An anisotropic reference matrix for image steganography

A lot of image steganographic techniques (also called data hiding) conceal secret data by utilizing a reference matrix (RM), and some new types of RMs, such as the turtle shell, an octagon-shaped shell, the Sudoku table, and so on, have been proposed in recent years. In this article, we present a novel type of RM called anisotropic RM. By employing a full search strategy, we have found the optimal parameters for constructing the anisotropic RM. To judge the performance of a parameter set quickly, a theoretical peak signal-to-noise ratio (PSNR) evaluation method is proposed. In addition, we extend the RM to three-dimensional (3D) space. Experimental results show that our data hiding scheme, based on the anisotropic RM, has a better quality stego image than previous methods. Moreover, the 3D RM works better than the traditional 2D RM using the same embedding capacity.     

Scalable medical data compression and transmission using wavelet transform for telemedicine applications

In this paper, a novel medical data compression algorithm, termed layered set partitioning in hierarchical trees (LSPIHT) algorithm, is presented for telemedicine applications. In the LSPIHT, the encoded bit streams are divided into a number of layers for transmission and reconstruction. Starting from the base layer, by accumulating bit streams up to different enhancement layers, we can reconstruct medical data with various signal-to-noise ratios (SNRs) and/or resolutions. Receivers with distinct specifications can then share the same source encoder to reduce the complexity of telecommunication networks for telemedicine applications. Numerical results show that, besides having low network complexity, the LSPIHT attains better rate-distortion performance as compared with other algorithms for encoding medical data.     

An error resilient scheme for image transmission over noisychannels with memory

This article addresses the use of a joint source-channel coding strategy for enhancing the error resilience of images transmitted over a binary channel with additive Markov noise. In this scheme, inherent or residual (after source coding) image redundancy is exploited at the receiver via a maximum a posteriori (MAP) channel detector. This detector, which is optimal in terms of minimizing the probability of error, also exploits the larger capacity of the channel with memory as opposed to the interleaved (memoryless) channel. We first consider MAP channel decoding of uncompressed two-tone and bit-plane encoded grey-level images. Next, we propose a scheme relying on unequal error protection and MAP detection for transmitting grey-level images compressed using the discrete cosine transform (DCT), zonal coding, and quantization. Experimental results demonstrate that for various overall (source and channel) operational rates, significant performance improvements can be achieved over interleaved systems that do not incorporate image redundancy.     

S-Detector: an enhanced security model for detecting Smishing attack for mobile computing

Recently the mobile computing technology has been generally used to people with the development of the IT technology. The mobile computing environment has provided a convenient environment via the intelligent devices such as tablet PC, smartphone, etc. However, many security threats in the mobile computing environment exist. Therefore, secure elements to protect against security threats are needed. In particular, Short Message Service phishing (Smishing) damage has continued to increase with the normalization of mobile computing environment. We discuss and analyze the security considerations about Smishing in mobile computing environments. In addition, we propose an enhanced security model for detecting Smishing attack (we called “S-Detector”). The proposed model is applied to a Naive Bayes classifier to improve the Smishing attack detection in smart devices. This model distinguishes normal text message and Smishing message. And this is mainly used to filter by using statistical learning method. As a result, it is possible to analyze a text message and effectively detect SMS phishing. Finally, we demonstrate the efficiency of our model through the evaluation and analysis of our proposed model.     

采用Hilbert扫描序列短重码统计的盲隐写检测方法

在LSB行扫描序列中,基于短重码间距统计的隐写检测方法对LSB匹配等隐写技术具有良好的盲检测性能。然而此方法只能利用相邻码元的相关性,影响检测性能。理论证明了短重码间距统计量的检测能力与重码累积成功概率、短重码维数有关,并提出采用Hilbert扫描序列以提高重码累积成功概率比率来提升检测性能的盲隐写检测方法。该方法在图像LSB Hilbert扫描序列中,基于码元相同短重码统计量的分布特征,通过Poisson分布显著性检验检测隐写信息,可充分利用Hilbert曲线良好的局部相关保持特性,不仅利用了相邻码元的相关性,还利用了局部区域码元的相关性。理论分析和实验结果表明了本文方法在有效控制虚警率的前提下,具有较好的隐写检测性能。     

An electromagnetic energy coupler for medical applications

An applicator for coupling electromagnetic (EM) energy into a dielectric material such as tissue is described. The applicator is a section of a surface strip transmission line which has proved to couple the EM energy efficiently and with minimal radiation leakage around the body. It is also small, light, and flat, and hence, suitable for medical applications.     

Adaptive predictive multiplicative autoregressive model for medical image compression

In this paper, an adaptive predictive multiplicative autoregressive (APMAR) method is proposed for lossless medical image coding. The adaptive predictor is used for improving the prediction accuracy of encoded image blocks in our proposed method. Each block is first adaptively predicted by one of the seven predictors of the JPEG lossless mode and a local mean predictor. It is clear that the prediction accuracy of an adaptive predictor is better than that of a fixed predictor. Then the residual values are processed by the MAR model with Huffman coding. Comparisons with other methods [MAR, SMAR, adaptive JPEG (AJPEG)] on a series of test images show that our method is suitable for reversible medical image compression.     

An ultra-low-power RF transceiver for WBANs in medical applications

A 2.4 GHz ultra-low-power RF transceiver with a 900 MHz auxiliary wake-up link for wireless body area networks(WBANs)in medical applications is presented.The RF transceiver with an asymmetric architecture is proposed to achieve high energy efficiency according to the asymmetric communication in WBANs.The transceiver consists of a main receiver(RX)with an ultra-low-power free-running ring oscillator and a high speed main transmitter(TX)with fast lock-in PLL.A passive wake-up receiver(WuRx)for wake-up function with a high power conversion efficiency(PCE)CMOS rectifier is designed to offer the sensor node the capability of work-on-demand with zero standby power.The chip is implemented in a 0.18μm CMOS process.Its core area is 1.6 mm~2. The main RX achieves a sensitivity of-55 dBm at a 100 kbps OOK data rate while consuming just 210μA current from the 1 V power supply.The main TX achieves +3 dBm output power with a 4 Mbps/500 kbps/200 kbps data rate for OOK/4 FSK/2 FSK modulation and dissipates 3.25 mA/6.5 mA/6.5 mA current from a 1.8 V power supply. The minimum detectable RF input energy for the wake-up RX is-15 dBm and the PCE is more than 25%.     

一种用于医疗无线体域网的超低功耗射频收发机

本文提出了一种用于医疗无线体域网的2.4GHz频段超低功耗射频收发机,同时该收发机还带有900MHz频段辅助唤醒通信链路。根据无线体域网的非对称通信特点,我们提出了一种带有高能效非对称架构的射频收发机。该射频收发机包含一个带有超低功耗自由振荡环形振荡器的主接收机和带有快速锁定锁相环频率综合器的高速主发射机。我们还设计了一种带有高转换效率CMOS整流器的无源唤醒接收机,它使得传感器节点不仅具有按需工作的功能,同时还具有几乎为零的待机功耗。该芯片在0.18um CMOS工艺上实现,芯片核心面积为1.6mm2。主接收机在接收100Kbps数据率的OOK调制信号时,可以实现-55dBm的灵敏度;在1V电源电压下,仅消耗210uA电流。主发射机的发射功率为 3dBm,可以实现最大数据率为4Mbps/500Kbps/200Kbps的OOK/4FSK/2FSK调制,在1.8V电源电压下分别消耗3.25mA/6.5mA/6.5mA电流。无源唤醒接收机的最小可检测输入射频能量为-15dBm,整流器的转换效率大于25%     

An adaptive Gaussian model for satellite image deblurring

The deconvolution of blurred and noisy satellite images is an ill-posed inverse problem, which can be regularized within a Bayesian context by using an a priori model of the reconstructed solution. Since real satellite data show spatially variant characteristics, we propose here to use an inhomogeneous model. We use the maximum likelihood estimator (MLE) to estimate its parameters and we show that the MLE computed on the corrupted image is not suitable for image deconvolution because it is not robust to noise. We then show that the estimation is correct only if it is made from the original image. Since this image is unknown, we need to compute an approximation of sufficiently good quality to provide useful estimation results. Such an approximation is provided by a wavelet-based deconvolution algorithm. Thus, a hybrid method is first used to estimate the space-variant parameters from this image and then to compute the regularized solution. The obtained results on high resolution satellite images simultaneously exhibit sharp edges, correctly restored textures, and a high SNR in homogeneous areas, since the proposed technique adapts to the local characteristics of the data.     

一种改进的图像去噪与增强模型

图像的去噪和增强越来越成为制约后续图像一系列处理的瓶颈,为此提出一种图像去噪与增强的混合模型。该模型在各向异性扩散模型的基础上引入边缘指示函数,达到快速识别边缘的目的;在冲击滤波器的基础上,引入含有时间变量的边缘判定函数,适时控制冲击幅度的大小。而后将上述两个模型结合起来,克服了扩散模型仅依赖梯度信息来控制扩散进程的弊端,也适时地锐化了边缘。实验表明该混合模型不仅可以很好地去除图像的斑点噪声,并且可以达到图像增强效果。     

An object-background image model for predictive video coding

The author proposes a three-state image model whose elements are named object, background, and new-scene after their representation of the corresponding structural situation. An interframe state transition diagram controlled by luminance differences is introduced. The model is then utilized in a background luminance updating algorithm based on a maximum a posteriori probability. Applications of the model and the updating algorithm in predictive video coding at low bit rate (64 kb/s and less) are suggested     

An algorithmic solution for computing circle intersection areas and its applications to wireless communications

A novel iterative algorithm for the efficient computation of the intersection areas of an arbitrary number of circles is presented. The algorithm, on the basis of a trellis structure, hinges on two geometric results, which allow the existence check and the computation of the area of the intersection regions generated by more than three circles by simple algebraic manipulations of the intersection areas of a smaller number of circles. The presented algorithm is a powerful tool for the performance analysis of wireless networks and finds many applications, ranging from sensor to cellular networks. As an example of practical application, an insightful study of the uplink outage probability in a wireless network with cooperative access points as a function of the transmission power and access point density is presented. Copyright © 2012 John Wiley & Sons, Ltd.     

基于攻击策略的复杂网络拓扑结构优化模型

针对不考虑攻击策略时以自然连通度作为复杂网络抗毁性测度存在的缺陷,用节点的改进总连通度作为目标函数建立了基于按度攻击策略的复杂网络拓扑结构优化模型,并运用收敛速度较快的自适应局部增强微分进化算法(ADMPDE)对复杂网络的拓扑结构优化进行仿真分析。仿真结果验证了该模型的合理性和算法的适用性,优化后的复杂网络的度大节点比例提高了32%,其抗毁性得到明显改进,并得到一些有意义的结论。相关内容可为该领域的进一步研究提供有价值的参考。     

An image pixel based variational model for histogram equalization

In this paper, we develop an image pixel based histogram equalization model for image contrast enhancement. The approach is to propose a variational model containing an energy functional to adjust the pixel values of an input image directly so that the resulting histogram can be redistributed to be uniform. This idea is different from existing histogram equalization algorithms where a histogram based on the input image is constructed, a mapping is determined to output a uniform histogram and then the pixel values of the input image are adjusted based on the mapping. In the variational model, a mean brightness term is incorporated to preserve the brightness of the input image, and a geometry constraint can also be added to keep the geometry structure of the input image. Theoretically, the existence of the minimizer of the proposed model, and the convergence of the proposed algorithm are given. Experimental results are reported to demonstrate that the performance of the proposed model are competitive with the other testing histogram equalization methods for several testing images.     

An occupancy model for image retrieval and similarity evaluation

The analysis of visual information often involves the manipulation of enormous volumes of data. If some tolerance is allowed in the results, orders of magnitude improvement in efficiency can be achieved in such analysis by appropriate selective processing, without necessarily considering all the data features. To guarantee that the error introduced does not exceed the allowed limit, a certain minimum proportion of the data must be involved in the analysis. This proportion cannot be determined arbitrarily. It should be chosen based on some formal methods, with a consideration of the error inherent in the data. This paper presents some techniques for improving the retrieval efficiency in image-based information systems, with performance guarantees on the reliability of results. Using the statistical theory of occupancy, it develops a model for the formal selection of the minimal subset of image features to be involved in histogram-based similarity evaluation. This guarantees that decisions based on the minimum proportion are always the same as (or close to) the one that would have been reached by considering all the features. Results on real and simulated data show the performance of the model on speedup, robustness, scalability, and performance guarantees.     

一种改进的双变量收缩模型图像去噪

针对噪声图像低频子带含有噪声的特点,给出了一种改进的局部自适应双变量收缩模型的图像去噪算法,对于高频子带用局部自适应双变量模型进行去噪,而对低频子带用具有局部自适应的高斯模型进行去噪。该算法既体现了尺度内的聚类性,又体现了尺度间的相关性且具有很好的局部自适应性,在实验中用离散小波变换进行去噪。实验结果表明,这种改进的算法无论从峰值信噪比,还是从主观视觉效果上都要优于传统的去噪算法。     

An algorithm for fast adaptive image binarization with applications in radiotherapy imaging

Locally adaptive image binarization with a sliding-window threshold can be an effective tool for various image processing tasks. We have used the method for the detection of bone ridges in radiotherapy portal images. However, a straight-forward implementation of sliding-window processing is too time consuming for routine use. Therefore, we have developed a new thresholding criterion suitable for incremental update within the sliding window, and we show that our algorithm gives better results on difficult portal images than various publicly available adaptive thresholding routines. For small windows, the routine is also faster than an adaptive implementation of the Otsu algorithm that uses interpolation between fixed tiles, and the resulting images are equally good.     

利用模糊特征改进Snakes模型的图像分割

利用模糊特征自适应地控制曲线法向力场改进参数主动轮廓模型,改进后的模型可以对弱边缘、无边缘区域和纹理图像进行分割。曲线法向力场加速了曲线收敛到目标区域边界,改进了抓取范围和提取凹区域的能力。对弱边缘图像、医学图像和纹理的分割实验表明,新方法具有良好的性能。     

An optimized CNN-based quality assessment model for screen content image

Most existing convolutional neural network (CNN) based models designed for natural image quality assessment (IQA) employ image patches as training samples for data augmentation, and obtain final quality score by averaging all predicted scores of image patches. This brings two problems when applying these methods for screen content image (SCI) quality assessment. Firstly, SCI contains more complex content compared to natural image. As a result, qualities of SCI patches are different, and the subjective differential mean opinion score (DMOS) is not appropriate as qualities of all image patches. Secondly, the average score of image patches does not represent the quality of entire SCI since the human visual system (HVS) is sensitive to image patches containing texture and edge information. In this paper, we propose a novel quadratic optimized model based on the deep convolutional neural network (QODCNN) for full-reference (FR) and no-reference (NR) SCI quality assessment to overcome these two problems. The contribution of our algorithm can be concluded as follows: 1) Considering the characteristics of SCIs, a valid network architecture is designed for both NR and FR visual quality evaluation of SCIs, which makes the networks learn the feature differences for FR-IQA; 2) with the consideration of correlation between local quality and DMOS, a training data selection method is proposed to fine-tune the pre-trained model with valid SCI patches; 3) an adaptive pooling approach is employed to fuse patch quality to obtain image quality, owns strong noise robust and effects on both FR and NR IQA. Experimental results verify that our model outperforms both current no-reference and full-reference image quality assessment methods on the benchmark screen content image quality assessment database (SIQAD). Cross-database evaluation shows high generalization ability and high effectiveness of our model.