Due to the low efficiency of manual inspection of pointer pressure gauges, numerous computer vision-based automatic reading recognition systems have been proposed in recent years. However, most existing work primarily focuses on using cameras or inspection robots for daily monitoring of gauges. Periodic calibration of gauges is equally important, as it ensures their optimal performance and imposes stricter precision requirements on inspections. To address these challenges, this study proposes a lightweight multitask interactive network (LMI-Net) and develops an intelligent robotic-assisted measurement system. LMI-Net, which generates a large number of feature maps through computationally inexpensive operation of a lightweight module, realizes multiple tasks via keypoint detection and instance segmentation branches. The keypoint detection branch identifies the position of the main scale and the center of the gauge, while the instance segmentation branch generates a segmentation mask of the pointer. Through our proposed multiple feature fusion (MFF) reading method, the value and order of the main scale are identified, the segmentation mask of the pointer is fitted to a straight line using a regional interpolation linear fitting method, and on the basis of geometric relationships, these pieces of feature information are fused to obtain a reading. Experimental results on a dataset containing multiple types of gauge demonstrate that with the proposed method, the reading error is controlled to within ±1/5 of the minimum resolution interval, which complies with the verification regulation for pointer pressure gauges.

In minimally invasive surgery, surgical instruments are typically inserted through small incisions in the patient’s body, which serve as the remote center of motion (RCM). In robot-assisted minimally invasive surgery, developing control algorithms that comply with RCM constraints is highly challenging due to the nonlinear nature of robot motion models and the stringent precision requirements necessary for patient safety. This article introduces an enhanced zeroing neural network (EZNN) model for controlling redundant manipulators while maintaining RCM constraints. The proposed model eliminates the need for pseudoinverse matrix computations and features an explicit dynamic form. It guarantees finite-time convergence and demonstrates robustness through the application of nonlinear activation functions (AFs). These properties are rigorously validated using Lyapunov theory. Simulation and experimental results indicate that the EZNN model surpasses Jacobian-based algorithms and recurrent neural networks (RNNs) in terms of efficiency and stability, all while ensuring adherence to RCM constraints.

Recovering function signatures is a cornerstone of binary program analysis, yet it remains a challenging task. Existing methods either rely on disassembly-based constraints, which struggle with cross-architecture compatibility and scalability, or adopt learning-based approaches that are resource-intensive and often inaccurate.

State machines are essential for enhancing protocol analysis to identify vulnerabilities. However, inferring state machines from network protocol implementations is challenging due to complex code syntax and semantics. Traditional dynamic analysis methods often miss critical state transitions due to limited coverage, while static analysis faces path explosion issues. To overcome these challenges, we introduce a novel state machine inference approach utilizing Large Language Models (LLMs), named ProtocolGPT. This method employs retrieval augmented generation technology to enhance a pre-trained model with specific knowledge from protocol implementations. Through effective prompt engineering, we accurately identify and infer state machines. To the best of our knowledge, our approach represents the first state machine inference that leverages the source code of protocol implementations. Our evaluation of six protocol implementations shows that our method achieves a precision of over 90 %, outperforming the baselines by more than 30 %. Furthermore, integrating our approach with protocol fuzzing improves coverage by more than 20 % and uncovers two 0-day vulnerabilities compared to baseline methods.

A promising ultrasound (US) image acquisition requires experienced sonographers holding the probe with proper force and pose to ensure an excellent acoustic coupling. To enable a robotic ultrasound system (RUSS) to acquire the sonographers’ skills from ultrasound image demonstrations, this paper proposes a cutting-edge framework that integrates an expert technique discrimination network and a robotic strategy generation network to learn expert scanning skills. In this framework, the expert technique discrimination network focuses on learning expert scanning techniques from the pre- and post-frame ultrasound images. Furthermore, to acquire expert scanning skills and obtain a standard image view, we design a knowledge-based algorithm grounded on inverse reinforcement learning (IRL) to generate a series of scanning policies concerning the expert technique discrimination network. Both simulations and experiments are conducted to validate the effectiveness of the proposed framework by comparing it with MI-GPSR and PTR. The scanning success rate and trajectory tracking error of the algorithm in the simulation environment are 68% and 12.0782, respectively, while in the phantom environment are 94% and 11.8367. The results demonstrate good performance in the task of imitating expert techniques for autonomous scanning. Note to Practitioners—The motivation for this work originates from the need for ultrasound scanning tasks, such as carotid plaque and thyroid scans, to follow specific procedures. In clinical practice, sonographers require extensive learning and training to acquire these scanning skills. Traditional autonomous robotic ultrasound research often focuses on achieving the final standard view, neglecting the logical flow of the scanning process. Moreover, current studies on imitation learning for robotic ultrasound typically require not only ultrasound images from expert demonstrations but also additional data like probe position, adding complexity and potential errors to the data collection process. The proposed method addresses this by designing a framework that learns and comprehends expert techniques solely from image demonstrations. This endows RUSS with the ability to perform a human-like ultrasound scanning task. This work can be applied in the field of autonomous ultrasound robotics to assist sonographers in achieving more precise scans.

This article proposes a novel control strategy based on a broad fuzzy neural network (BFNN) which is subjected to contact with the unknown environment. Compared with the conventional fuzzy neural network (NN), a prominent feature can be achieved by taking the advantage of the broad learning system (BLS) to explicitly tackle the problem of how to choose a sufficient number of NN units to approximate the unknown dynamic model. Aiming at providing a soft compliant contact scheme without the requirement of the environment model, an adaptive impedance learning is developed to establish the optimal interaction between the robot and the environment. Meanwhile, the problems related to the state constraints are addressed by incorporating a barrier Lyapunov function (BLF) into the design of a trajectory tracking controller. The proposed method can achieve desired tracking and interaction performance while guaranteeing the stability of the closed-loop system. In addition, simulation and experimental studies are performed to verify the effectiveness of BFNN under optimal impedance control with a two degree-of-freedom (DOF) manipulator and a Baxter robot, respectively.