This work firstly reported preparation of cobalt-doped CaCu3Ti4O12 (CCCTO) thin film by a sol-gel modified method. It was concluded that a relatively high dielectric constant ε' (2326, at 1 kHz), low ...dielectric loss tan δ (0.012, at 1 kHz) and high nonlinear coefficient α (4.9) were simultaneously obtained in the CaCu2.95Co0.05Ti4O12 thin film at room temperature. The decrease of the dielectric loss was associated with the increase in the density of the insulating grain boundary layer, which was governed by the grain size reduction and densification of CCCTO films due to Co doping. Monovalent cation Cu+ detected by X-ray photoelectron spectroscopy (XPS) of the CaCu2.95Co0.05Ti4O12 thin film decreased leakage current. These excellent electrical properties provided a viable solution to the application of CCTO materials in capacitive-varistors.
•We prepared cobalt-doped CaCu3Ti4O12 thin films by sol-gel method.•Doping of Co significantly decreased the dielectric loss tan δ (0.012) in CaCu2.95Co0.05Ti4O12 thin film.•The decrease of tan δ was attributed to the increase of the density of insulating grain boundary layer.•The doping of Co enhanced the nonlinear characteristics of CCTO films.
Background: Developing an accurate computer-aided diagnosis (CAD) system of MR brain images is essential for medical interpretation and analysis. In this study, we propose a novel automatic CAD ...system to distinguish abnormal brains from normal brains in MRI scanning. Methods: The proposed method simplifies the task to a binary classification problem. We used discrete wavelet packet transform (DWPT) to extract wavelet packet coefficients from MR brain images. Next, Shannon entropy (SE) and Tsallis entropy (TE) were harnessed to obtain entropy features from DWPT coefficients. Finally, generalized eigenvalue proximate support vector machine (GEPSVM), and GEPSVM with radial basis function (RBF) kernel, were employed as classifier. We tested the four proposed diagnosis methods (DWPT + SE + GEPSVM, DWPT + TE + GEPSVM, DWPT + SE + GEPSVM + RBF, and DWPT + TE + GEPSVM + RBF) on three benchmark datasets of Dataset-66, Dataset-160, and Dataset-255. Results: The 10 repetition of K-fold stratified cross validation results showed the proposed DWPT + TE + GEPSVM + RBF method excelled not only other three proposed classifiers but also existing state-of-the-art methods in terms of classification accuracy. In addition, the DWPT + TE + GEPSVM + RBF method achieved accuracy of 100%, 100%, and 99.53% on Dataset-66, Dataset-160, and Dataset-255, respectively. For Dataset-255, the offline learning cost 8.4430s and online prediction cost merely 0.1059s. Conclusions: We have proved the effectiveness of the proposed method, which achieved nearly 100% accuracy over three benchmark datasets.
(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer ...vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.
Gesture recognition has been studied for decades and still remains an open problem. One important reason is that the features representing those gestures are not sufficient, which may lead to poor ...performance and weak robustness. Therefore, this work aims at a comprehensive and discriminative feature for hand gesture recognition. Here, a distinctive Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor and modified Hu moments are suggested on the platform of a Kinect sensor. Firstly, two algorithms are designed to extract the fingertip-emphasized features, including palm center, fingertips, and their gradient orientations, followed by the finger-emphasized Fourier descriptor to construct the FGFF descriptors. Then, the modified Hu moment invariants with much lower exponents are discussed to encode contour-emphasized structure in the hand region. Finally, a weighted AdaBoost classifier is built based on finger-earth mover’s distance and SVM models to realize the hand gesture recognition. Extensive experiments on a ten-gesture dataset were carried out and compared the proposed algorithm with three benchmark methods to validate its performance. Encouraging results were obtained considering recognition accuracy and efficiency.
Over the last decade, Medical Imaging has become an essential component in many fields of bio-medical research and clinical practice. Biologists study cells and generate 3D confocal microscopy data ...sets, virologists generate 3D reconstructions of viruses from micrographs, radiologists identify and quantify tumors from MRI and CT scans, and neuroscientists detect regional metabolic brain activity from PET and functional MRI scans. On the other hand, Image Processing includes the analysis, enhancement, and display of images captured via various medical imaging technologies. Image reconstruction and modeling techniques allow instant processing of 2D signals to create 3D images. In addition, image processing and analysis can be used to determine the diameter, volume, and vasculature of a tumor or organ, flow parameters of blood or other fluids, and microscopic changes that have not previously been discernible....
It is very important to early detect abnormal brains, in order to save social and hospital resources. The wavelet-energy was a successful feature descriptor that achieved excellent performances in ...various applications; hence, we proposed a novel wavelet-energy based approach for automated classification of MR brain images as normal or abnormal. SVM was used as the classifier, and biogeography-based optimization (BBO) was introduced to optimize the weights of the SVM. The results based on a 5 × 5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO-KSVM in terms of sensitivity and accuracy. The study offered a new means to detect abnormal brains with excellent performance.
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The growth characteristics of eutectic Si in unmodified and Sr-modified Al–12.7%Si alloys were investigated by microstructure-correlated crystallographic analyses. For the unmodified ...alloys, the formation of repeated single-orientation twin variants enables rapid growth of eutectic Si according to the twin plane re-entrant edge (TPRE) mechanism. Microscopically, Si crystals are plate-like elongated in one 〈110〉 direction that is not in accordance with the 〈112〉 growth assumed by the TPRE model. The 〈110〉 growth direction is realized by paired 〈112〉 zigzag growth on parallel twinning planes, leading to alternate disappearance and creation of 141° re-entrants. As each twinning plane is associated with three re-entrants, Si crystals may extend in three co-planar 〈110〉 directions and cause the formation of equilateral plates. With the formation of α-Al around eutectic Si, the number of re-entrants is reduced. The planar isotropic growth of eutectic Si becomes anisotropic, leading to the formation of long plates. The reduction of the number of re-entrants also accounts for the width and thickness changes over the length of Si plates. This complex growth mode results in Si crystals exposing only their low-energy {111} planes to the melt. For the Sr-modified alloys, substantial changes appear in the eutectic Si morphology, attributable to the restricted TPRE growth and the impurity induced twinning (IIT) growth. The former enhances lateral growth by forming new twins with parallel twinning planes, while the latter leads to isotropic growth by forming differently oriented twins.
The continuous improvements in the area of medical imaging, makes the patient monitoring a crucial concern. The internet of things (IoT) embedded in a medical technologies to collect data from human ...body through sensors, wireless connectivity etc. The junction of medicine and IT like medical informatics will transform healthcare, curbing cost, make more efficient, and saving lives. Various computerized techniques are implemented in the area of Artificial Intelligence (AI) for the application of medical imaging to diagnose the infected regions in the images and videos such as WCE and pathology. The famous stomach infections are ulcer, polyp, and bleeding. Stomach cancer is the most common infection and a leading cause of human deaths worldwide. In the USA, since 2019, a total of 27,510 new cases are reported including 17,230 men and 10,230 women. While the number of deaths is 11,140 consists of 6,800 men and 4,340 women. The manual diagnosis of these stomach infections is a difficult and agitated process therefore it is required to design a fully automated system using AI. In this article, we presented a fully automated system for stomach infection recognition based on deep learning features fusion and selection. In this design, ulcer images are assigned manually and support to a saliency-based method for ulcer detection. Later, pre-trained deep learning model named VGG16 is employing and re-trained using transfer learning. Features of re-trained model are extracted from two consecutive fully connected layers and fused by array-based approach. Besides, the best individuals are selected through the metaheuristic approach name PSO along mean value-based fitness function. The selected individuals are finally recognized through Cubic SVM. The experiments are conducted on Private collected dataset and achieved an accuracy of 98.4%, which is best as compared to existing state-of-the-art techniques.
Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification ...performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions.
First, we used maximum inter-class variance (ICV) to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch's t-test (WTT). Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC.
The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36 ± 0.94) was better than the linear kernel of 91.47 ± 1.02 and the radial basis function (RBF) kernel of 86.71 ± 1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions (Anterior Cingulate, Caudate Nucleus, Cerebellum, Cingulate Gyrus, Claustrum, Inferior Frontal Gyrus, Inferior Parietal Lobule, Insula, Lateral Ventricle, Lentiform Nucleus, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterial Cingulate, Precentral Gyrus, Precuneus, Subcallosal Gyrus, Sub-Gyral, Superior Frontal Gyrus, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, Thalamus, Transverse Temporal Gyrus, and Uncus). The results were coherent with existing literatures.
The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning.
Synthetic aperture radar (SAR) ship detection is widely used in cutting-edge applications such as environmental protection, traffic monitoring, search, and rescue. Lightweight detection algorithms ...are more important for practical applications. Although there has been extensive research in this field, there are some problems with the existing lightweight algorithms. For example, it is easy to misjudge targets that are mixed with the background, and the detection effect is not ideal for targets with few samples in the dataset. The root cause of these problems lies in the fact that the useless information in the background is relatively close to the target, and existing algorithms are too simplistic in fusing features at different levels, resulting in algorithms not being robust enough when facing these problems. Therefore, this article proposes a multiscale feature pyramid network (FPN)-based detection network (MFPNet), which introduces a spatial information-focusing module in the feature fusion channel to enhance the target's features to suppress interference information in the background and reduce misjudgment. Then, optimize the FPN and extract the importance of different resolution features based on network contribution to identifying multiscale targets. Experiments have shown that the MFPNet has better detection performance compared to existing algorithms on public datasets.
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