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The channel capacity performance of ultra-wide bandwidth systems using the dual-pulse receiver is optimized with respect to the time delay between the reference and adjacent data pulses. Approximate and theoretical expressions for the signal-to-interference-plus-noise ratio are derived. Numerical results show that the optimized receiver can provide a significant capacity improvement of up to 3.4 bits/Sec/GHz.

Food security is a major worldwide issue nowadays. One of the supporting indicators of the food security level is the trend of the global agriculture output per capita. In this study, grain data from China between 1997 and 2007 is used as a means to indicate the level of grain security. The inputs for this study are based on 3 categories, productive indexes, consumptive indexes, disaster indexes, in total there are 11 input indexes to the system with 2 membership functions (MFs) for each input. The system output is the level of the grain security, where the target data is based on a previous study of China grain security level. We use an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the grain security level. In this case data pre-processing with the Principal Component Analysis (PCA) technique was used to reduce inputs to 6 to avoid too many rule parameters which would affect the optimization performance of the model. A Multi-Layer-Perceptron-Neural-Network (MLP-NN) model is used to compare with the performance of ANFIS. The result of this study shows that the resulting regression value in the case of ANFIS is around 0.99 which is better than that for the NN, which is around 0.60. Hence the ANFIS model is shown to offer better predictor of grain security level. It may also be an attractive method to explore further as a means for food security early warning monitoring systems.

In automotive applications, the ability of optical wireless receiver amplifiers to detect fleeting signals at low energy is crucial. A new Photoparametric Amplifier (PPA) mode of operation is discussed in which the applied bias to the photodetector is minimised to maximise sensitivity for such applications. This is shown to be advantageous in terms of the desirable characteristics for PPA operation, leading to improved conversion efficiency and the potential for low noise operation. Furthermore, in experimental configuration, a nondegenerate mode is discussed which leads to optimum high gain at idler frequency. The original optical modulated signal at baseband frequency can be recovered again at high gain.

Some recent applications of methods from Intelligent Systems Engineering (ISE) to key research problem areas in optical networking are reviewed. A brief introduction to ISE for a general technical audience is given, focusing on the methods that will appear in the applications considered. Progress in generic network design and optimization techniques applicable to the optical domain is outlined, after which specific examples are described. In particular, the use of fuzzy logic in optical CDMA, the employment of genetic algorithms to optimize indoor optical wireless transmission, the use of artificial neural networks for predistortion in radio over fiber and the determination of optimum routes in optical core networks are presented. The paper concludes with a discussion of the future directions for ISE in optical networking.

This paper presents a hybrid evolutionary algorithm (HGAPSO) to maximize utilization and improve the Quality of Service (QoS) in expanding networks. Two meta-heuristic optimization algorithms, namely a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are combined to find the feasible solution within a search space of telecommunication networks. By employing a local search based priority encoding method, each individual in the GA and each particle in PSO is represented as a potential solution for the routing problem. The performance of HGAPSO is compared to both the GA and PSO alone for finding the K-shortest paths, demonstrating its superiority.

Electric noses (e-noses), taking their inspiration from the human olfactory system, have been extensively used in food quality control and human disease monitoring. This chapter presents the e-nose as a potential candidate for health monitoring and disease and pest detection on tomato plants. Two common problems in greenhouse tomatoes, namely powdery mildew and spider mites, are considered. An experimental arrangement is described based on a commercial 13-sensor e-nose where tomato plants are grown in an isolated, controlled environment inside a greenhouse. Attention is paid to the preliminary results of data post-processing using two different techniques. First, Principal Component Analysis is employed and demonstrates clear evolution of the components as the plants develop disease or infestation. Subsequently, Grey System Theory enables the identification of clear groupings in the sensor responses and thus the reduction of the model, producing stronger trend differences in the Principal Component between healthy and unhealthy plants. The results, although preliminary, show that the e-nose with appropriate data post-processing is a promising approach to monitoring the development of tomato plant diseases and infestations.

Eye bacteria are vital to the diagnosis of eye disease, which makes the classification of such bacteria necessary and important. This chapter aims to classify different kinds of eye bacteria after the data were collected by an Electronic Nose. First the Multi-layer perceptron (MLP) and decision tree (DT) were introduced as the algorithm and the base classifiers. After that, the bagging technique was introduced to both algorithms and showed that the accuracy of the MLP had been significantly improved. Moreover, bagging to the DT not only reduced the misclassification rate, but enabled DT to select the most important features, and thus, decreased the dimension of the data facilitating an enhanced training and testing process.

The diagnosis of plant diseases is an important part of commercial greenhouse crop production and can enable continued disease and pest control. A plant subject to infection typically releases exclusive volatile organic compounds (VOCs) which may be detected by appropriate sensors. In this work, an Electronic Nose (EN) is employed as an alternative to Gas Chromatography - Mass Spectrometry (GC-MS) to sample the VOCs emitted by control and artificially infected tomato plants. A case study in which powdery mildew and spider mites may be present on tomato plants is considered. The data from the EN was analyzed and visualized using Fuzzy C-Mean Clustering (FCM) and Self-Organizing Maps (SOM). The VOC samples from healthy plants were successfully distinguished from the infected ones using the clustering techniques. This study suggests that the proposed methodology is promising for enhancing the automated detection of crop pests and diseases and may be an attractive tool to be deployed in horticultural settings.

The aim of Multisensor Data Fusion (MDF) is to eliminate redundant, noisy or irrelevant information and thus find an optimal subset from an array of high dimensionality. An important feature of MDF is that the signals are constantly evolving instead of being static. This provides an opportunity for Evolutionary Computation (EC) algorithms to be developed to solve MDF tasks. This chapter describes the application of three EC algorithms to widely used datasets. Comparative studies were performed so that relative advantage and disadvantages of the different approaches could be investigated. From this study, authros found that ECs performed in the feature selection stage can greatly reduce the dataset dimensionality and hence enhance the MDF system performance; when being used in a way to represent knowledge, ECs can dramatically increase rules when input data are not clustered.

This chapter presents the initial studies on the detection of two common diseases and pests, the powdery mildew and spider mites, on greenhouse tomato plants by measuring the chemical volatiles emitted from the tomato plants as the disease develops using a Field Asymmetric Ion Mobility Spectrometry (FAIMS) device. The processing on the collected FAIMS measurements using PCA shows that clear increment patterns can be observed on all the experimental plants representing the gradual development of the diseases. Optimisation on the number of dispersion voltages to be used in the FAIMS device shows that reducing the number of dispersion voltages by a factor up to 10, preserves the key development patterns perfectly, though the amplitudes of the new patterns are reduced significantly.

The cost of laying optical fiber to the home means that digital transmission using copper twisted pairs is still widely used to provide broadband Internet access via Digital Subscriber Line (DSL) techniques. However, copper transmission systems were optimally designed for voice transmission and cause distortion of high bandwidth digital information signals. Thus equalization is needed to ameliorate the effects of the distortion. To avoid wasting precious bandwidth, it is desirable that the equalization is blind, operating without training sequences. This chapter concerns the use of a popular blind adaptive equalization algorithm, namely the Constant Modulus Algorithm (CMA) that penalizes deviations from a fixed value in the modulus of the equalizer output signal. The CMA is set in the context of blind equalization, with particular focus on systems that sample at fractions of the symbol time. Illustrative examples show the performance of the CMA on an ideal noiseless channel and in the presence of Gaussian noise. Realistic data simulations for microwave and DSL channels confirm that the CMA is capable of dealing with the non-ideal circumstances that will be encountered in practical transmission scenarios.

In this chapter, the authors introduce the principles of some of the most widely used supervised and unsupervised Pattern Recognition (PR) techniques and assess behaviour and performances. A dataset acquired from a set of experiments conducted at University of Warwick is employed to construct a case study in which the techniques will be applied. The chapter will also evaluate the integration of PR methods with an Electronic Nose (EN) device to develop and implement a plant diagnosis tool based on discriminating the Organic Volatile Compounds (VOC) released by plants when attacked by pest. The chapter concludes with a performance comparison and a brief discussion of how an appropriate PR technique can be coupled with an EN to produce a greenhouse plant pest and disease diagnosis system for day-to-day utilisation. Some consideration of further work is also presented.

The diversity of future technologies requiring ad hoc networks to operate within unpredicted situations will mean an increase in the required flexibility of the actual protocols used for communicating information. A methodology is proposed to genetically evolve the optimum ad hoc network communication protocol under any given network scenario. The methodology creates and dynamically adapts the communication protocol based upon an alphabet of characteristics and performance metrics using simple protocol mapping techniques and minimisation of a fitness function via a genetic selection process. A scenario has been created to evaluate the performance of the methodology in finding the optimum solution. Preliminary results show that the methodology is able to find the global optimum within several runs. The methodology could be enhanced using Field Programmable Gate Array (FPGA) hardware nodes for real time performance and distributed control.

This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.

The network coding problem (NCP) is an NP-hard combinatorial problem, and genetic algorithms (GAs) have recently been applied to address this problem. This paper reports a novel ripple-spreading GA (RSGA) for the NCP. In contrast to existing GAs where a chromosome directly represents a solution, the proposed RSGA separates chromosomes and solutions by introducing a purpose-designed pre-problem for the NCP. In the pre-problem, the nodes in the NCP are projected into an artificial space, in which some ripple epicenters are randomly generated. Then a specially parameterized ripple-spreading process is employed such that as ripples (starting from the epicenters) spread out in the artificial space, the incoming signals and outgoing signals of all nodes will be individually determined, according to the amplitudes of the ripples which have reached the node. Changing the values of the ripple-spreading parameters will result in different information flows in the networks. Therefore, a simple binary-string based GA, unlike existing GAs which employ permutation representations for the NCP, can be used to optimize the values of the ripple-spreading parameters, in order to find a good solution to the NCP. A potential advantage of the RSGA is its scalability in complex networks, where permutation representation based GAs may face serious memory-efficiency problems. The effectiveness of the proposed RSGA is illustrated in the context of some experiments.

In this paper, regular low-density parity-check (LDPC) codes have been applied for the first time to a low cost spectrum-sliced WDM system. The Gaussian approximation of the noise in such system has been commonly used in previous studies, and there is a standard LDPC code for symmetric additive white Gaussian noise (AWGN) channels. However, in the system considered here the actual noise character is asymmetric and chi-square distributed. Due to the specific channel property, an adaptive decoding algorithm based on the precise chi-square noise statistics has been proposed. Simulations are conducted for various code rates. The results show that such a forward error correction scheme provides significant coding gain for a dedicated system and the adaptive algorithm outperforms decoding with conventional Gaussian approximations.

A genetic algorithm controlled multispot transmitter is proposed as an alternative approach to optimising the SNR for single element receivers in fully diffuse mobile indoor optical wireless communication systems. Results are presented that show by dynamically controlling the powers of individual diffusing spots, a consistent SNR distribution can be created in a deployment environment, independent of the room illumination sources, reflection characteristics and user movement patterns. The advantageous adaptability of the genetic algorithm approach removes the need for bespoke system design and provides a more cost effective system, capable of deployment in multiple locations, whilst improving end user friendliness. The case study scenarios considered in this paper are based around the use of a single element receiver with a FOV = 55° in two non-identically illuminated rooms. A genetic algorithm is then utilised to show the concepts capability to reduce the dynamic range of the SNR, referenced against the peak SNR, by up to 13% when empty. Furthermore, the algorithm is then shown to be capable of reducing the user movement induced SNR perturbation by up to 11%, and maintaining to within 21% of this optimised case, the SNR distribution.

The optical wireless (OW) channel has attracted recent interests from researchers as a complement to the already crowded RF spectrum. The OW offers many advantages over RF, such as unregulated bandwidth and improved security and less interference with existing systems, for example. The modulation schemes developed for OW are limited by the combined power and bandwidth requirements [1], since the communication link is often exposed to intense background noise and channel dynamics. In order to improve the link quality and immunity, rule-based fuzzy logic (FL) control and adaptive neuro-fuzzy inference system (ANFIS) are applied to modulation optimisation under different channel variation. It has been demonstrated that the FL control model can be used to realise real time parameter optimisation, with preset instructions or a learned control pattern. The obtained control model has shown the ability to learn from the desired parameter optimisation, and training results agree well with preset instructions. Since the control model is rule based, it can be easily migrated into different scenarios. This is certainly helpful for high speed applications where link quality is vital.

As more intelligence and control mechanisms are added into optical networks, the need for the deployment of a reliable and secure management system using efficient control techniques has become increasingly relevant. While some of available control and management methods are applicable to different types of network architectures, many of them are not adequate for all-optical networks. These emerging transparent optical networks have particularly unique features and requirements in terms of security and quality of service thus requiring a much more targeted approach in terms of network management. In particular, the peculiar behavior of all-optical components and architectures bring forth a new set of challenges for network security. In this article, we briefly overview security and management issues that arise in all-optical networks. We then discuss the key management functions that are responsible for ensuring the secure and continued functioning of the network. Consequently, we present a framework for the realization of an appropriate management system that can meet the challenges posed by all-optical networks .

Delivery of the required QoS in IP-over-DWDM networks would face uncertain environment capacity problems due to demand variations over different periods of network operation. Therefore, future demand uncertainties should be considered in planning DWDM transport backbones for capacity sensitive telecommunication applications with enhanced quality of service. This paper, presents a genetic algorithm optimization approach for designing robust optical cores by solving the routing and wavelength assignment problem for different demand conditions. This improves the network performance in stochastic uncertain demand environments. The simulation results demonstrate the effectiveness of genetic algorithm for optimization robust optical bandwidth planning against uncertainties.

As a relatively new information theory, network coding has already resulted in a significant influence on many research areas such as communication system, network protocol, wireless network, and network security. The optimization of network coding, which aims to minimize network coding resources such as coding nodes and links, has recently attracted the attention of some researchers, who have so far focused their efforts mainly on static network coding problem (SNCP). For instance, they make the common assumption that a target rate is always achievable at every sink as long as coding is allowed at all nodes, which is unrealistic due to the dynamic nature of most real-world networks, where any link could be disconnected at any time. This paper is concerned with how to address the dynamic network coding problem (DNCP). To this end, a general formulation of DNCP is described, and then the design of an effective Genetic Algorithm (GA) for the DNCP is reported. The new problem formulation not only considers the minimization of network coding resources, but also takes into account the maximization of the rate actually achieved. The proposed GA adopts a new permutation representation, which not only makes evolutionary operations free of feasibility problems, but also makes it easy to integrate useful problem-specific heuristic rules into the algorithm. Experimental results illustrate the effectiveness of the proposed model and algorithm for DNCP.

A genetic algorithm controlled multispot transmitter is demonstrated to be capable of optimizing the received power distribution for single element receivers in fully diffuse mobile indoor optical wireless systems. By dynamically modifying the intensity of individual diffusion spots, the transmitter is capable of compensating for changes in receiver alignment, user movement and surface reflectivity characteristics, with negligible impact to bandwidth and RMS delay spread. The dynamic range, referenced against the peak received power, can be reduced by up to 27% when the room is empty, and up to 26% with user movement and variable receiver alignment. Furthermore, received power perturbation, induced by user movement, is reduced from 10% to 2.5%. This method shows potential for providing a highly adaptable solution of overcoming channel variability whilst also reducing receiver complexity.

We present an up to date review of various channel models used for optical wireless communication systems so far. Various channel models for both indoor and outdoor optical wireless communication systems are examined. For indoor environment different configurations of transmitter and receiver positions are discussed. For outdoor environment, the factors affecting the link performance such as atmospheric turbulence are investigated in terms of channel modelling. Finally, both the indoor and outdoor channel models are respectively compared in terms of computation speed, complexity and accuracy of performance.

The use of spread spectrum technology continues to be of interest in optical communication networks. Fiber-optic systems offer access to large bandwidth and ultra-high signal processing speeds are possible in the optical medium. Optical code-division multiple-access (O-CDMA) is therefore evolving into a promising candidate for next-generation broadband communications, due to its potential for enhanced information security, simplified network control and management, as well as the fair division of bandwidth. This paper presents a brief review of the O-CDMA technique followed by discussion of recent developments at Warwick. In particular, the development of a new set of padded orthogonal codes to reduce multiple-access interference (MAI), the employment of M-ary pulse-position modulation (PPM) to improve system capacity and fuzzy logic based power control.

A genetic algorithm controlled multispot transmitter is proposed as an alternative approach to optimizing the power distribution for single element receivers in fully diffuse mobile indoor optical wireless communication systems. By specifically tailoring the algorithm, it is shown that by dynamically altering the intensity of individual diffusion spots, a consistent power distribution, with negligible impact on bandwidth and rms delay spread, can be created in multiple rooms independent of reflectivity characteristics and user movement patterns. This advantageous adaptability removes the need for bespoke system design, aiming instead for the use of a more cost effective, optimal transmitter and receiver capable of deployment in multiple scenarios and applications. From the simulations conducted it is deduced, that implementing a receiver with a FOV = 55 degrees in conjunction with either of two notable algorithms, the dynamic range of the rooms, referenced against the peak received power, can be reduced by up to 26% when empty, and furthermore to within 12% of this optimized case when user movement perturbs the channel.

The effect of dispersion on a spectrum-sliced wavelength division multi-plexed system with an optical preamplifier receiver is investigated using a theoretical model based on the statistics to improve understanding of spectrum slicing in a realistic situation, where dispersion significantly impacts the signal in transmission. The results show that the optically preamplified receiver delivers increased transmission capacity and a substantially improved power budget compared to a pin receiver. The results are obtained using the saddlepoint approximation and compared to the customary Gaussian approximation, which is formed to be reasonably accurate in predicting the optimum bandwidth but conservative in sensitivity predictions.

Two-dimensional optical CDMA has been proved to be a promising access technique providing many distinctive merits. A system using fiber Bragg gratings to spectrally slice the incoherent light from a low cost light source is proposed. The optical beat noise in such an incoherent system is studied as the dominant noise source. The sliced wavelengths are thereafter encoded according a predetermined pattern. A modified prime hop code (MPHC) scheme is employed to lower the power loss and reduce the BER degradation caused by the beat noise. Comparison between MPHC and prime hop codes (PHCs) regarding the beat noise indicates that MPHC is very suitable for the proposed incoherent system

In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of longitudinal dispersion coefficient is presented. This hybrid method utilizes Genetic Algorithms (GAs) to identify variables that are being input into a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), which simplifies the neural network structure and makes the training process more efficient. Once input variables are determined, GNMM processes the data using an MLP with the back-propagation algorithm. The MLP is presented with a series of training examples and the internal weights are adjusted in an attempt to model the input/output relationship. GNMM is able to extract regression rules from the trained neural network. The effectiveness of GNMM is demonstrated by means of case study data, which has previously been explored by other authors using various methods. By comparing the results generated by GNMM to those presented in the literature, the effectiveness of this methodology is demonstrated.

The key features of spectrum slicing are discussed in the context of the provision of low cost customer access as an alternative to laser systems. The history of the method is presented followed by an outline of performance analysis techniques. Following from this, system results are introduced from on-off keying leading to promising outcomes from the use of more sophisticated coding and modulation methods. There is also discussion of the use of shared optical preamplifiers that can deliver increased throughput and sensitivity benefits of almost 11 dB.

This paper presents the integration of quality of service (QoS) requirements in routing and wavelength assignment (RWA) problems to design wavelength-routed dense wavelength division multiplexing (DWDM) optical networks. A genetic algorithm (GA) optimization approach is employed to solve this complex and multi-constraint problem under heavy load demand matrices employing the COST239 test bench. Simulation results demonstrate that the GA is efficient to establish both bandwidth and propagation delay optimization lightpaths.

The nonlinearity aspects of two widely used types of external intensity modulators, Electroabsorption modulators (EAMs) and Mach-Zehnder modulators (MZMs) are investigated in the context of multicarrier high speed optical wireless systems. The effect of number of subcarriers on system performance is examined and quantitative performance parameters are calculated to illustrate the comparative performance between the two types of modulators.

Network management is an indispensable constituent of communication systems since it is responsible for ensuring the secure and continuous functioning of any network. An integral part of any management system is fault management, which is responsible for detecting failures and isolating failed components. The key functions of fault management include fault detection, fault localization, and fault notification. They also include protection and restoration services in the event of transmission failure of links and nodes. In this paper, we propose an extension for fault management in support of the emerging generalized multi-protocol label switching framework for distributed control of optical networks.

This paper reviews the use of a photoparametric amplifier as a versatile yet elegant solution to the optical to electrical conversion stage found within optical communication systems. Basic operational principles are described along with application examples, in particular focusing on point-to-point free space links and indoor optical wireless scenarios. Broader applications, such as subcarrier multiplexing fiber systems and radio over fiber links, are also highlighted. Gain equations for upconversion and baseband detection are presented, along with a novel baseband detection double heterodyne photoparametric amplifier.

A comparative study of various multiple sub-carrier modulation (MSM) techniques suitable for optical wireless communication systems is presented. The key features of each modulation scheme are examined in the context of employability for multi-spectral optical wireless applications. A review of recent techniques for improving power efficiency of MSM systems is also presented. As a special case of MSM, the performance of orthogonal frequency division multiplexing (OFDM) scheme in an optical wireless channel is examined and several techniques to reduce the peak-to-average power ratio (PAPR), employed for the wireless radio domain, are studied for suitability of implementation in the multi-spectral indoor optical wireless environment.

As more intelligence and control mechanisms are added to optical networks, the deployment of an efficient and secure management system, using suitable control and monitoring methods, is highly desirable. Whilst some of the available management mechanisms are applicable to different types of network architectures, many of these are not adequate for all-optical networks (AONs). An important implication of using AON components in communication systems is that available methods that are used to manage and monitor the health of the network may no longer be appropriate. Therefore, without additional control mechanisms a break in the core of an optical network might not be detectable. In this paper we propose a novel hardware-based control unit that can participate in some tasks for fault management of AONs. We present the key functionality of this device focusing particularly on its performance evaluation and cost analysis. We conclude the paper with a discussion concerning the applicability and implementation of this device in AON management systems.

A technique is presented which permits an enhanced frequency response for optical wireless receivers. A cascade bootstrap approach has been employed which has been combined with coil peaking to deliver a bandwidth improvement of around 7.2 times in comparison with a conventional high impedance front end. A PIN photodiode has been used as the detector, and the technique improve on the limitations normally incurred because of device capacitance, stray capacitance, and also transit time effects. As an example of the application of the method, an initial 236 MHz bandwidth high impedance front-end configuration is shown to be capable of a bandwidth of 1.7 GHz with the same gain. The noise penalty is compatible with the change in bandwidth, and not degraded by the bandwidth cubed factor which equalisation tends to produce for the same bandwidth extension.

The concept of a simple adaptable bandwidth system is investigated which can be implemented to maintain reliability of the communication link in a non-directed diffused indoor optical wireless environment under varying channel conditions. The proposed system is found to be more sensitive to any variations in signal-to-noise ratio (SNR) at higher noise magnitudes compared to SNR variations at lower noise magnitudes.

Acoustic Emission (AE) can be used to discriminate the different types of damage occurring in composite materials, because any AE signal contains useful information about the damage mechanisms. A major issue in the use of the AE technique is how to discriminate the AE signatures which are due to the different damage mechanisms. Conventional studies have focused on the analysis of different parameters of such signals, say the frequency. But in previous publications where the frequency is employed to differentiate between events, only one frequency is considered and this frequency was not enough to thoroughly describe the behavior of the composite material. So we introduced the second frequency. A Fast Fourier Transform (FFT) is then applied to the signals resulting from the two frequencies to discriminate different failure mechanisms. This was achieved by using self-organizing map and Fuzzy C-means to cluster the AE data. The result shows that the two approaches have been very successful.

A genetic algorithm controlled multispot transmitter is proposed as an alternative approach to optimising the power distribution for single element receivers in fully diffuse mobile indoor optical wireless communication systems. Results are presented that show by dynamically controlling the powers of individual diffusion spots, a consistent power distribution, with negligible impact to bandwidth and rms delay spread, can be created in multiple rooms, independent of the reflection characteristics and user movement patterns. The advantageous adaptability of the genetic algorithm approach removes the need for bespoke design and provides for a more cost effective system, capable of deployment in multiple locations whilst improving end user friendliness. Results have been presented based around the use of a single element receiver with a FOV = 55deg in conjunction with two algorithms that are shown to be capable of reducing the dynamic range, referenced against the peak received power, by up to 26% when empty and furthermore, maintaining to the within 7% of this optimised case, the power distribution when the user perturbs the channel through movement.

This paper describes an investigation into the performance of a Neural Network (NN) based Electronic Nose (EN) system, which can discriminate the aroma of different tea grades. The EN system comprising of an array of four tin-oxide gas sensors was used to sniff thirteen randomly selected tea grades, which were exemplars of eight categories in terms of aroma profiles. The mean and peak of the transient signals generated by the gas sensors, as a result of aroma sniffing, were treated as the feature vectors for the analysis. Principal Component Analysis (PCA) was used to visualise the different categories of aroma profiles. In addition, K-means and Kohonen’s Self Organising Map (SOM) cluster analysis indicated there were eight clusters in the dataset. Data classification was performed using supervised NN classifiers; namely the Multi-Layer Perceptron (MLP) network, Radial Basis Function (RBF) network, and Constructive Probabilistic Neural Network (CPNN) were used for aroma classification. The results were that the three NNs performed as follows: 90.77, 92.31, and 93.85%, respectively in terms of classification accuracy. Hence the performance of the proposed method of aroma analysis demonstrates that it is possible to use NN based EN to assist with the tea quality monitoring procedure during the tea grading process. In addition the results indicate the possibility for standardization of the tea aroma in numeric terms.

An integrated artificial intelligence optimised cellular radio channel allocation and call dropping algorithm is presented. The components of the system comprise a new genetic algorithm (GA) based channel allocation scheme, and a novel application of fuzzy logic to call dropping technique. The new method is compared to both random allocation and to a conventional allocation approach. The results show improvements of 30% in the signal-to-interference ratio and 10% in the uniformity of the traffic across the cells in the system. Furthermore, up to 80% fewer calls are dropped using the new methodology

In emerging all-optical networks (AONs), efficient monitoring and estimation of signal quality along a lightpath are of highest interest because of their importance in diagnosing and assessing the overall health of the network. This is because transmission in these networks is limited by a number of effects such as optical crosstalk and amplified spontaneous emission. In particular, crosstalk is additive and can be exploited to perform service disruption attacks upon the whole network. Since these attacks can spread rapidly through the network, causing additional awkward failures and triggering multiple undesirable alarms, they must be detected and identified at any point in the network where they may occur. Due to network transparency, this requires particularly the availability of expert diagnostic techniques to measure and control the smallest granular component, the wavelength channel. However, to monitor all wavelengths at several detection points in a node is likely to be a very expensive solution. In this paper we briefly overview optical crosstalk mechanisms that arise in AON components. We then propose a hardware-based method for monitoring performance degradation in AON components that can participate in some tasks for performance management of AONs.

Performance management is still a major complication for all-optical networks (AONs). In particular, signal quality monitoring is too difficult in AONs as the analogue nature of optical signals means that miscellaneous transmission impairments aggregate and can impact the signal quality enough to reduce the Quality of Service (QoS) without precluding all network services. This results in the continuous monitoring and identification of the impairments becoming challenging in the event of transmission failures. However, a simple and reliable signal quality monitoring method does not exist at present. Despite new methods for performance monitoring having been proposed, no robust standards or techniques exist to date for guaranteeing the QoS in AONs. Therefore, the need for more sophisticated mechanisms that assist managing and assessing the proper function of AONs is highly desirable. In this work we present the design and internal architecture of a hardware-based Monitoring Device Block (MDB) that can be used in some tasks for performance management of AONs.

The need of effective packet transmission to deliver advanced performance in wireless networks creates the need to find shortest network paths efficiently and quickly. This paper addresses a reduced uncertainty-based hybrid evolutionary algorithm (RUBHEA) to solve dynamic shortest path routing problem (DSPRP) effectively and rapidly. Genetic algorithm (GA) and particle swarm optimization (PSO) are integrated as a hybrid algorithm to find the best solution within the search space of dynamically changing networks. Both GA and PSO share context of individuals to reduce uncertainty in RUBHEA. Various regions of search space are explored and learned by RUBHEA. By employing a modified priority encoding method, each individual in both GA and PSO are represented as a potential solution for DSPRP. A complete statistical analysis has been performed to compare the performance of RUBHEA with various state-of-the-art algorithms. It shows that RUBHEA is considerably superior (reducing the failure rate by up to 50%) to similar approaches with increasing number of nodes encountered in the networks.

All-Optical Networks (AONs) are a relatively new technology for very high data rate communications, flexible switching and broadband application support. More specifically, they provide transparency features allowing routing and switching of data without interpretation or regression of signals within the network. AONs contain only transparent optical components and therefore differ to a large extent from the optical networks currently used. In particular, the behaviors of AON components and architectures bring forth a new set of challenges for network security. As a result, AONs have unique features and requirements in terms of security and quality of service that require a highly targeted approach in terms of network management. This paper presents and analyzes an algorithm for multiple failure localization and identification in AONs. This algorithm utilizes fewer monitoring information than is required by other approaches. As a direct consequence, it offers the benefit of relaxing the high cost and complexity of signal quality monitoring for future AON management solutions. The paper concludes with a discussion tacking into consideration the interaction of this algorithm with typical path recovery schemes.

This paper aims to illustrate for the first time the applicability of emergence within the design, modeling and simulation of wireless routing protocols for the security of information exchange in the presence of a malicious node. Our methodology focuses on the black hole problem and delivers a set of results that describe a protocol prototype within a number of set characteristic pairs. For this purpose, and motivated by the ever growing number of protocol implementations, we define an adaptive system that, through a set of simple rules, has the capability of selecting the fittest structure within a well defined environment capable of dealing with the problem under question.

All-optical networks (AONs) are emerging as a promising technology for very high data rate communications, flexible switching and broadband application support. They contain only optical components and vary to a large extent from traditional optical networks. Due to this specific variation, AONs have unique features and requirements in terms of security and quality of service (QoS) that require a very targeted approach in terms of network management. The paper discusses management issues and requirements that arise from using optical components in communication systems. It then proposes a management framework for the realization of an appropriate network management system (NMS) that can meet the challenges posed by AONs.

All-optical networks (AONs) are a relatively new technology for high data-rate communications. They contain only optical components and are intrinsically different from optical networks currently being used. More specifically, AONs provide transparent features that allow routing and data switching without necessitating the interpretation or regression of signals within the network. Consequently, AON components and architectures bring forth a set of new challenges in network security. The paper presents an overview of security issues in AONs and proposes the design criteria for network management in AONs. In addition, there is a discussion concerning the implementation of such a system within the authors' research group.

In this paper we present the security features of a novel protocol stack designed for carrying signalling information in the UMTS Core Network (CN) and more specifically for its Packet Switched domain. We compare its features with proposed protocols by the Internet and Telecommunications Community, such as the IPsec and Stream Control Transmission Protocol (SCTP) architectures, moving on to analysing FCNS's advantages over these proposals. We evaluate our protocol stack and reveal future work, and other uses of it, in various operating environments.