Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force signal was transformed into a two-dimensional image using continuous wavelet transform (CWT), short-time Fourier transform (STFT), and Gramian angular summation field (GASF) methods. The generated images were then fed into the proposed convolutional neural network (CNN) model for further analysis. The calculation results show that the accuracy of tool wear state recognition proposed in this paper was above 90%, which was higher than the accuracy of AlexNet, ResNet, and other models. The accuracy of the images generated using the CWT method and identified with the CNN model was the highest, which is attributed to the fact that the CWT method can extract local features of an image and is less affected by noise. Comparing the precision and recall values of the model, it was verified that the image obtained by the CWT method had the highest accuracy in identifying tool wear state. These results demonstrate the potential advantages of using a force signal transformed into a two-dimensional image for tool wear state recognition and of applying CNN models in this area. They also indicate the wide application prospects of this method in industrial production.

Goal: In the context of robot-assisted laminectomy surgery, an analytical force model is introduced to guarantee procedural safety. The aim of the method is to intraoperatively monitor the cutting depth via modeling the milling status. Methods: The theoretical dynamic model for the surgical milling process is based on the flute geometry of the ball-end milling tool. A particle swarm optimization algorithm is exploited to calibrate the model using the local average force, and to validate it using the denoised dynamic force. A wear detection method based on the fast Fourier transform is proposed to determine the quality of the tool geometry and to avoid using worn tools, which may lead to imprecise and unsafe operations. Results: Milling experiments were performed on machined fresh bovine femur bones. The experimental results thus obtained from the mechanical model are in good accordance with the numerical model. The proposed method can monitor the current cutting depth with an accuracy of ±0.1 mm in regions located within the depth [0.8-1.2 mm], and ±0.2 mm within [1.2-1.6 mm]. Conclusion: The proposed model can successfully estimate the milling force and the cutting depth intraoperatively in experimental conditions. Significance: This approach has the potential to improve the safety of laminectomy operations in humans, and make it more accessible to younger surgeons by lowering the required manual skills threshold.

Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labeled training sample per class. Despite the prototype based approaches have achieved substantial success, existing models are limited to the imaging scenarios with considerably distinct objects and not highly complex background, e.g., natural images. This makes such models suboptimal for medical imaging with both conditions invalid. To address this problem, we propose a novel DetailSelf-refinedPrototypeNetwork (DSPNet) to construct high-fidelity prototypes representing the object foreground and the background more comprehensively. Specifically, to construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modeling the multimodal structures with clustering and then fusing each in a channel-wise manner. Considering that the background often has no apparent semantic relation in the spatial dimensions, we integrate channel-specific structural information under sparse channel-aware regulation. Extensive experiments on three challenging medical image benchmarks show the superiority of DSPNet over previous state-of-the-art methods. The code and data are available at https://github.com/tntek/DSPNet.

This study presents a compact multi-band fork-shaped multiple input multiple output (MIMO) antenna design that integrates Carrel's log-periodic concept with the planar log-periodic array (PLPA) mechanism to improve C, Ku, K, Ka, and Q band performance. The suggested MIMO antenna full size is 54 × 44 × 1.58 mm³. The study empirically shows improved isolation and high gain of wideband MIMO antenna, addressing the requirements for modern wireless communication. The suggested self-multi-band coupling counteraction, characterized by a straightforward antenna design and extensive self-decoupling capabilities, is expected to offer considerable application value for compact and wideband MIMO systems. The observed data supports the assertion that the proposed MIMO antenna is designed to operate across a variety of frequency bands, including the C-band (4.4-5.21 GHz), Ku-band (14.30-17.64 GHz), K-band (20.10-28.10 GHz), Ka-band (28.95-30.76 GHz) and (32.38-36.42 GHz), and a segment of the Q-band (38.34-39.55 GHz). The proposed MIMO antenna's wideband performance is achieved by applying the Carrel log-periodic theory, which prescribes the geometric scaling of radiating elements. Moreover, it was found that a microstrip feed line with a diameter of 50 Ω was noteworthy in conjunction with the carrel concept. The microstrip feed line is connected to two orthogonal branches. Decoupling structures eliminate the necessity for the MIMO antenna to achieve its maximum gain of 7.21 to 9.53 dBi and exceptional element isolation of 21.54 dB. MIMO performance measurements, encompassing envelope correlation coefficient (ECC), diversity gain (DG), and channel capacity loss (CCL), demonstrate favorable characteristics and field correlation performance over the operational spectrum band. The design suggested suits Ku/K-band and multi-band MIMO applications across diverse wireless systems in communication.

The decompressive laminectomy is a common treatment for lumbar spinal stenosis. Generally, surgeons use grinding tools to remove laminae under the guidance of intraoperative medical images. To improve accuracy and reduce surgeons' burdens, robot-assisted surgery is gaining acceptance. This paper proposes a method to plan grinding paths and velocities based on 3-D medical images in the context of robot-assisted decompressive laminectomies. As the lesion areas to be grinded are irregular, an interactive method with 3-D reconstruction is designed for surgeons to transfer discrete information about grinding paths and velocities to the robot system. The path generation strategy is based on a ray casting algorithm after space registrations, while the velocity generation strategy is based on the virtual force and mechanical analysis is used to optimize temporal efficiency and stability. A complete system is developed to test and explore the feasibility of this method. Results suggest that robot-assisted decompressive laminectomies can be performed well.