Degradation of 2,4-D By Ozone And Light

The decomposition of 2,4-dichlorophenoxyacetic acid in aqueous solutions has bsen studied using ozone and ultraviolet radiation at different pH, ozone production, and initial concentration of pesticide. A mathematical model which incorporates the rate of mass transfer of ozone, the oxidation kinetics of 2,4-D and the kinetics of formation and oxidation of the intermediates was used. A global rate constant was calculated and an empirical equation which correlated that constant with ozone production, initial concentration of 2,4-D and pH at the same temperature was determined.     

Linear reconstruction measure steered nearest neighbor classification framework

The linear reconstruction measure (LRM), which determines the nearest neighbors of the query sample in all known training samples by sorting the minimum L₂-norm error linear reconstruction coefficients, is introduced in this paper. The intuitive interpretation and mathematical proofs are presented to reveal the efficient working mechanism of LRM. Through analyzing the physical meaning of coefficients and regularization items, we find that LRM provides more useful information and advantages than the conventional similarity measure model which calculates the distance between two entities (i.e. conventional point-to-point, C-PtP). Inspired by the advantages of LRM, the linear reconstruction measure steered nearest neighbor classification framework (LRM-NNCF) is designed with eight classifiers according to different decision rules and models of LRM. Evaluation on several face databases and the experimental results demonstrate that these proposed classifiers can achieve greater performance than the C-PtP based 1-NNs and competitive recognition accuracy and robustness compared with the state-of-the-art classifiers.     

Gradient orientation pattern matching with the Hamming distance

This paper presents a novel pattern matching technique that is robust to illumination changes and the occlusion problem. The technique is based on the matching of gradient orientations in place of traditional image features such as intensities or gradients. Gradient orientations depend on the texture in an image. They are known to be insensitive to changes of image intensities that are often caused by time-varying illuminations or the auto-gain control (AGC) function of the camera. Moreover,the proposed method employs a voting strategy in the process of matching gradient orientations. The method works remarkably well even when a large part of the pattern is occluded with a foreign object. Consequently, the proposed method is robust to both irregular lighting conditions and the occlusion problem.     

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.     

More than twenty years of advancements on Frontiers in handwriting recognition

In this paper the results of the research in handwriting/handwritten character recognition in about the last quarter of a century are reported, illustrating the results presented during the International Workshop on Frontiers in Handwriting Recognition (IWFHR) and the International Conference on Frontiers in Handwriting Recognition (ICFHR).     

Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition

Due to the noise disturbance and limited number of training samples, within-set and between-set sample covariance matrices in canonical correlation analysis (CCA) usually deviate from the true ones. In this paper, we re-estimate within-set and between-set covariance matrices to reduce the negative effect of this deviation. Specifically, we use the idea of fractional order to respectively correct the eigenvalues and singular values in the corresponding sample covariance matrices, and then construct fractional-order within-set and between-set scatter matrices which can obviously alleviate the problem of the deviation. On this basis, a new approach is proposed to reduce the dimensionality of multi-view data for classification tasks, called fractional-order embedding canonical correlation analysis (FECCA). The proposed method is evaluated on various handwritten numeral, face and object recognition problems. Extensive experimental results on the CENPARMI, UCI, AT&T, AR, and COIL-20 databases show that FECCA is very effective and obviously outperforms the existing joint dimensionality reduction or feature extraction methods in terms of classification accuracy. Moreover, its improvements for recognition rates are statistically significant on most cases below the significance level 0.05.     

An efficient parallel-network packet pattern-matching approach using GPUs

In the past few years, the increase in interest usage has been substantial. The high network bandwidth speed and the large amount of threats pose challenges to current network intrusion detection systems, which manage high amounts of network traffic and perform complicated packet processing. Pattern matching is a computationally intensive process included in network intrusion detection systems. In this paper, we present an efficient graphics processing unit (GPU)-based network packet pattern-matching algorithm by leveraging the computational power of GPUs to accelerate pattern-matching operations and subsequently increase the overall processing throughput. According to the experimental results, the proposed algorithm achieved a maximal traffic processing throughput of over 2 Gbit/s. The results demonstrate that the proposed GPU-based algorithm can effectively enhance the performance of network intrusion detection systems.     

离散小波变换Haar-LL的行人检测研究

提出一种基于二维离散Haar小波变换的局部二值模式(LBP)与局部梯度模式(LGP)的特征融合方法。对图像进行二维离散Haar小波变换,得到4个不同频率的子图像,对低频部分子图像提取LBP特征,对3个高频部分子图像提取LGP特征,将3个LGP特征并接融合后与LBP特征串接融合进行行人检测。在Matlab环境下利用支持向量机(SVM)对INRIA数据集进行5组实验,分别将该方法与梯度方向直方图(HOG)、金字塔梯度方向直方图(PHOG)、LBP、LGP进行检测率、检测时间、光照鲁棒性以及噪声鲁棒性对比。综合各项实验数据表明,该方法在光照鲁棒性以及噪声鲁棒性方面都能取得更好的效果。     

A three-dimensional model for suspension-to-membrane-wall heat transfer in circulating fluidized beds

A three-dimensional model is proposed for both furnace-side and wall-side heat transfer in circulating fluidized beds with membrane walls. Following previous publications (Int. J. Heat Mass Transfer (2003a, b)), a core-annulus flow structure is employed in the model, with consideration of the membrane wall influence on bed hydrodynamics. The model couples radiation, conduction and convection on the furnace side to conduction and convection on the wall side. Radiation in the wall layer is simulated by the moment method. A finite-element method is employed to solve the set of non-linear, partial differential equations. The solution is demonstrated for a typical example. The model gives predictions of suspension-to-wall heat transfer which show satisfactory agreement with published experimental data.