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.
4468971
Tracking a maneuvering target using neural fuzzy network
A fast target maneuver detection and highly accurate tracking technique using a neural fuzzy network based on a Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? The traditional maneuver detection algorithms, such as variable dimension filter (VDF) and input estimation (IE) etc., are computation intensive and difficult to implement in real time. To solve this problem, neural network algorithms have been issued recently. However, normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size. Furthermore, the way to decide the network structure is heuristic. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. By generating possible target trajectories including maneuver information to train the SONFIN, the trained SONFIN can detect when the maneuver occurred, the magnitude of maneuver values and when the maneuver disappeared. Without having to change the structure of the Kalman filter or modeling the maneuvering target, this new algorithm, SONFIN, can always find an economic network size with a fast learning process. Simulation results show that KF-SONFIN is superior to traditional IE and VDF methods in estimation accuracy.
11189498
On the spanning connectivity of graphs
Abstract A k-container C ( u , v ) of G between u and v is a set of k internally disjoint paths between u and v . A k -container C ( u , v ) of G is a k * - container if it contains all vertices of G . A graph G is k * - connected if there exists a k * -container between any two distinct vertices. The spanning connectivity of G , κ * ( G ) , is defined to be the largest integer k such that G is w * -connected for all 1 ⩽ w ⩽ k if G is a 1 * -connected graph. In this paper, we prove that κ * ( G ) ⩾ 2 δ ( G ) - n ( G ) + 2 if ( n ( G ) / 2 ) + 1 ⩽ δ ( G ) ⩽ n ( G ) - 2 . Furthermore, we prove that κ * ( G - T ) ⩾ 2 δ ( G ) - n ( G ) + 2 - | T | if T is a vertex subset with | T | ⩽ 2 δ ( G ) - n ( G ) - 1 .
12748109
Using leases to support server-driven consistency in large-scale systems
The paper introduces volume leases as a mechanism for providing cache consistency for large scale, geographically distributed networks. Volume leases are a variation of leases, which were originally designed for distributed file systems. Using trace driven simulation, we compare two new algorithms against four existing cache consistency algorithms and show that our new algorithms provide strong consistency while maintaining scalability and fault tolerance. For a trace based workload of Web accesses, we find that volumes can reduce message traffic at servers by 40% compared to a standard lease algorithm, and that volumes can considerably reduce the peak load at servers when popular objects are modified.
5812336
A Key Embedded Video Codec for Secure Video Multicast
The problem of controlling access to multimedia multicasts requires the distribution and maintenance of keying information. This paper proposes a new key embedded video codec for the media-dependent approach that involves the use of a data-dependent channel, and can be achieved for multimedia by using data embedding techniques. Using data embedding to convey keying information can provide an additional security and reduce the demand of bandwidth. In this paper, we develop a new statistical data embedding algorithm on compression-domain, then, by combining FEC (Forward Error Correction) algorithm and our proposed key information frame format. After a series of simulation experiments, we find that the new codec can satisfy the special demand of media-dependent approach. At the same time, the codec provides good performance.
32008979
Isomorphic Path Decompositions of Crowns
1407089
Volume Leases for Consistency in Large-Scale Systems
This article introduces volume leases as a mechanism for providing server-driven cache consistency for large-scale, geographically distributed networks. Volume leases retain the good performance, fault tolerance, and server scalability of the semantically weaker client-driven protocols that are now used on the Web. Volume leases are a variation of object leases, which were originally designed for distributed file systems. However, whereas traditional object leases amortize overheads over long lease periods, volume leases exploit spatial locality to amortize overheads across multiple objects in a volume. This approach allows systems to maintain good write performance even in the presence of failures. Using trace-driven simulation, we compare three volume lease algorithms against four existing cache consistency algorithms and show that our new algorithms provide strong consistency while maintaining scalability and fault-tolerance. For a trace-based workload of Web accesses, we find that volumes can reduce message traffic at servers by 40 percent compared to a standard lease algorithm, and that volumes can considerably reduce the peak load at servers when popular objects are modified.