- 标题
- 摘要
- 关键词
- 实验方案
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A Novel Weighted Integral Energy Functional (WIEF) Algorithm: Augmented Reality (AR) for Visualising the Blood Vessels in Breast Implant Surgeries
摘要： The use of Augmented Reality (AR) for visualising blood vessels in surgery is still at the experimental stage and has not been implemented due to limitations in terms of accuracy and processing time. The AR also hasn't applied in breast surgeries yet. As there is a need for a plastic surgeon to see the blood vessels before he cuts the breast and before putting the implant, this paper aims to improve the accuracy of augmented videos in visualising blood vessels during Breast Implant Surgery. The proposed system consists of a Weighted Integral Energy Functional (WIFE) algorithm to increase the accuracy of the augmented view in visualising the occluded blood vessels that covered by fat in the operating room. The results on breast area shows that the proposed algorithm improves video accuracy in terms of registration error to 0.32 mm and processing time to 23 sec compared to the state-of-the-art method. The proposed system focuses on increasing the accuracy in augmented view in visualising blood vessels during Breast Implant Surgery as it reduces the registration error. Thus, this study concentrates on looking at the feasibility of the use of Augmented Reality technology in Breast Augmentation surgeries.
关键词： Breast Augmentation,Augmented Reality,Random Decision Forests,Surgical Planning,Vascular Pulsation,Wavelet Decomposition
更新于2025-09-19 17:15:36
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[IEEE 2018 18th Mediterranean Microwave Symposium (MMS) - Istanbul, Turkey (2018.10.31-2018.11.2)] 2018 18th Mediterranean Microwave Symposium (MMS) - PSO Based Approach to the Synthesis of a Cylindrical-Rectangular Ring Microstrip Conformal Antenna Using SVR Models with RBF and Wavelet Kernels
摘要： In this work, particle swarm optimization (PSO) based approach to the synthesis of a cylindrical-rectangular ring microstrip conformal antenna using support vector regression (SVR) models is presented. Resonant frequency of the antenna is obtained by PSO of trained SVR models. Radial basis function (RBF) and wavelet kernel functions are used in SVR models. Simulation examples are given and the results are compared.
关键词： support vector regression,rectangular ring microstrip antenna,particle swarm optimization,wavelet kernel,conformal antennas
更新于2025-09-19 17:15:36
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Design of New Wavelet Packets Adapted to High-Resolution SAR Images With an Application to Target Detection
摘要： High resolution in synthetic aperture radar (SAR) leads to new physical characterizations of scatterers which are anisotropic and dispersive. These behaviors present an interesting source of diversity for target detection schemes. Unfortunately, such characteristics have been integrated and have been naturally lost in monovariate single-look SAR images. Modeling this lost behavior as nonstationarity, wavelet analysis has been successful in retrieving this information. However, the sharp-edge of the used wavelet functions introduces undesired high side-lobes for the strong scatterers present in the images. In this paper, a new family of parameterized wavelets, designed specifically to reduce those side lobes in the SAR image decomposition, is proposed. Target detection schemes are then explored using this spectro-angular diversity and it can be shown that in high-resolution SAR images, the non-Gaussian and robust framework leads to better results.
关键词： wavelet packets,High resolution,robust adaptive detection,synthetic aperture radar (SAR)
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deep Hybrid Wavelet Network for Ice Boundary Detection in Radra Imagery
摘要： This paper proposes a deep convolutional neural network approach to detect ice surface and bottom layers from radar imagery. Radar images are capable to penetrate the ice surface and provide us with valuable information from the underlying layers of ice surface. In recent years, deep hierarchical learning techniques for object detection and segmentation greatly improved the performance of traditional techniques based on hand-crafted feature engineering. We designed a deep convolutional network to produce the images of surface and bottom ice boundary. Our network take advantage of undecimated wavelet transform to provide the highest level of information from radar images, as well as multilayer and multi-scale optimized architecture. In this work, radar images from 2009-2016 NASA Operation IceBridge Mission are used to train and test the network. Our network outperformed the state-of-the-art accuracy.
关键词： Deep learning,Wavelet transform,Holistically nested edge detection,Radar,Ice Boundary detection
更新于2025-09-19 17:15:36
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BaB8O12F2: a promising deep-UV birefringent material
摘要： Seismic prediction has been a huge challenge because of the great uncertainties contained in the seismic data. Deep learning (DL) has been successfully applied in many fields and brought revolutionary changes, such as computer vision and natural language processing. The traditional artificial neural networks have been studied to improve the accuracy and resolution of seismic prediction for years, but not DL. In this paper, we develop a new architecture for seismic reservoir characterization based on the DL technique. We apply the convolutional neural network (CNN), which is a DL framework, to predict lithology and have achieved better results compared with traditional methods. We also propose to use continuous wavelet transforms (CWTs) to get a time–frequency spectrum for neural networks. CWTs help to make full use of the frequency content of the post-stack seismic data. According to the difference in the convolution layers and the organization of the input data, we propose four DL architectures for seismic lithology prediction, namely the deep neural networks (DNNs), the CNNs, the CWT-DNNs and the CWT-CNNs. All of these four architectures are applied in the case study. The final results on blind wells, profile and horizontal slice show that CWT-CNN models have the best performance on post-stack seismic lithology prediction. CWT maps contain more information about thinner layers and convolution layers are better at feature extraction from CWT maps. The CWT-CNN model has higher accuracy and resolution, especially on medium and thin layer prediction.
关键词： Wavelet transform,Neural networks,Asia,Image processing
更新于2025-09-19 17:15:36
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Biorthogonal Halfband Perfect Reconstruction Filterbank for Multimodal Image Fusion
摘要： To design a wavelet filter bank which helps to detect more prominently continuous changes in tumor cells. In this paper, a method of designing two-channel wavelet base FIR filter bank using factorization of a half band filter is presented. Here factorization is done considering maximum vanishing moments for construction of decomposition as well as reconstruction filters. The 14 order maximally flat halfband filter is proposed with factorization, leading to design of 8/8 orthogonal and 9/7 & 6/10 symmetric filters. The fusion performance of designed wavelet filter bank is evaluated using various performance metrics, like, cross entropy, standard deviation, mean square error and PSNR. The results are compared with the Daubechies filter bank where db4 is used for implementation. It is clear from the results that designed filter bank improves fusion performance. Further, proposed filters have maximum number of vanishing moments which gives smooth scaling and wavelet functions and consequently provides flat frequency response. The designed 8/8 orthogonal wavelet filters are implemented in fusion application for multimodal biomedical images of a subject for detection of an abnormality (cancerous growth). The novelty of this method is the adaptive design of the filterbank for the given multimodal images, so that fused images shall have more clarity, further, it will help to improve the results with enhancement in the entropy levels of fused image.
关键词： Spectral Factorization,Medical Image Processing,Filter Banks,Multimodal Image Fusion,Wavelet Transforms,Half Band Filter
更新于2025-09-19 17:15:36
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Nonlinear Ultrasonic Detection Method for Delamination Damage of Lined Anti-Corrosion Pipes Using PZT Transducers
摘要： Lined anti-corrosion pipes are widely used in oil and gas, petrochemical, pharmaceutical industries. However, defects, especially delamination, may occur in the production and service of pipes which result in safety accidents. Based on nonlinear ultrasonic theory, this paper studied the delamination detection method using the nonlinear harmonics for lined anti-corrosion pipes. The response characteristics of the anti-corrosion pipe were obtained through a sweep sine response experiment and the preferred excitation frequency was determined. The Wavelet Packet transform and Hilbert–Huang transform is applied for signal process and feature extraction. Then, a series of experiments were carried out and the results were analyzed and discussed. The results showed that a second-order and third-order nonlinear coefficient increased with the delamination damage. The amplitude of second-harmonic is much stronger than the third-order one. The mean squared error of the nonlinear coefficient, which was processed by Wavelet Packet transform and Hilbert–Huang transform, is smaller than wavelet packet transform and Discrete Fourier transform or processed only Hilbert–Huang transform. The higher harmonics can describe the change of delamination damage, which means that the nonlinear ultrasonic detection method could use for damage detection of anti-corrosion pipe. The nonlinear higher-harmonic is sensitive to delamination damage. The nonlinear ultrasonic method has the potential for damage detection for lined anti-corrosion pipes.
关键词： lined anti-corrosion pipes,Hilbert–Huang transform,delamination damage,nonlinear ultrasonic,wavelet packet transform
更新于2025-09-19 17:15:36
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Photoelectron spectroscopy of solvated dicarboxylate and alkali metal ion clusters, M <sup>+</sup> [O <sub/>2</sub> C(CH <sub/>2</sub> ) <sub/>2</sub> CO <sub/>2</sub> ] <sup>2?</sup> [H <sub/>2</sub> O] <sub/>n</sub> (M = Na, K; <i>n</i> = 1–6)
摘要： Image denoising is one of the most important directions in image processing. Medical images are often affected by noise and interference from the environment and equipment during acquisition, conversion, and transmission, resulting in degradation. This paper mainly introduces a new convolutional neural network structure for medical image denoising - deep neural network based on wavelet domain (deep wavelet denoising net DWDN). Our DWDN model exhibits high effectiveness in general medical image denoising tasks and is more excellent in the details of image.
关键词： denoising,wavelet transform,medical image,convolutional neural network
更新于2025-09-19 17:15:36
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Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance
摘要： The development of computerized automated diagnostic systems ensures more effective health screening in plants. In this way, the damage caused by diseases can be reduced by early detection. Light reflections from plant leaves are known to carry information about plant health. In the study, healthy and fusarium diseased peppers (capsicum annuum) was detected from the reflections obtained from the pepper leaves with the aid of spectroradiometer. Reflections were taken from four groups of pepper leaves (healthy, fusarium-diseased, mycorrhizal fungus, fusarium-diseased and mycorrhizal fungus) grown in a closed environment at wavelengths between 350 nm and 2500 nm. Pepper disease detection takes place in two stages. In the first step, the feature vector is obtained. In the second step, the feature vectors of the input data are classified. The feature vector consist of the coefficients of wavelet decomposition and the statistical values of these coefficients. Artificial Neural Networks (ANN), Naive Bayes (NB) and K-nearest Neighbor (KNN) were used for classification. In detection the health case of pepper, the average success rates of different classification algorithms for the first two groups (diseased and healthy peppers) were calculated as 100% for KNN, 97.5% for ANN and 90% for NB. Likewise, these rates for the classification of all groups were calculated as 100% for KNN, 88.125% for ANN and 82% for NB. Overall, the results have shown that leaf reflections can be successfully used in disease detection.
关键词： Wavelet,Spectral reflectance,Machine learning algorithms,Pepper disease detection,Classification
更新于2025-09-19 17:15:36
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[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Study on a Multi-channel Switchable and Environment Self-adaptive Ultrasonic Sensor in an Erbium-doped Fiber Ring Laser
摘要： The usefulness of the information contained in biomedical data relies heavily on the reliability and accuracy of the methods used for its extraction. The conventional assumptions of stationarity and autonomicity break down in the case of living systems because they are thermodynamically open, and thus constantly interacting with their environments. This leads to an inherent time-variability and results in highly nonlinear, time-dependent dynamics. The aim of signal analysis usually is to gain insight into the behavior of the system from which the signal originated. Here, a range of signal analysis methods is presented and applied to extract information about time-varying oscillatory modes and their interactions. Methods are discussed for the characterization of signals and their underlying nonautonomous dynamics, including time-frequency analysis, decomposition, coherence analysis and dynamical Bayesian inference to study interactions and coupling functions. They are illustrated by being applied to cardiovascular and EEG data. The recent introduction of chronotaxic systems provides a theoretical framework within which dynamical systems can have amplitudes and frequencies which are time-varying, yet remain stable, matching well the characteristics of life. We demonstrate that, when applied in the context of chronotaxic systems, the methods presented facilitate the accurate extraction of the system dynamics over many scales of time and space.
关键词： phase coherence,coupling function,Biomedical signal analysis,dynamical Bayesian inference,wavelet bispectrum,cardiovascular system,time-frequency analysis,brain dynamics,time-dependent dynamics
更新于2025-09-19 17:13:59