基于可见/近红外光谱分析技术的猪肉肉糜品质检测研究

以225个猪肉肉糜样本为研究对象,利用可见/近红外光谱分析技术对猪肉肉糜主要品质指标的的快速检测进行了研究.光谱经小波去噪后,采用偏最小二乘法和支持向量机定量分析方法分别建立了肉糜中肌内脂肪、蛋白质和水分含量的可见/近红外光谱预测模型.其中,肌内脂肪的支持向量机定量预测模型最优,校正相关系数rcal和预测相关系数rva...     

Application of wavelet filtering techniques to Lau interferometric fringe analysis for measurement of small tilt angles

This communication presents the results of investigation undertaken towards testing the viability of wavelet filtering techniques in analyzing the Lau interferometric fringes. Towards this, the applicability of Haar, Daubechies and Symlet wavelet based filtering schemes have been tested. Of these schemes, the Symlet based filtering scheme gives the best results in terms of improved signal to noise ratio. This scheme is then used to analyze Lau interferograms recorded in the experimental arrangement for tilt angle measurement. The processed interferograms yield improved accuracy and precision of measurement.     

Laser ultrasonic propagation imaging method in the frequency domain based on wavelet transformation

A wavelet-transformed ultrasonic propagation imaging method capable of ultrasonic propagation imaging in the frequency domain was developed and applied as a new structural damage or flaw visualization algorithm. Since the wavelet-transformed ultrasonic propagation imaging method has strong frequency selectivity, it can visualize the propagation of ultrasonic waves of a specific frequency (for example, to isolate ultrasonic mode of interest and a damage-related ultrasonic wave). The strong frequency selectivity of the wavelet-transformed ultrasonic propagation imaging method was demonstrated, isolating only the zeroth-order asymmetrical mode of the fundamental Lamb wave modes in an anisotropic carbon fiber-reinforced plastic plate with a thickness of 5 mm. The wavelet-transformed ultrasonic propagation imaging method can also convert a complex time domain multiple wavefield into a simple frequency domain single wavefield. This feature enables easy interpretation of the results, and facilitates the precise evaluation of the location and size of structural damage or flaws. We demonstrated this capability by detecting a disbond in a sandwich structure made of Al-alloy skins and a foam core. A disbond with a diameter of 20 mm, which is representative of a common manufacturing flaw, was successfully detected, localized, and evaluated. Since a method to determine the allowable maximum pulse repetition frequency depending on target materials and structures was found by investigating the residual wave caused from the previous laser impinging, our laser ultrasonic system can scan rapidly the target with an optimal pulse repetition rate. In addition, the proposed wavelet-transformed ultrasonic propagation imaging method can visualize damage or flaw without the need for reference data from the intact state of the structure. Hence, we propose the wavelet-transformed ultrasonic propagation imaging approach for automatic inspection of in-service engineering structures, or in-process quality inspection in manufacturing.     

低碳化学品火焰红外光谱辐射特性研究

低碳化学品火灾事故风险高、危害极大,探究低碳化学品火焰光谱特性对探测识别该类火灾危害污染意义重大,但目前国内外对大型低碳化学品火灾事故产生有毒、有害的硫化物（SO_X）和氮化物（NO_X）气体等相关研究较少。通过搭建1.2～12 μm红外波段火焰光谱测试实验平台,对二硫化碳、92#汽油和酒精进行5,14和20 cm三种不同燃烧尺度火焰光谱测试,探究火焰燃烧尺度对高温火焰分子辐射光谱的影响。随着燃烧尺度的增大,火焰辐射强度增强和特征波段出现增宽现象。分析5 cm 燃烧尺度下四种典型化学品中液化天然气（LNG）、丙烯腈、乙腈和95#汽油不同的火焰光谱特征。通过用傅里叶变换红外光谱仪测量高温黑体炉的不同温度,对火焰光谱信号进行辐射定标,得出准确的辐射定标系数,从而得到高温火焰分子发射的辐射亮度值。并且与HITRAN数据库模拟大气压1 atm、温度1 300 K单一的SO₂,H₂O,CO₂和NO₂分子辐射光谱进行对比分析。其中高温火焰分子光谱主要有7.3～7.6,8.7和4.0 μm SO₂波段、1.8～2.1和6.4 μm H₂O波段和4.2～4.6 μm CO₂波段,以及2.5～2.9 μm H₂O和CO₂共同波段。高温NO₂气体未达到红外光谱仪的检测限,通过HITRAN数据库模拟可知6.0～6.4,3.4和2.4 μm NO₂波段。为了进一步区分各种化学品火焰光谱,对定标后的火焰光谱信号进行归一化处理,用db2小波基函数进行6层分解得到高频部分近似系数和低频部分的细节系数,通过对比不同化学品高温火焰光谱的近似和细节系数的差异。结果表明,二硫化碳火焰光谱特征和小波分析的化学品火焰光谱特征,可作为区分低碳化学品与油料重要依据,并为后续遥感探测低碳化学品特征污染物、组分浓度反演以及识别评估其污染危害奠定重要基础。     

Generalized wavelet transform based on the convolution operator in the linear canonical transform domain

The wavelet transform (WT) and linear canonical transform (LCT) have been shown to be powerful tool for optics and signal processing. In this paper, firstly, we introduce a novel time-frequency transformation tool coined the generalized wavelet transform (GWT), based on the idea of the LCT and WT. Then, we derive some fundamental results of this transform, including its basis properties, inner product theorem and convolution theorem, inverse formula and admissibility condition. Further, we also discuss the time-fractional-frequency resolution of the GWT. The GWT is capable of representing signals in the time-fractional-frequency plane. Last, some potential applications of the GWT are also presented to show the advantage of the theory. The GWT can circumvent the limitations of the WT and the LCT.     

色彩再现的多光谱图像压缩

针对多光谱图像在色度高保真再现等领域的应用, 为提高压缩效率, 进一步存储传输, 提出了WF系列编码方法, 设计了APWS_RA算法, 进而提出了一种低复杂度、光照稳定性好且支持跨设备再现的WF_APWS_RA压缩算法。首先研究了现有的基于光谱均方误差的小波嵌入编码原理, 提出了色感应失真准则和视觉特性矩阵W;同时, 优化了APWS编码算法的码率分配, 形成APWS_RA编码算法;最后, 结合以上技术, 以色度误差准则指导编码, 对视觉加权后的多光谱图像数据进行aPWS_rA编码, 命名为WF_APWS_RA。WF_APWS_RA算法融合人眼视觉特性矩阵W, 利用吸引力传播(Affinity Propagation)聚类挖掘加权图像的谱间冗余, 小波变换去除其空间冗余, 最后结合误差补偿机制和码率分配策略进行小波编码。实验表明, 在相同比特率下, 较现有低复杂度经典编码算法, WF系列编码方法能更有效地保留光谱中的色度信息, APWS_RA算法的重建光谱误差最小, WF_APWS_RA算法的重建色度精度优势明显。     

Image composition optimization based on feature match and detail preserved

Poisson composition and gradient domain are typical and traditional composition technologies. Their main function is to create a seamless composited image. However, when the original image and the target image have quite a few differences with regard to their features (color, sharpness, noise, texture and so on), the composited image is unrealistic. We use some innovative methods to deal with the problem and there are three main stages considered in the paper. The first stage is to deal with the original image's color ahead of schedule. This is to make the original image as similar as the target image and it contributes to get a realistic composited image. The second stage is to achieve the image's multi-scale composition with wavelet pyramid. The third stage is that we use BLF filter and an image pyramid to preserve the composited image's detail after the image composition. Image composition's optimization is based on the three stages.     

Visually weighted reconstruction of compressive sensing MRI

Compressive sensing (CS) enables the reconstruction of a magnetic resonance (MR) image from undersampled data in k-space with relatively low-quality distortion when compared to the original image. In addition, CS allows the scan time to be significantly reduced. Along with a reduction in the computational overhead, we investigate an effective way to improve visual quality through the use of a weighted optimization algorithm for reconstruction after variable density random undersampling in the phase encoding direction over k-space. In contrast to conventional magnetic resonance imaging (MRI) reconstruction methods, the visual weight, in particular, the region of interest (ROI), is investigated here for quality improvement. In addition, we employ a wavelet transform to analyze the reconstructed image in the space domain and fully utilize data sparsity over the spatial and frequency domains. The visual weight is constructed by reflecting the perceptual characteristics of the human visual system (HVS), and then applied to ?₁ norm minimization, which gives priority to each coefficient during the reconstruction process. Using objective quality assessment metrics, it was found that an image reconstructed using the visual weight has higher local and global quality than those processed by conventional methods.     

Exploiting the wavelet structure in compressed sensing MRI

Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space sampling. According to structured sparsity theories, fewer measurements are required for tree sparse data than the data only with standard sparsity. Intuitively, more accurate image reconstruction can be achieved with the same number of measurements by exploiting the wavelet tree structure in MRI. A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data. In contrast to conventional compressed sensing MRI (CS-MRI) that only relies on the sparsity of MR images in wavelet or gradient domain, we exploit the wavelet tree structure to improve CS-MRI. This tree-based CS-MRI problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved by an iterative scheme. Simulations and in vivo experiments demonstrate the significant improvement of the proposed method compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms.