Engineering Science and Technology-An International Journal-Jestech最新文献

Integrated quantum-classical hybrid architectures for robust lung lesion segmentation in volumetric CT video data samples 基于集成量子经典混合架构的体积CT视频数据样本鲁棒肺病变分割

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102272

Sai Babu Veesam , Lalitha Kumari Pappala , Aravapalli Rama Satish , Sravan Kumar Chirumamilla , Vunnava Dinesh Babu , Shonak Bansal , Krishna Prakash , Mohamad A. Alawad , Mohammad Tariqul Islam

Segmentation of lung lesions in volumetric CT data is crucial for the clinical aspects of diagnosis, therapy planning, and monitoring disease progression. Currently, deep learning applications are unable to model spatiotemporal coherency alongside anatomical consistency and uncertainty-aware refinement across sequential slices. In this study, we propose a hybrid quantum–classical framework that would accommodate multiple innovative modules. The architecture features a Quantum Latent Entanglement Consistency validator to establish spatiotemporal coherence across slices by maximizing von Neumann entropy. A Quantum-Classical Interventional Gradient Alignment ensures the harmony of gradients between classical CNN encoders and quantum discriminators. Further, the Temporal Quantum Attention for Boundary Stabilization captures the temporal context in the boundary refinement using controlled quantum gates. Alongside these, a Quantum-Enhanced Structural Similarity Feedback mechanism is proposed that exploits anatomical priors for retrofitting spatial lesion structures, as well as a Hybrid Quantum Adversarial Ensemble Validation, which provides confidence-aware validity through disagreement modeling. Collection and experimental evaluations over LIDC IDRI, NSCLC-Radiomics, and MosMedData datasets depict that the entirety of the systems significantly increases the Dice Similarity Coefficient by 5–7%, holds Hausdorff Distance lower at 10–12%, narrows down the over-segmentation errors by 8–10%, while reducing overall false positives near lung boundaries by 15% or even less. This represents a significant advancement toward fusing quantum learning with clinical-grade imaging pipelines, demonstrating clear improvements in segmentation stability, precision, and trustworthiness in real-world settings.

体积CT数据中肺病变的分割对于临床诊断、治疗计划和监测疾病进展至关重要。目前，深度学习应用程序无法模拟时空一致性以及跨序列切片的解剖一致性和不确定性感知细化。在这项研究中，我们提出了一个混合量子-经典框架，将容纳多个创新模块。该架构具有量子潜在纠缠一致性验证器，通过最大化冯·诺伊曼熵来建立跨片的时空相干性。量子-经典干涉梯度对准保证了经典CNN编码器和量子鉴别器之间梯度的和谐。此外，用于边界稳定的时间量子注意在使用受控量子门的边界细化中捕获时间上下文。除此之外，还提出了一种量子增强结构相似性反馈机制，该机制利用解剖先验来改造空间病变结构，以及一种混合量子对抗集成验证，该验证通过分歧建模提供信心感知有效性。对LIDC IDRI、NSCLC-Radiomics和MosMedData数据集的收集和实验评估表明，整个系统显着将Dice Similarity Coefficient提高了5-7%，将Hausdorff Distance降低了10-12%，将过度分割错误降低了8-10%，同时将肺边界附近的总体假阳性降低了15%甚至更少。这代表了将量子学习与临床级成像管道融合的重大进步，在现实世界的分割稳定性、精度和可信度方面有了明显的提高。

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引用次数: 0

Constrained optimal formation control for nonlinear multi-agent systems using data-driven adaptive neural networks 基于数据驱动自适应神经网络的非线性多智能体系统约束最优编队控制

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102269

Saleh Mobayen , Mai The Vu , Reza Rahmani , Hamid Toshani , Wudhichai Assawinchaichote , Paweł Skruch

This paper presents a constrained optimal adaptive control strategy for formation control in nonlinear multi-agent systems (MASs) using a data-driven approach. In contrast to traditional methods that require detailed system models, the proposed method employs Locally Linearized Dynamic Models (LLDMs), in which key parameters as Pseudo-Partial Derivatives (PPDs) are estimated adaptively from input–output data. This removes the need for explicit mathematical modeling and broadens the method’s applicability to uncertain systems. To address actuator limitations and reduce control effort, a performance criterion incorporating control constraints is defined, and the problem is reformulated as a Constrained Quadratic Program (CQP) with control increments as optimization variables. A Projection Recurrent Neural Network (PRNN) is developed to solve this CQP in real time, which ensures convergence of the numerical optimizer and guarantees closed-loop stability using Lyapunov analysis and singular value approach. The proposed algorithm achieves robust, model-free formation control, explicitly manages input constraints, and enables fast convergence. Simulation results show that this approach outperforms existing data-driven methods under uncertainty, which demonstrates its potential for applications in multi-agent system applications.

提出了一种基于数据驱动的非线性多智能体系统约束最优自适应控制策略。与传统方法需要详细的系统模型相比，该方法采用局部线性化动态模型（lldm），其中关键参数作为伪偏导数（PPDs）自适应地从输入输出数据中估计。这消除了对显式数学建模的需要，并扩大了该方法对不确定系统的适用性。为了解决执行器的限制和减少控制工作量，定义了包含控制约束的性能标准，并将问题重新表述为以控制增量为优化变量的约束二次规划（CQP）。利用李雅普诺夫分析和奇异值法，建立了一种投影递归神经网络（PRNN）来实时求解该CQP，保证了数值优化器的收敛性和闭环稳定性。该算法实现了鲁棒性、无模型的编队控制，明确地管理输入约束，并实现了快速收敛。仿真结果表明，该方法在不确定条件下优于现有的数据驱动方法，证明了其在多智能体系统中的应用潜力。

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引用次数: 0

DGait: Robust gait recognition using dynamic ST-GCN with global aware attention 步态：基于全局感知注意力的动态ST-GCN鲁棒步态识别

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102267

Md. Khaliluzzaman , Kaushik Deb

Gait recognition, a promising behavioral soft biometric technology, has a significant research area in security and computer vision. Nowadays, joint position-based approaches are of significant interest in gait recognition. ST-GCN, the spatio-temporal graph convolutional network, is employed on the joint stream to identify the gait feature from the spatial–temporal graph, prone to provide attention to dynamic information. Many methods utilize multi-scale operations to integrate long-range relationships among joints. However, these approaches fail to assign equal significance to all joints, leading to an incomplete perception of long-range joint connections. Furthermore, considering the joint stream solely may fail to extract the discriminative features produced by motion and bone structures. This paper presents a multi-stream dynamic spatio-temporal graph convolution (DSTGCN) approach with attention, denoted as DGait. It leverages bone and joint data from the spatial frames and joint-motion data from successive frames to early fusion of informative skeleton features. An improved HOP-extraction approach is introduced to provide equal importance to the relationship between further and closer joints while avoiding redundant dependencies. To address the limitations of ST-GCN, Global Aware Attention (GAA) is incorporated into the ST-GCN units, enhancing the capability for dynamically correlating the spatial–temporal joints. The suggested model exhibits remarkable accuracy on widely used CASIA-B, OUMVLP-Pose, and GREW datasets. The CASIA-B reveals an average accuracy of 96.94 %, 93.56 %, and 90.78 % for the normal walking, carrying-bag, and clothing conditions, respectively. The OUMVLP-Pose and GREW exhibit an average and rank-1 accuracy of 92.7 % and 72.6 %, respectively.

步态识别是一种很有发展前景的行为软生物识别技术，在安全和计算机视觉领域有着重要的研究领域。目前，基于关节位置的方法是步态识别的重要研究方向。在关节流上采用时空图卷积网络ST-GCN，从时空图中识别步态特征，易于关注动态信息。许多方法利用多尺度操作来整合关节之间的远程关系。然而，这些方法不能对所有关节赋予同等的重要性，导致对远距离关节连接的不完整感知。此外，仅考虑关节流可能无法提取由运动和骨结构产生的区别特征。本文提出了一种带注意的多流动态时空图卷积（DSTGCN）方法，记为DGait。它利用来自空间框架的骨骼和关节数据以及来自连续框架的关节运动数据来早期融合信息骨骼特征。引入了一种改进的hop提取方法，在避免冗余依赖的同时，对更远和更近的关节之间的关系提供同等的重视。为了解决ST-GCN的局限性，在ST-GCN单元中加入了全局感知注意（Global Aware Attention， GAA），增强了动态关联时空节点的能力。该模型在广泛使用的CASIA-B、OUMVLP-Pose和grow数据集上显示出显著的准确性。CASIA-B在正常行走、携带包和穿衣服条件下的平均准确率分别为96.94%、93.56%和90.78%。OUMVLP-Pose和grow的平均准确率和rank-1准确率分别为92.7%和72.6%。

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引用次数: 0

Characterizing the FRA curves of transformer tertiary helical windings by deriving transfer functions from FRA data 利用铁磁数据推导传递函数，对变压器三级螺旋绕组铁磁曲线进行了表征

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102268

Zhi Zhang

Interpreting frequency response analysis (FRA) data presents a formidable challenge in transformer fault diagnosis. Previous attempts to derive transfer functions (TF) for characterizing FRA curves have been both desirable and unsuccessful. The collected FRA data aims to represent the mechanical conditions of the transformer windings under examination. Nonetheless, the techniques applied to FRA results for assessing mechanical integrity face inherent uncertainty due to the lack of a direct link between the measured data and the electrical characteristics of an equivalent circuit (EC) consisting of resistance, inductance, and capacitance (RLC) components. As such, a rigorous analysis of the FRA data becomes crucial for a comprehensive assessment and interpretation of the mechanical state of these windings. The proposed investigation into TF is designed to offer a detailed mathematical interpretation of FRA characteristics, potentially enabling the early detection of potential faults through the derived TF and relevant parameters. This research paper revolves around the computation of TFs for power transformer helical windings. Consequently, a strong correlation emerges between the recorded FRA curves and the computed TF curves, affirming the precision of TF estimation and its significant contribution to advance FRA technology.

在变压器故障诊断中，频响分析（FRA）数据的解释是一个巨大的挑战。以前试图推导传递函数（TF）来表征FRA曲线的尝试既有可取的，也有失败的。收集的FRA数据旨在表示被检查的变压器绕组的机械状况。尽管如此，由于测量数据与等效电路（EC）（由电阻、电感和电容（RLC）组成）的电气特性之间缺乏直接联系，应用于评估机械完整性的FRA结果的技术面临固有的不确定性。因此，对FRA数据的严格分析对于全面评估和解释这些绕组的机械状态至关重要。该研究旨在为FRA特征提供详细的数学解释，从而通过推导出的TF和相关参数及早发现潜在故障。本研究围绕电力变压器螺旋绕组的热载荷计算展开。结果表明，实测的FRA曲线与计算的TF曲线之间存在很强的相关性，证实了TF估计的精度及其对FRA技术进步的重要贡献。

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引用次数: 0

A comprehensive review of noise-cancellation antenna sensors in ultra-high frequency: techniques, challenges, and future directions 超高频消噪天线传感器：技术、挑战和未来方向综述

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102271

Zongxing Wei , Mohamadariff Othman , Tarik Abdul Latef , Hazlee Azil Illias , S. M. Kayser Azam , Tengku Faiz Tengku Mohmed Noor Izam , Muhammad Ubaid Ullah , Mohamed Alkhatib , Mousa I. Hussein

This paper provides a comprehensive review of ultra-high frequency (UHF) noise-cancellation antenna (NCA) sensors. It identifies the critical challenges posed by noise interference in UHF bands and their impact on signal quality, particularly in partial discharge (PD) detection applications. The paper summarises the various types of noise present in the UHF range and highlights the importance of advanced design methods to enhance signal integrity. A significant contribution of this work is the detailed analysis of several noise-cancellation (NC) techniques, including the integrated feedline approach, embedded filter antenna technique, slot design modification, parasitic element incorporation, and shorting pin integration. These are systematically evaluated for their effectiveness in reducing interference. The review also provides a comparative analysis using tabular data, covering performance metrics such as NC implementation, radiation nulls (RN) frequency, bandwidth, gain, and other parameters. In addition, the paper identifies the most suitable techniques for PD detection and discusses their practical limitations. By highlighting potential directions for future research, this study offers valuable insights for advancing UHF antenna sensor design and its application in industrial PD monitoring systems.

本文综述了超高频（UHF）噪声消除天线（NCA）传感器的研究进展。它确定了UHF频段噪声干扰带来的关键挑战及其对信号质量的影响，特别是在局部放电（PD）检测应用中。本文总结了超高频范围内存在的各种类型的噪声，并强调了采用先进的设计方法来提高信号完整性的重要性。这项工作的一个重要贡献是详细分析了几种噪声消除（NC）技术，包括集成馈线方法、嵌入式滤波器天线技术、槽设计修改、寄生元件集成和短引脚集成。系统地评估它们在减少干扰方面的有效性。该综述还提供了使用表格数据的比较分析，包括性能指标，如NC实现、辐射零值（RN）频率、带宽、增益和其他参数。此外，本文确定了最适合PD检测的技术，并讨论了它们的实际局限性。通过强调未来研究的潜在方向，本研究为推进UHF天线传感器的设计及其在工业PD监测系统中的应用提供了有价值的见解。

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引用次数: 0

Optimization of modification parameters of face gear pair based on TCA-NRBPNN-HYPE hybrid drive model 基于TCA-NRBPNN-HYPE混合驱动模型的面齿轮副修形参数优化

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102274

Kun He , Ronghao Li , Yongquan Chen , Yachao Jia , Guolong Li

To enhance the meshing characteristics of the face gear pair and determine the optimal modification parameters, an optimization model of modification parameters, based on the TCA-NRBPNN-HYPE (Tooth Contact Analysis-Newton Raphson Back Propagation Neural Network-Hyperparameter Optimization) hybrid drive model is proposed. Firstly, a dual weight modification curve is introduced to modify the tooth surface of face gears, and the TCA model is employed to accurately obtain the meshing characteristics parameters of the modified gear pair, including contact position, transmission error, and contact stress. based on the modification parameters and TCA results, an NRBPNN prediction model is established to achieve mapping from modification parameters to meshing characteristics. Finally, the HYPE optimization model is applied to globally optimize the prediction results and obtain the optimal modification parameter combination. The results show that the optimal design reduces the contact position parameter from 4.15 to 1.50, the transmission error from 2.98″ to 0.314″, and the contact stress from 566.30 MPa to 292.33 MPa. These results indicate that the proposed method effectively improves the meshing characteristics and reliability of face gear pair.

为了提高面齿轮副的啮合特性，确定最优修形参数，提出了一种基于TCA-NRBPNN-HYPE（齿接触分析- newton Raphson反向传播神经网络-超参数优化）混合驱动模型的修形参数优化模型。首先，引入双权值修形曲线对面齿轮齿面进行修形，利用TCA模型精确获取修形后齿轮副的啮合特性参数，包括接触位置、传动误差和接触应力。基于修正参数和TCA结果，建立了NRBPNN预测模型，实现了修正参数与网格特征的映射。最后，应用HYPE优化模型对预测结果进行全局优化，得到最优修正参数组合。结果表明：优化设计后，接触位置参数由4.15降至1.50，传动误差由2.98″降至0.314″，接触应力由566.30 MPa降至292.33 MPa。结果表明，该方法有效地改善了面齿轮副的啮合特性和可靠性。

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引用次数: 0

A FEM-ANN framework to estimate the on-diagonal elements of the impedance matrix in a Cochlear Implant 一种估算人工耳蜗阻抗矩阵对角元素的FEM-ANN框架

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102273

M.J. Hernández-Gil , A. Ramos-de-Miguel , D. Greiner , D. Benítez , G. Montero , J.M. Escobar

Accurate estimation of the impedance matrix is essential for optimizing cochlear implant (CI) performance, yet the on-diagonal terms, that represent the contact impedances of electrodes, remain poorly characterized in existing models. In this work, we first analyze these on-diagonal terms and highlight their impact on electric field distribution. We then revisit the classic linear extrapolation approach and introduce two novel extrapolation methods to enhance prediction accuracy. To capture patient-specific variability, real impedance measurements are incorporated into a resistive–conductive finite-element method (FEM) model, whose matrices serve as the basis for a supervised neural network. The network is trained and validated on a diverse dataset of FEM-derived impedance matrices, enabling robust generalization across electrode configurations. Benchmarking against state-of-the-art techniques shows that our hybrid FEM-ANN framework reduces prediction error for diagonal terms. Moreover, when used in multipolar stimulation strategies, the ANN-based impedance matrices yield comparable focalization while requiring lower electrical power. Our results demonstrate that combining physical modeling with data-driven methods produces more reliable and efficient impedance estimates, paving the way for improved CI fitting and patient outcomes.

阻抗矩阵的准确估计对于优化人工耳蜗（CI）性能至关重要，然而，在现有模型中，代表电极接触阻抗的对角线项仍然很差。在这项工作中，我们首先分析了这些对角线项，并强调了它们对电场分布的影响。然后，我们回顾了经典的线性外推方法，并引入了两种新的外推方法来提高预测精度。为了捕获患者特异性的可变性，实际阻抗测量被纳入电阻-导电有限元方法（FEM）模型，其矩阵作为监督神经网络的基础。该网络在fem衍生阻抗矩阵的不同数据集上进行训练和验证，从而实现跨电极配置的鲁棒泛化。对最先进技术的基准测试表明，我们的混合FEM-ANN框架减少了对角项的预测误差。此外，当用于多极刺激策略时，基于人工神经网络的阻抗矩阵可以产生相当的聚焦，同时需要更低的电力。我们的研究结果表明，将物理建模与数据驱动方法相结合，可以产生更可靠、更有效的阻抗估计，为改善CI拟合和患者预后铺平了道路。

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引用次数: 0

Design and implementation of PIλDμ controller for ROVs: Thruster modeling, controller parameter optimization, and FPGA realization rov pi - λ dμ控制器的设计与实现：推力器建模、控制器参数优化及FPGA实现

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2025-12-22 DOI: 10.1016/j.jestch.2025.102261

Hakan Ersoy, Berke Akgül, Emin Akpınar, Aslihan Kartci, Umut Engin Ayten

Remotely operated and autonomous underwater vehicles (ROVs/AUVs) operate in a harsh environment dominated by nonlinear hydrodynamics, strong coupling, and wave–current disturbances. In most of the existing literature, surge-axis motion is still regulated by integer-order PID controllers that are tuned either heuristically or via single-scenario optimization. Such designs often exhibit limited robustness: their performance degrades significantly under severe noise, targeted wave excitation, or time-varying operational profiles. These limitations motivate the use of fractional-order control and more systematic tuning procedures. This paper investigates fractional-order

{PI}^{λ} D^{μ}

(FOPID) controllers for surge control and compares two popular meta-heuristics, Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA), in comparison with classical PID. A fourth-order surge plant model is first obtained via system identification of experimental data from a BlueRobotics T200 thruster. Then, PSO and DEA are used to tune both PID and

{PI}^{λ} D^{μ}

parameters over a multi-scenario cost function that combines step-response quality, disturbance rejection, and control effort. The resulting controllers are evaluated under four increasingly demanding tests: noiseless step tracking, severe white-noise excitation, sinusoidal “storm” disturbance, and a final scenario with time-varying set-points under the same storm condition. Across all 16 scalar performance metrics (IAE, ISE, and, ITAE over four tests), the DEA-tuned

{PI}^{λ} D^{μ}

achieves the best value in 12 cases, consistently outperforming both PID designs and the PSO-based

{PI}^{λ} D^{μ}

. In the most demanding final test (multi-level reference + storm), it reduces the integral time-weighted absolute error ITAE from 0.1065 (best PID) to 0.0893, i.e., by approximately 16%, while preserving competitive control effort. These results provide quantitative evidence that DEA-tuned

{PI}^{λ} D^{μ}

offers a more robust and energy-aware solution for single-axis surge control in ROV/AUV applications.

遥控和自主水下航行器（rov / auv）在非线性流体动力学、强耦合和波流干扰的恶劣环境中工作。在大多数现有文献中，浪涌轴运动仍然由启发式或单场景优化调谐的整阶PID控制器调节。这种设计通常表现出有限的鲁棒性：在严重噪声、目标波激励或时变操作剖面下，它们的性能显著下降。这些限制促使使用分数阶控制和更系统的调优过程。本文研究了分数阶pi - λ dμ (FOPID)控制器在喘振控制中的应用，并将两种常用的元启发式算法粒子群优化（PSO）和差分进化算法（DEA）与经典PID进行了比较。首先通过对BlueRobotics T200推进器实验数据的系统识别，得到了一个四阶调压装置模型。然后，PSO和DEA用于在结合阶跃响应质量，干扰抑制和控制努力的多场景成本函数上调整PID和PIλDμ参数。所得到的控制器在四种要求越来越高的测试中进行评估：无噪声步进跟踪、严重白噪声激励、正弦“风暴”干扰，以及在相同风暴条件下具有时变设值的最终场景。在所有16个标量性能指标（IAE， ISE和ITAE超过四次测试）中，dea调谐的PIλDμ在12种情况下达到最佳值，始终优于PID设计和基于pso的PIλDμ。在最苛刻的最终测试（多级参考+风暴）中，它将积分时间加权绝对误差ITAE从0.1065（最佳PID）降低到0.0893，即大约16%，同时保持竞争性控制努力。这些结果提供了定量证据，表明dea调谐PIλDμ为ROV/AUV应用中的单轴浪涌控制提供了更强大和能量感知的解决方案。

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引用次数: 0

State-of-the-art soft robotic systems for unstructured and real-world environments: A systematic review 用于非结构化和现实世界环境的最先进的软机器人系统：系统综述

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2025-12-22 DOI: 10.1016/j.jestch.2025.102264

Arameh Eyvazian , Yooseob Song , Chibukhchyan Hovhannes , Ardeshir Savari , Narinderjit Singh Sawaran Singh

Soft robotics has emerged as a transformative paradigm in automation, offering unprecedented compliance, adaptability, and safety for operation in unstructured and dynamic environments. This study systematically reviews the latest advances in soft robotic systems, spanning novel material innovations, intelligent hybrid architectures, and cutting-edge actuation and control strategies. Key developments in integration with artificial intelligence, computer vision, and machine learning are highlighted, enabling enhanced perception, autonomy, and adaptive behavior. Application-driven case studies in healthcare, exploration, and search-and-rescue showcase the evolving capabilities of soft robots in challenging real-world settings. Persistent challenges, such as untethered operation, robust sensorimotor integration, scalable fabrication, and interpretable AI, are discussed alongside emerging multidisciplinary solutions. The review concludes by outlining future research directions, emphasizing the need for unified codesign approaches and collaboration across robotics, materials science, AI, and biology. This synthesis provides a roadmap for advancing next-generation soft robotic systems, aiming to bridge the gap between laboratory innovations and impactful deployment in complex, unpredictable environments, in support of industrial and innovation progress.

软机器人已经成为自动化的变革范例，为非结构化和动态环境中的操作提供前所未有的合规性、适应性和安全性。本研究系统地回顾了软机器人系统的最新进展，包括新型材料创新、智能混合架构以及前沿驱动和控制策略。重点介绍了与人工智能、计算机视觉和机器学习集成的关键发展，从而增强了感知、自主性和自适应行为。医疗保健、勘探和搜救领域的应用程序驱动案例研究展示了软机器人在具有挑战性的现实环境中不断发展的能力。持续的挑战，如不受约束的操作，强大的感觉运动集成，可扩展的制造和可解释的人工智能，与新兴的多学科解决方案一起讨论。该综述总结了未来的研究方向，强调需要统一的协同设计方法和机器人、材料科学、人工智能和生物学之间的协作。这一综合为推进下一代软机器人系统提供了路线图，旨在弥合实验室创新与复杂、不可预测环境中有影响力的部署之间的差距，以支持工业和创新进展。

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引用次数: 0

Hybrid fragile image watermarking for tamper detection, localization and dual self-recovery 用于篡改检测、定位和双重自恢复的混合脆弱图像水印

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2025-12-22 DOI: 10.1016/j.jestch.2025.102266

Aditya Kumar Sahu , Monalisa Sahu

This paper presents a novel image watermarking framework that effectively addresses the issue of random block mapping. This phenomenon compromises tampered regions and their corresponding recovery blocks, resulting in irretrievable image data. To mitigate the random block mapping issue, a crisscross block mapping strategy (CrCsBMS) is proposed to enhance the robustness of block mapping by ensuring non-randomised reference allocation. The authentication bit generation leverages Gram-Schmidt Orthonormalization (GSO), extracting pivotal image characteristics, such as mean intensity, variance, and edge strength, thereby fortifying the integrity verification mechanism. The hybrid embedding strategy integrates discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) to maintain an optimal balance between imperceptibility and embedding capacity, while distortion compensated quantization index modulation (DC-QIM) is employed for recovery bit encoding. A dual self-recovery mechanism incorporating bilinear interpolation-based inpainting and an 8-neighborhood method with a 255-color range scaling (255-CRS) is introduced, significantly augmenting recovery efficiency and ensuring precise restoration of tampered pixels. Experimental analysis demonstrates superior imperceptibility, robustness against image processing attacks, and reduced computational complexity compared to contemporary techniques. The proposed scheme achieves an average PSNR of 52.22 dB, an SSIM of 0.9983, and a payload capacity of 1 bit per pixel, surpassing existing self-recovery watermarking frameworks in both accuracy and resilience.

本文提出了一种新的图像水印框架，有效地解决了随机块映射问题。这种现象危及篡改区域及其相应的恢复块，导致不可恢复的图像数据。为了缓解随机块映射问题，提出了一种交错块映射策略（CrCsBMS），通过确保非随机引用分配来增强块映射的鲁棒性。认证位的生成利用Gram-Schmidt正交规格化（GSO），提取关键图像特征，如平均强度、方差和边缘强度，从而加强完整性验证机制。混合嵌入策略将离散小波变换（DWT）、离散余弦变换（DCT）和奇异值分解（SVD）相结合，在隐密性和嵌入容量之间保持最佳平衡，同时采用失真补偿量化指标调制（DC-QIM）进行恢复位编码。采用双线性插值法和8邻域255色范围缩放法（255-CRS）的双重自恢复机制，大大提高了恢复效率，确保了篡改像素的精确恢复。实验分析表明，与当代技术相比，优越的不可感知性，对图像处理攻击的鲁棒性以及降低的计算复杂性。该方案的平均PSNR为52.22 dB， SSIM为0.9983，有效载荷容量为1比特/像素，在精度和弹性方面都优于现有的自恢复水印框架。

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