5221251
On Stochastic High-Level Petri Nets
28815316
Stability analysis of buffer priority scheduling policies using Petri nets
A Petri net approach to determining the conditions for stability of a re-entrant system with buffer priority scheduling policy is described in this paper. The concept of buffer boundedness based on the dynamic behavior of the marking in the system model is emphasized. The method is used to demonstrate the stability of the first buffer first served (FBFS) and the last buffer first served (LBFS) scheduling policies. finally a sufficient condition for instability of systems with a positive feedback loop (PFL) is established, and an example is given.
15949067
Security authentication of 3G-WLAN interworking
With the development of wireless Internet, the interworking of the third generation (3G) wide area wireless networks and wireless local area networks (WLAN) has become a focus of research. 3G systems have their advantages in charging management, roaming, and security facilities, and WLAN systems have the advantages of high bandwidth and low investment cost. Interworking of 3G-WLAN offers characteristics that complement each other perfectly. Several authentication protocols have been proposed to improve security. In this paper, we firstly introduce the architectures of 3G-WLAN interworking and discuss existing problems related to authentication and key agreement procedures. Then, we present two schemes by applying authentication based on EAP-TLS (extensible authentication protocol-transport layer security) and EAP-TTLS (tunneled TLS) into integrated 3G and WLAN to achieve high end-to-end security and authentication for the users. Finally, we conduct a series of performance evaluation on the energy consumption and latency of standard authentication protocols.
16150333
Self-healing group key distribution with time-limited node revocation for wireless sensor networks
Abstract A novel key distribution scheme with time-limited node revocation is proposed for secure group communications in wireless sensor networks. The proposed scheme offers two important security properties: the seal-healing re-keying message distribution which features periodic one-way re-keying with implicitly authentication, efficient tolerance for the lost re-keying messages, and seamless Traffic Encryption Key (TEK) switch without disrupting ongoing data transmissions; and the time-limited dynamic node attachment and detachment, so that both forward and backward secrecy is assured by dual directional hash chains. It is shown that the communication and computation overhead of the proposed protocol is light, and the protocol is robust under poor communication channel quality and frequent group node topology change.
12254119
Wireless network security
18052662
Probabilistic analysis on mesh network fault tolerance
Mesh networks are among the most important interconnection network topologies for large multicomputer systems. Mesh networks perform poorly in tolerating faults in the view of worst-case analysis. On the other hand, such worst cases occur very rarely in realistic situations. In this paper, we study the fault tolerance of 2-D and 3-D mesh networks under a more realistic model in which each network node has an independent failure probability. We first observe that if the node failure probability is fixed, then the connectivity probability of these mesh networks can be arbitrarily small when the network size is sufficiently large. Thus, it is practically important for multicomputer system manufacture to determine the upper bound for node failure probability when the probability of network connectivity and the network size are given. We develop a novel technique to formally derive lower bounds on the connectivity probability for 2-D and 3-D mesh networks. Our study shows that these mesh networks of practical size can tolerate a large number of faulty nodes thus are reliable enough for multicomputer systems. For example, it is formally proved that as long as the node failure probability is bounded by 0.5%, a 3-D mesh network of up to a million nodes remains connected with a probability larger than 99%.
5057977
ZPL: A Machine Independent Programming Language for Parallel Computers
The goal of producing architecture-independent parallel programs is complicated by the competing need for high performance. The ZPL programming language achieves both goals by building upon an abstract parallel machine and by providing programming constructs that allow the programmer to "see" this underlying machine. This paper describes ZPL and provides a comprehensive evaluation of the language with respect to its goals of performance, portability, and programming convenience. In particular, we describe ZPt's machine-independent performance model, describe the programming benefits of ZPL's region-based constructs, summarize the compilation benefits of the language's high-level semantics, and summarize empirical evidence that ZPL has achieved both high performance and portability on diverse machines such as the IBM SP-2, Cray T3E, and SGI Power Challenge.
206626471
Noisy Speech Segmentation/Enhancement with Multiband Analysis and Neural Fuzzy Networks
Background noise added to speech can decrease the performance of speech segmentation and enhancement. To solve this problem, new methods have been developed in this thesis. First, a new speech segmentation method (ATF-based SONFIN algorithm) is proposed in fixed noise-level environment. This method contains the multiband analysis and a neural fuzzy network, and it achieves higher recognition rate than the TF-based robust algorithm by 5%. In addition, a new speech segmentation method called RTF-based RSONFIN algorithm is proposed for variable noise-level environment. The RTF-based RSONFIN algorithm contains a recurrent neural fuzzy network. This method contains the multiband analysis and achieve higher recognition rate than the TF-based robust algorithm by 12%.
322920
Introduction to the special issue on statistical language modeling
The goal of statistical language modeling (SLM) is to estimate the likelihood (or probability) of a word string. SLM is fundamental to many natural language applications like automatic speech recognition (ASR) [Jelinek 1990], statistical machine translation (SMT) [Brown et al. 1993], and Asian language text input [Gao et al. 2002a]. The research on SLM basically involves two main tasks: modeling and estimation. Modeling is to determine the structure of a statistical model; estimation is to determine the free parameters of the model using training data. SLM usually uses a parametric model with Maximum Likelihood Estimation (MLE) and various smoothing methods to tackle data sparseness problems. Different statistical models have been proposed in the past, but n-gram models (in particular, bigram and trigram models) still dominate SLM research. SLM has recently been demonstrated as an effective framework for a few new applications, such as question answering [Berger 2001], text summarization, paraphrasing [Barzilay and Lee 2004], and information retrieval [Croft and Lafferty 2003]. However, these new applications come with new challenges. For example, in the SLM approaches to information retrieval, a language model has to be trained on a single document, an extremely small training set; while in ASR, a language model is typically trained on a million word corpus. The recent development of related techniques stimulates new modeling and estimation methods that are beyond the scope of the traditional approaches. Two representative examples of such techniques are statistical parsing and discriminative training. With the ever-increasing popularity of SLM, we think that it is the right time to assemble a special issue reflecting recent advances in both its theory and applications. It __________________________________________________________________________________________
20511547
Recurrent learning algorithms for designing optimal controllers of continuous systems
Proposes a recurrent learning algorithm for designing the controllers of continuous dynamical systems in optimal control problems. The controllers are in the form of unfolded recurrent neural nets embedded with physical laws from classical control techniques. The learning algorithm is characterized by a double forward-recurrent-loops structure for solving both temporal recurrent and structure recurrent problems. The first problem results from the nature of general optimal control problems, where the objective functions are often related to (evaluated at) some specific time steps or system states only, causing missing learning signals at some steps or states. The second problem is due to the high-order discretization of continuous systems by the Runge-Kutta method that we perform to increase accuracy. This discretization transforms the system into several identical interconnected subnetworks, like a recurrent neural net expanded in the time axis. Two recurrent learning algorithms with different convergence properties are derived; first- and second-order learning algorithms. Their computations are local and performed efficiently as net signal propagation. We also propose two new nonlinear control structures for the 2D guidance problem and the optimal PI control problem. Under the training of the recurrent learning algorithms, these controllers can be easily tuned to be suboptimal for given objective functions. Extensive computer simulations show the controllers' optimization and generalization abilities.
16309496
Scaling to the end of silicon with EDGE architectures
Microprocessor designs are on the verge of a post-RISC era in which companies must introduce new ISAs to address the challenges that modern CMOS technologies pose while also exploiting the massive levels of integration now possible. To meet these challenges, we have developed a new class of ISAs, called explicit data graph execution (EDGE), that will match the characteristics of semiconductor technology over the next decade. The TRIPS architecture is the first instantiation of an EDGE instruction set, a new, post-RISC class of instruction set architectures intended to match semiconductor technology evolution over the next decade, scaling to new levels of power efficiency and high performance.