Avionics Module Fault Diagnosis Algorithm Based on Hybrid Attention Adaptive Multi-Scale Temporal Convolution Network.

Since the reliability of the avionics module is crucial for aircraft safety, the fault diagnosis and health management of this module are particularly significant. While deep learning-based prognostics and health management (PHM) methods exhibit highly accurate fault diagnosis, they have disadvantages such as inefficient data feature extraction and insufficient generalization capability, as well as a lack of avionics module fault data. Consequently, this study first employs fault injection to simulate various fault types of the avionics module and performs data enhancement to construct the P2020 communications processor fault dataset.

Fractional Fourier Image Transformer for Multimodal Remote Sensing Data Classification.

With the recent development of the joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data, deep learning methods have achieved promising performance owing to their locally sematic feature extracting ability. Nonetheless, the limited receptive field restricted the convolutional neural networks (CNNs) to represent global contextual and sequential attributes, while visual image transformers (VITs) lose local semantic information. Focusing on these issues, we propose a fractional Fourier image transformer (FrIT) as a backbone network to extract both global and local contexts effectively.

Joint Dense Residual and Recurrent Attention Network for DCE-MRI Breast Tumor Segmentation.

Breast cancer detection largely relies on imaging characteristics and the ability of clinicians to easily and quickly identify potential lesions. Magnetic resonance imaging (MRI) of breast tumors has recently shown great promise for enabling the automatic identification of breast tumors. Nevertheless, state-of-the-art MRI-based algorithms utilizing deep learning techniques are still limited in their ability to accurately separate tumor and healthy tissue.

CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network.

Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat-top-hat strategy.

Adaptive current harmonics suppression strategy for grid-tie inverters.

Since the observed angle of a phase-locked loop (PLL) is relatively accurate even under distorted grid conditions, the mathematical model of the voltage errors caused by the switching modulation and the dead-time effect are derived as a function of the grid voltage angle in this paper. Based on the model analysis, an adaptive compensation algorithm is proposed to suppress the grid-side current harmonics in three-phase converters. The proposed algorithm fits the unmeasurable voltage errors by a truncated Fourier series expansion, and then takes it as an equivalent disturbance in the current control loop to achieve harmonic compensation.

[An endpoint-detection algorithm of surface electromyography insensitive to electrocardiogram interference].

Surface electromyography (sEMG) has been widely used in the study of clinical medicine, rehabilitation medicine, sports, etc., and its endpoints should be detected accurately before analyzing. However, endpoint detection is vulnerable to electrocardiogram (ECG) interference when the sEMG recorders are placed near the heart.

Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering.

Background: Pulmonary nodules in computerized tomography (CT) images are potential manifestations of lung cancer. Segmentation of potential nodule objects is the first necessary and crucial step in computer-aided detection system of pulmonary nodules. The segmentation of various types of nodules, especially for ground-glass opacity (GGO) nodules and juxta-vascular nodules, present various challenges.

Topology adaptive vessel network skeleton extraction with novel medialness measuring function.

Vessel tree skeleton extraction is widely applied in vascular structure segmentation, however, conventional approaches often suffer from the adjacent interferences and poor topological adaptability. To avoid these problems, a robust, topology adaptive tree-like structure skeleton extraction framework is proposed in this paper. Specifically, to avoid the adjacent interferences, a local message passing procedure called Gaussian affinity voting (GAV) is proposed to realize adaptive scale-growing of vessel voxels.

Fuzzy speed function based active contour model for segmentation of pulmonary nodules.

Pulmonary nodules are potential manifestation of lung cancer. Accurate segmentation of juxta-vascular nodules and ground glass opacity (GGO) nodules is an important and active area of research in medical image processing. At present, the classical active contour models (ACM) for segmentation of pulmonary nodules may cause the problem of boundary leakage.

Multimodal medical volumetric data fusion using 3-D discrete shearlet transform and global-to-local rule.

Traditional two-dimensional (2-D) fusion framework usually suffers from the loss of the between-slice information of the third dimension. For example, the fusion of three-dimensional (3-D) MRI slices must account for the information not only within the given slice but also the adjacent slices. In this paper, a fusion method is developed in 3-D shearlet space to overcome the drawback.

Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model.

The segmentation and detection of various types of nodules in a Computer-aided detection (CAD) system present various challenges, especially when (1) the nodule is connected to a vessel and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO) characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult to define the boundaries. Traditional segmentation methods may cause problems of boundary leakage and "weak" local minima. This paper deals with the above mentioned problems.

[Detection of lung mini-nodules using multi-feature tracking].

How to accurately identify mini-nodules in a large amount of high resolution computed tomography (HRCT) images is always a significant and difficult issue in lung nodule computer-aided detection (CAD). This paper describes a new mini-nodules detection method which is based on a multi-feature tracking algorithm. Our detection method began after running the Da-Jing algorithm and morphological operation to extract the lung region of every HRCT image in a sequence.

An optical model for translucent volume rendering and its implementation using the preintegrated shear-warp algorithm.

In order to efficiently and effectively reconstruct 3D medical images and clearly display the detailed information of inner structures and the inner hidden interfaces between different media, an Improved Volume Rendering Optical Model (IVROM) for medical translucent volume rendering and its implementation using the preintegrated Shear-Warp Volume Rendering algorithm are proposed in this paper, which can be readily applied on a commodity PC. Based on the classical absorption and emission model, effects of volumetric shadows and direct and indirect scattering are also considered in the proposed model IVROM. Moreover, the implementation of the Improved Translucent Volume Rendering Method (ITVRM) integrating the IVROM model, Shear-Warp and preintegrated volume rendering algorithm is described, in which the aliasing and staircase effects resulting from under-sampling in Shear-Warp, are avoided by the preintegrated volume rendering technique.

[On evaluating the robot-based experimental system for biomechanical experiment of human knee].

This is a report on how we use the hybrid force-displacement control method to load the human knee and analyze the effect and value of our robot experimental system through the biomechanical experiments of total meniscal resection of human knee. The whole robot control system can load continuously on the specimens, thus overcoming the shortcomings of the traditional loading methods which can only load discretely. In the meantime, by using the robot-based testing system, the force (torque) of the specimens and the spatial position under the force can be measured in real-time, which overcomes the shortcomings caused by the separation of force (torque) measurement from displacement measurement and so greatly improves the measurement accuracy.

[Research of joint-robotics-based design of biomechanics testing device on human spine].

This paper introduces the hardware and software of a biomechanical robot-based testing device. The bottom control orders, posture and torque data transmission, and the control algorithms are integrated in a unified visual control platform by Visual C+ +, with easy control and management. By using hybrid force-displacement control method to load the human spine, we can test the organizational structure and the force state of the FSU (Functional spinal unit) well, which overcomes the shortcomings due to the separation of the force and displacement measurement, thus greatly improves the measurement accuracy.

[CAD for identifying malignant lung nodules in early diagnosis: a survey].

It is of paramount importance for the diagnosis and therapy of lung cancer, even for the increasing of 5-year survival rate in that the early dignosis of malignant pulmonary nodules are made by intelligent identification successfully. As it stands, in intelligent identification of pulmonary nodules, computer-aided detection/diagnosis (CAD) plays the most important role. The key points of intelligent identification of pulmonary nodules are (1) Detecting pulmonary nodules based on the characterization of nodule appearance; (2) Measuring accurately the nodule size; (3) Computing accurately the growth rate.

[A preliminary research on multi-source medical image fusion].

Multi-modal medical image fusion has important value in clinical diagnosis and treatment. In this paper, the multi-resolution analysis of Daubechies 9/7 Biorthogonal Wavelet Transform is introduced for anatomical and functional image fusion, then a new fusion algorithm with the combination of local standard deviation and energy as texture measurement is presented. At last, a set of quantitative evaluation criteria is given.

[Fast multi-resolution volume rendering method of large medical data sets].

A Fast Multi-resolution Volume Rendering Method (FMVRM) based on wavelet and Shear-Warp is herein proposed. In this method, the medical volume data is compressed using wavelet transformation first. Then based on the set resolution, the medical volume data is decompressed guided by Opacity transfer function (OTF).

[A hybrid volume rendering method using general hardware].

In order to improve the effect and efficiency of the reconstructed image after hybrid volume rendering of different kinds of volume data from medical sequential slices or polygonal models, we propose a hybrid volume rendering method based on Shear-Warp with economical hardware. First, the hybrid volume data are pre-processed by Z-Buffer method and RLE (Run-Length Encoded) data structure. Then, during the process of compositing intermediate image, a resampling method based on the dual-interpolation and the intermediate slice interpolation methods is used to improve the efficiency and the effect.

[Application of GVF snake model in segmentation of whole body bone SPECT image].

Limited by the imaging principle of whole body bone SPECT image, the gray value of bladder area is quite high, which affects the image's brightness, contrast and readability. In the meantime, the similarity between bladder area and focus makes it difficult for some images to be segmented automatically. In this paper, an improved Snake model, GVF Snake, is adopted to automatically segment bladder area, preparing for further processing of whole body bone SPECT images.

[Multimodal medical image registration using cubic spline interpolation method].

Based on the characteristic of the PET-CT multimodal image series, a novel image registration and fusion method is proposed, in which the cubic spline interpolation method is applied to realize the interpolation of PET-CT image series, then registration is carried out by using mutual information algorithm and finally the improved principal component analysis method is used for the fusion of PET-CT multimodal images to enhance the visual effect of PET image, thus satisfied registration and fusion results are obtained. The cubic spline interpolation method is used for reconstruction to restore the missed information between image slices, which can compensate for the shortage of previous registration methods, improve the accuracy of the registration, and make the fused multimodal images more similar to the real image. Finally, the cubic spline interpolation method has been successfully applied in developing 3D-CRT (3D Conformal Radiation Therapy) system.

[Segmentation of whole body bone SPECT image based on BP neural network].

In this paper, BP neural network is used to segment whole body bone SPECT image so that the lesion area can be recognized automatically. For the uncertain characteristics of SPECT images, it is hard to achieve good segmentation result if only the BP neural network is employed. Therefore, the segmentation process is divided into three steps: first, the optimal gray threshold segmentation method is employed for preprocessing, then BP neural network is used to roughly identify the lesions, and finally template match method and symmetry-removing program are adopted to delete the wrongly recognized areas.

An intelligent control method based on fuzzy logic for a robotic testing system for the human spine.

In previous biomechanical studies of the human spine, we implemented a hybrid controller to investigate load-displacement characteristics. We found that measurement errors in both position and force caused the controller to be less accurate than predicted. As an alternative to hybrid control, a fuzzy logic controller (FLC) has been developed and implemented in a robotic testing system for the human spine.

MRS algorithm: a new method for searching myocardial region in SPECT myocardial perfusion images.

First, the necessity of automatically segmenting myocardium from myocardial SPECT image is discussed in Section 1. To eliminate the influence of the background, the optimal threshold segmentation method modified for the MRS algorithm is explained in Section 2. Then, the image erosion structure is applied to identify the myocardium region and the liver region.

Constrained motion control of flexible robot manipulators based on recurrent neural networks.

In this paper, a neural network approach is presented for the motion control of constrained flexible manipulators, where both the contact force everted by the flexible manipulator and the position of the end-effector contacting with a surface are controlled. The dynamic equations for vibration of flexible link and constrained force are derived. The developed control, scheme can adaptively estimate the underlying dynamics of the manipulator using recurrent neural networks (RNNs).