ADALINE based robust control in robotics: a Riemann-Liouville fractional differintegration based learning scheme

This paper presents an approach to improve the performance of intelligent sliding model control achieved by the use of a fundamental constituent of soft computing, named Adaptive Linear Element (ADALINE). The proposed scheme is based on the fractional calculus. A previously considered tuning scheme is revised according to the rules of fractional order differintegration. After a comparison with the integer order counterpart, it is seen that the control system with the proposed adaptation scheme provides (1) better tracking performance, (2) suppression of undesired drifts in parameter evolution and (3) a very high degree of robustness and insensitivity to disturbances. The claims are justified through some simulations utilizing the dynamic model of a two degrees of freedom (DOF) direct drive robot arm and overall, the contribution of the paper is to introduce the fractional order calculus into a robust and nonlinear control problem with some outperforming features that are absent when the integer order differintegration operators are adopted.     

Automated analysis of images in documents for intelligent document search

Authors use images to present a wide variety of important information in documents. For example, two-dimensional (2-D) plots display important data in scientific publications. Often, end-users seek to extract this data and convert it into a machine-processible form so that the data can be analyzed automatically or compared with other existing data. Existing document data extraction tools are semi-automatic and require users to provide metadata and interactively extract the data. In this paper, we describe a system that extracts data from documents fully automatically, completely eliminating the need for human intervention. The system uses a supervised learning-based algorithm to classify figures in digital documents into five classes: photographs, 2-D plots, 3-D plots, diagrams, and others. Then, an integrated algorithm is used to extract numerical data from data points and lines in the 2-D plot images along with the axes and their labels, the data symbols in the figure’s legend and their associated labels. We demonstrate that the proposed system and its component algorithms are effective via an empirical evaluation. Our data extraction system has the potential to be a vital component in high volume digital libraries.     

Adaptive sliding mode-like fuzzy logic control for nonlinear systems

Sliding mode-like fuzzy logic control （SMFC） algorithm for nonlinear systems is presented in this paper. Firstly dead zone parameters of sliding mode control （SMC） are selftuned by proper adaptive laws and then combined into fuzzy logic system （FLS） to compose the opportune fuzzy logic control （FLC）, which is equivalent to the predesigned SMC controller with self-tuning parameters. Robustness and invariance to the uncertainties of the closed-loop systems are improved and chattering of the SMC is eliminated. Finally simulation results of numerical examples show that the proposed control algorithm is efficient and feasible.     

Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects. Recommended by Editorial Board member Hyo-Choong Bang under the direction of Editor Hyun Seok Yang. This work was supported by the Korea Research Foundation Grant (MOEHRD) KRF-2005-204-D00002, the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MOST) R0A-2007-000-10017-0 and Engineering Research Institute at Seoul National University. Daewon Lee received the B.S. degree in Mechanical and Aerospace Engineering from Seoul National University (SNU), Seoul, Korea, in 2005, where he is currently working toward a Ph.D. degree in Mechanical and Aerospace Engineering. He has been a member of the UAV research team at SNU since 2005. His research interests include applications of nonlinear control and vision-based control of UAV. H. Jin Kim received the B.S. degree from Korea Advanced Institute of Technology (KAIST) in 1995, and the M.S. and Ph.D. degrees in Mechanical Engineering from University of California, Berkeley in 1999 and 2001, respectively. From 2002–2004, she was a Postdoctoral Researcher and Lecturer in Electrical Engineering and Computer Science (EECS), University of California, Berkeley (UC Berkeley). From 2004–2009, she was an Assistant Professor in the School of in Mechanical and Aerospace Engineering at Seoul National University (SNU), Seoul, Korea, where she is currently an Associate Professor. Her research interests include applications of nonlinear control theory and artificial intelligence for robotics, motion planning algorithms. Shankar Sastry received the B.Tech. degree from the Indian Institute of Technology, Bombay, in 1977, and the M.S. degree in EECS, the M.A. degree in mathematics, and the Ph.D. degree in EECS from UC Berkeley, in 1979, 1980, and 1981, respectively. He is currently Dean of the College of Engineering at UC Berkeley. He was formerly the Director of the Center for Information Technology Research in the Interest of Society (CITRIS). He served as Chair of the EECS Department from January, 2001 through June 2004. In 2000, he served as Director of the Information Technology Office at DARPA. From 1996 to 1999, he was the Director of the Electronics Research Laboratory at Berkeley (an organized research unit on the Berkeley campus conducting research in computer sciences and all aspects of electrical engineering). He is the NEC Distinguished Professor of Electrical Engineering and Computer Sciences and holds faculty appointments in the Departments of Bioengineering, EECS and Mechanical Engineering. Prior to joining the EECS faculty in 1983 he was a Professor with the Massachusetts Institute of Technology (MIT), Cambridge. He is a member of the National Academy of Engineering and Fellow of the IEEE.     

Elastic block ciphers: method, security and instantiations

We introduce the concept of an elastic block cipher which refers to stretching the supported block size of a block cipher to any length up to twice the original block size while incurring a computational workload that is proportional to the block size. Our method uses the round function of an existing block cipher as a black box and inserts it into a substitution- permutation network. Our method is designed to enable us to form a reduction between the elastic and the original versions of the cipher. Using this reduction, we prove that the elastic version of a cipher is secure against key-recovery attacks if the original cipher is secure against such attacks. We note that while reduction-based proofs of security are a cornerstone of cryptographic analysis, they are typical when complete components are used as sub-components in a larger design. We are not aware of the use of such techniques in the case of concrete block cipher designs. We demonstrate the general applicability of the elastic block cipher method by constructing examples from existing block ciphers: AES, Camellia, MISTY1, and RC6. We compare the performance of the elastic versions to that of the original versions and evaluate the elastic versions using statistical tests measuring the randomness of the ciphertext. We also use our examples to demonstrate the concept of a generic key schedule for block ciphers.

Logistic混沌网络会议加密系统的设计

文章采用Logistic混沌序列对实时视频传输系统进行数据的分块加／解密,并从算法的安全性、效率等方面进行了性能分析,该算法具有良好的实时性、快速性和易实施性,可以有效地防止敏感信息被轻易的窃取、篡改和传播,有效保障网络视频传输的流畅性及安全性。     

一种新结构的DM-multipath与动态负载平衡

对于存储设备,Linux 在多路径 I/O方面主要有两种类型的解决方案：位于I/O调度器之上的顶层方案(BIO映射器)和位于底层的子系统具体方案(如IDE,SCSI)。因为实施它们的所在存储堆栈中的位置不同,每种方案各有优势。通过与第一种方案比较,介绍具体硬件子系统之上的另一种解决方案,新结构中重点实现移动内核当前多路径层——多路径设备映射器 (dm-multipath)到I/O调度层之下,这种方案能够有效地支持动态的负载平衡。     

基于块I/O的RAID设计

磁盘阵列(RAID)是当前能够提供存储系统高可用性和高可靠性的一项重要技术。它通过软硬件的冗余和奇偶校验提供数据的重构和恢复。针对当前在RAID控制软件设计的过程中面临多次数据拷贝的问题,文中提出了一种基于块I/O的RAID系统设计。它利用最新的Linux内核所提供的BIO机制,插入到SCSI Target的中间层进行数据I/O的处理。它能屏蔽掉上层不同的设备驱动类型,提供到IP-SAN的无缝链接。实验表明,该设计能够减少数据的传输延迟,最大限度地提高数据传输过程中的吞吐率,避免了多次昂贵的内存拷贝操作。     

基于JND门限的小波域彩色图像数字水印算法

提出了一种基于小波域的彩色图像数字水印算法,先将图像从RGB色彩空间转化到YIQ色彩空间,在YIQ模型中取其亮度分量Y,依据块能量的大小选取合适的嵌入点,结合JND自适应地嵌入水印,然后再反变回RGB色彩空间。理论分析与仿真结果表明应用该算法实现的水印具有很好的不可见性和鲁棒性。     

Improved Cloud Storage Encryption Using Block Cipher-Based DNA Anti-Codify Model

When it comes to data storage, cloud computing and cloud storage providers play a critical role. The cloud data can be accessed from any location with an internet connection. Additionally, the risk of losing privacy when data is stored in a cloud environment is also increased. A variety of security techniques are employed in the cloud to enhance security. In this paper, we aim at maintaining the privacy of stored data in cloud environment by implementing block-based modelling to boost the privacy level with Anti-Codify Technique (ACoT) and block cipher-based algorithms. Initially, the cipher text is generated using Deoxyribo Nucleic Acid (DNA) model. Block-cipher-based encryption is used by ACoT, but the original encrypted file and its extension are broken up into separate blocks. When the original file is broken up into two separate blocks, it raises the security level and makes it more difficult for outsiders to cloud data access. ACoT improves the security and privacy of cloud storage data. Finally, the fuzzy-based classification is used that stores various access types in servers. The simulation results shows that the ACoT-DNA method achieves higher entropy against various block size with reduced computational cost than existing methods.