Parallel transfer optical packet switches

For efficient utilization of bandwidth in optical packet switching, the guard time Tg between packets should only be a small fraction of the packet transmission time Td. Since the guard time Tg of existing packet switching approaches must be larger than the reconfiguration time Tsw of optical switches, this imposes a stringent demand on the switch reconfiguration time Tsw as the transmission rate of optical fibers increases. By using batch transfer of packets or multiple switching fabrics in parallel, the requirement on the switch reconfiguration time can be significantly relaxed. The utilization of the transmission links can be greatly improved because the guard time between packets is no longer constrained by the switch reconfiguration time. © 2009 IEEE.published_or_final_versio

Fixed channel assignment in cellular radio networks using a modified genetic algorithm

With the limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel assignment becomes increasingly important. However, finding a conflict-free channel assignment with the minimum channel span is NP hard. Therefore, we formulate the problem by assuming a given channel span. Our objective is to obtain a conflict-free channel assignment among the cells, which satisfies both the electromagnetic compatibility (EMC) constraints and traffic demand requirements. We propose an approach based on a modified genetic algorithm (GA). The approach consists of a genetic-fix algorithm that generates and manipulates individuals with fixed size (i.e., in binary representation, the number of ones is fixed) and a minimum-separation encoding scheme that eliminates redundant zeros in the solution representation. Using these two strategies, the search space can be reduced substantially. Simulations on the first four benchmark problems showed that this algorithm could achieve at least 80%, if not 100%, convergence to solutions within reasonable time. In the fifth benchmark problem, our algorithm found better solutions with shorter channel span than any existing algorithms. Such significant results indicate that our approach is indeed a good method for solving the channel-assignment problem. © 1998 IEEE.published_or_final_versio

Centralized broadcast scheduling in packet radio networks via genetic-fix algorithms

An important, yet difficult, problem in the design of a packet radio network is the determination of a conflict-free broadcast schedule at a minimum cycle length. In this letter, we first formulate the problem via a within-two-hop connectivity matrix and then, by assuming a known cycle length, determine a conflict-free scheduling pattern using a centralized approach that exploits the structure of the problem via a modified genetic algorithm. This algorithm, called genetic-fix, generates and manipulates individuals with fixed size (i.e., in binary representation, the number of ones is fixed) and therefore, can reduce the search space substantially. We also propose a method to find a reasonable cycle length and shorten it gradually to obtain a near-optimal one. Simulations on three benchmark problems showed that our approach could achieve 100% convergence to solutions with optimal cycle length within reasonable time.published_or_final_versio

Improving the performance of optical burst switching with large control overhead

In optical burst switching (OBS) system, the throughput decreases rapidly with increase in control packet processing time Tcp. The negative impact of Tcp will become significant as the optical fiber transmission rate increases. By analyzing the relationship between the throughput and Tcp, we attempt to improve the throughput. Different possible solutions are discussed. We found that using extra random offset time can significantly improve the throughput at the expense of increase in data burst delay. ©2008 IEEE.published_or_final_versio

Performance Model of Multichannel Deflection-Routed All-Optical Networks With Packet Injection Control

Deflection routing is a feasible approach to resolve the output contention problem in packet-switched networks when buffering of packets is not practical. In this paper, we investigate the performance of multichannel deflection-routed networks with no packet injection control, strict packet injection control, and a simple token-bucket-based packet injection control. The analytical performance models of multichannel deflection-routed networks with strict packet injection control are derived. Simulation results show that the analytical models can accurately predict the performance regardless of the network topology, number of channels, and packet injection control methods. We observed that the end-to-end throughput-delay and the packet re-transmission performance at sources can be largely improved by using simple packet injection control mechanisms such as the proposed token-bucket-based method.postprin

Performance improvement methods for burst-switched networks

In this paper, we present a performance model of optical burst switching (OBS) that can explain the degradation of OBS throughput performance when the control packet processing time increases. We then use the proposed performance model to investigate three feasible methods to improve OBS performance without significantly increasing the implementation complexity: addition of simple fiber delay lines (FDLs), random extra offset time, and window-based channel scheduling (WBS). Additional FDLs can eliminate the negative impact caused by the variation of the offset time between control packets and data bursts. The random extra offset time approach does not require any additional hardware and computational capability in the nodes. If higher computational capability is available, WBS in general can provide better throughput improvement than that of random extra offset time when FDLs are used in the nodes to compensate the processing time. Simulation results show that a combination of the proposed methods can significantly improve OBS performance. © 2011 Optical Society of America.published_or_final_versio

The decomposition of a blocking model for connection-oriented networks

Two general-purpose decomposition methods to calculate the blocking probabilities of connection-oriented networks are presented. The methods are based on either the call status or the link status of the networks, and can significantly reduce the required computational times. A heuristic is presented to simplify the application of the proposed decomposition methods on networks with irregular topologies. Numerical examples are given to demonstrate the applications of the proposed methods. © 2004 IEEE.published_or_final_versio

Performance model of deflection-routed multi-slot batch-transfer networks

published_or_final_versionWith the recently proposed multi-slot batch-transfer (MSBT) architecture, we can build optical packet switches using slow switching fabrics with reconfiguration time larger than the guard time between packets. Since MSBT switches can provide multichannel capability with no additional hardware, we propose to combine the multichannel and deflection routing approaches for packet contention resolution in MSBT networks. As there is no analytical performance model available, we derive the required model in this paper. Simulations show that the model is very accurate. © 2008 IEEE.link_to_subscribed_fulltextThe IEEE Global Telecommunications Conference (GLOBECOM 2008), New Orleans, LO., USA, 30 November-4 December 2008

On wavelength-routed networks with reversible wavelength channels

published_or_final_versio

Cast-Gan: Learning to Remove Colour Cast from Underwater Images

Underwater images are degraded by blur and colour cast caused by the attenuation of light in water. To remove the colour cast with neural networks, images of the scene taken under white illumination are needed as reference for training, but are generally unavailable. As an alternative, one can use surrogate reference images taken close to the water surface or degraded images synthesised from reference datasets. However, the former still suffer from colour cast and the latter generally have limited colour diversity. To address these problems, we exploit open data and typical colour distributions of objects to create a synthetic image dataset that reflects degradations naturally occurring in underwater photography. We use this dataset to train Cast-GAN, a Generative Adversarial Network whose loss function includes terms that eliminate artefacts that are typical of underwater images enhanced with neural networks. We compare the enhancement results of Cast-GAN with four state-of-the-art methods and validate the cast removal with a subjective evaluation