Search Results (47)

Surface Acoustic Wave (SAW) technology, long central to analog signal processing and RF filtering, is undergoing a major renewal. Driven by advances that decouple SAWs from traditional piezoelectric materials and fixed-function devices, the field is gaining unprecedented control over acoustic, optical, and electronic interactions at the micro and nanoscale. This review synthesizes these developments across four fronts: new physical mechanisms for SAW manipulation, emerging material platforms, ranging from thin films to 2D systems, along with reconfigurable device architectures and circuits, and the expanding landscape of applications they enable. Optical methods are reshaping how SAWs are generated and controlled, bypassing the limits of conventional electromechanical coupling. Coherent optical excitation of high-Q SAW cavities via Brillouin-like optomechanical interactions now grants access to modes in non-piezoelectric substrates such as diamond and silicon, while on-chip SAW excitation in photonic waveguides through backward stimulated Brillouin scattering opens new integrated sensing routes. In parallel, magneto-acoustic experiments have revealed nonreciprocal SAW diffraction from resonant scattering in magnetoelastic gratings. On the device side, ZnO thin-film transistors integrated on LiNbO₃ exploit acoustoelectric coupling to realize voltage-tunable phase shifters; UHF Z-shaped delay lines achieve high sensitivity in a compact footprint; and parametric synthesis of wideband, multi-stage lattice filters targets 5G-class performance. Atomistic simulations show that SAW propagation in 2D MXene films can be engineered via surface terminations, while aerosol jet printing and SAW-assisted particle patterning provide agile, cleanroom-light fabrication of microfluidic and magnetic components. These advances enable applications ranging from hybrid quantum systems and quantum links to lab-on-a-chip particle control, SBS-based and UHF sensing, reconfigurable RF front-ends, and soft robotic actuators based on patterned magnetic composites. At the same time, optical techniques offer non-contact probes of dissipation, and MXenes and other emerging materials open new regimes of acoustic control. Conclusively, they are transforming SAW technology into a versatile, programmable platform for mediating complex interactions in next-generation electronic, photonic, and quantum systems. Full article

Sustainable energy systems such as anaerobic digestion (AD) bioreactors exhibit complex nonlinear dynamics that complicate the monitoring of key stability indicators using conventional laboratory-based methods. As a preliminary investigation, this pilot study explores the feasibility of using machine learning-based soft sensing to estimate Total Volatile Fatty Acids (TVFA(M)) from routinely measured physicochemical parameters. Using a short-term laboratory dataset obtained from controlled CO₂ biomethanisation experiments, several regression models were benchmarked, including an attention-based deep learning architecture (TabNet), multi-architecture artificial neural networks (ANNs), gradient-boosting ensembles (CatBoost, XGBoost, LightGBM), and classical kernel-based approaches. Model performance was evaluated under a cross-validated framework to assess predictive capability and consistency across folds within the limited experimental scope. Among the tested models, TabNet achieved highly competitive performance, yielding an R² of 0.8551, an RMSE of 0.0090, and an MAE of 0.0067. To support model transparency and interpretability, Explainable Artificial Intelligence (XAI) techniques based on SHapley Additive exPlanations (SHAP) were applied, identifying pCO₂ as the dominant contributor to TVFA(M) predictions within the studied operational range. The results demonstrate the potential of explainable machine learning models as soft sensors for TVFA(M) estimation under controlled laboratory conditions. Although restricted to controlled laboratory conditions and a short observation period, this pilot study demonstrates the potential of explainable machine learning models for TVFA(M) estimation and provides a methodological benchmark for future validation using larger and more diverse datasets. Full article

The highly squinted mode, as an operational configuration of synthetic aperture radar (SAR), fulfills specific remote sensing demands. Under equivalent conditions, it necessitates a higher pulse repetition frequency (PRF) than the side-looking mode but produces inferior imaging quality, thereby constraining its widespread application. By applying the sparse SAR imaging method to highly squinted SAR systems, imaging quality can be enhanced while simultaneously reducing PRF requirements and expanding swath. Hyperparameters in sparse SAR imaging critically influence reconstruction quality and computational efficiency, making hyperparameter optimization (HPO) a persistent research focus. Inspired by HPO techniques in the deep unfolding network (DUN), we modified the iterative soft-thresholding algorithm (ISTA) employed in fast sparse SAR reconstruction based on approximate observation operators. Our adaptation enables adaptive regularization parameter tuning during iterations while accelerating convergence. To improve the robustness of this enhanced algorithm under realistic SAR echoes with noise, we integrated hypergradient descent (HD) to automatically adjust the ISTA step size after regularization parameter convergence, thereby mitigating overfitting. The proposed method, named Hyper-ISTA-GHD, adaptively selects regularization parameters and step sizes. It achieves high-precision, rapid imaging for highly squinted SAR. Owing to its training-free iterative minimization framework, this approach exhibits superior generalization capabilities compared to existing DUN methods and demonstrates broad applicability across diverse SAR imaging modes and scene characteristics. Simulations show that the hyperparameter selection and reconstruction results of the proposed method are almost consistent with the optimal values of traditional methods under different signal-to-noise ratios and sampling rates, but the time consumption is only one-tenth of that of traditional methods. Comparative experiments on the generalization performance with DUN show that the generalization performance of the proposed method is significantly better than DUN in extremely sparse scenarios. Full article

Agricultural robotics and artificial intelligence (AI) are becoming essential to building more sustainable, efficient, and resilient food systems. As climate change, food security pressures, and labour shortages intensify, the integration of intelligent technologies in agriculture has gained strategic importance. This systematic review provides a consolidated assessment of AI and robotics research in agriculture from 2000 to 2025, identifying major trends, methodological trajectories, and underexplored domains. A structured search was conducted in the Scopus database—which was selected for its broad coverage of engineering, computer science, and agricultural technology—and records were screened using predefined inclusion and exclusion criteria across title, abstract, keywords, and eligibility levels. The final dataset was analysed through descriptive statistics and science-mapping techniques (VOSviewer, SciMAT). Out of 4894 retrieved records, 3673 studies met the eligibility criteria and were included. As with all bibliometric reviews, the synthesis reflects the scope of indexed publications and available metadata, and potential selection bias was mitigated through a multi-stage screening workflow. The analysis revealed four dominant research themes: deep-learning-based perception, UAV-enabled remote sensing, data-driven decision systems, and precision agriculture. Several strategically relevant but underdeveloped areas also emerged, including soft manipulation, multimodal sensing, sim-to-real transfer, and adaptive autonomy. Geographical patterns highlight a strong concentration of research in China and India, reflecting agricultural scale and investment dynamics. Overall, the field appears technologically mature in perception and aerial sensing but remains limited in physical interaction, uncertainty-aware control, and long-term autonomous operation. These gaps indicate concrete opportunities for advancing next-generation AI-driven robotic systems in agriculture. Funding sources are reported in the full manuscript. Full article

The integration of artificial intelligence (AI) and micro/nanorobotics is fundamentally reshaping biosensing by enabling autonomous, adaptive, and high-resolution biological analysis. These miniaturized robotic systems fabricated using advanced techniques such as photolithography, soft lithography, nanoimprinting, 3D printing, and self-assembly can navigate complex biological environments to perform targeted sensing, diagnostics, and therapeutic delivery. AI-driven algorithms, mainly those in machine learning (ML) and deep learning (DL), act as the brains of the operation, allowing for sophisticated modeling, genuine real-time control, and complex signal interpretation. This review focuses recent advances in the design, fabrication, and functional integration of AI-enabled micro/nanorobots for biomedical sensing. Applications that demonstrate their potential range from quick point-of-care diagnostics and in vivo biosensing to next-generation organ-on-chip systems and truly personalized medicine. We also discuss key challenges in scalability, energy autonomy, data standardization, and closed-loop control. Collectively, these advancements are paving the way for intelligent, responsive, and clinically transformative biosensing systems. Full article

Multi-Unmanned Aerial Vehicle (Multi-UAV) cooperative search represents a cutting-edge research direction in the field of unmanned aerial vehicle applications. The use of multi-UAV systems for low-altitude target search and area surveillance has become an effective means of enhancing security capabilities. In practical scenarios, UAVs rely on onboard sensors to acquire environmental information; however, due to the limited perceptual range of these sensors, their observation capabilities are inherently local and constrained. This paper investigates the problem of multi-UAV cooperative search in partially observable low-altitude environments, where each UAV possesses a circular sensing range with a finite radius. Target location information is only obtained when a target enters the field of view of any UAV. The objective is to achieve cooperative search and sustain continuous surveillance while ensuring safety among UAVs and with the environment. To address this challenge, we propose a novel multi-agent deep reinforcement learning (MADRL) algorithm named Normalizing Graph Attention Soft Actor-Critic (NGASAC). This algorithm integrates a normalizing flow (NL) layer and a multi-head graph attention network (MHGAT). The normalizing flow technique maps traditional Gaussian sampling to a more complex action distribution, thereby enhancing the expressiveness and flexibility of the policy. Simultaneously, by constructing a multi-head graph attention network that captures “obstacle–target” relationships, the algorithm improves the UAVs’ ability to learn and reason about complex spatial topologies, leading to significantly better performance in cooperative search and stable surveillance of hidden targets. Simulation results demonstrate that the NGASAC algorithm markedly outperforms baseline methods such as Multi-Agent Soft Actor-Critic (MASAC), Multi-Agent Proximal Policy Optimization (MAPPO), and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) across multiple evaluation metrics, including success rate, task time, and obstacle avoidance capability. Furthermore, it exhibits strong generalization performance and robustness. Full article

This study introduces a novel dual-model deep learning framework based on Bidirectional Gated Recurrent Units (Bi-GRUs) with the Attention Mechanism to predict intermediate-pressure (IP) turbine casing temperatures in a 370 MW coal-fired power plant under varying operational regimes, including startup, shutdown, and load-following conditions. Accurate temperature prediction is critical, as thermal gradients induce significant stresses in the turbine casing, potentially causing fatigue crack initiation. To mitigate sensor failures, which lead to costly downtime in power generation systems, the proposed soft sensor leverages an extensive dataset collected over one year from Unit 4 of the Opole Power Plant. The dataset is partitioned into shutdown and active regimes to capture distinct thermal dynamics, enhancing model adaptability. The framework employs advanced preprocessing techniques and state detection heuristics to improve prediction robustness. Experimental results show that the dual-model approach outperforms traditional machine learning models (Random Forest Regressor, XGBoost) and single-model deep learning baselines (LSTM, Single Attentive Bi-GRU), achieving a mean squared error (MSE) of 2.97 °C and a mean absolute error (MAE) of 1.07 °C on the test set, while also maintaining low prediction latency suitable for real-time applications. This superior performance stems from a tailored architecture, optimized via Hyperband tuning and a strategic focus on distinct operational regimes. This work advances soft sensing in power systems and provides a practical, real-time solution for stress monitoring and control, particularly as coal plants in Poland face increased cycling demands due to the growth of renewable energy sources, rising from 7% in 2010 to 25% by 2025. The approach holds potential for broader application in industrial settings requiring robust temperature prediction under variable conditions. Full article

Soft robotic grippers have emerged as crucial tools for safe and adaptive manipulation of delicate and different objects, enabled by their compliant structures. These grippers need embedded sensing that offers proprioceptive and exteroceptive feedback in order to function consistently. Resistive sensing is unique among transduction processes since it is easy to use, scalable, and compatible with deformable materials. The three main classes of resistive sensors used in soft robotic grippers are systematically examined in this review: ionic sensors, which are emerging multimodal devices that can capture both mechanical and environmental cues; tactile sensors, which detect contact, pressure distribution, and slip; and strain sensors, which monitor deformation and actuation states. Their methods of operation, material systems, fabrication techniques, performance metrics, and integration plans are all compared in the survey. The results show that sensitivity, linearity, durability, and scalability are all trade-offs across sensor categories, with ionic sensors showing promise as a new development for multipurpose soft grippers. There is also a discussion of difficulties, including hysteresis, long-term stability, and signal processing complexity. In order to move resistive sensing from lab prototypes to reliable, practical applications in domains like healthcare, food handling, and human–robot collaboration, the review concludes that developments in hybrid material systems, additive manufacturing, and AI-enhanced signal interpretation will be crucial. Full article

Biopolymers have emerged as a transformative class of materials that reconcile high-performance functionality with environmental stewardship. Their inherent capacity for controlled degradation and biocompatibility has driven rapid advancements across electronics, sensing, actuation, and healthcare. In flexible electronics, these polymers serve as substrates, dielectrics, and conductive composites that enable transient devices, reducing electronic waste without compromising electrical performance. Within sensing and actuation, biodegradable polymer matrices facilitate the development of fully resorbable biosensors and soft actuators. These systems harness tailored degradation kinetics to achieve temporal control over signal transduction and mechanical response, unlocking applications in in vivo monitoring and on-demand drug delivery. In healthcare, biodegradable polymers underpin novel approaches in tissue engineering, wound healing, and bioresorbable implants. Their tunable chemical architectures and processing versatility allow for precise regulation of mechanical properties, degradation rates, and therapeutic payloads, fostering seamless integration with biological environments. The convergence of these emerging applications underscores the pivotal role of biodegradable polymers in advancing sustainable technology and personalized medicine. Continued interdisciplinary research into polymer design, processing strategies, and integration techniques will accelerate commercialization and broaden the impact of these lower eCO₂ value materials across diverse sectors. This perspective article comments on the innovation in these sectors that go beyond the applications of biodegradable materials in packaging applications. Full article

The convergence of carbon nanomaterials and functional polymers has led to the emergence of smart carbon–polymer nanocomposites (CPNCs), which possess exceptional potential for next-generation sensing and actuation systems. These hybrid materials exhibit unique combinations of electrical, thermal, and mechanical properties, along with tunable responsiveness to external stimuli such as strain, pressure, temperature, light, and chemical environments. This review provides a comprehensive overview of recent advances in the design and synthesis of CPNCs, focusing on their application in multifunctional sensors and actuator technologies. Key carbon nanomaterials including graphene, carbon nanotubes (CNTs), and MXenes were examined in the context of their integration into polymer matrices to enhance performance parameters such as sensitivity, flexibility, response time, and durability. The review also highlights novel fabrication techniques, such as 3D printing, self-assembly, and in situ polymerization, that are driving innovation in device architectures. Applications in wearable electronics, soft robotics, biomedical diagnostics, and environmental monitoring are discussed to illustrate the transformative impact of CPNCs. Finally, this review addresses current challenges and outlines future research directions toward scalable manufacturing, environmental stability, and multifunctional integration for the real-world deployment of smart sensing and actuation systems. Full article

Wearable and implantable Lab-on-Chip (LoC) biosensors are revolutionizing healthcare by enabling continuous, real-time monitoring of physiological and biochemical parameters in non-clinical settings. These miniaturized platforms integrate sample handling, signal transduction, and data processing on a single chip, facilitating early disease detection, personalized treatment, and preventive care. This review comprehensively explores recent advancements in LoC biosensing technologies, emphasizing their application in skin-mounted patches, smart textiles, and implantable devices. Key innovations in biocompatible materials, nanostructured transducers, and flexible substrates have enabled seamless integration with the human body, while fabrication techniques such as soft lithography, 3D printing, and MEMS have accelerated development. The incorporation of nanomaterials significantly enhances sensitivity and specificity, supporting multiplexed and multi-modal sensing. We examine critical application domains, including glucose monitoring, cardiovascular diagnostics, and neurophysiological assessment. Design considerations related to biocompatibility, power management, data connectivity, and long-term stability are also discussed. Despite promising outcomes, challenges such as biofouling, signal drift, regulatory hurdles, and public acceptance remain. Future directions focus on autonomous systems powered by AI, hybrid wearable–implantable platforms, and wireless energy harvesting. This review highlights the transformative potential of LoC biosensors in shaping the future of smart, patient-centered healthcare through continuous, minimally invasive monitoring. Full article

Polymer-based photonic sensors are emerging as cost-effective, scalable alternatives to conventional silicon and glass photonic platforms, offering unique advantages in flexibility, functionality, and manufacturability. This review provides a comprehensive assessment of recent advances in polymer photonic sensing technologies, focusing on material systems, fabrication techniques, device architectures, and application domains. Key polymer materials, including PMMA, SU-8, polyimides, COC, and PDMS, are evaluated for their optical properties, processability, and suitability for integration into sensing platforms. High-throughput fabrication methods such as nanoimprint lithography, soft lithography, roll-to-roll processing, and additive manufacturing are examined for their role in enabling large-area, low-cost device production. Various photonic structures, including planar waveguides, Bragg gratings, photonic crystal slabs, microresonators, and interferometric configurations, are discussed concerning their sensing mechanisms and performance metrics. Practical applications are highlighted in environmental monitoring, biomedical diagnostics, and structural health monitoring. Challenges such as environmental stability, integration with electronic systems, and reproducibility in mass production are critically analyzed. This review also explores future opportunities in hybrid material systems, printable photonics, and wearable sensor arrays. Collectively, these developments position polymer photonic sensors as promising platforms for widespread deployment in smart, connected sensing environments. Full article

Stretchable display technology has rapidly evolved, enabling a new generation of flexible electronics with applications ranging from wearable healthcare and smart textiles to implantable biomedical devices and soft robotics. This review systematically presents recent advances in stretchable displays, focusing on intrinsic stretchable materials, wavy surface engineering, and hybrid integration strategies. The paper highlights critical breakthroughs in device architectures, energy-autonomous systems, durable encapsulation techniques, and the integration of artificial intelligence, which collectively address challenges in mechanical reliability, optical performance, and operational sustainability. Particular emphasis is placed on the development of high-resolution displays that maintain brightness and color fidelity under mechanical strain, and energy harvesting systems that facilitate self-powered operation. Durable encapsulation methods ensuring long-term stability against environmental factors such as moisture and oxygen are also examined. The fusion of stretchable electronics with AI offers transformative opportunities for intelligent sensing and adaptive human–machine interfaces. Despite significant progress, issues related to large-scale manufacturing, device miniaturization, and the trade-offs between stretchability and device performance remain. This review concludes by discussing future research directions aimed at overcoming these challenges and advancing multifunctional, robust, and scalable stretchable display systems poised to revolutionize flexible electronics applications. Full article

The contemporary challenge in remote sensing lies in the precise retrieval of increasingly abundant and high-resolution remotely sensed images (RS image) stored in expansive data warehouses. The heightened spatial and spectral resolutions, coupled with accelerated image acquisition rates, necessitate advanced tools for effective data management, retrieval, and exploitation. The classification of large-sized images at the pixel level generates substantial data, escalating the workload and search space for similarity measurement. Semantic-based image retrieval remains an open problem due to limitations in current artificial intelligence techniques. Furthermore, on-board storage constraints compel the application of numerous compression algorithms to reduce storage space, intensifying the difficulty of retrieving substantial, sensitive, and target-specific data. This research proposes an innovative hybrid approach to enhance the retrieval of remotely sensed images. The approach leverages multilevel classification and multiscale feature extraction strategies to enhance performance. The retrieval system comprises two primary phases: database building and retrieval. Initially, the proposed Multiscale Multiangle Mean-shift with Breaking Ties (MSMA-MSBT) algorithm selects informative unlabeled samples for hyperspectral and synthetic aperture radar images through an active learning strategy. Addressing the scaling and rotation variations in image capture, a flexible and dynamic algorithm, modified Deep Image Registration using Dynamic Inlier (IRDI), is introduced for image registration. Given the complexity of remote sensing images, feature extraction occurs at two levels. Low-level features are extracted using the modified Multiscale Multiangle Completed Local Binary Pattern (MSMA-CLBP) algorithm to capture local contexture features, while high-level features are obtained through a hybrid CNN structure combining pretrained networks (Alexnet, Caffenet, VGG-S, VGG-M, VGG-F, VGG-VDD-16, VGG-VDD-19) and a fully connected dense network. Fusion of low- and high-level features facilitates final class distinction, with soft thresholding mitigating misclassification issues. A region-based similarity measurement enhances matching percentages. Results, evaluated on high-resolution remote sensing datasets, demonstrate the effectiveness of the proposed method, outperforming traditional algorithms with an average accuracy of 86.66%. The hybrid retrieval system exhibits substantial improvements in classification accuracy, similarity measurement, and computational efficiency compared to state-of-the-art scene classification and retrieval methods. Full article

Soft robotics represents a transformative approach to automation, focusing on the development of robots constructed from flexible, compliant materials that mimic biological systems. Being different from traditional rigid robots, soft robots are engineered to adapt and operate efficiently in complex, unstructured environments, making them highly appropriate for applications that require delicate manipulation, safe human–robot interaction, and mobility on unstable terrain. The key principles, materials, and fabrication techniques of soft robotics are explored in this study, highlighting their versatility in industries such as healthcare, agriculture, and search-and-rescue operations. The essence of soft robotic systems lies in their ability to deform and respond to environmental stimuli. The system enables new paradigms in automation for tasks that demand flexibility, such as handling fragile objects, navigating narrow spaces, or interacting with humans. Emerging materials, such as elastomers, hydrogels, and shape-memory alloys, are driving innovations in actuation and sensing mechanisms, expanding the capabilities of soft robots in applications. We also examine the challenges associated with the control and energy efficiency of soft robots, as well as opportunities for integrating artificial intelligence and advanced sensing to enhance autonomous decision-making. Through case studies and experimental data, the potential of soft robotics is reviewed to revolutionize sectors requiring adaptive automation, ultimately contributing to safer, more efficient, and sustainable technological advancements than present robots. Full article