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361.
单扫描时空编码磁共振成像是一种新型超快速磁共振成像技术,它对磁场不均匀和化学位移伪影有较强的抵抗性,但是其固有的空间分辨率较低,因此通常需要进行超分辨率重建,以在不增加采样点数的情况下提高时空编码磁共振图像的空间分辨率.然而,现有的重建方法存在迭代求解时间长、重建结果有混叠伪影残留等问题.为此,本文提出了一种基于深度神经网络的单扫描时空编码磁共振成像超分辨率重建方法.该方法采用模拟样本训练深度神经网络,再利用训练好的网络模型对实际采样信号进行重建.数值模拟、水模和活体鼠脑的实验结果表明,该方法能快速重建出无残留混叠伪影、纹理信息清楚的超分辨率时空编码磁共振图像.适当增加训练样本数量以及在训练样本中加入适当的随机噪声水平,有助于改善重建效果. 相似文献
362.
Multivariable time series forecasting is an important topic of machine learning, and it frequently involves a complex mix of inputs, including static covariates and exogenous time series input. A targeted investigation of this input data is critical for improving prediction performance. In this paper, we propose the fusion transformer (FusFormer), a transformer-based model for forecasting time series data, whose framework fuses various computation modules for time series input and static covariates. To be more precise, the model calculation consists of two parallel stages. First, it employs a temporal encoder–decoder framework for extracting dynamic temporal features from time series data input, which analyzes and integrates the relative position information of sequence elements into the attention mechanism. Simultaneously, the static covariates are fed to the static enrichment module, which is inspired by gated linear units, to suppress irrelevant information and control the extent of nonlinear processing. Finally, the prediction results are calculated by fusing the outputs of the above two stages. Using Mooney viscosity forecasting as a case study, we demonstrate considerable forecasting performance improvements over existing methodologies and verify the effectiveness of each component of FusFormer via ablation analysis, and an interpretability use case is conducted to visualize temporal patterns of time series. The experimental results prove that FusFormer can achieve accurate Mooney viscosity prediction and improve the efficiency of the tire production process. 相似文献
363.
In this paper, aiming to solve the problem of vital information security as well as neural network application in optical encryption system, we propose an optical image encryption method by using the Hopfield neural network. The algorithm uses a fuzzy single neuronal dynamic system and a chaotic Hopfield neural network for chaotic sequence generation and then obtains chaotic random phase masks. Initially, the original images are decomposed into sub-signals through wavelet packet transform, and the sub-signals are divided into two layers by adaptive classification after scrambling. The double random-phase encoding in 4f system and Fresnel domain is implemented on two layers, respectively. The sub-signals are performed with different conversions according to their standard deviation to assure that the local information’s security is guaranteed. Meanwhile, the parameters such as wavelength and diffraction distance are considered as additional keys, which can enhance the overall security. Then, inverse wavelet packet transform is applied to reconstruct the image, and a second scrambling is implemented. In order to handle and manage the parameters used in the scheme, the public key cryptosystem is applied. Finally, experiments and security analysis are presented to demonstrate the feasibility and robustness of the proposed scheme. 相似文献
364.
Recently, with the rise of deep learning, text classification techniques have developed rapidly. However, the existing work usually takes the entire text as the modeling object and pays less attention to the hierarchical structure within the text, ignoring the internal connection between the upper and lower sentences. To address these issues, this paper proposes a Bert-based hierarchical graph attention network model (BHGAttN) based on a large-scale pretrained model and graph attention network to model the hierarchical relationship of texts. During modeling, the semantic features are enhanced by the output of the intermediate layer of BERT, and the multilevel hierarchical graph network corresponding to each layer of BERT is constructed by using the dependencies between the whole sentence and the subsentence. This model pays attention to the layer-by-layer semantic information and the hierarchical relationship within the text. The experimental results show that the BHGAttN model exhibits significant competitive advantages compared with the current state-of-the-art baseline models. 相似文献
365.
Large-scale Multiple-Input Multiple Output (MIMO) is the key technology of 5G communication. However, dealing with physical channels is a complex process. Machine learning techniques have not been utilized commercially because of the limited learning capabilities of traditional machine learning algorithms. We design a deep learning hybrid precoding scheme based on the attention mechanism. The method mainly includes channel modeling and deep learning encoding two modules. The channel modeling module mainly describes the problem formally, which is convenient for the subsequent method design and processing. The model design module introduces the design framework, details, and main training process of the model. We utilize the attention layer to extract the eigenvalues of the interference between multiple users through the output attention distribution matrix. Then, according to the characteristics of inter-user interference, the loss minimization function is used to study the optimal precoder to achieve the maximum reachable rate of the system. Under the same condition, we compare our proposed method with the traditional unsupervised learning-based hybrid precoding algorithm, the TTD-based (True-Time-Delay, TTD) phase correction hybrid precoding algorithm, and the deep learning-based method. Additionally, we verify the role of attention mechanism in the model. Extensive simulation results demonstrate the effectiveness of the proposed method. The results of this research prove that deep learning technology can play a driving role in the encoding and processing of MIMO with its unique feature extraction and modeling capabilities. In addition, this research also provides a good reference for the application of deep learning in MIMO data processing problems. 相似文献
366.
《Advanced Optical Materials》2017,5(22)
The orthogonal luminescence encoding technique is developed for the first time based on a novel near‐infrared (NIR) rechargeable upconverting persistent luminescence (UCPL) composites. The orthogonal encoding signals with multicolor upconversion emission and long persistent phosphorescence can be activated and work independently in the presence and absence of 980 nm excitation. The orthogonal encoding technique allows for a marked increase in the coding capacity compared with conventional luminescence encoding strategies. This strategy can offer a greatly increased coding capacity with less naked‐eye certifiable colors to decrease the decoding error rate induced by the chromatic aberration of the similar colors, which can be effectively decoded by the portable charge‐coupled device. In addition, the obtained NIR rechargeable UCPL materials show great potential applications for anticounterfeiting, rewriteable data encryption and decryption, zero‐background bioimaging, and noninvasive photo‐biostimulation, etc. 相似文献
367.
Inorganic photoluminescence (PL) phosphors (including upconversion (UC), down-shifting (DS), and persistent (PersL) materials) with tunable outputs, high quantum yields (QYs), and excellent photostability have attracted tremendous attention in advanced information hiding and encoding (IHE). The three kinds of phosphors endow security patterns in different IHE levels owing to their unique optical features. For security applications, it is very necessary to review how to boost optical performance and achieve multi-level anti-counterfeiting. Herein, diversely pivotal approaches on the achievement of multicolor emissions, high QYs, and excellent photostability are summarized. Full learning of these methods is promising to design superior inorganic phosphors. Based on the appealing optical properties of inorganic PL materials, a progressively improved IHE level is revealed by using unitary inorganic PL material, the couples of UCL, DS, and PersL, and further combinations together with external stimuli. This review not only deepens an understanding of designing high-performance inorganic PL phosphors, but also gets inside into the construction of high-performance IHE. 相似文献
368.
阐述了相位型滤波器的伪随机编码设计中的改进方法：对全复值调制数据随机编码形成双振幅相位型滤波器。与极小欧氏距离最佳滤波器（ＭＥＤＯＦ）相比较，其性能有很大改善。 相似文献
369.
An efficient technique for the design of multiple-valued logic circuits using polarization-encoded optical shadow-casting is presented. As an illustration, a trinary full subtracter is designed using this technique. The resulting logic unit is compared with those obtained by using other algorithms. 相似文献
370.
A modified single-point imaging (SPI) technique using a variable phase encoding interval is proposed. This method is based on the minimization of the phase encoding interval for further signal-to-noise ratio (SNR) optimization. This is particularly beneficial when the maximum gradient amplitude limits an optimal phase encoding interval, and the resulting SNR suffers from T(2)-related signal attenuation. Theoretical calculation of the SNR and simulation of the point spread function (PSF) for the different experimental parameters are presented. Experiments using a rubber sample (T(2)* approximately 73 micros) and a tooth (bi-exponential relaxation: T(2,1)*=111 micros and T(2,1)*=872 micros) showed a significant increase in SNR (>3 and >2, respectively) when compared with images acquired with conventional SPI. 相似文献