In the endoscopic images of bladder, accurate segmentation of different grade bladder tumor from blurred boundary regions and highly variable shapes is of great significance for doctors' diagnosis and patients' later treatment. We propose a nested attentional feature fusion segmentation network (NAFF-Net) based on the encoder-decoder structure formed by the combination of weighted pyramid pooling module (WPPM) and nested attentional feature fusion (NAFF). Among them, WPPM applies the cascade of atrous convolution to enhance the overall perceptual field while introducing adaptive weights to optimize multi-scale feature extraction, NAFF integrates deep semantic information into shallow feature maps, effectively focusing on edge and detail information in bladder tumor images. Additionally, a weighted mixed loss function is constructed to alleviate the impact of imbalance between positive and negative sample distribution on segmentation accuracy. Experiments illustrate the proposed NAFF-Net achieves better segmentation results compared to other mainstream models, with a MIoU of 84.05%, MPrecision of 91.52%, MRecall of 90.81%, and F1-score of 91.16%, and also achieves good results on the public datasets Kvasir-SEG and CVC-ClinicDB. Compared to other models, NAFF-Net has a smaller number of parameters, which is a significant advantage in model deployment.
Physicians , Urinary Bladder Neoplasms , Humans , Urinary Bladder Neoplasms/diagnostic imaging , Urinary Bladder/diagnostic imaging , Semantics , Image Processing, Computer-Assisted
Bacterial adhesion and colonization on material surfaces pose a serious problem for healthcare-associated devices. Cationic amphiphilic polymer brushes are usually used as surface coatings in antibacterial materials to endow an interface with excellent bactericidal efficiency, but they are easily contaminated, which puts a great limitation on their application. Herein, novel antibacterial copolymer brush surfaces containing geminized cationic amphiphilic polymers (pAGC8) and thermoresponsive poly(N-isopropylacrylamide) polymers (pNIPAm) have been synthesized. Surface functionalization of polymer brushes was investigated by X-ray photoelectron spectroscopy, spectroscopic ellipsometry, atomic force microscopy, and water contact angle measurements. A proportion of AGC8 and NIPAm units in copolymer brushes has been adjusted to obtain a high-efficiency bactericidal surface with minimal interference to its self-cleaning property. The killing and releasing efficiency of the optimized surface simultaneously reached up to above 80% for both Staphylococcus aureus and Escherichia coli bacteria, and the bactericidal and self-cleaning abilities are still excellent even after three kill-release cycles. Such a novel copolymer brush system provides innovative guidance for the development of high-efficiency antibacterial materials in biomedical application.
Anti-Bacterial Agents , Polymers , Anti-Bacterial Agents/chemistry , Anti-Bacterial Agents/pharmacology , Staphylococcus aureus/chemistry , Escherichia coli/chemistry , Polymers/chemistry , Polymers/pharmacology , Photoelectron Spectroscopy , Surface Properties
To meet the enormous demand for smart manufacturing, industrial robots are playing an increasingly important role. For industrial operations such as grinding 3C products, numerous demands are placed on the compliant interaction ability of industrial robots to interact in a compliant manner. In this article, an adaptive compliant control framework for robot interaction is proposed. The reference trajectory is obtained by single-point demonstration and DMP generalization. The adaptive feedforward and impedance force controller is derived in terms of position errors, and they are input into an admittance controller to obtain the updated amount of position deviation. The compliant interaction effect is achieved, which is shown that the grinding head fits on the curved surface of a computer mouse, and the interaction force is within a certain expected range in the grinding experiment based on the performance an Elite robot. A comparative experiment was conducted to demonstrate the effectiveness of the proposed framework in a more intuitive way.
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.
