparticles

SSRN

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SSRN 2025年

作者： Nasiri, Rohollah Guagliano, Giuseppe Van Gastel, Dirkje Sanei, Reihaneh Madadelahi, Masoud Tanriverdi, Selim Jain, Saumey Fayazbaksh, Farzaneh Lee, Sang Won Zhu, Yangzhi Joensson, Haakan N. Russom, Aman Herland, Anna Division of Nanobiotechnology Department of Protein Science Science for Life Laboratory KTH Royal Institute of Technology Solna Sweden AIMES Center for the Advancement of Integrated Medical and Engineering Sciences Department of Neuroscience Karolinska Institute Solna Sweden Department of Chemistry Materials and Chemical Engineering "G. Natta" Politecnico di Milano Milan Italy Division of Micro and Nanosystems Department of Intelligent Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Terasaki Institute for Biomedical Innovation Los AngelesCA90064 United States

Detection and monitoring of lactate and glucose levels in biological fluids and cell cultures are critical for understanding metabolic disorders. While electrochemical biosensors are commonly used, traditional enzymatic sensors face stability, reproducibility, and cost challenges. To address these issues, we developed non-enzymatic sensors using nanostructured electrodes, specifically nickel oxide (NiO)-based, for lactate and glucose detection. The sensors were fabricated by drop-casting a NiO/Nafion/ethanol dispersion onto the working electrode, with performance evaluated through cyclic voltammetry and amperometry. Optimal sensitivity and linearity were achieved at a working voltage of ~0.5 V. To address glucose interference, sensors were modified with Prussian Blue, creating a dual sensor for lactate and glucose. The sensors respectively exhibited linear responses for lactate (0.1 – 5 mM) and glucose (0.1 – 5 mM) with detection limits of 0.60 mM and 0.35 mM, and it showed sensitivities of 0.43 ± 0.05 µA/mM (lactate) and 0.82 ± 0.02 µA/mM (glucose) in phosphate buffer solution. Validation against the lactate and glucose assay kits in a cell culture medium showed good agreement, highlighting the potential of these enzyme-free electrochemical sensors for metabolic analysis. Finally, the sensor was integrated with a microfluidic device, demonstrating its potential as a flow-through, enzyme-free metabolic sensor for future organ-on-chip applications. © 2025, The Authors. All rights reserved.

关键词： Cyclic voltammetry

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Nano device fabrication for in-memory and in-sensor reservoir computing

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International Journal of Extreme Manufacturing 2025年第1期7卷 46-71页

作者： Yinan Lin Xi Chen Qianyu Zhang Junqi You Renjing Xu Zhongrui Wang Linfeng Sun Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)School of PhysicsBeijing Institute of TechnologyBeijing 100081People’s Republic of China Beijing Key Laboratory of Nanophotonics&Ultrafine Optoelectronic Systems School of PhysicsBeijing Institute of TechnologyBeijing 100081People’s Republic of China Department of Electrical and Electronic Engineering The University of Hong KongPokfulam RoadHong Kong Special Administrative Region of ChinaPeople’s Republic of China ACCESS-AI Chip Center for Emerging Smart Systems InnoHK CentersHong Kong Science ParkHong Kong Special Administrative Region of ChinaPeople’s Republic of China School of Microelectronics Southern University of Science and TechnologyShenzhen 518055People’s Republic of China Thrust of Microelectronics of Function Hub The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400People’s Republic of China

Recurrent neural networks(RNNs)have proven to be indispensable for processing sequential and temporal data,with extensive applications in language modeling,text generation,machine translation,and time-series *** their versatility,RNNs are frequently beset by significant training expenses and slow convergence times,which impinge upon their deployment in edge AI *** computing(RC),a specialized RNN variant,is attracting increased attention as a cost-effective alternative for processing temporal and sequential data at the ***’s distinctive advantage stems from its compatibility with emerging memristive hardware,which leverages the energy efficiency and reduced footprint of analog in-memory and in-sensor computing,offering a streamlined and energy-efficient *** review offers a comprehensive explanation of RC’s underlying principles,fabrication processes,and surveys recent progress in nano-memristive device based RC systems from the viewpoints of in-memory and in-sensor RC *** covers a spectrum of memristive device,from established oxide-based memristive device to cutting-edge material science developments,providing readers with a lucid understanding of RC’s hardware implementation and fostering innovative designs for in-sensor RC ***,we identify prevailing challenges and suggest viable solutions,paving the way for future advancements in in-sensor RC technology.

关键词： reservoir computing memristive device fabrication compute-in-memory in-sensor computing

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Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction Effects

arXiv

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arXiv 2025年

作者： Kostelecky, Anna Mareike Stefansson, Ivar Bringedal, Carina Ghosh, Tufan Dahle, Helge K. Helmig, Rainer Institute for Modelling Hydraulic and Environmental Systems Univeristy of Stuttgart Stuttgart Germany Department of Mathematics University of Bergen Bergen Norway Center for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Bergen Norway Department of Computer science Electrical engineering and Mathematical sciences Western Norway University of Applied Sciences Bergen Norway

Instantaneous heat transfer between different phases is a common assumption for modeling heat transfer in porous media, known as Local Thermal Equilibrium (LTE). This assumption may not hold in certain technical and environmental applications, especially in systems with large temperature gradients, large differences in thermal properties, or high velocities. Local Thermal Non-Equilibrium (LTNE) models aim to describe heat transfer processes when the LTE assumption may fail. In this work, we compare three continuum-scale models from the pore to the representative elementary volume (REV) scale. Specifically, dual-network and REV-scale models are evaluated against a pore-resolved model, which we perceive as a reference in the absence of experimental results. Different effective models are used to obtain upscaled properties on the REV scale and to compare resulting temperature profiles. The systems investigated are fully saturated, consisting of one fluid and one solid phase. This study focuses on purely conductive systems without significant differences in thermal properties. Results show that LTE holds then for low interfacial resistances. However, for large interfacial resistances, solid and fluid temperatures differ. The REV-scale model with effective parameters obtained by homogenization leads to similar results as the pore-resolved model, whereas the dual-network model shows greater deviation due to its fixed spatial resolution. Among the evaluated effective parameter formulations for the REV-scale model, only the homogenization-based approach captures the LTNE behavior, as it incorporates the interfacial heat transfer coefficient. Convection is relevant for most practical applications, and its impact will be addressed in a follow-up article. © 2025, CC BY.

关键词： Electric heating

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Runaway electron generation in disruptions mitigated by deuterium and noble gas injection in SPARC

arXiv

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arXiv 2025年

作者： Ekmark, I. Hoppe, M. Tinguely, R.A. Sweeney, R. Fülöp, T. Pusztai, I. Department of Physics Chalmers University of Technology GöteborgSE-41296 Sweden Department of Electrical Engineering KTH Royal Institute of Technology StockholmSE-11428 Sweden Plasma Science and Fusion Center Massachusetts Institute of Technology CambridgeMA01239 United States Commonwealth Fusion Systems DevensMA United States

One of the critical challenges in future high current tokamaks is the avoidance of runaway electrons during disruptions. Here, we investigate disruptions mitigated with combined deuterium and noble gas injection in SPARC. We use multi-objective Bayesian optimization and take into account limits on the maximum runaway current, the transported fraction of the heat loss, and the current quench time. The simulations are conducted using the numerical framework Dream (Disruption Runaway Electron Analysis Model). We show that during deuterium operation, runaway generation can be avoided with material injection, even when we account for runaway electron generation from DD-induced Compton scattering. However, when including the latter, the operational space of successful mitigation is reduced. During deuterium–tritium operation, acceptable levels of runaway current and transported heat losses are only obtainable at the highest levels of achievable injected deuterium densities. Furthermore, disruption mitigation is found to be more favourable when combining deuterium with neon, compared to deuterium combined with helium or argon. Copyright © 2025, The Authors. All rights reserved.

关键词： Electrons

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Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes

引用

Medical Image Analysis 2025年 103卷 103580页

作者： Manzano-Patrón, J.P. Deistler, Michael Schröder, Cornelius Kypraios, Theodore Gonçalves, Pedro J. Macke, Jakob H. Sotiropoulos, Stamatios N. Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham United Kingdom Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen & Tübingen AI Center Germany School of Mathematical Sciences University of Nottingham United Kingdom Belgium Department of Computer Science and Department of Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI framework for parametric spherical deconvolution of diffusion MRI data of the brain. We demonstrate its utility for estimating white matter fibre orientations, mapping uncertainty of voxel-based estimates and performing probabilistic tractography by spatially propagating fibre orientation uncertainty. We conduct an extensive comparison against established Bayesian methods based on Markov-Chain Monte-Carlo (MCMC) and find that: a) in-silico training can lead to calibrated SBI networks with accurate parameter estimates and uncertainty mapping for both single- and multi-shell diffusion MRI, b) SBI allows amortised inference of the posterior distribution of model parameters given unseen observations, which is orders of magnitude faster than MCMC, c) SBI-based tractography yields reconstructions that have a high level of agreement with their MCMC-based counterparts, equal to or higher than scan-rescan reproducibility of estimates. We further demonstrate how SBI design considerations (such as dealing with noise, defining priors and handling model selection) can affect performance, allowing us to identify optimal practices. Taken together, our results show that SBI provides a powerful alternative to classical Bayesian inference approaches for fast and accurate model estimation and uncertainty mapping in MRI. © 2025 The Authors

关键词： Monte Carlo methods

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Advancements in UAV-based Integrated Sensing and Communication: A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Ahmed, Manzoor Nasir, Ali Arshad Masood, Mudassir Memon, Kamran Ali Qureshi, Khurram Karim Khan, Feroz Khan, Wali Ullah Xu, Fang Han, Zhu School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China Interdisciplinary Research Center for Communication Systems and Sensing King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China University of Luxembourg Luxembourg City1855 Luxembourg Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Unmanned aerial vehicle (UAV)-based integrated sensing and communication (ISAC) systems are poised to revolutionize next-generation wireless networks by enabling simultaneous sensing and communication (S&C). This survey comprehensively reviews UAV-ISAC systems, highlighting foundational concepts, key advancements, and future research directions. We explore recent advancements in UAV-based ISAC systems from various perspectives and objectives, including advanced channel estimation (CE), beam tracking, and system throughput optimization under joint sensing and communication S&C constraints. Additionally, we examine weighted sum rate (WSR) and sensing trade-offs, delay and age of information (AoI) minimization, energy efficiency (EE), and security enhancement. These applications highlight the potential of UAV-based ISAC systems to improve spectrum utilization, enhance communication reliability, reduce latency, and optimize energy consumption across diverse domains, including smart cities, disaster relief, and defense operations. The survey also features summary tables for comparative analysis of existing methodologies, emphasizing performance, limitations, and effectiveness in addressing various challenges. By synthesizing recent advancements and identifying open research challenges, this survey aims to be a valuable resource for developing efficient, adaptive, and secure UAV-based ISAC systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Anisotropic Exchange Spin Model to Investigate the Curie Temperature Dispersion of Finite-Size L10-FePt Magnetic Nanoparticles

arXiv

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arXiv 2025年

作者： Ochiai, Kohei Tsuyama, Tomoyuki Shimizu, Sumera Zhang, Lei Watanabe, Jin Kudo, Fumito Zhu, Jian-Gang Okuno, Yoshishige Resonac Corporation Research Center for Computational Science and Informatics 8 Ebisu-cho Kanagawa-ku Kanagawa. Yokohama221-8517 Japan Resonac Hard Disk Corporation Research & Development Center 5-1 Yawatakaigan-dori Chiba Ichihara290-0067 Japan Data Storage Systems Center Carnegie Mellon University PittsburghPA15213 United States Electrical and Computer Engineering Department Carnegie Mellon University PittsburghPA15213 United States Materials Science and Engineering Department Carnegie Mellon University PittsburghPA15213 United States

We developed an anisotropic spin model that accounts for magnetic anisotropy and evaluated the Curie temperature (Tc) dispersion due to finite size effects in L10-FePt nanoparticles. In heat-assisted magnetic recording (HAMR) media, a next-generation magnetic recording technology, high-density recording is achieved by locally heating L10-FePt nanoparticles near their Tc and rapidly cooling them. However, variations in Tc caused by differences in particle size and shape can compromise recording stability and areal density capacity, making the control of Tc dispersion critical. In this study, we constructed atomistic LLG models to explicitly incorporate the spin exchange anisotropy of L10-FePt, based on parameters determined by first-principles calculations. Using this model, we evaluated the impact of particle size on Tc dispersion. As a result, (1) the Tc dispersion critical to the performance of HAMR can be reproduced, whereas it was previously underestimated by isotropic models and (2) approximately 70% of the experimentally observed Tc dispersion can be attributed to particle size effects. This research highlights the role of exchange anisotropy in amplifying finite-size effects and underscores the importance of size control in HAMR media. Copyright © 2025, The Authors. All rights reserved.

关键词： Curie temperature

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Surface Integration Modulated Low-Temperature Synthesis for High-Quality Halide Perovskite Single Crystals

SSRN

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SSRN 2025年

作者： Zeng, Shengqiao Xue, Bin Zhang, Bin Yang, Bin Xie, Xiangfan Hao, Chuanyun Wang, Xingzhu Qian, Lihua Petrov, Andrey A. Niu, Guangda Choy, Wallace C.H. Xiao, Shuang Southern University of Science and Technology China Huazhong University of Science and Technology China College of Engineering Physics Shenzhen Technology University Guangdong Shenzhen518118 China Engineering and Research Center for Integrated New Energy Photovoltaics & Energy Storage Systems of Hunan Province School of Electrical Engineering University of South China Hunan Hengyang421001 China College of Physics Huazhong University of Science and Technology Hubei Wuhan430074 China Faculty of Materials Science Shenzhen MSU-BIT University Shenzhen518172 China Laboratory of New Materials for Solar Energetics Faculty of Materials Science Lomonosov Moscow State University Moscow119991 Russia Research Institute of Huazhong University of Science and Technology in Shenzhen Guangdong Shenzhen518052 China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan430074 China Department of Electrical and Electronic Engineering The University of Hong Kong Pokfulam Road Hong Kong

Advancements in optoelectronic devices are largely contingent on the availability of superior-quality semiconductor materials, such as halide perovskites. However, quickly producing halide perovskite single crystals (SCs) often leads to compromised material properties hindering high-end applications. To address this challenge, we developed a refined crystal growth methodology, low-temperature inverse temperature crystallization (LITC), tailored to enhance the quality of perovskite SCs while maintaining a relatively fast growth rate. Taking the synthesis of MAPbBr3 SCs as a model, isopropyl alcohol (IPA) was introduced into the precursors as an additive. The affinity of IPA for the crystal surface enables a modulated surface integration process and simultaneously impacts the charge of colloids within the precursor solutions to suppress undesirable nucleation. Thereafter, MAPbBr3 SCs can successfully grow under near-equilibrium conditions from 44 °C to 48 °C. Notably, a narrow full width at half maximum of 0.012° in the rocking curve of high-resolution X-ray diffraction was achieved, outperforming most inverse temperature crystallization (ITC) methods for SC synthesis. X-ray detectors fabricated with LITC SCs exhibited markedly improved performances. With meticulous surface integration control, this work advances the synthesis of high-quality perovskite SCs and paves the way for elevating the performance and durability of optoelectronic devices. © 2025, The Authors. All rights reserved.

关键词： X ray detectors

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Deficient Excitation in Parameter Learning

arXiv

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arXiv 2025年

作者： Cao, Ganghui Wang, Shimin Guay, Martin Wang, Jinzhi Duan, Zhisheng Polycarpou, Marios M. State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Massachusetts Institute of Technology CambridgeMA02139 United States Queen’s University KingstonONK7L 3N6 Canada KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

This paper investigates parameter learning problems under deficient excitation (DE). The DE condition is a rank-deficient, and therefore, a more general evolution of the well-known persistent excitation condition. Under the DE condition, a proposed online algorithm is able to calculate the identifiable and non-identifiable subspaces, and finally give an optimal parameter estimate in the sense of least squares. In particular, the learning error within the identifiable subspace exponentially converges to zero in the noise-free case, even without persistent excitation. The DE condition also provides a new perspective for solving distributed parameter learning problems, where the challenge is posed by local regressors that are often insufficiently excited. To improve knowledge of the unknown parameters, a cooperative learning protocol is proposed for a group of estimators that collect measured information under complementary DE conditions. This protocol allows each local estimator to operate locally in its identifiable subspace, and reach a consensus with neighbours in its non-identifiable subspace. As a result, the task of estimating unknown parameters can be achieved in a distributed way using cooperative local estimators. Application examples in system identification are given to demonstrate the effectiveness of the theoretical results developed in this paper. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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