Publications

Precise decoupling and calibration of multiaxis force sensor (MFS) is crucial in engineering applications. This work presents a novel nonlinear decoupling and calibration approach to meet the physical coupling characteristics of the structural strain-deformation transducers. It deals with the most force–voltage responses by the linear prime modeling and the gross error deviation by the nonlinear error modeling. Such a prime-error framework is naturally derived from the conventional least-square (LS) decoupling model with the delicate nonlinear error modeling by the multivariate Bernstein polynomials. The two- and three-axis force sensors are tested and compared with the proposed Bernstein-based prime-error model (BPEM), the LS decoupling model (LSM), and error-based neural network (eNN) learning model, extreme learning machine (ELM), as well as the error-based support vector machine (e-SVM), the interpretable nonlinear decoupling model (IND). Results demonstrate that the proposed BPEM provides an accurate, practical, and effective scheme for modeling and calibrating MFSs.

Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. However, most existing models lack the unique biological characteristics. To solve this problem, we proposed a high-fidelity virtual liver model incorporating biological characteristics, such as the viscoelastic, anisotropic and nonlinear biological characteristics. Besides, to the best of our knowledge, our study is the first to introduce the viscoplasticity of biological tissues to improve the fidelity of the liver model. This mothod was proposed to describe the viscoplastic characteristics of the diseased liver resection process, when the liver is in a state of excessive deformation and loss of elasticity, however, there are few works focusing on this problem. The 3DMax2020 and OpenGL4.6 were used to build a liver surgery simulation platform, and the PHANTOM OMNI manual controller was used to sense the feedback force during the operation. The proposed model was verified from three aspects of accuracy, fidelity and real-time performance. The experimental results show that the proposed virtual liver model can enhance visual perception ability, improve deformation accuracy and fidelity.

Brain-machine interfaces (BMIs) have emerged as a promising technology for restoring motor function in paralyzed individuals through direct neural control of prosthetic devices. While conventional decoding algorithms have achieved considerable success, they often overlook the fundamental biological properties of neural information processing. This paper presents a novel approach using Spiking Neural Networks (SNNs), a neuromorphic computing paradigm that closely mimics biological neural dynamics through event-driven processing and spike-timing-dependent plasticity. A SNN-based decoder was implemented for offline decoding of intracortical neural recordings from the primary motor cortex (M1) and dorsal premotor cortex (PMd) to continuous 2D cursor movements in a macaque monkey. This approach leverages the temporal processing capabilities of SNNs to capture the complex, time-varying nature of neural representations, potentially enabling more naturalistic and adaptive BMI control.

This paper proposed a three-part framework for deploying an environmental monitoring system (EMS). An enhanced gateway which supports Bluetooth based local wireless access and GPRS based remote communication is designed to be an interface between the client tier and a deployed ZigBee wireless sensor network. The binding mechanism which associates a service on one node with a service on another node is used so as to allow communication between two services to be performed without the need to specify the address. An implementation for verifying the EMS including the gateway as well as the sMotes is presented, and experimental results show that, the proposed architecture can achieve the environmental monitoring performance, and except the gateway, other nodes in the system work on sleep mode at most of the time, which will improve the lifetime of the whole system efficiently.

The current development and the application status of the home network are introduced briefly, then, a two-tier architecture of home control system (HCS) based on ZigBee multi-hop mesh network is presented. In addition to the basic data acquisition and processing functions, the gateway which has been designed and fabricated supports ZigBee based wireless access interface, Bluetooth based local interface and GPRS based remote interface. The hardware architecture and protocol software, as well as the communication control method are proposed in detail. Under the constructed test environment, the performance results show that the proposed scheme is more convenient for home control services, and except the gateway, all of the router nodes in the system work on sleep mode at most of the time, which will improve the lifetime of the whole system efficiently.

Multi-Agent Reinforcement Learning (MARL) has made remarkable progress in recent years. However, in most MARL methods, agents share a policy or value network, which is easy to result in similar behaviors of agents and thus limits the flexibility of the method to handle complex tasks. To enhance the diversity of agent behaviors, we propose a novel method, Making Decisions from Different Perspectives (MDDP). This method enables agents to switch flexibly between different policy roles and make decisions from different perspectives, which can improve the adaptability of policy learning in complex scenarios. Specifically, in MDDP, we design a new Self-attention and Gated Recurrent Unit (GRU) based Dueling Architecture Network (SG-DAN) to estimate the individual <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -values. SGDAN contains two components: the new Self-Attention based Role-switching network (SAR) and the capable GRU-based State value Estimation network (GSE). SAR takes charge of action advantage estimation and GSE is responsible for state value estimation. Experimental results on the challenging StarCraft II micromanagement benchmark not only verify the modeling reasonability of MDDP but also demonstrate its performance superiority over the related advanced approaches.

This work reports an adaptive path-following controller for a multijoint snake robot (MSR) to improve the adaptability of the robot to the environment. The new strategy estimates the time-varying parameters of the system and the external interference to adjust the motion state of the robot in real time. Estimations are used to compensate for the joint torque of an MSR, thus reducing the fluctuation peak of path-following errors. In addition, this work designs an anti-sideslip line-of-sight (LOS) guidance strategy to avoid the deviation of the direction angle. The method can improve the tracking accuracy of an MSR, and the position errors enable the system to achieve uniformly ultimate boundedness (UUB). The angle errors converge to the origin to achieve stability. Experimental results demonstrate that the novel method can accurately estimate the time-dependent parameters, sideslip, and interference, raise the convergent speed of errors, and reduce the fluctuation peak.

Despite strong representation ability, deep convolutional neural networks (CNNs) are largely hindered in practical human activity recognition (HAR) deployment due to high computational cost, which is often unaffordable on resource-limited wearable devices. In this article, to bridge the gap between on-device HAR and deep learning, we present a collaborative compression scheme to reduce the runtime of HAR with an acceptable performance degradation, which combines channel pruning and tensor decomposition to simultaneously handle sparsity and low-rankness when fully considering mutual interference in one network consisting of efficient 1-dimensional convolutional kernels. Our method includes two main stages. Concretely, given a target compression ratio, a global compression ratio decision optimization is first performed to automatically decide per-layer compression ratio by measuring compression sensitivity, without requiring labor-exhaustive human intervention. Then a multi-step collaborative compression is iteratively implemented to remove the least important compression unit based on an improved importance metric until the per-layer target compression ratio is attained. Extensive experiments on multiple HAR benchmarks show that our approach considerably outperforms previous compression strategies. For example, it can achieve around 50% FLOPs reduction with only an accuracy drop of 0.25% and 0.15% on UCI-HAR and PAMAP2, respectively. Actual implementation is evaluated on an embedded platform.

In wireless networks, it is cost-effective to estimate the node-to-anchor distance by using the distribution characteristics of nodes. Unfortunately, in practice, the network topology may be irregular, which makes the estimated distances inaccurate. In this paper, by analyzing the distribution characteristics of nodes, we propose a novel scheme called Accurate Analytical-based Multi-hop Localization (AAML), which can greatly enhance estimation accuracy with requiring less communication and lower energy consumption. We first develop a new method that uses the common numbers of neighbors to estimate the distances between pairs of directly connected nodes during the network initialization phase. We then employ an optimal weighted matrix and hyperbolic estimation to reduce the effect of cumulative error. Finally, we utilize the weighted Taylor series to further improve the estimation accuracy; and utilize the geometric constraint to rectify the inaccurate estimated location, based on the relationship between the estimated locations and the anchor node locations. The proposed AAML method has been validated through analysis and experiments over different irregular networks and parameters, and AAML is demonstrated to be superior to existing methods in terms of estimated accuracy and energy consumption. The code is publicly available at https://pan.baidu.com/s/1k5o4aNGqa6iuxNskxRMMdw, pwd: n6ht.

Laryngoscopes are essential tools in transoral robotic surgery (TORS) for visualizing and accessing the larynx. However, the rigidity and non-adjustable design of conventional laryngoscopes limit their ability to reach certain lesion areas, increasing patient invasiveness. This study presents a novel shape and stiffness adjustable laryngoscope to address these challenges. The proposed laryngoscope features a spring-tendon layout that allows for stable bending through tendon actuation by a single motor. A lockable mechanism enhances stiffness, enabling the instrument to be securely positioned at various bending angles. We evaluated the performance of the spring-tendon layout in reducing the hysteresis error by 26%, while the lockable mechanism significantly improves the stiffness of the manipulator under a large load of 20 N by an average of 481% and up to 1462%. Experimental results from a physical prototype demonstrate the robustness of the proposed design in withstanding disturbances of varying magnitudes applied from different locations, and validate the prototype's ability to expose areas of the throat that are inaccessible with a traditional laryngoscope. The bendable, lockable laryngoscope significantly improves access, stability, and visualization in TORS, providing enhanced capabilities for surgeons performing complex procedures. This innovative design provides a promising solution to the current limitations in laryngoscopy, potentially enhancing the precision and minimally invasive nature of TORS, and ultimately leading to improved patient outcomes.