Combining deep learning and bird sound recognition strongly supports monitoring bird species and maintaining ecological balance. However, in outdoor environments, the extraction of bird sound features is often hindered by environmental noise, making it challenging for models to learn the fine-grained features of bird sounds fully. And single-scale feature extraction is harrowing to cover the time–frequency domain feature information of bird sounds in multiple dimensions. To address these issues, this paper proposes a multi-grained detail-enhanced and patch-aware network. The model utilizes densely connected time delay neural network as the backbone network and introduces the multi-grained detail-enhanced convolution, which combines vanilla convolutions with differential convolutions in the horizontal, vertical, angular, and central levels, and incorporates multi-grained pooling strategies to learn fine-grained acoustic features at different levels. To further overcome the limitations of single-scale feature extraction, the branch patch-aware attention module is proposed. This module collaboratively captures local details and global contextual information through a multi-branch structure and patch partitioning of different sizes. On the three datasets, the method achieved accuracies of 96.29%, 86.51%, and 97.40%, respectively. This achievement demonstrates the precise capture and parsing ability of the method for audio feature information.

Segmentation of the pancreatic computed tomography (CT) image in the Internet of Medical Things (IoMT) environment faces dual challenges of feature robustness and accurate recognition of small organs. As a clinically critical but anatomically challenging organ, the pancreas exhibits small volume, high inter-patient variability, and low contrast with surrounding tissues, making automatic pancreas segmentation a representative and difficult task in abdominal CT analysis. Existing automatic, machine-centric segmentation methods often perform unsatisfactorily across different medical institutions due to variations in imaging devices, changes in scanning protocols, as well as the irregular shape and blurred boundaries of the pancreas. To address this problem, this paper proposes a SimCLR-based Contrastive Swin-UNet model (ContSwinU), which integrates contrastive learning with the Swin Transformer architecture to achieve feature robustness learning and high-precision pancreas segmentation. Specifically, ContSwinU leverages SimCLR to learn robust feature representations, thereby enhancing the model’s generalization capability across diverse scenarios. Additionally, by incorporating the hierarchical window attention mechanism of the Swin Transformer, the model effectively balances local texture and global structural information, improving segmentation accuracy for the pancreas as a small organ. Experimental results on a public pancreas CT dataset demonstrate that ContSwinU achieves an IoU of 0.8041, a Dice coefficient of 0.8872, and a recall of 0.9014, significantly outperforming mainstream baseline methods. These results indicate that the proposed framework is well suited for challenging small-organ segmentation tasks and has the potential to be extended to other organs and multi-center IoMT scenarios. This study provides an effective solution for pancreas segmentation in IoMT environments and has substantial clinical application value.

Automated navigation control of microrobots in complex environments is essential for applications such as targeted drug delivery and micromanipulation. Recently, machine learning (ML) has shown great potential for automated microrobot control but still lacks accuracy and smoothness. In this work, we propose a novel Learning-from-Demonstration (LfD)-based control and navigation framework to achieve precise and smooth motion control of microrobots. This work represents an early attempt to directly utilize expert-provided data for designing a learning-based microrobot controller. The framework begins by collecting a small dataset of expert demonstrations (several thousand episodes) , from which the controller learns compensatory behaviors and task-specific adaptability, eliminating the need for extensive exploration or parameter retuning. Based on this data, a time-series neural network is then developed to process the microrobot’s historical states and control actions, allowing the system to capture sequential dependencies and transitions for smooth and accurate path tracking. For demonstration, we take the magnetic microswarm as an illustrative example. Systematic simulations and comparative experiments validate the proposed framework, demonstrating its superior performance in tracking accuracy and smoothness, validating the efficacy of ML for low-level microrobot control.

Motion control of magnetic microswarms has attracted extensive attention due to its significance in microrobots-based biomedical applications such as targeted drug delivery. However, such reconfigurable microswarms are subject to complex interactions between individuals and environments which make accurate modeling challenging. These complexities of microswarms poses challenges for precise motion control, as traditional controllers often rely on precise mathematical models and manual parameter tuning that limits their scalability and efficiency. Learning-based methods, such as Deep Reinforcement Learning (DRL), offer an alternative but require large datasets (usually on the order of millions) and extensive exploration which may cause the microswarms instability in physical environments due to unreasonable actions during early training therefore results in the sim-to-real gap. Moreover, traditional DRL focuses on instantaneous state-action mappings, neglecting the sequential dependencies critical for accurate motion control, leading to low tracking accuracy in complex scenarios. To address these challenges, we propose a Learning from Demonstration (LfD)-based motion control framework, which inherently encode compensatory behaviors and task-specific adaptability into neural networks, enabling adaptive performance even under unmodeled disturbances. Furthermore, the neural networks consider a time series of microswarm states to determine the future control actions, enabling the system to learn sequential dependencies and transitions between states so as to ensure smooth and accurate motion control. Simulations and comparative experiments validate our framework’s effectiveness and demonstrate superior control accuracy and adaptability to microswarm’s shape changes.

ABSTRACT Malignant lung nodules can significantly affect patients' normal lives and, in severe cases, threaten their survival. Owing to the heterogeneity of computed tomography scans and the varying sizes of nodules, physicians often face challenges in diagnosing this condition. Therefore, a novel adaptive multi‐channel fusion network (AMCF‐Net) is proposed for computer‐aided diagnosis of lung nodules. First, a Multi‐Channel Fusion Model module is designed, which divides the channels into two parts in specific proportions, effectively extracting multi‐scale channel information while reducing network parameters. After the feature maps output at each layer of the AMCF‐Net, a novel adaptive depth‐wise separable convolution with a squeeze‐and‐excitation module is designed to adaptively integrate the feature maps of various stages of the AMCF‐Net, ensuring that the key lesions of lung nodules are not lost during classification. Finally, a hybrid loss scheme based on an adaptive mixing ratio is proposed to solve the problem of an imbalanced number of positive and negative nodule samples in the dataset. The model achieved the following test results: an accuracy of 90.22%, a specificity of 98.19%, an F1‐score of 86.57%, a sensitivity of 86.49%, and a G‐mean of 87.72%. Compared with other advanced networks, AMCF‐net delivers high‐precision lung nodule classification with minimal inference cost. Related codes have been released at: https://github.com/GuYuIMUST/AMCF‐net .

With the growing significance of non-intrusive speech quality assessment in speech systems, existing methods predominantly rely on neural networks to extract low-order features. Typically, these features undergo a low-dimensional linear transformation, yielding the network’s output. However, the intercorrelation between feature points is often overlooked. In this paper, we explore the concept of kernel method, which maps features into high dimensional space through dot product, in order to enhance the extraction of relationships among all feature points. Considering the unique advantages of tensors in complex data representation, we extend the utilization of tensor network and propose a novel framework that incorporates a matrix product state (MPS) layer to predict mean opinion score (MOS). By integrating the MPS layer, our model can transform low-order features into higher-order representations, facilitating linear transformation in a high dimensional space without increasing the number of parameters. Furthermore, we propose a loss function that concurrently assesses regression and classification biases, along with correlation with real MOS labels. Experimental results demonstrate that our proposed model consistently outperforms the baseline system across all evaluation metrics and surpasses state-of-the-art models on the test set.

Toxic Speech (TS) is widely spread in social media and is increasingly harmful to the online networking environment. At the same time, words and their variants containing implicit toxic semantics are influenced by the humanistic and social environment of the language and are extremely rich in form and shape, and the lack of background knowledge related to the "discourse body" of existing research has led to the confusion of Implicit Toxic Speech (ITS) with Non-Toxic Speech (NTS). Therefore, this paper proposes the Prompt-enhanced Attention Head Fusion (PAHF) toxic text classification model based on the generative language model. We optimize the hidden layer vectors by intervening in the hidden layer information through prompt, then filter the vectors by combining linear probes to locate those vectors in the hidden layer information that are strongly correlated with the encoding of the background knowledge, and finally construct an attention fusion mechanism to fuse the filtered features, which provides a higher detection accuracy compared with the existing algorithms.

In this work, we study the optimal parameter design and microrobotic navigation control of the parallel-mobile-coil system (PMCS) that consists of three mobile electromagnetic coils. With motion driven by a parallel mechanism, the three coils can move in 3D large space and keep as close as possible to the controlled microrobot for magnetic actuation. Although promising for microrobotic applications, how to design such a type of system for a specific workspace requirement is untackled. Regarding this issue, we propose a computational design method, by which one can calculate the structural parameters of a PMCS starting from a required cylindrical workspace. With the derived performance metrics for motion actuation and magnetic actuation of the PMCS, the system actuation performance (composed of motion and magnetic actuation) is optimized. Utilizing the design method, we optimally construct a prototype PMCS for microrobotic navigation. We then conduct experiments to validate the demanding field/force generation capability of the PMCS and demonstrate the navigation control of different types of magnetic microrobots. In particular, we design closed-loop motion controllers for both torque and force-driven microrobots, using which automated large-workspace and high-accuracy trajectory tracking is realized. Note to Practitioners—This work is motivated by the recent wide interest in magnetic microrobots. Driven by external magnetic fields, magnetic microrobots can navigate in a wireless manner for targeted delivery/therapy. To promote microrobot applications to the human body, a magnetic actuation system with large workspace is desirable. However, due to the fast decay of magnetic field, the commonly used stationary coil-based magnetic actuation systems have the workspace scalability problem. Thus, several mobile-coil-based systems have been designed. In this work, we propose an optimal design method for the PMCS, using which one can design a PMCS starting from a cylindrical workspace with performance being optimized. We construct a PMCS prototype with a workspace of$\Phi 230 \times 100$mm3, and we then study the automated microrobotic navigation control methods for the PMCS. Controllers are designed for different types of magnetic microrobots, and experiments show that, using the controllers, the PMCS can perform automated large-workspace microrobotic navigation control with high accuracy.

Untethered microrobots possess a promising perspective for micromanipulation applications. With specifically designed morphologies and structures, microrobots are able to perform controllable delivery of target objects. However, the manipulation process still lacks autonomy, to achieve which the mechanism of picking, transporting, and releasing behaviors needs further investigation. In this article, we propose to achieve automated microrobotic manipulation using magnetic microswarms with multimodal morphology. The microswarm is composed of around 11–21 million$\text{Fe}_{3}\mathrm{O}_{4}$nanoparticles (1.0$\text{--}1.8\,\mu$L particle suspension). When exposed to different dynamic magnetic fields, the swarm could exhibit corresponding forms. We realize precise and controllable cargo picking and releasing by exploiting the fluid fields of different swarm forms. In order to quantitatively describe these behaviors, we design a finite-state machine. A super-twisting sliding-mode controller has been formulated for the motion control of swarms. The disturbances are compensated via a disturbance observer. To enable automated micromanipulation in obstructed scenarios, a path planner inspired by rapidly exploring random tree algorithm is designed for path planning when obstacles exist. We also propose an enhanced-genetic algorithm to optimally transport multiple objects to the target position. Experiments demonstrate that our method could effectively transport micro-objects with different sizes and shapes. The precise selectivity of the method is validated when multiple objects exist in the working environment. Finally, the long-distance delivery ability and adaptivity to various friction situations of our strategy are demonstrated. This work explores a concise, untethered, and automated micromanipulation strategy, provides a new automatic tool for micromanipulation tasks, and extends the application potential of swarm microrobotics.

Untethered magnetic microrobots with control-lable locomotion property and multiple functions have attracted lots of attention in recent years. Owing to the small scale, micro-robots with automatic navigation possess a promising perspec-tive for biomedical applications including precise delivery and targeted therapy in confined and narrow space, especially for in-vivo scenario. However, the practical working environment for microrobots can be various, dynamic, and complicated, and path planning algorithm applicable for both dynamic obstacle avoidance and planning in maze-like environments still remains a challenge. Furthermore, considering the sizes, different types of microrobots may occupy different proportions of the field of vision. The safe distance between the waypoints and the obstacles needs to be taken into thoughts. In this work, we proposed a reinforcement learning-based strategy capable of real-time path planning for microrobots in different scales. The reference moving direction at each control period is provided by a deep Q network (DQN) according to the local surrounding environment, and the corresponding control magnetic field is generated via a 3-axis Helmholtz coil system. A distur-bance observer (DOB) is responsible for the locomotion state observation and direction error compensation. Experiments demonstrate the effectiveness of our proposed strategy using microrobots with different locomotion mechanisms and scales, in both virtual dynamic obstacle environments and channel-like environments.

Magnetic field is a favorable power source for actuation and control of micro-/nanorobots. To overcome the fast decay of magnetic field for large-workspace microrobotic actuation, mobile field source-based systems have been proposed. In this work, we report a new mobile-coil system, i.e., QuadMag. It consists of four electromagnetic coils, whose motion is actuated by a parallel mechanism. Compared to previous systems with three mobile coils, e.g., DeltaMag, the additional coil in the QuadMag increases the degree-of-freedom (DoF) for magnetic control. However, to control QuadMag, new control methods should be developed for the over-constrained parallel mechanism and for the field/force of the four coils. We derive the Jacobian matrix for the differential motion of the parallel mechanism and then formulate the field, force and simultaneous field and force control methods for magnetic actuation. Comparative experiments validate the enhanced actuation efficiency when controlling torque-driven helical microrobots. Moreover, the magnetic actuation dexterity is also enhanced by the additional coil. We conduct simulated navigation experiments and prove the actuation capability of QuadMag for 3D force-driven microrobot navigation with controlled robot orientation.

Based on deep learning technology, non-intrusive methods have received increasing attention for synthetic speech quality assessment since it does not need reference signals. Meanwhile, i-vector has been widely used in paralinguistic speech attribute recognition such as speaker and emotion recognition, but few studies have used it to estimate speech quality. In this paper, we propose a neural-network-based model that splices the deep features extracted by convolutional neural network (CNN) and i-vector on the time axis and uses Transformer encoder as time sequence model. To evaluate the proposed method, we improve the previous prediction models and conduct experiments on Voice Conversion Challenge (VCC) 2018 and 2016 dataset. Results show that i-vector contains information very related to the quality of synthetic speech and the proposed models that utilize i-vector and Transformer encoder highly increase the accuracy of MOSNet and MBNet on both utterance-level and system-level results.

Soft microrobotics have recently been an active field that advances microrobotics with new robot design, locomotion, and applications. In this paper, we study the ferrofluid robot (FR), which has soft nature and exhibits paramagnetism. Currently, the FR locomotion is usually realized by magnetic force. To enable the FR with more locomotion modes for environment and task adaptability, we program three dynamic field forms and realize three corresponding torque-actuated locomotion modes: Rolling, Wobbling, and Oscillating. The torque actuation of the FR is formulated, and the three locomotion modes are characterized. With the implementation of automated tracking and control algorithms, the controllability of these modes is testified. We then fabricate different environments to validate the adaptability of the FR that can switch its locomotion mode accordingly. Finally, utilizing the oscillating mode and wobbling mode, we demonstrate the transport of lipophilic and hydrophilic cargoes, respectively, showing the task adaptability.

A dynamic self-assembly is a promising approach for inducing the collective behavior of agents to perform coordinated tasks at small scales. However, efficient pattern formation and navigation in environments with complex conditions remain a challenge. In this article, we propose a strategy for micromanipulation using dynamically self-assembled magnetic droplets with needle guidance. An iron needle was controlled by a three-degree-of-freedom (3-DoF) manipulator and magnetized by precessing magnetic fields. The process of self-assembly was optimized based on real-time vision feedback and a genetic algorithm. Affected by the locally induced field gradient near the needle, reconfigurable assembled magnetic droplets were formed beneath the air-liquid interface with high time efficiency, and the geometric center of the pattern was determined. Following the magnetized needle, assembled patterns were navigated along preplanned paths and exhibited reversible pattern expansion and shrinkage. Moreover, cargo can be trapped and caged by exploiting the induced fluid flow around the assembled droplets. To perform cargo transportation tasks in a multiple-obstacle environment, an optimal path planner with obstacle-avoidance capability was designed based on the particle swarm optimization (PSO) algorithm. Experiments demonstrated effective pattern formation, navigation, cargo trapping, and obstacle-avoidance transportation. The proposed method opens new prospects of using a dynamically self-assembled pattern as an untethered end-effector for micromanipulation.Note to Practitioners—This article was motivated by the recent interest in utilizing the collective behavior of small-scale active agents to perform micromanipulation tasks. Driven by external magnetic fields, building blocks are gathered and assembled, yielding a dynamically stable pattern. To perform practical tasks, efficient pattern formation, control, and navigation are required. Besides, obstacles often exist in the working environment, challenging pattern navigation, and manipulation tasks. The strategy presented here is developed for micromanipulation using dynamically self-assembled magnetic droplets with needle guidance. The three-axis Helmholtz coil system is applied to rotate the droplets and magnetize the iron needle. Algorithms are designed to guide and optimize the pattern formation, navigation, and cargo trapping process. Magnetic droplets are real-time tracked, and ordered assembled patterns are formed in an optimized way. Following the needle, the pattern was navigated and performed cargo manipulation tasks with obstacle-avoidance capability. Experimental results have validated the proposed strategy in pattern formation, navigation, and cargo manipulation in a multiple-obstacle environment.

The small size and wireless actuation of microrobots make them potential candidates for minimally invasive medicine. To advance microrobots to future clinical application, microrobotics researchers have investigated a number of key issues, in which swarm control is a primary challenge and is attracting increasing attention. As a single microrobot has limited volume and surface area, clinically relevant tasks, includingin-vivotracking, usually require simultaneous control of a large swarm of microrobots. Unlike macroscale robots, implementing on-board actuators and sensors for microrobots is challenging, which differentiates swarm microrobotics from other swarm robotics approaches. This article systematically summarizes the state of the art for this emerging field, including actuation systems with different power sources, swarm behaviors modeling and simulation, swarm control strategies, and targeted biomedical applications. Actuation principles of microrobot swarms are categorized in detail, and critical comparisons are made to provide guidance and insight for future swarm microrobotics researchers. Considering the unique features of swarm microrobotics compared to traditional swarm robotics, this article also emphasizes the modeling, simulation, and control of microrobot swarms. Furthermore, recent biomedical applications of microrobot swarms are summarized to illustrate specific application scenarios. Finally, we provide an assessment of the future directions of swarm microrobotics.

Reconfigurable microrobotic swarms and controllable active matter systems have drawn extensive attention recently. Developing effective actuation strategies and control schemes that enable embodied intelligence of microscopic swarms are both major challenges. In this work, we realize the generation of an elliptical paramagnetic nanoparticle swarm (EPNS) with enhanced dexterity for adaptive locomotion, and subsequently a fuzzy control strategy is developed for automatically tuning pattern deformation, orientation, and position of the swarm. By adjusting the input field, the aspect ratio of the EPNS will change accordingly, and we demonstrate its adaptive navigation through curved and narrowed channel by performing pattern reconfigurations. Moreover, using the proposed control strategy, precise matches can be reached between the controlled swarms and the desired patterns. Finally, to show the high compatibility of the control strategy, we employ ribbon-like colloidal swarms driven by oscillating magnetic field, and the results also validate the effectiveness of the strategy.

Mechanical rubbing of blood clot using miniature magnetic helical robots is a potential way for thrombolysis. In this paper, we report a new strategy for this issue based on mobile coils. Previously, we proposed the concept of magnetic actuation with parallel mobile coils, in which multiple coils can move in 3D space. Enabled by mobility of the coils, additional degree-of-freedom (DOF) could be utilized for actuation performance optimization. Besides the primary helical propulsion by rotating magnetic fields, our strategy aims to optimize the coil motion to make the magnetic force contributes the most to the helical robot forward motion. For this goal, modeling of the magnetic field and force of multiple mobile coils are presented, based on which an optimization algorithm is formulated to output the best coil motion. For validation, an enhanced mobile coil system having a workspace of Φ500 mm ×150 mm is constructed based on the parallel mobile coil concept. Simulations show the effectiveness of the proposed strategy, whose effective workspace for a specific task can also be obtained. After implementing the proposed strategy, preliminary experiments using clot analog demonstrate that the removal speed is accelerated over 50% compared to that without coil motion optimization.

Large workspace localization of magnetic robots is important for medical applications. This paper presents a novel localization strategy to achieve simultaneous localization and actuation of magnetic robots using hall-effect sensors. We integrate 25 sensors into a sensing probe and mount it on to the mobile-coil system, which realizes accurate sensing and actuation of magnetic devices within a cylindrical workspace of ϕ500 mm×150 mm. Simulation results show the average localization error using the proposed method is 1.7 mm. A verification experiment is conducted to prove the design advantages; Another two experiments are conducted to demonstrate the simultaneous actuation and localization of a torque-driven robot and a force-driven floating robot respectively. For the force-driven floating robot, the average variation between the localization results and the desired trajectory is less than 2 mm.

Magnetic helical microswimmers can be propelled by rotating magnetic field and are adept at passing through narrow space. To date, various magnetic actuation systems and control methods have been developed to drive these microswimmers. However, steering their spacial movement in a large workspace is still challenging, which could be significant for potential medical applications. In this regard, this paper designs an eye-in-hand stereo-vision module and corresponding refraction-rectified location algorithm. Combined with the motor module and the coil module, the mobile-coil system is capable of generating dynamic magnetic fields in a large 3D workspace. Based on the system, a robust triple-loop stereo visual servoing strategy is proposed that operates simultaneous tracking, locating, and steering, through which the helical swimmer is able to follow a long-distance 3D path. A scaled-up magnetic helical swimmer is employed in the path following experiment. Our prototype system reaches a cylindrical workspace with a diameter more than 200 mm, and the mean error of path tracking is less than 2 mm.

Recently, swarm control of micro-/nanorobots has drawn much attention in the field of microrobotics. This paper reports a mobile paramagnetic nanoparticle swarm with the capability of active shape deformation that can improve its environment adaptability. We show that, by applying elliptical rotating magnetic fields, a swarm pattern called the elliptical paramagnetic nanoparticle swarm (EPNS) would be formed. When changing the field ratio-α (i.e. the strength ratio between the minor axis and major axis of the elliptical field), the shape ratio-β of the EPNS (i.e. the length ratio between the major axis and minor axis) will change accordingly. However, automatically control this shape deformation process has difficulties because the deformation dynamics has strong nonlinearity, model variation and long time requirement. To solve this problem, we propose a fuzzy logic-based control scheme that utilizes the knowledge and control experience from skilled human operators. Experiments show that the proposed control scheme can stably maneuver the shape deformation of the EPNS with small overshoot, which cannot be achieved by conventional PI control. Moreover, experimental results show that, with the automatic shape deformation control, shape of the EPNS is controlled with high reversibility and also can be well maintained during the planar rotational and translational locomotion of the EPNS.

Microrobotic delivery possesses a promising perspective for precision medicine and has attracted much attention recently. However, its automation remains challenging, especially with complex environmental conditions, such as obstacles and obstructed optical feedback. In this article, we propose an automated control approach for a new type of magnetic microrobot, i.e., the multifunctional magnetic spore (Mag-Spore), which has good potential for targeted delivery. By the surface functionalization of the spore with Fe3O4nanoparticles and carbon quantum dots (QDs), it can be remotely actuated and tracked by an electromagnetic coil system and the fluorescence microscopy, respectively. Our control approach uses fluorescence imaging for vision feedback, which enhances the recognition and tracking of Mag-Spores, obstacles, and cells. Then, information of the obstacles, targeted cells, and Mag-Spores for planning and control is identified by image processing, and an optimal path planner with obstacle-avoidance capability is designed based on the particle swarm optimization (PSO) algorithm. To make the Mag-Spore follow the planed path accurately, a robust model predictive trajectory-tracking controller is synthesized. Simulations are conducted to validate the proposed control approach and tune the control parameters. Experiments demonstrate the effective targeted delivery of the Mag-Spore by using the proposed automated control method under the guidance of fluorescence imaging. Note to Practitioners-This article was motivated by the recent wide interest of precise targeted delivery using biohybrid magnetic microrobots. Driven by external magnetic fields, microrobots accomplish the targeted delivery tasks. In practical applications, obstacles and obstructed optical feedback often exist that make the delivery task challenging. The Mag-Spore presented here has a hollow structure, so that the cargo-carrying capability is maximized and supported by the proposed automated control techniques, and the delivery precision and efficiency are promised in multiple-obstacle scenarios. In addition, the control method has the robustness to model uncertainties and external disturbances that should be considered and well solved in applications. Fluorescence imaging, a common way for observing biomaterials, is compatible with the proposed control scheme and the developed software so that the recognition and tracking of the Mag-Spore and other biomaterials are improved. Moreover, the self-established plug-and-play (PnP) electromagnetic magnetic coil system has the feature of easy installation and configuration on fluorescence microscopes. Simulations and experiments validate the effectiveness of our method in fluorescence-guided targeted delivery using magnetic microrobots.

Swarm control is one of the primary challenges in microrobotics. For the automated control of such a microrobotic system with small size and large population, conventional methods using precise robot models and robot-robot communications lose effectiveness due to the complex locomotion of micro/nano agents in a swarm and difficult implementation of onboard actuators and sensors for individual motion control and motion feedback. This article proposes a statistics-based approach and reports the fully automated control of a swarm of paramagnetic nanoparticles including the swarm pattern formation, identification, tracking, motion control, and real-time distribution monitoring/control. By establishing the swarm statistics, collective behaviors of a nanoparticle swarm can be quantitatively analyzed by computers. Algorithms are designed based on the statistics to automatically generate and identify the vortex-like paramagnetic nanoparticle swarm (VPNS), which present robustness to the dose and initial distribution of the nanoparticle swarm. In order to robustly track a VPNS, a statistics-based tracking method is proposed, in which 500 boundary points of the VPNS are extracted and the VPNS distribution is optimally recognized. And, with the proposed gathering improvement control, experiments show that over 70% nanoparticles can be gathered in the VPNS. Furthermore, an automated motion control scheme for the VPNS is proposed which shows high-accuracy trajectory tracking performance (tracking error: <; 5% body length). Besides, real-time monitoring of the distribution region/density and control of the distribution area for a nanoparticle swarm are also realized by using the statistics. Experimental results validate the feasibility of the proposed method in automated control of paramagnetic nanoparticle swarms.

In this paper, a novel magnetic manipulation system using mobile coils for remote actuation of magnetic untethered devices in an enlarged workspace is proposed and studied. A parallel mechanism is implemented to actuate the mobile coils. A proof-of-concept prototype is designed and constructed, namely the DeltaMag, which includes three electromagnetic coils for generating magnetic fields and three motors for actuation of the coils. It has good space utilization: ratio between the diameter of the workspace and the diameter of the whole prototype reaches 0.7.A calibrated mathematical model is developed for the field distribution of a single coil, which has an average error of 8.75%. Then, we introduce a calculation method for the 3D magnetic field at any working position for the configuration of multiple parallel mobile coils. Moreover, an embedded system is established for actuating the parallel mechanism, whose pose is fed back via serial communication for magnetic field computation. A vision based approach is developed for closed-loop control of the parallel mechanism. Furthermore, experiments demonstrate the capabilities of the DeltaMag for manipulation of a magnetic catheter mock-up and a magnetic capsule mock-up in a workspace with a diameter more than 200 mm.

A linear frequency modulation (LFM) signal is a typical non‐stationary signal, and its parameter estimation has always been among the core issues in the field of signal processing. In this study, a novel method for parameter estimation of LFM signal is proposed using the simplified linear canonical transform and evaporation‐rate‐based water cycle algorithm. According to the results obtained from the theoretical analysis and experimental simulation, the proposed method possesses the advantages of easy implementation, high precision and independence on initial values compared with the concise fractional Fourier transform (CFRFT)‐based and fractional Fourier transform‐based methods. Moreover, the proposed parameter estimation method is successfully applied to optical measurement with improving measurement accuracy.

Microrobots are considered as promising tools for biomedical applications. However, the imaging of them becomes challenges in order to be further applied on in vivo environments. Here we report the magnetic navigation of a paramagnetic nanoparticle-based swarm using ultrasound images. The swarm can be generated using simple rotating magnetic fields, resulting in a region containing particles with a high area density. Ultrasound images of the swarm shows a periodic changing of imaging contrast. The reason for such dynamic contrast has been analyzed and experimental results are presented. Moreover, this swarm exhibits enhanced ultrasound imaging in comparison to that formed by individual nanoparticles with a low area density, and the relationship between imaging contrast and area density is testified. Furthermore, the microrobotic swarm can be navigated near a solid surface at different velocities, and the imaging contrast show negligible changes. This method allows us to localize and navigate a microrobotic swarm with enhanced ultrasound imaging indicating a promising approach for imaging of microrobots.

Microrobotic cargo delivery possesses promising perspective for precision medicine, and has attracted much attention recently. However, its automation remains challenging, especially with complex environmental conditions, such as obstacles and obstructed optical feedback. In this paper, we propose an automated control approach for a new microrobotic cargo carrier, i. e. the multifunctional magnetic spore (Mag-Spore). By surface functionalization of the spore with Fe3O4 nanoparticles and carbon quantum dots, it can be remotely actuated and tracked by an electromagnetic coil system and the fluorescence microscopy, respectively. Our strategy utilizes fluorescence imaging for vision feedback, which enhances the recognition and tracking of Mag-Spores and cells. Then, information of the cells and Mag-Spores for planning and control is identified via image processing, and an optimal path planner with obstacle avoidance capability is designed based on the Particle Swarm Optimization (PSO)algorithm. To make the Mag-Spore follow the planed path accurately, an observer-based trajectory tracking controller is synthesized. Simulations and experiments are conducted to demonstrate the effectiveness of the proposed control approach.