51888770
Statistical performance guarantees in large-scale cross-path packet switch
Recently, a quasi-static routing scheme called path switching was proposed for large-scale packet switch systems. The path-switched Clos network is called a cross-path switch. The cross-path switch is capable of supporting multirate and multicast traffic. In this paper, we develop a general framework for it to handle multimedia traffic. We study the statistical behavior of the cross-path switch using exponential bounded burstiness (EBB) processes as source session traffic models. With characterizing the service guarantees by service curves, we derive a set of statistical bounds on the delay, backlog and departure processes at the switch on a per-session basis. These bounds are necessary for providing per-session statistical quality of service (QoS) guarantees in the cross-path switch and achieving high system utilization.
40366616
Parallel routing algorithms in Benes-Clos networks
A new parallel algorithm for route assignment in Benes-Clos network is studied. In packet switching systems, switch fabrics must be able to provide internally conflict-free paths simultaneously and to accommodate packets requesting for connections in real-time as they arrive at the inputs. Most known sequential route assignment algorithms, such as the looping algorithm for Benes (1962) networks or Clos (1953) networks, are designed for circuit switching systems where the switching configuration can be rearranged at a relatively low speed. Most existing parallel routing algorithms are not practical for packet switching because they either assume the set of connection requests is a full permutation or fail to deal with output contentions among the set of input packets. We develop a parallel routing algorithm by solving a set of Boolean equations which are derived from the connection requests and the symmetric structure of the Benes network. Our approach can handle both the partial permutations and the output contention problem easily. The time complexity of our algorithm is O(log/sup 2/N), where N is the network size. Furthermore, we extend the algorithm and show that it can be applied to the Clos network if the number of central modules is a power of two.
16028378
Improving the Performance of Infomax Using Statistical Signal Processing Techniques
In this paper, we present a new method that speeds up the convergence of the infomax algorithm proposed by Bell and Sejnowski. One effect of the infomax algorithm is that the 2nd order and 4th order statistical correlations are reintroduced to the signals during the learning process due to the optimization with respect to the complete signal statistics. We show that repetitively forcing 2nd and 4th order correlations to zero speeds up the convergence and improves separating sources with fewer data points.
6758538
Separable equilibrium state probabilities via time reversal in Markovian process algebra
The reversed compound agent theorem (RCAT) is a compositional result that uses Markovian process algebra (MPA) to derive the reversed process of certain interactions between two continuous time Markov chains at equilibrium. From this reversed process, together with the given, forward process, the joint state probabilities can be expressed as a product-form, although no general algorithm has previously been given. This paper first generalises RCAT to multiple (more than two) cooperating agents, which removes the need for multiple applications and inductive proofs in cooperations of an arbitrary number of processes. A new result shows a simple stochastic equivalence between cooperating, synchronised processes and corresponding parallel, asynchronous processes. This greatly simplifies the proof of the new, multi-agent theorem, which includes a statement of the desired product-form solution itself as a product of given state probabilities in the parallel components. The reversed process and product-form thus derived rely on a solution to certain rate equations and it is shown, for the first time, that a unique solution exists under mild conditions--certainly for queueing networks and G-networks.
7698976
Segmenting Images Corrupted by Correlated Noise
Image segmentation is fundamental to many image analysis problems. It aims to partition a digital image into a set of nonoverlapping homogeneous regions. The main contribution of this paper is the development of a new segmentation procedure which is designed to segment images corrupted by correlated noise. This new segmentation procedure is based on Rissanen's minimum description length (MDL) principle and consists of two components: 1) an MDL-based criterion in which the "best" segmentation is defined as its minimizer; and 2) a merging algorithm which attempts to locate this minimizer. The performance of this procedure is illustrated via a simulation study, with promising results.
42876940
An HMM-based speech recognition IC
This paper presents the design, simulation and measurement results of a Hidden Markov Model (HMM) based isolated word recognizer IC with double mixtures. Table look-up technique is employed in this design. The chip operates at 20 MHz at 3.3 V. The recognition time is 0.5 s for a 50-word speech library. The speech IC has been verified with 467 test speech data and the recognition accuracy is 93.8%. A reference software recognizer using the same algorithm and speech library has a recognition accuracy of 94.2%. The new speech IC that uses a table look up to reduce the complexity of the circuit has approximately the same recognition accuracy as an ideal software recognizer.
207739442
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources
An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.