1,147 results
Search Results
2. Dynamic Compass Models and Gathering Algorithms for Autonomous Mobile Robots.
- Author
-
Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Prencipe, Giuseppe, Zaks, Shmuel, Katayama, Yoshiaki, Tomida, Yuichi, and Imazu, Hiroyuki
- Abstract
This paper studies a gathering problem for a system of asynchronous autonomous mobile robots that can move freely in a two-dimensional plane. We consider robots equipped with inaccurate (incorrect) compasses which may point a different direction from other robots' compasses. A gathering problem is that the robots are required to eventually gather at a single point which is not given in advance from any initial configuration. In this paper, we propose several inaccurate compass models and give two algorithms which solve the gathering problem on these models. One algorithm is the first result dealing with the compasses whose indicated direction may change in every beginning of execution cycles of robots. It solves the problem when compasses point different at most π/8 from the (absolute) north. The other one solves the problem when the compasses never change its pointed direction and their difference is at most π/3 among robots. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
3. Labeling Schemes with Queries.
- Author
-
Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Prencipe, Giuseppe, Zaks, Shmuel, Korman, Amos, and Kutten, Shay
- Abstract
Recently, quite a few papers studied methods for representing network properties by assigning informative labels to the vertices of a network. Consulting the labels given to any two vertices u and v for some function f (e.g. "distance(u,v)") one can compute the function (e.g. the graph distance between u and v). Some very involved lower bounds for the sizes of the labels were proven. In this paper, we demonstrate that such lower bounds are very sensitive to the number of vertices consulted. That is, we show several almost trivial constructions of such labeling schemes that beat the lower bounds by large margins. The catch is that one needs to consult the labels of three vertices instead of two. We term our generalized model labeling schemes with queries. Additional contributions are several extensions. In particular, we show that it is easy to extend our schemes for tree to work also in the dynamic scenario. We also demonstrate that the study of the queries model can help in designing a scheme for the traditional model too. Finally, we demonstrate extensions to the non-distributed environment. In particular, we show that one can preprocess a general weighted graph using almost linear space so that flow queries can be answered in almost constant time. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
4. From Renaming to Set Agreement.
- Author
-
Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Prencipe, Giuseppe, Zaks, Shmuel, Mostefaoui, Achour, Raynal, Michel, and Travers, Corentin
- Abstract
The M-renaming problem consists in providing the processes with a new name taken from a new name space of size M. A renaming algorithm is adaptive if the size M depends on the number of processes that want to acquire a new name (and not on the total number n of processes). Assuming each process proposes a value, the k-set agreement problem allows each process to decide a proposed value in such a way that at most k different values are decided. In an asynchronous system prone to up to t process crash failures, and where processes can cooperate by accessing atomic read/write registers only, the best that can be done is a renaming space of size M = p + t where p is the number of processes that participate in the renaming. In the same setting, the k-set agreement problem cannot be solved for t ≥ k. This paper focuses on the way a solution to the renaming problem can help solving the k-set agreement problem when k ≤ t. It has several contributions. The first is a t-resilient algorithm (1 ≤ t < n) that solves the k-set agreement problem from any adaptive (n + k − 1)-renaming algorithm, when k = t. The second contribution is a lower bound that shows that there is no wait-free k-set algorithm based on an (n + k − 1)-renaming algorithm that works for any value of n, when k < t. This bound shows that, while a solution to the (n + k − 1)-renaming problem allows solving the k-set agreement problem despite t = k failures, such an additional power is useless when k < t. In that sense, in an asynchronous system made up of atomic registers, (n + k − 1)-renaming allows progressing from k > t to k = t, but does not allow bypassing that frontier. The last contribution of the paper is a wait-free algorithm that constructs an adaptive (n + k − 1)-renaming algorithm, for any value of the pair (t,k), from a failure detector of the class $\Omega^k_*$ (this last algorithm is a simple adaptation of an existing renaming algorithm). [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
5. Fast Periodic Graph Exploration with Constant Memory.
- Author
-
Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Prencipe, Giuseppe, Zaks, Shmuel, Gąsieniec, Leszek, Klasing, Ralf, and Martin, Russell
- Abstract
We consider the problem of periodic exploration of all nodes in undirected graphs by using a finite state automaton called later a robot. The robot, using a constant number of states (memory bits), must be able to explore any unknown anonymous graph. The nodes in the graph are neither labelled nor colored. However, while visiting a node v the robot can distinguish between edges incident to it. The edges are ordered and labelled by consecutive integers 1,...,d(v) called port numbers, where d(v) is the degree of v. Periodic graph exploration requires that the automaton has to visit every node infinitely many times in a periodic manner. Note that the problem is unsolvable if the local port numbers are set arbitrarily, see [8]. In this context, we are looking for the minimum function π(n), such that, there exists an efficient deterministic algorithm for setting the local port numbers allowing the robot to explore all graphs of size n along a traversal route with the period π(n). Dobrev et al. proved in [13] that for oblivious robots π(n) ≤ 10n. Recently Ilcinkas proposed another port labelling algorithm for robots equipped with two extra memory bits, see [20], where the exploration period π(n) ≤ 4n − 2. In the same paper, it is conjectured that the bound 4n − O(1) is tight even if the use of larger memory is allowed. In this paper, we disprove this conjecture presenting an efficient deterministic algorithm arranging the port numbers, such that, the robot equipped with a constant number of bits is able to complete the traversal period in π(n) ≤ 3.75n − 2 steps hence decreasing the existing upper bound. This reduces the gap with the lower bound of π(n) ≥ 2n − 2 holding for any robot. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
6. A Connectivity Based Partition Approach for Node Scheduling in Sensor Networks.
- Author
-
Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Aspnes, James, Scheideler, Christian, Arora, Anish, Madden, Samuel, and Ding, Yong
- Abstract
This paper presents a Connectivity based Partition Approach (CPA) to reduce the energy consumption of a sensor network by sleep scheduling among sensor nodes. CPA partitions sensors into groups such that a connected backbone network can be maintained by keeping only one arbitrary node from each group in active status while putting others to sleep. Nodes within each group swap between active and sleeping status occasionally to balance the energy consumption. Unlike previous approaches that partition nodes geographically, CPA is based on the measured connectivity between pairwise nodes and does not depend on nodes' locations. In this paper, we formulate node scheduling as a constrained optimal graph partition problem, and propose CPA as a distributed heuristic partition algorithm. CPA can ensure k-vertex connectivity of the backbone network for its partition so as to achieve the trade-off between saving energy and preserving network communication quality. Moreover, simulation results show that CPA outperforms other approaches in complex environments where the ideal radio propagation model does not hold. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
7. Mobile Anchor-Free Localization for Wireless Sensor Networks.
- Author
-
Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Aspnes, James, Scheideler, Christian, Arora, Anish, Madden, Samuel, and Xu, Yurong
- Abstract
In this paper, we consider how to localize individual nodes in a wireless sensor network when some subset of the network nodes can be in motion at any given time. For situations in which it is not practical or cost-efficient to use GPS or anchor nodes, this paper proposes an Anchor-Free Mobile Geographic Distributed Localization (MGDL) algorithm for wireless sensor networks. Taking advantage of the accelerometers that are present in standard motes, MGDL estimates the distance moved by each node. If this distance is beyond a threshold, then this node will trigger a series of mobile localization procedures to recalculate and update its location in the node itself. Such procedures will be stopped when the node stops moving. Data collected using Tmote Invent nodes (Moteiv Inc.) and simulations show that the proposed detection method can efficiently detect the movement, and that the localization is accurate and the communication is efficient in different static and mobile contexts. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
8. Efficient and Distributed Access Control for Sensor Networks.
- Author
-
Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Aspnes, James, Scheideler, Christian, Arora, Anish, Madden, Samuel, and Liu, Donggang
- Abstract
Sensor networks are often used to sense the physical world and provide observations for various uses. In hostile environments, it is critical to control the network access to ensure the integrity, availability, and at times confidentiality of the sensor data. This paper develops efficient methods for distributed access control in sensor networks. The paper starts with a baseline approach, which provides a more flexible and efficient way to enforce access control when compared with previous solutions. This paper then extends the baseline approach to enable privilege delegation, which allows a user to delegate its privilege to other users without using a trusted server, and broadcast query, which allows a user to access the network at a large scale efficiently. The privilege delegation and broadcast query are very useful in practice; none of the current solutions can achieve these two properties. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
9. A Simpler Proof of Preemptive Total Flow Time Approximation on Parallel Machines.
- Author
-
Bampis, Evripidis, Jansen, Klaus, Kenyon, Claire, and Leonardi, Stefano
- Abstract
We consider the classical problem of scheduling jobs in a multiprocessor setting in order to minimize the flow time (total time in the system). The performance of the algorithm, both in offline and online settings, can be significantly improved if we allow preemption: i.e., interrupt a job and later continue its execution. Preemption is inherent to make a scheduling algorithm efficient [7,8]. Minimizing the total flow time on parallel machines with preemption is known to be NP-hard on m ≥ 2 machines. Leonardi and Raz [8] showed that the well known heuristic shortest remaining processing time (SRPT) performs within a logarithmic factor of the optimal offline algorithm on parallel machines. It is not known if better approximation factors can be reached and thus SRPT, although it is an online algorithm, becomes the best known algorithm in the off-line setting. In fact, in the on-line setting, Leonardi and Raz showed that no algorithm can achieve a better bound. In this work we present a nicer and simpler proof of the approximation ratio of SRPT. The proof presented in this paper combines techniques from the original paper of Leonardi and Raz [8] with those presented in a later paper on approximating total flow time when job preemption but not job migration is allowed [2] and on approximating total flow time non-clairvoyantly [3], that is when the processing time of a job is only known at time of completion. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
10. Lifetime Maximization of Sensor Networks Under Connectivity and k-Coverage Constraints.
- Author
-
Gibbons, Phillip B., Abdelzaher, Tarek, Aspnes, James, Rao, Ramesh, Mo, Wei, Qiao, Daji, and Wang, Zhengdao
- Abstract
In this paper, we study the fundamental limits of a wireless sensor network's lifetime under connectivity and k-coverage constraints. We consider a wireless sensor network with n sensors deployed independently and uniformly in a square field of unit area. Each sensor is active with probability p, independently from others, and each active sensor can sense a disc area with radius rs. Moreover, considering the inherent irregularity of a sensor's sensing range caused by time-varying environments, we model the sensing radius rs as a random variable with mean r0 and variance r02σs2. Two active sensors can communicate with each other if and only if the distance between them is smaller than or equal to the communication radius rc. The key contributions of this paper are: (1) we introduce a new definition of a wireless sensor network's lifetime from a novel probabilistic perspective, called ω-lifetime (0 ≤ ω ≤ 1). It is defined as the expectation of the time interval during which the probability of guaranteeing connectivity and k-coverage simultaneously is at least ω; and (2) based on the analysis results, we propose a near-optimal scheduling algorithm, called PIS (Pre-planned Independent Sleeping), to achieve the network's maximum ω-lifetime, which is validated by simulation results, and present a possible implementation of the PIS scheme in the distributed manner. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
11. Wireless Communication in Random Geometric Topologies.
- Author
-
Nikoletseas, Sotiris E., Rolim, José D. P., Kučera, Luděk, and Kučera, Štěpán
- Abstract
The present paper deals with communication in random geometric topologies; in particular, we model modern wireless ad hoc networks by random geometric topologies. The paper has two goals: the first is to implement the network power control mechanism extended by several wireless engineering features and to use the model to assess communicational properties of two typical random geometric topologies known from the literature in real-life conditions. The second goal is to suggest a modification of the "LowDegree" algorithm (one of the two studied topologies), which preserves excellent power requirements of the model, but matches the performance of the standard "UnitDisk" algorithm in terms of higher total network throughput. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
12. A Novel All-Optical Neural Network Based on Coupled Ring Lasers.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Chen, Ying, Zhu, Qi-guang, and Li, Zhi-quan
- Abstract
An all-optical neural network based on coupled ring lasers is proposed in this paper. Each laser in the network has a different wavelength, representing one neuron. The network status is determined by the wavelength of the network's light output. Inputs to the network are in the optical power domain. The nonlinear threshold function required for neural-network operation is achieved optically by interaction between the lasers. A simple laser model developed in the paper has illuminated the behavior of the coupled lasers. An experimental system is implemented using single mode fiber optic components at wavelengths near 1550 nm. A number of functions are implemented to demonstrate the practicality of the new network. From the experiment, a conclusion can be obtained that the neural network is particularly robust against input wavelength variations. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
13. A Design and Implementation of Reconfigurable Architecture for Neural Networks Based on Systolic Arrays.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Wang, Qin, Li, Ang, Li, Zhancai, and Wan, Yong
- Abstract
This paper proposes a reconfigurable architecture for VLSI implementation of BP neural networks with on-chip learning. Basing on systolic arrays, this architecture can flexibly adapt to neural networks with different scales, transfer functions or learning algorithms by reconfiguration of basic processing components,. Three kinds of reconfigurable processing units (RPU) are proposed firstly basing on the analysis of neural network's reconfiguration. Secondly, the paper proposes a reconfigurable systolic architecture and the method of mapping BP networks into this architecture. The implementation of an instance on FPGA is introduced in the last. The results show that this flexible architecture can also achieve a high learning speed of 432M CUPS (Connection Updated Per Second) at 100MHz using 22 multipliers. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
14. Natural Language Human-Machine Interface Using Artificial Neural Networks.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Majewski, Maciej, and Kacalak, Wojciech
- Abstract
In this paper there is a natural language interface presented, which consists of the intelligent mechanisms of human identification, speech recognition, word and command recognition, command syntax and result analysis, command safety assessment, technological process supervision as well as human reaction assessment. In this paper there is also a review of the selected issues on recognition of speech commands in natural language given by the operator of the technological device. A view is offered of the complexity of the recognition process of the operator's words and commands using neural networks made of a few layers of neurons. The paper presents research results of speech recognition and automatic recognition of commands in natural language using artificial neural networks. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
15. Automatic Recognition and Evaluation of Natural Language Commands.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Majewski, Maciej, and Kacalak, Wojciech
- Abstract
New applications of artificial neural networks are capable of recognition and verification of effects and safety of commands given by the operator of the technological device. In this paper there is a review of the selected issues on estimation of results and safety of the operator's commands as well as supervision of the technological process. A view is offered of the complexity of effect analysis and safety assessment of commands given by the operator using neural networks. The first part of the paper introduces a new concept of modern supervising systems of the technological process using a natural language human-machine interface and discusses the general topics and issues. The second part is devoted to a discussion of more specific topics of the automatic command verification that have led to interesting new approaches and techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
16. A Constraint Satisfaction Adaptive Neural Network with Dynamic Model for Job-Shop Scheduling Problem.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Xing, Li-Ning, Chen, Ying-Wu, and Shen, Xue-Shi
- Abstract
It is well known, the Job-Shop Scheduling Problem (JSSP) is the most complicated and typical problem of all kinds of production scheduling problems, the allocation of resources over time to perform a collection of tasks. The current method has several shortcomings in solving the JSSP. In this paper, we correct these deficiencies by introducing a dynamic model that is based on an analysis of the run-time behavior of CSANN algorithm. At the same time, this paper proposes several new heuristics in order to improve the performance of CSANN. The computational simulations have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
17. Modeling and Optimization of High-Technology Manufacturing Productivity.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Xu, Sheng, Zhao, Hui-Fang, Sun, Zhao-Hua, and Bao, Xiao-Hua
- Abstract
As more and more industries experience the globalization of business activities, measuring productivity performance has become an area of concern for companies and policy makers in Europe, the United States, Japan and so on. A novel way about nonlinear regression modeling of high-technology manufacturing (HTM) productivity with the support vector machines (SVM) is presented in this paper. Optimization of labor productivity (LP) is also presented in this paper, which is based on chaos and uses the SVM regression model as the objective function. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
18. Neural Network Based Posture Control of a Human Arm Model in the Sagittal Plane.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Liu, Shan, Wang, Yongji, and Huang, Jian
- Abstract
In this paper posture control of a human arm in the sagittal plane is investigated by means of model simulations. The arm is modeled by a nonlinear neuromusculoskeletal model with two degrees of freedom and six muscles. A multilayer perceptron network is used in this paper, and effectively adapted by Levenberg-Marquardt training algorithm. The duration of next movement is regulated according as current feedback states. Simulation Results indicate that this method can maintain two joints at different location in allowable bound. The control scheme provides novel insight into neural prosthesis control and robotic control. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
19. Identification of Cell-Cycle Phases Using Neural Network and Steerable Filter Features.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Yang, Xiaodong, Li, Houqiang, Zhou, Xiaobo, and Wong, Stephen T.C.
- Abstract
In this paper, we aim to address the cell phase identification problem, and two important aspects, the feature extraction methods and the classifier design, are discussed. In our study, we first propose extracting high frequency information of different orientations using Steerable filters. Next, we employ a multi-layer neural network using the back-propagation algorithm to replace K-Nearest Neighbor (KNN) classifier which has been implemented in the Cellular Image Quantitator (CELLIQ) system [3]. Experimental results provide a comparison between the proposed steerable filter features and existing regular features which have been used in published papers [3, 5]. From the comparison, it can be concluded that Steerable filter features can effectively represent the cells in different phases and improve the classification accuracy. Neural network also has a better performance than KNN currently deployed in CELLIQ system [3]. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
20. Mining Protein Interaction from Biomedical Literature with Relation Kernel Method.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Eom, Jae-Hong, and Zhang, Byoung Tak
- Abstract
Many interaction data still exist only in the biomedical literature and they require much effort to construct well-structured data. Discovering useful knowledge from large collections of papers is becoming more important for efficient biological and biomedical researches as genomic research advances. In this paper, we present a relation kernel-based interaction extraction method to extract knowledge efficiently. We extract protein interactions of from text documents with relation kernel and Yeast was used as an example target organism. Kernel for relation extraction is constructed with predefined interaction corpus and set of interaction patterns. The proposed method only exploits shallow parsed documents. Experimental results show that the proposed kernel method achieves a recall rate of 79.0% and precision rate of 80.8% for protein interaction extraction from biomedical document without full parsing efforts. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
21. Multiple-Point Bit Mutation Method of Detector Generation for SNSD Model.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, and Tan, Ying
- Abstract
In self and non-self discrimination (SNSD) model, it is very important to generate a desirable detector set since it decides the performance and scale of the SNSD model based task. By using the famous principle of negative selection in natural immune system, a novel generating algorithm of detector, multiple-point bit mutation method, is proposed in this paper. It utilizes random multiple-point mutation to look for non-self detectors in a large range in the whole space of detectors, such that we can obtain a required detector set in a reasonable computation time. This paper describes the work procedure of the proposed detector generating algorithm. We tested the algorithm by using many datasets and compared it with the Exhaustive Detector Generating Algorithm in details. The experimental results show that the proposed algorithm outperforms the Exhaustive Detector Generating Algorithm both in computational complexities and detection performance. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
22. A Neural Network Decision-Making Mechanism for Robust Video Transmission over 3G Wireless Network.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Wen, Jianwei, Dai, Qionghai, and Jin, Yihui
- Abstract
This paper addresses the important issues of error control for video transmission over 3G, considering the fact that wireless video delivery faces the huge challenge of the high error rate and time variability in wireless channel. This paper proposes a real world statistics based event-trigger bit error rate (BER) model, which can describe and handle the time-varying wireless channel error characteristics better. Moreover, a recurrent neural network is employed to decide the state transfer as a mechanism. Simulation results and comparisons demonstrate effectiveness and efficiency of the proposed method in term of visual performance and transmission efficiency over a variety of wireless channel conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
23. The LD-CELP Gain Filter Based on BP Neural Network.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Zhang, Gang, Xie, Keming, Zhao, Zhefeng, and Xue, Chunyu
- Abstract
The recommendation G.728 depends on the Levinson-Durbin (L-D) algorithm to update its gain filter coefficients. In this paper, it is contrasted with BP neural network method. Because quantizer has not existed at optimizing gain filter, the quantization SNR can not be used to evaluate its performance. This paper proposes a scheme to estimate SNR so that gain predictor can be separately optimized with quantizer. Using BP neural network filter, the calculation quantity is only 6.7 percent of L-D method's and its average segment SNR is about 0.156dB higher than G.728. It is also used to evaluate the case that excitation vector is 16 or 20 samples, respectively, the BP neural network algorithm has similarly good result. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
24. Intelligent Built-in Test (BIT) for More-Electric Aircraft Power System Based on Hybrid Generalized LVQ Neural Network.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Liu, Zhen, Lin, Hui, and Luo, Xin
- Abstract
This paper proposes a hybrid neural network model based on the Generalized Learning Vector Quantization(GLVQ) learning algorithm and applies this proposed method to the BIT system of More-Electric Aircraft Electrical Power System (MEAEPS). This paper first discusses the feasibility of application unsupervised neural networks to the BIT system and the representative Generalized LVQ (GLVQ) neural network is selected due to its good performance in clustering analysis. Next, we adopt a new form of loss factor to modify the original GLVQ algorithm in order to make it more suitable for our application. Since unsupervised networks cannot distinguish the similar classes, we add a LVQ layer to the GLVQ network to construct a hybrid neural network model. Finally, the proposed method has been applied to the intelligent BIT system of the MEAEPS, and the results show that the proposed method is promising to improve the performance of the BIT system. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
25. Generalized Minimum Variance Neuro Controller for Power System Stabilization.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Ko, Hee-Sang, Lee, Kwang Y., Kang, Min-Jae, and Kim, Ho-Chan
- Abstract
This paper presents a power system stabilizer design that uses a generalized minimum variance-inverse dynamic neuro controller, which is the combination of the inverse dynamic neural model, the generalized minimum variance, and the neuro compensator. An inverse dynamic neural model represents the inverse dynamics of the system. The inverse dynamic neural model is trained to provide control input into the system, which makes the plant output reach the target value at the next sampling time. Once the inverse dynamic neural model is trained, it does not require retuning for cases with other types of disturbances. In this paper, a generalized minimum variance control scheme is adapted to prevent unstable system performance caused by non-minimum phase characteristics. In addition, a neural compensator is designed to compensate for modeling errors. The proposed control scheme is tested in a multimachine power system. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
26. Feeder Load Balancing Using Neural Network.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Ukil, Abhisek, Siti, Willy, and Jordaan, Jaco
- Abstract
The distribution system problems, such as planning, loss minimization, and energy restoration, usually involve the phase balancing or network reconfiguration procedures. The determination of an optimal phase balance is, in general, a combinatorial optimization problem. This paper proposes optimal reconfiguration of the phase balancing using the neural network, to switch on and off the different switches, allowing the three phases supply by the transformer to the end-users to be balanced. This paper presents the application examples of the proposed method using the real and simulated test data. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
27. Study of Neural Networks for Electric Power Load Forecasting.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Wang, Hui, Li, Bao-Sen, Han, Xin-Yang, Wang, Dan-Li, and Jin, Hong
- Abstract
Electric Power Load Forecasting is important for the economic and secure operation of power system, and highly accurate forecasting result leads to substantial savings in operating cost and increased reliability of power supply. Conventional load forecasting techniques, including time series methods and stochastic methods, are widely used by electric power companies for forecasting load profiles. However, their accuracy is limited under some conditions. In this paper, neural networks have been successfully applied to load forecasting. Forecasting model with Neural Networks is set up based on the analysis of the characteristics of electric power load, and it works well even with rapidly changing weather conditions. This paper also proposes a novel method to improve the generalization ability of the Neural Networks, and this leads to further increasing accuracy of load forecasting. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
28. Grasping Control of Robot Hand Using Fuzzy Neural Network.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Chen, Peng, Hasegawa, Yoshizo, and Yamashita, Mitushi
- Abstract
In this paper, we propose a grasping control method for robot hand using fuzzy theory and partially- linearized neural network. The robot hand has Double-Octagon Tactile Sensor (D.O.T.S), which has been proposed in our previous papers, to detect grasping force between the grasped object and the robot fingers. Because the measured forces are fluctuant due to the measuring error and vibration of the hand, the tactile information is ambiguous. In order to quickly control the grasping force to prevent the grasped object sliding out off the robot fingers, we apply the possibility theory to deal with the ambiguous problem of the tactile information, and use the partially- linearized neural network (P.L.N.N) to construct a fuzzy neural network. The method proposed in this paper is verified by applying it to practical grasping control of breakable objects, such as eggs, fruits, etc. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
29. PD Control of Overhead Crane Systems with Neural Compensation.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Toxqui, Rigoberto Toxqui, Yu, Wen, and Li, Xiaoou
- Abstract
This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compensated effectively. This new neural control can resolve the two problems for overhead crane control: 1) decrease steady-state error of normal PD control. 2) guarantee stability via neural compensation. By Lyapunov method and input-to-state stability technique, we prove that these robust controllers with neural compensators are stable. Real-time experiments are presented to show the applicability of the approach presented in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
30. Identification and Control of Dynamic Systems Based on Least Squares Wavelet Vector Machines.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Li, Jun, and Liu, Jun-Hua
- Abstract
A novel least squares support vector machines based on Mexican hat wavelet kernel is presented in the paper. The wavelet kernel which is admissible support vector kernel is characterized by its local analysis and approximate orthogonality, and we can well obtain estimates for regression by applying a least squares wavelet support vector machines (LS-WSVM). To test the validity of the proposed method, this paper demonstrates that LS-WSVM can be used effectively for the identification and adaptive control of nonlinear dynamical systems. Simulation results reveal that the identification and adaptive control schemes suggested based on LS-WSVM gives considerably better performance and show faster and stable learning in comparison to neural networks or fuzzy logic systems. LS-WSVM provides an attractive approach to study the properties of complex nonlinear system modeling and adaptive control. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
31. Implementable Adaptive Backstepping Neural Control of Uncertain Strict-Feedback Nonlinear Systems.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Chen, Dingguo, and Yang, Jiaben
- Abstract
Presented in this paper is neural network based adaptive control for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. A popular recursive design methodology - backstepping is employed to systematically construct feedback control laws and associated Lyapunov functions. The significance of this paper is to make best use of available signals, avoid unnecessary parameterization, and minimize the node number of neural networks as on-line approximators. The design assures that all the signals in the closed loop are semi-globally uniformly, ultimately bounded and the outputs of the system converges to a tunable small neighborhood of the desired trajectory. Novel parameter tuning algorithms are obtained on a more practical basis. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
32. Fractional Order Digital Differentiators Design Using Exponential Basis Function Neural Network.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Liao, Ke, Yuan, Xiao, Pu, Yi-Fei, and Zhou, Ji-Liu
- Abstract
In this paper, the topic of fractional order digital differentiators (FODD) is designed using neural networks approximation method. First, FODD amplitude response is given in the form of sum of exponential basis functions. Then, the exponential basis function neural network is used to approximate FODD amplitude response. Finally, some examples compared with others' method are given to illustrate the advantages of this paper approach. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
33. Object Detection Using Unit-Linking PCNN Image Icons.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Gu, Xiaodong, Wang, Yuanyuan, and Zhang, Liming
- Abstract
A new approach to object detection using image icons based on Unit-linking PCNN (Pulse Coupled Neural Network) is introduced in this paper. Unit-linking PCNN, which has been developed from PCNN exhibiting synchronous pulse bursts in cat and monkey visual cortexes, is a kind of time-space-coding SNN (Spiking Neural Network). We have used Unit-linking PCNN to produce the global image icons with translation and rotation invariance. Unit-linking PCNN image icon (namely global image icons) is the 1-dimentional time series, and is a kind of image feature extracted from the time information that Unit-linking PCNN code the 2-dimentional image into. Its translation and rotation invariance is a good property in object detection. In addition to translation, rotation invariance, the object detection approach in this paper is also independent of scale variation. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
34. Evolutionary Cellular Automata Based Neural Systems for Visual Servoing.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Lee, Dong-Wook, Park, Chang-Hyun, and Sim, Kwee-Bo
- Abstract
This paper presents an evolutionary cellular automata based neural systems (Evolutionary CANS) for visual servoing of RV-M2 robot manipulator. The architecture of CANS consist of a two-dimensional (2-D) array of basic neurons. Each neuron of CANS has local connections only with contiguous neuron and acts as a form of pulse according to the dynamics of the chaotic neuron model. CANS are generated from initial cells according to the cellular automata (CA) rule. Therefore neural architecture is determined by both initial pattern of cells and production rule of CA. Production rules of CA are evolved based on a DNA coding. DNA coding has the redundancy and overlapping of gene and is apt for representation of the rule. In this paper we show the general expression of CA rule and propose translating method from DNA code to CA rule. In addition, we present visual servoing application using evolutionary CANS. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
35. The Clustering Solution of Speech Recognition Models with SOM.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Du, Xiu-Ping, and He, Pi-Lian
- Abstract
This paper first introduces the system requirement and the system flow of the auto-plotting system. As the data points needed by the auto-plotting system coming from the remote speech signals, to reach high recognition accuracy, the Hidden Markov Model (HMM) approach was chosen as the speech recognition approach. Then the paper is detailed on the speaker dependent (SD), speaker independent (SI) and speaker adaptive (SA) speech recognition methods. We proposed the n-speech models SD system as the recognition system to gain the highest recognition performance in varying speech environments. However the system required that searching for the optimal model from the database should finish in 5 minutes, so the paper finally describes how the Self-Organizing Map (SOM) was used to pre clustering to the n-speech models, to decrease the time for speech recognition and results evaluation and decrease matching time, Experiments show the n-speech models SD system can select the best-matching model in the limited time and improve the average speech recognition accuracy to 97.2. It ideally suits the system requirements. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
36. An Incremental Linear Discriminant Analysis Using Fixed Point Method.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Chen, Dongyue, and Zhang, Liming
- Abstract
Linear Discriminant Analysis (LDA) is a very powerful method in pattern recognition. But it is difficult to realize online processing for data stream. In this paper, a new adaptive LDA method is proposed. We decompose the online LDA problem into two adaptive PCA problems and develop a fixed point adaptive PCA to implement adaptive LDA. Online updating of in-class scatter matrix Sw(t) and covariance matrix Cx(t) are derived in this paper. Simulation results show that the proposed method has no learning rate, fast convergence and less time-consuming. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
37. Improved Locally Linear Embedding Through New Distance Computing.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Wang, Heyong, Zheng, Jie, Yao, Zhengan, and Li, Lei
- Abstract
Locally linear embedding (LLE) is one of the methods intended for dimensionality reduction, which relates to the number K of nearest-neighbors points to be initially chosen. So, in this paper, we want that the parameter K has little influence on the dimension reduction, that is to say, the parameter K can be widely chosen while not influence the effect of dimension reduction. Therefore, we propose a method of improved LLE, which uses new distance computing for weight of K nearest-neighbors points in LLE. Thus, even when the number K is little, the improved LLE can get good results of dimension reduction, while the traditional LLE needs a larger number of K to get the same results. When the number K of the nearest neighbors gets larger, test in this paper has proved that the improved LLE can still get correct results. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
38. A Modified Constructive Fuzzy Neural Networks for Classification of Large-Scale and Complicated Data.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Wang, Lunwen, Wu, Yanhua, Tan, Ying, and Zhang, Ling
- Abstract
Constructive fuzzy neural networks (i.e., CFNN) proposed in [1] cannot be used for non-numerical data. In order to use CFNN to deal with non-numerical complicated data, rough set theory is adopted to improve the CFNN in this paper. First of all, we use rough set theory to extract core set of non-numerical attributes and decrease number of dimension of samples by reducing redundancy. Secondly, we can pre-classify the samples according to non-numerical attributes. Thirdly, we use CFNN to classify the samples according to numerical attributes. The proposed method not only increases classification accuracy but also speeds up classification process. Finally, the classification of wireless communication signals is given as an example to illustrate the validation of the proposed method in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
39. A Novel Input Stochastic Sensitivity Definition of Radial Basis Function Neural Networks and Its Application to Feature Selection.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Wang, Xi-Zhao, and Zhang, Hui
- Abstract
For a well-trained radial basis function neural network, this paper proposes a novel input stochastic sensitivity definition and gives its computational formula assuming the inputs are modelled by normal distribution random variables. Based on this formula, one can calculate the magnitude of sensitivity for each input (i.e. feature), which indicates the degree of importance of input to the output of neural network. When there are redundant inputs in the training set, one always wants to remove those redundant features to avoid a large network. This paper shows that removing redundant features or selecting significant features can be completed by choosing features with sensitivity over a predefined threshold. Numerical experiment shows that the new approach to feature selection performs well. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
40. Identification of Mixing Matrix in Blind Source Separation.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Li, Xiaolu, and He, Zhaoshui
- Abstract
Blind identification of mixing matrix approach and the corresponding algorithm are proposed in this paper. Usually, many conventional Blind Source Separation (BSS) methods separate the source signals by estimating separated matrix. Different from this way, we present a new BSS approach in this paper, which achieves BSS by directly identifying the mixing matrix, especially for underdetermined case. Some experiments are conducted to check the validity of the theory and availability of the algorithm in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
41. Comparative Study of Extreme Learning Machine and Support Vector Machine.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Wei, Xun-Kai, Li, Ying-Hong, and Feng, Yue
- Abstract
Comparative study of extreme learning machine (ELM) and support vector machine (SVM) is investigated in this paper. A cross validation method for determining the appropriate number of neurons in the hidden layer is also proposed in this paper. ELM proposed by Huang, et al [3] is a novel machine-learning algorithm for single hidden-layer feedforward neural network (SLFN), which randomly chooses the input weights and hidden-layer bias, and analytically determines the output weights optimally instead of tuning them. This algorithm tends to produce good generalization ability and obtain least experience risk simultaneously with solid foundations. Benchmark tests of a real Tennessee Eastman Process (TEP) are carried out to validate its superiority. Compared with SVM, this proposed algorithm is much faster and has better generalization performance than SVM in the case studied in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
42. An SVM Classification Algorithm with Error Correction Ability Applied to Face Recognition.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Wang, Chengbo, and Guo, Chengan
- Abstract
This paper presents an SVM classification algorithm with predesigned error correction ability by incorporating the error control coding schemes used in digital communications into the classification algorithm. The algorithm is applied to face recognition problems in the paper. Simulation experiments are conducted for different SVM-based classification algorithms using both PCA and Fisherface features as input vectors respectively to represent the images with dimensional reduction, and performance analysis is made among different approaches. Experiment results show that the error correction SVM classifier of the paper outperforms other commonly used SVM-based classifiers both in recognition rate and error tolerance. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
43. Gradient Based Fuzzy C-Means Algorithm with a Mercer Kernel.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Park, Dong-Chul, Tran, Chung Nguyen, and Park, Sancho
- Abstract
In this paper, a clustering algorithm based on Gradient Based Fuzzy C-Means with a Mercer Kernel, called GBFCM (MK), is proposed. The kernel method adopted in this paper implicitly performs nonlinear mapping of the input data into a high-dimensional feature space. The proposed GBFCM(MK) algorithm is capable of dealing with nonlinear separation boundaries among clusters. Experiments on a synthetic data set and several real MPEG data sets show that the proposed algorithm gives better classification accuracies than both the conventional k-means algorithm and the GBFCM. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
44. Exponential Stability of Delayed Stochastic Cellular Neural Networks.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Liao, Wudai, Xu, Yulin, and Liao, Xiaoxin
- Abstract
In view of the character of saturation linearity of output functions of neurons of the cellular neural networks, the method decomposing the state space to sub-regions is adopted to study almost sure exponential stability on delayed cellular neural networks which are in the noised environment. When perturbed terms in the model of the neural network satisfy Lipschitz condition, some algebraic criteria are obtained. The results obtained in this paper show that if an equilibrium of the neural network is the interior point of a sub-region, and an appropriate matrix related to this equilibrium has some stable degree to stabilize the perturbation, then the equilibrium of the delayed cellular neural network can still remain the property of exponential stability. All results in the paper is only to compute eigenvalues of matrices. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
45. Stability Analysis of Reaction-Diffusion Recurrent Cellular Neural Networks with Variable Time Delays.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Zheng, Weifan, Zhang, Jiye, and Zhang, Weihua
- Abstract
In this paper, the global exponential stability of a class of recurrent cellular neural networks with reaction-diffusion and variable time delays was studied. When neural networks contain unbounded activation functions, it may happen that equilibrium point does not exist at all. In this paper, without assuming the boundedness, monotonicity and differentiability of the active functions, the algebraic criteria ensuring existence, uniqueness and global exponential stability of the equilibrium point of neural networks are obtained. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
46. Global Asymptotical Stability of Cohen-Grossberg Neural Networks with Time-Varying and Distributed Delays.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Chen, Tianping, and Lu, Wenlian
- Abstract
In this paper, we discuss delayed Cohen-Grossberg neural networks with time-varying and distributed delays and investigate their global asymptotical stability of the equilibrium point. The model proposed in this paper is universal. A set of sufficient conditions ensuring global convergence and globally exponential convergence for the Cohen-Grossberg neural networks with time-varying and distributed delays are given. Most of the existing models and global stability results for Cohen-Grossberg neural networks, Hopfield neural networks and cellular neural networks can be obtained from the theorems given in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
47. Global Asymptotical Stability in Neutral-Type Delayed Neural Networks with Reaction-Diffusion Terms.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Qiu, Jianlong, and Cao, Jinde
- Abstract
In this paper, the global uniform asymptotical stability is studied for delayed neutral-type neural networks by constructing appropriate Lyapunov functional and using the linear matrix inequality (LMI) approach. The main condition given in this paper is dependent on the size of the measure of the space, which is usually less conservative than space-independent ones. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed criteria. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
48. A Neural Model on Cognitive Process.
- Author
-
Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Wang, Rubin, Yu, Jing, and Zhang, Zhi-kang
- Abstract
In this paper we studied a new dynamic evolution model on phase encoding in population of neuronal oscillators under condition of different phase, and investigated neural information processing in cerebral cortex and dynamic evolution under action of different stimulation signal. It is obtained that evolution of the averaging number density along with time in space of three dimensions is described in different cluster of neuronal oscillators firing action potential at different phase space by means of method of numerical analysis. The results of numerical analysis show that the dynamic model proposed in this paper can be used to describe mechanism of neurodynamics on attention and memory. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
49. Universally Composable Identity-Based Encryption.
- Author
-
Nguyen, Phong Q., Nishimaki, Ryo, Manabe, Yoshifumi, and Okamoto, Tatsuaki
- Abstract
Identity-based encryption (IBE) is one of the most important primitives in cryptography, and various security notions of IBE (e.g., IND-ID-CCA2, NM-ID-CCA2, IND-sID-CPA etc.) have been introduced. The relations among them have been clarified recently. This paper, for the first time, investigates the security of IBE in the universally composable (UC) framework. This paper first defines the UC-security of IBE, i.e., we define the ideal functionality of IBE, $\mathcal{F}_\mathrm{IBE}$. We then show that UC-secure IBE is equivalent to conventionally-secure (IND-ID-CCA2-secure) IBE. Keywords: identity-based encryption, IND-ID-CCA2, universal composition. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
50. How to Construct Sufficient Conditions for Hash Functions.
- Author
-
Nguyen, Phong Q., Sasaki, Yu, Naito, Yusuke, Yajima, Jun, Shimoyama, Takeshi, Kunihiro, Noboru, and Ohta, Kazuo
- Abstract
Wang et al. have proposed collision attacks for various hash functions. Their approach is to first construct a differential path, and then determine the conditions (sufficient conditions) that maintain the differential path. If a message that satisfies all sufficient conditions is found, a collision can be generated. Therefore, in order to apply the attack of Wang et al., we need techniques for constructing differential paths and for determining sufficient conditions. In this paper, we propose the "SC algorithm", an algorithm that can automatically determine the sufficient conditions. The input of the SC algorithm is a differential path, that is, all message differentials and differentials of the chaining variables. The SC algorithm then outputs the sufficient conditions. The computation time of the SC algorithm is within few seconds. In applying the method of Wang et al. to MD5, there are 3 types of sufficient conditions: conditions for controlling the carry length when differentials appear in the chaining variables, conditions for controlling the output differentials of the Boolean function when the input variables of the function have differentials and conditions for controlling the relationship between the carry effect and left rotation operation. Sufficient conditions for SHA-1, SHA-0 and MD4 consist of only Type 1 and Type 2. Type 3 is unique to MD5. The SC algorithm can construct Type 1 and Type 2 conditions; we use the method of Liang et al. to construct Type 3 conditions. The complexity of the collision attack depends on the number of sufficient conditions needed. The SC algorithm constructs the fewest possible sufficient conditions. To check the feasibility of the SC algorithm, we apply it to the differential path of MD5 given by Wang et al. It is shown to yield 12 fewer conditions than the latest work on MD5. The SC algorithm is applicable to the MD-family and the SHA-family. This paper focuses on the sufficient conditions of MD5, but only as an example. Keywords: Hash Function, Collision Attack, Differential Path, Sufficient Condition. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.