15011396
Adaptive resource management for mobile multimedia communications in asymmetric wireless network
In the multi-service wireless networks, the bandwidth asymmetry is the distinctive characteristic. To avoid wasting uplink and downlink bandwidth in asymmetric network, admissible region for the nonreal-time (NRT) calls has been proposed. But in that scheme the static guard capacity for RT and NRT calls cannot adapt to traffic variation. In addition, the differentiated QoS services to users are not considered. In this paper, we propose an adaptive guard channel reservation (AGCR) scheme, which has the following novelties: i) the guard capacity for each class is dynamically adjusted so as to adapt to dynamic traffic, ii) the blocking probabilities of classes with higher priority are kept below certain targets and iii) differentiated QoS services to users are provided by maintaining the blocking probability ratios over different classes at the predefined targets. Simulation results have demonstrated the proposed AGCR scheme can keep the blocking probabilities of classes with higher priority below certain targets, track the ratios close to the targets well while maintaining a high bandwidth utilization
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%.
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.
37744315
Research on Network Architecture with Trustworthiness and Controllability
In this paper, the architecture of trustworthy and controllable networks is discussed to meet arising application requirements. After reviewing the lessons and experiences of success and failure in the Internet and summarizing related work, we analyze the basic targets of providing trustworthiness and controllability. Then, the anticipant architecture is introduced. Based on the resulting design, several trustworthy and controllable mechanisms are also discussed.
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.
5934964
Fuzzy control for guaranteeing absolute delays in Web servers
This paper presents a fuzzy control approach that guarantees absolute delays in Web servers. Previous work has proposed the use of classical PI controllers for delay guarantees. However, a disadvantage of the classical PI controller is that the system model, which is obtained by system identification, mismatches the real system and inevitably degrades the performance of the Web system. In contrast with classical PI controllers, fuzzy controllers are nonlinear and therefore independent of the accurate model of the plant, i.e. the controlled system. Hence, fuzzy controllers seem to be very suitable for Web servers. Our experiments show that fuzzy controllers indeed perform better than PI controllers presented in earlier papers
5592923
Temperature control with a neural fuzzy inference network
Although multilayered backpropagation neural networks (BPNNs) have demonstrated high potential in adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained online to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared, with respect to set point regulation, ramp-point tracking, and the influence of unknown impulse noise and large parameter variation in the temperature control system. The proposed NFIN scheme has the best control performance.
44754516
Noisy Speech Segmentation/Enhancement with Multiband Analysis and Neural Fuzzy Networks
This paper addresses the problem of speech segmentation and enhancement in the presence of noise. We first propose a new word boundary detection algorithm by using a neural fuzzy network (called ATF-based SONFIN algorithm) for identifying islands of word signals in fixed noise-level environment. We further propose a new RTF-based RSONFIN algorithm where the background noise level varies during the procedure of recording. The adaptive time-frequency (ATF) and refined time-frequency (RTF) parameters extend the TF parameter from single band to multiband spectrum analysis, and help to make the distinction of speech and noise signals clear. The ATF and RTF parameters can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. Due to the self-learning ability of SONFIN and RSONFIN, the proposed algorithms avoid the need of empirically determining thresholds and ambiguous rules. The RTF-based RSONFIN algorithm can also find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level by processing the temporal relations. Our experimental results show that both in the fixed and variable noise-level environment, the algorithms that we proposed achieved higher recognition rate than several commonly used word boundary detection algorithms and reduced the recognition error rate due to endpoint detection.
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 __________________________________________________________________________________________
16836522
Real-time gang schedulings with workload models for parallel computers
Gang scheduling has been shown to be an effective job scheduling policy for parallel computers that combines elements of space sharing and time sharing. We propose new policies to enable gang scheduling to adapt to environments with real-time constraints. Our work, to our best knowledge, is the first work to attempt to address the real-time aspects of gang scheduling. Our system guided by a metric, called "task utilization workload", can schedule both real-time and non-real-time tasks at the same time. We report simulation results with a family of scheduling algorithms based on our proposed metric. Our scheme is designed to be a practical scheme to be used for large scale industrial and commercial parallel systems. Preliminary simulation results also show that our proposed policy is an effective scheme to perform real-time scheduling, while scheduling non-real-time jobs with fairness and good throughput.
41121683
Decomposition Methods for Linear Support Vector Machines
In this letter, we show that decomposition methods with alpha seeding are extremely useful for solving a sequence of linear support vector machines (SVMs) with more data than attributes. This strategy is motivated by Keerthi and Lin (2003), who proved that for an SVM with data not linearly separable, after C is large enough, the dual solutions have the same free and bounded components. We explain why a direct use of decomposition methods for linear SVMs is sometimes very slow and then analyze why alpha seeding is much more effective for linear than nonlinear SVMs. We also conduct comparisons with other methods that are efficient for linear SVMs and demonstrate the effectiveness of alpha seeding techniques in model selection.