Img FS: a transparent cryptography for stored images using a filesystem in userspace

Real-time encryption and decryption of digital images stored on end-user devices is a challenging task due to the inherent features of the images. Traditional software encryption applications generally suffered from the expense of user convenience, performance efficiency, and the level of security provided. To overcome these limitations, the concept of transparent encryption has been proposed. This type of encryption mechanism can be implemented most efficiently with kernel file systems. However, this approach has some disadvantages since developing a new file system and attaching it in the kernel level requires a deep understanding of the kernel internal data structure. A filesystem in userspace(FUSE) can be used to bridge the gap. Nevertheless, current implementations of cryptographic FUSE-based file systems suffered from several weaknesses that make them less than ideal for deployment. This paper describes the design and implementation of Img FS, a fully transparent cryptographic file system that resides on user space. Img FS can provide a sophisticated way to access, manage, and monitor all encryption and key management operations for image files stored on the local disk without any interaction from the user. The development of Img FS has managed to solve weaknesses that have been identified on cryptographic FUSE-based implementations. Experiments were carried out to measure the performance of Img FS over image files’ read and write against the cryptographic service, and the results indicated that while Img FS has managed to provide higher level of security and transparency, its performance was competitive with other established cryptographic FUSE-based schemes of high performance.     

Infrared Image Target Segmentation Processing Based On Space-Time Combination

This paper puts forward a scheme of target time-domain segmentation on the basis of analyzing time-domain characteristics of sequence images and targets＇ gray level characteristics in time-domain. On the suggestion of multi-variable joint distribution probability, we discover a new target segmentation method based on space-time joint on the help of the method that makes use of space-time fusing to enhance targets. And the new method has considered both targets＇ gray level distributing characteristics in single-frame image and their gray levels and moving speed continuities in bordered some frame images. Joining both of the segmented images and the realization of time-domain processing and single-frame processing parallel can greatly cut calculating time and apparently improve the effect of image segmentation. The result of experiment indicates that it is feasible to fuse two-frame image of single-frame target enhancing and time-domain target enhancing first and segment them secondly, and it can also greatly improve the precision and universality of segmentation.     

A new numerical method on American option pricing

Mathematically, the Black-Scholes model of American option pricing is a free boundary problem of partial differential equation. It is well known that this model is a nonlinear problem, and it has no closed form solution. We can only obtain an approximate solution by numerical method, but the precision and stability are hard to control, because the singularity at the exercise boundary near expiration date has a great effect on precision and stability for numerical method. We propose a new numerical method, FDA method, to solve the American option pricing problem, which combines advantages the Semi-Analytical Method and the Front-Fixed Difference Method. Using the FDA method overcomes the difficulty resulting from the singularity at the terminal of optimal exercise boundary. A large amount of calculation shows that the FDA method is more accurate and stable than other numerical methods.     

数据集成中不一致性数据相似性比较的加权算法

Reducing inconsistency is the key problem to improve data quality during data integration. In this paper,we first present a weighted algorithm of similarity coefficient which is superior to traditional algorithms if the source data have multiple characteristic items,all of which have to be taken into account ,especially during the complex infor-mation integration. Secondly ,we apply it to the experiment of telecommunication customers integrating,the results ofdata clustering show it has high feasibility and precision performance.     

Novel serpentine structure design method considering confidence level and estimation precision

Due to the importance of metal layers in the product yield,serpentine test structures are usually fabricated on test chips to extract parameters for yield prediction.In this paper,the confidence level and estimation precision of the average defect density on metal layers are investigated to minimize the randomness of experimental results and make the measured parameters more convincing.On the basis of the Poisson yield model,the method to determine the total area of all serpentine test structures is obtained using the law of large numbers and the Lindeberg-Levy theorem.Furthermore,the method to determine an adequate area of each serpentine test structure is proposed under a specific requirement of confidence level and estimation precision.The results of Monte Carlo simulation show that the proposed method is consistent with theoretical analyses.It is also revealed by wafer experimental results that the method of designing serpentine test structure proposed in this paper has better performance.     

Bagging Neural Networks for Predicting Water Consumption

Neural network ensembles can improve the generalization and stabilization ability of learning systems effectively through aggregating numbers of neural networks. It is considered as a promising kind of engineering neural computing technology. In this paper, we construct a learning model based on bagged neural network ensembles by bagging BP（Back Propagation） networks. Through analyzing the historical data of civic water consumption, the model can predict water consumptions efficiently for various kinds of users and several of periods of time. Our experiments and practical applications show that the model has high predicting precision, good stabilization as well as feasibility.     

A floating point conversion algorithm for mixed precision computations

The floating point number is the most commonly used real number representation for digital computations due to its high precision characteristics. It is used on computers and on single chip applications such as DSP chips. Double precision (64-bit) representations allow for a wider range of real numbers to be denoted. However, single precision (32-bit) operations are more efficient. Recently, there has been an increasing interest in mixed precision computations which take advantage of single precision efficiency on 64-bit numbers. This calls for the ability to interchange between the two formats. In this paper, an algorithm that converts floating point numbers from 64- to 32-bit representations is presented. The algorithm was implemented as a Verilog code and tested on field programmable gate array (FPGA) using the Quartus II DE2 board and Agilent 16821A portable logic analyzer. Results indicate that the algorithm can perform the conversion reliably and accurately within a constant execution time of 25 ns with a 20 MHz clock frequency regardless of the number being converted.     

Sensor Placement for Sensing Coverage and Data Precision in Wireless Sensor Networks

We present a novel paradigm of sensor placement concerning data precision and estimation. Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network. These sensors can cooperate with each other to obtain a precise estimate of the quantity in a real-time manner. We consider a problem on planning a minimum-cost scheme of sensor placement with desired data precision and resource consumption. Measured data is modeled as a Gaussian random variable with a changeable variance. A gird model is used to approximate the problem. We solve the problem with a heuristic algorithm using branch-and-bound method and tabu search. Our experiments demonstrate that the algorithm is correct in a certain tolerance, and it is also efficient and scalable.     

Biped walking on level ground with torso using only one actuator

It is well known that a biped robot needs actuators to walk stably on level ground. Till now, a biped robot with torso has needed at least two actuators to achieve this. Would it be possible for this kind of robot to walk on level ground with only one actuator？ This paper responds in the affirmative and proposes a simple control strategy for a planar biped robot with torso. In this control method, there is only one low gain proportional-derivative （PD） controller between the torso and the stance leg, while the swing leg remains totally free. The PD controller utilizes states of both the torso （angle and angular velocity） and the stance leg. The numerical simulations show that, by adopting this controller, a planar biped robot with torso can walk stably on level ground, and that the robot can walk with a wide range of speeds and high energy efficiency by changing the control parameters. Four period-one gaits, one of which is stable while the other three are unstable, are found by simulations. According to the literature surveyed, we are the first to have a torso only driven biped robot walk stably on level ground.     

PWM型VLSI神经网络在故障诊断中的应用

An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information, each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.     

基于Chirp-Z变换的LFMCW系统测距算法仿真研究

首先介绍了用Chirp-Z变换提高液位测量精度的算法原理,然后仿真出了线性调频连续波雷达(LFMCW)在快速傅里叶变换(FFT)、线性调频Z变换(CZT)在理想回波条件下的测距精度;同时分析并比较了两种测距方法对测距精度造成的影响。理论计算和数字仿真结果表明,此算法可显著提高计算效率和液面雷达的测距精度。     

基于小波变换模极大值特征的多模医学图像融合算法研究

文章提出了一种基于小波变换模极大值特征的多模医学图像融合算法,将待融合的图像进行小波变换提取其模极大值特征,在特征域上根据小波变换模极大值的特征实施不同权重的加权融合计算,实施逆变换重建融合图像。并对人脑的MRI-PET图像进行了融合,实验结果表明该方法能有效地将解剖信息和功能信息融合在一起,并保留原始图像的解剖结构特征。     

一种类DCT的8×8整数变换

整数变换因其高效的计算性能而被最新的国际视频编码标准 H.2 6 4采纳 ,我国的第一个音视频编码标准AVS也将采用 8× 8整数变换。变换模块的性能直接影响编解码器的性能 ,所以选择性能优异的变换基尤为重要。本文在 8× 8类 DCT整数变换原理的基础上 ,提出了一种结合能量集中率和去相关效率的变换基变换性能评价方法 ,同时考虑计算复杂度 ,在理论上选出了几组性能较好的整数变换基。通过分析视频序列测试结果 ,讨论得出不同变换基的适用场合 ,并且验证了理论评价结果的正确性     

线性离散动态系统连续化的研究

本文对线性离散动态系统的连续化问题进行了研究·应用文[1]研究结果,给出了非带限信号采样率应满足的条件,导出了其拉氏变换与Z变换间的反演公式,并对一些文献中的模糊与不确之处作了澄清。     

一种基于双树复小波变换的图像融合方法

提出了一种基于双树复小波变换的图像融合方法。采用双树复小波变换对源图像进行分解后,该方法首先对各频域分别定义一种活性测度和匹配测度,再通过相应的匹配测度来计算各频域的融合因子,然后采用加权与选择相结合的规则融合高频系数和低频系数,得到融合图像的各频域系数。最后,采用双树复小波逆变换重构得到融合图像。实验表明,该融合方法具有良好的客观评价性能和主观视觉效果。     

Web应用中数据库交互行为验证

采用定理证明和逆向工程的方法,对Web应用中的数据库交互行为进行验证。使用Z规格说明描述需求模型,根据数据库交互的源代码和转换规则得到实现模型。从实现模型中获取Web应用的相关性质,通过Z/EVES定理证明器验证这些性质是否在需求模型的 Z规格说明中得到满足。在此基础上,设计该方法的验证框架,并开发相应的原型系统。通过图书馆数据库管理系统实例证明该方法的有 效性。     

应用小波理论分析稳压器压力信号特征

压水堆的稳压器起着控制核动力一回路装置压力的作用。该文介绍了稳压器机理建模的思路。基于描述两相状态的两区模型,分析了稳压器压力信号的内在物理机制。指出当尺度系数按整数划分时,信号的连续小波变换可以按照数值求和的方法进行计算;当尺度系数为分数甚或无理数时,必须采用频域Z变换法进行计算,并给出了该算法。定义了压力信号的小波时间平均功率谱和L ipsch itz指数,对压力信号的局部奇异性进行定量描述,反映了核动力装置的运行特点,为系统运行监测提供有效支持。     

Hierarchical kernel-based rotation and scale invariant similarity

Image similarity measure has been widely used in pattern recognition and computer vision. We usually face challenges in terms of rotation and scale changes. In order to overcome these problems, an effective similarity measure which is invariant to rotation and scale is proposed in this paper. Firstly, the proposed method applies the log-polar transform to eliminate the rotation and scale effect and produces a row and column translated log-polar image. Then the obtained log-polar image is passed to hierarchical kernels to eliminate the row and column translation effects. In this way, the output of the proposed method is invariant to rotation and scale. The theoretical analysis of invariance has also been given. In addition, an effective template sets construction method is presented to reduce computational complexity and to improve the accuracy of the proposed similarity measure. Through the experiments with several image data sets we demonstrate the advantages of the proposed approach: high classification accuracy and fast.     

ARMA模型法生成粉红噪声的研究

针对现有粉红噪声的生成方法所存在的计算过程复杂,与理想粉红噪声相比偏差较大等问题,本文提出了一种利用自回归滑动平均(ARMA)模型法生成粉红噪声的新方法。首先,构造一个待定系数的ARMA模型,并通过Z变换和功率谱估计的公式进行推导;其次,利用已知的粉红噪声模拟滤波器的传递函数H(s)和双线性Z变换法推导出IIR数字滤波器的传递函数H(z),进而得到粉红噪声的ARMA模型;最后,利用MATLAB对生成的粉红噪声进行功率谱估计并与理想的粉红噪声进行对比。由MATLAB仿真结果可知,利用该方法生成的粉红噪声与理想的粉红噪声拟合度更高,完全符合粉红噪声的各项性能要求。     

电视精密跟踪控制系统的设计和应用

本文介绍了一个用于实际的电视精密跟踪控制系统,该控制系统在外场跟踪飞行体时稳 定可靠,跟踪精度为数角秒. 文中论述了人参与的复合捕获控制系统,找到了该系统参数的设计准则,即在切换时要满 足相对变化量△≤15％. 其次,重点论述了三阶无静差电视精密跟踪控制系统的设计方法,即利用Z变换直接求 解,使得系统满足最佳条件 σmax/ Ki=0和 2σmax/ K2i>0,从而,使得该控制系统满足了 高稳定度和高精度的技术指标,并在实际中获得了应用.