Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging

Early and accurate diagnosis of Parkinson’s disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with ¹²³I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the ¹²³I-Ioflupane SPECT scans (as obtained from the Parkinson’s progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.     

A fuzzy DEA model with undesirable fuzzy outputs and its application to the banking sector in India

Data envelopment analysis (DEA) is a widely used technique for measuring the relative efficiencies of decision making units (DMUs) with multiple inputs and multiple outputs. However, in real life applications, undesirable outputs may be present in the production process which needs to be minimized. The present study endeavors to propose a DEA model with undesirable outputs and further to extend it in fuzzy environment in view of the fact that input/output data are not always available in exact form in real life problems. We propose a fuzzy DEA model with undesirable fuzzy outputs which can be solved as crisp linear program for each α in (0, 1] using α-cut approach. Further, cross-efficiency technique is applied to increase the discrimination power of the proposed models and to rank the efficient DMUs at every α in (0, 1]. Moreover, for better understanding of the proposed methodology, we present a numerical illustration followed by an application to the banking sector in India. This is the first study which attempts to measure the performance of public sector banks (PuSBs) in India using fuzzy input/output data for the period 2009–2011. The results obtained from the proposed methodology not only depict the impact of undesirable output on the performance of PuSBs but also analyze efficiently the influence of the presence of uncertainty in the data over the efficiency results. The findings show that the efficiency results of many PuSBs vary with the variation in α during the selected period.     

一种适用于物联网设备的自适应跳频方法

由于2.4 GHz频段的免授权性和开放性,新的无线系统在该频段内的自由接入必定会受到同频系统的干扰。针对上述问题,采用自适应跳频技术保证通信双方在通信中自动适应信道变化,实时避开频率集中被干扰的频点,提高通信的可靠性和抗干扰能力。首先介绍自适应跳频技术的2种相关算法,时间同步及信道评估算法,接着介绍基于CC2500的物联网设备的具体实现流程,并采用公共信道实现跳频信令的传输,在基于CC2500的物联网设备上做了具体实践,最后通过实验分析表明,该跳频算法具有一定的实用性和先进性。     

Fast and robust face recognition via coding residual map learning based adaptive masking

Robust face recognition (FR) is an active topic in computer vision and biometrics, while face occlusion is one of the most challenging problems for robust FR. Recently, the representation (or coding) based FR schemes with sparse coding coefficients and coding residual have demonstrated good robustness to face occlusion; however, the high complexity of l₁-minimization makes them less useful in practical applications. In this paper we propose a novel coding residual map learning scheme for fast and robust FR based on the fact that occluded pixels usually have higher coding residuals when representing an occluded face image over the non-occluded training samples. A dictionary is learned to code the training samples, and the distribution of coding residuals is computed. Consequently, a residual map is learned to detect the occlusions by adaptive thresholding. Finally the face image is identified by masking the detected occlusion pixels from face representation. Experiments on benchmark databases show that the proposed scheme has much lower time complexity but comparable FR accuracy with other popular approaches.     

Probabilistic pseudo-morphology for grayscale and color images

Mathematical morphology offers popular image processing tools, successfully used for binary and grayscale images. Recently, its extension to color images has become of interest and several approaches were proposed. Due to various issues arising from the vectorial nature of the data, none of them imposed as a generally valid solution. We propose a probabilistic pseudo-morphological approach, by estimating two pseudo-extrema based on Chebyshev inequality. The framework embeds a parameter which allows controlling the linear versus non-linear behavior of the probabilistic pseudo-morphological operators. We compare our approach for grayscale images with the classical morphology and we emphasize the impact of this parameter on the results. Then, we extend the approach to color images, using principal component analysis. As validation criteria, we use the estimation of the color fractal dimension, color textured image segmentation and color texture classification. Furthermore, we compare our proposed method against two widely used approaches, one morphological and one pseudo-morphological.     

Multi-resolution feature fusion for face recognition

For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ “Gabor-feature hallucination”, which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation/super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods.     

An adaptive watermarking scheme for e-government document images

This paper proposes an adaptive watermarking scheme for e-government document images. The adaptive scheme combines the discrete cosine transform (DCT) and the singular value decomposition (SVD) using luminance masking. As a core of masking model in the human visual system (HVS), luminance masking is implemented to improve noise sensitivity. Genetic algorithm (GA), subsequently, is employed for the optimization of the scaling factor of the masking. Involving a number of steps, the scheme proposed through this study begins by calculating the mask of the host image using luminance masking. It is then continued by transforming the mask on each area into all frequencies domain. The watermark image, following this, is embedded by modifying the singular values of DCT-transformed host image with singular values of mask coefficient of host image and the control parameter of DCT-transformed watermark image using Genetic Algorithm (GA). The use of both the singular values and the control parameter respectively, in this case, is not only to improve the sensitivity of the watermark performance but also to avoid the false positive problem. The watermark image, afterwards, is extracted from the distorted images. The experiment results show the improved adaptive performance of the proposed scheme is in resistant to several types of attacks in comparison with the previous schemes; the adaptive performance refers to the adaptive parameter of the luminance masking functioned to improve the performance or robustness of an image from any attacks.     

Joint fingerprinting and encryption in hybrid domains for multimedia sharing in social networks

The advent of social networks and cloud computing has made social multimedia sharing in social networks become easier and more efficient. The lowered cost of redistribution, however, also invites much motivation for large-scale copyright infringement, so it is necessary to safeguard multimedia sharing for security and privacy. In this paper, we proposed a novel framework for joint fingerprinting and encryption (JFE) based on Cellular Automata (CA) and social network analysis (SNA) with the purpose of protecting media distribution in social networks. The motivation is to map the hierarchical community structure of social networks into the tree structure of Discrete Wavelet Transform (DWT) for fingerprinting and encryption. First, the fingerprint code is produced by using SNA. Then the obtained fingerprints are embedded into the DWT domain. Afterwards, CA is used for permutation in the DWT domain. Finally, the image is diffused with XOR operation in the spatial domain. The proposed method, to the best of our knowledge, is the first JFE method using CA and SNA in hybrid domains for security and privacy in social networks. The use of fingerprinting along with encryption can provide a double-layer protection for media sharing in social networks. Both theoretical analysis and experimental results validate the effectiveness of the proposed scheme.     

高速公路立交安全影响区范围确定è

为了便于高速公路安全的分析及事故预测模型的建立,明确了高速公路"立交安全影响区"这一概念,分别以500 m和100 m为统计单元,研究立交桥两侧5 km范围内的事故发生水平.结果表明:以500 m为间隔划分单元是合适的,能满足实际的需要,通过比较立交中心桩号两侧1 km、2 km、3 km和4 km范围内、外的事故发生水平,确定了立交安全影响区的范围,并给出了理论上立交区的事故水平分布图.     

切比雪夫序列初值精度有限时退化为周期序列

深入研究并分析切比雪夫序列的短周期现象,并通过数学证明,对于任意整数阶切比雪夫序列,在初值角度取有限小数时必会退化为短周期序列. 首先,通过证明得到切比雪夫序列退化为短周期序列的等价条件;然后,利用分类讨论的思想证明任意整数阶切比雪夫序列在初值角度取整数时,必会退化为短周期序列;最后,通过数学归纳法将这一结论扩展到任意有限精度初值上,得出切比雪夫序列在初值精度有限时退化为周期序列.