Handbook of image processing operators
Handbook of image processing operators
QoS routing in networks with uncertain parameters
IEEE/ACM Transactions on Networking (TON)
QoS routing in networks with inaccurate information: theory and algorithms
IEEE/ACM Transactions on Networking (TON)
An Approach to Flexible QoS Routing with Active Networks
AMS '02 Proceedings of the Fourth Annual International Workshop on Active Middleware Services
Human iris detection using fast cooperative modular neural nets and image decomposition
Machine Graphics & Vision International Journal
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
EURASIP Journal on Applied Signal Processing
Complex-valued multistate neural associative memory
IEEE Transactions on Neural Networks
ABE: providing a low-delay service within best effort
IEEE Network: The Magazine of Global Internetworking
Surveillance of video signals over computer networks
ACC'11/MMACTEE'11 Proceedings of the 13th IASME/WSEAS international conference on Mathematical Methods and Computational Techniques in Electrical Engineering conference on Applied Computing
Real-time transmission of video streaming over computer networks
EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology
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It is highly desirable to protect data traffic from unexpected changes as well as provide effective network utilization in the internetworking era. In this paper the QoS management issue that utilizing the active network technology is discussed. Such algorithm is based on the proposed work presented in [15]. Active networks seem to be particularly useful in the context of QoS support. The Active QoS Routing (AQR) algorithm which is based on On-demand routing is implemented incorporating the product of available bit rate and delay for finding the best path for dynamic networks using the active network test bed ANTS. It is inferred that with background traffic, the AQR finds alternative paths very quickly and the delay and subsequently the jitter involved are reduced significantly. In this paper the variant of AQR implemented is demonstrated to be more useful in reducing the jitter when the overall traffic in the network is heavy and has useful application in finding effective QoS routing in ad-hoc networks as well as defending DDoS attacks by identifying the attack traffic path using QoS regulations. The main achievement of this paper is the fast attack detection algorithm. Such algorithm based on performing cross correlation in the frequency domain between data traffic and the input weights of fast time delay neural networks (FTDNNs). It is proved mathematically and practically that the number of computation steps required for the presented FTDNNs is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.