基于改进LMD的齿轮故障特征增强方法

针对齿轮发生故障时信号的调制特点,采用相关分析对局部均值分解(Local Mean Decomposition,LMD)解调方法进行改进,并提出了基于改进LMD的故障特征增强方法。将该方法应用于实际齿轮断齿故障特征的提取,结果表明:该方法能有效地增强齿轮故障特征,其增强效果优于传统的Hilbert包络解调分析方法。     

Photoluminescence decay rate of silicon nanoparticles modified with gold nanoislands

We investigated plasmon-assisted enhancement of emission from silicon nanoparticles (ncs-Si) embedded into porous SiO _x matrix in the 500- to 820-nm wavelength range. In the presence in the near-surface region of gold nanoisland film, ncs-Si exhibited up to twofold luminescence enhancement at emission frequencies that correspond to the plasmon resonance frequency of Au nanoparticles. Enhancement of the photoluminescence (PL) intensity was attributed to coupling with the localized surface plasmons (LSPs) excited in Au nanoparticles and to increase in the radiative decay rate of ncs-Si. It has been shown that spontaneous emission decay rate of ncs-Si modified by thin Au film over the wide emission spectral range was accelerated. The emission decay rate distribution was determined by fitting the experimental decay curves to the stretched exponential model. The observed increase of the PL decay rate distribution width for the Au-coated nc-Si-SiO _x sample in comparison with the uncoated one was explained by fluctuations in the surface-plasmon excitation rate.

PACS

78. 67. Bf; 78.55.-m     

面向LTE深度覆盖的PTN接入层网络演进

随着LTE网络覆盖推向深入， PTN接入层网络的承载压力越来越大，本文通过详细分析不同场景的业务带宽需求，结合网络实际，提出PTN接入层网络演进模型，为PTN接入层规划和建设提供了重要的借鉴和参考。     

Effect of successive stimuli on sweetness intensity of gels and custards

Variations of tastant concentration during the consumption of food products were shown to enhance taste intensity. The aim of this study was to investigate the relation between the frequency at which tastant concentration is varied during the consumption of products and the occurrence of taste enhancement. For this purpose, the sweetness intensity of sequences of 4 successive sweet stimuli represented by cubes of a semi-solid gel or spoons of a model custard dessert was assessed. The intensity and the order of the stimuli within the sequences were varied to obtain 4 types of sucrose concentration profile (decreasing, increasing, middle peak and boundary peak) at 2 magnitudes of concentration differences (small and large). Sequences of 4 stimuli containing a constant sucrose concentration were used as a reference. The sweetness intensity of the sequences of successive stimuli was assessed using line scale and time-intensity ratings.Line scale ratings showed that the type of sequence had an effect on sweetness intensity. A sweetness enhancement relative to the reference was observed in sequences that ended with high-intensity stimuli, whereas sweetness suppression was observed in sequences that ended with low-intensity stimuli. The observed sweetness enhancement and suppression were attributed to serial position effects (i.e. recency effects).Time-intensity ratings indicated that each stimulus in the sequences was evaluated individually by the assessors. Sequential effects seem to have occurred during the continuous evaluation of successive stimuli, since preceding stimuli in the sequences affected the evaluation of posterior stimuli. Furthermore, the overall sweetness intensity of the different sequences was not enhanced in relation to the reference. The lack of taste enhancement in sequences exhibiting variations of sucrose concentration was attributed mainly to the long period at which sucrose concentration was varied during the evaluation of the sequences. It was confirmed that the frequency of variation of tastant concentration affects the occurrence of taste enhancement.     

A novel prognosis and segmentation of necrosis (dead cells) in contrast enhanced T1-weighted glioblastoma tumor with automatic contextual clustering

In recent years, mortality rate with high-grade tumor has been increased significantly especially with glioblastoma (GBM) brain tumor while compared to other malignant brain tumor. Here, the amount of dead cells accommodated with the tumor tissue in GBM brain tumor play a vital task and necessitate an earlier diagnosis of malignancy with the GBM tumor. It inspires to implement new automatic diagnosis system which detects the dead cells and tumor tissue with the GBM brain tumor, such that the survival rate of the diseased can easily be prognosis by the Radiologist and Neurosurgeon. The main objective of this article is to detect the amount of dead cells with respect to tumor tissue associated with the GBM brain tumor which desires the impact factor of the brain tumor. In this framework, initially, the new contrast enhancement modality is incorporated to enhance the gray information over the dead cells and the tumor tissue with the T1-weighted MRI GBM brain tumor. In this enhancement, the edges of the tumor cells and its dead cells are magnified efficiently. As the noises and outliers with MR image is unpredictable and it experiences the variable amount of noises over the local window, the contextual information over each pixel of the image is adaptively modified with respect to the amount of noise over local window using adaptive contextual clustering. The performance evaluation of the framework is investigated which exhibits the overwhelming result compared to conventional detection techniques.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.     

Frequency selective millimeter‐wave channel estimation based on subspace enhancement algorithms

In millimeter wave (mmW) communication systems, hybrid architecture, including the analog‐digital precoder and combiner matrices, is employed to take advantage of the multistream transceiver. In practice, mmW channel is assumed to be frequency‐selective, since the signal bandwidth is larger than the coherence bandwidth. Hence, orthogonal frequency‐division multiplexing signaling can be remedial. So far, most of the previous works on the frequency‐selective channel estimation have focused on the single measurement vector (SMV) form, whereas finding and exploiting the proper multimeasurement vector (MMV) model can improve upon the estimation procedure based on compressive sensing (CS) concepts. In fact, the estimation procedure based on the MMV model has a faster convergence speed than the SMV method specially, when the training frames are small. In this paper, we first extract the MMV model of the channel. In this model, the rank‐deficiency occurs as the number of training frames is less or equal to the sparsity level. Thus, the conventional estimation methods fail to provide the desirable performance. To overcome this issue, we propose two rank‐aware algorithms based on the enhancement of the observed signal subspace. The first algorithm assumes to know the sparsity level, while the second faces to the lack of knowledge about the sparsity level. The simulation results corroborate the fact that the proposed methods outperform the conventional CS algorithms such as Simultaneous Orthogonal Matching Pursuit.     

基于小波变换的分数阶微分算法在肝脏肿瘤CT图像纹理增强中的应用

图像纹理增强过程中容易丢失平滑区域纹理细节，而分数阶微分增强虽然能够非线性保留平滑区域纹理细节，但对频率分辨率敏感。针对这个问题，提出一种基于小波变换的分数阶微分纹理增强算法，应用于平扫计算机断层扫描（CT）图像的肝脏肿瘤区域的纹理增强。首先，通过小波变换将图像感兴趣区分解成多个子带分量；其次，基于分数阶微分定义构造一个带补偿参数的分数阶微分掩膜；最后，使用该掩膜与每个高频子带分量进行卷积并利用小波逆变换重组图像感兴趣区。实验结果表明，该方法在使用较大分数阶次显著增强肿瘤区域的高频轮廓信息的同时，有效地保留了低频平滑的纹理细节：增强后的肝细胞癌区域与原区域相比，信息熵平均增加36.56%，平均梯度平均增加321.56%，平均绝对差值平均为9.287；增强后的肝血管瘤区域与原区域相比，信息熵平均增加48.77%，平均梯度平均增加511.26%，平均绝对差值平均为14.097。     

结合引导滤波的自适应多曝光图像融合

针对引导滤波产生的光晕、梯度反转现象，以及图像融合边缘细节丢失的现象，提出一种改进引导滤波的自适应多曝光图像融合算法。在引导滤波中根据梯度信息设定权重函数，并结合图像像素点和一定区域的均值创建函数，共同实现不同区域的纹理特性自适应；利用平均亮度与对比度、饱和度及曝光适中度的关系，设置权值函数，使加权平均融合过程中的权重值不再是固定的数值，而能够根据不同的图像亮度自适应调整，权重值也不同，使得融合后的图像质量更好；将原序列图的细节信息叠加到改进的引导滤波图像中，构建纹理细节层。实验结果削弱了光晕及梯度反转现象，使图像更加真实，细节更加清晰，并且对有小光源的图像处理效果更好。算法结果明显优于多曝光融合算法及引导滤波的多曝光图像融合，在信息熵、互信息和边缘信息评价中分别取得最高2.5%、30%和30%左右的质量提升。     

Simulation of microwave's heating effect on coal seam permeability enhancement

As hydraulic fracturing was forbidden in some countries due to possible negative environmental impacts and enhanced coalbed methane(ECBM) was restricted by in-situ conditions, microwave heating was proposed to enhance coalbed permeability. One of the mechanisms of improving coal permeability with microwave irradiation is that thermal expansion caused by microwave heating. To study the influence of microwave's heating effect of coal samples, the simulations were conducted using a coupled electromagnetic, thermal and mechanical model in this paper. The temperature, Von-Mises stress and strain distribution of coal sample are recorded every 10 s. The permeability distribution is also obtained based on the relationship between strain and permeability from articles. It was found that volume average temperature, stress, strain and permeability increase almost linearly with time. The average permeability increased from 1.65×10~(-16) m~2 to 3.63×10~(-16)m~2 under 2.45 GHz and 500W microwave radiation after 300s. The significant increase proved microwave to be effective in coal seam permeability enhancement.