An effective genetic algorithm for network coding

The network coding problem (NCP), which aims to minimize network coding resources such as nodes and links, is a relatively new application of genetic algorithms (GAs) and hence little work has so far been reported in this area. Most of the existing literature on NCP has concentrated primarily on the static network coding problem (SNCP). There is a common assumption in work to date that a target rate is always achievable at every sink as long as coding is allowed at all nodes. In most real-world networks, such as wireless networks, any link could be disconnected at any time. This implies that every time a change occurs in the network topology, a new target rate must be determined. The SNCP software implementation then has to be re-run to try to optimize the coding based on the new target rate. In contrast, the GA proposed in this paper is designed with the dynamic network coding problem (DNCP) as the major concern. To this end, a more general formulation of the NCP is described. The new NCP model considers not only the minimization of network coding resources but also the maximization of the rate actually achieved at sinks. This is particularly important to the DNCP, where the target rate may become unachievable due to network topology changes. Based on the new NCP model, an effective GA is designed by integrating selected new problem-specific heuristic rules into the evolutionary process in order to better diversify chromosomes. In dynamic environments, the new GA does not need to recalculate target rate and also exhibits some degree of robustness against network topology changes. Comparative experiments on both SNCP and DNCP illustrate the effectiveness of our new model and algorithm

Multi-user indoor optical wireless communication system channel control using a genetic algorithm

A genetic algorithm controlled multispot transmitter is demonstrated that is capable of optimising the received power distribution for randomly aligned single element receivers in multiple fully diffuse optical wireless communications systems with multiple mobile users. Using a genetic algorithm to control the intensity of individual diffusion spots, system deployment environment changes, user movement and user alignment can be compensating for, with negligible impact on the bandwidth and root mean square delay spread. It is shown that the dynamic range, referenced against the peak received power, can be reduced up to 27% for empty environments and up to 26% when the users are moving. Furthermore, the effect of user movement, that can perturb the channel up to 8%, can be reduced to within 5% of the optimised case. Compared to alternative bespoke designs that are capable of mitigating optical wireless channel drawbacks, this method provides the possibility of cost-effectiveness for mass-produced receivers in applications where end-user friendliness and mobility are paramount

Bacteria classification using Cyranose 320 electronic nose

Background An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320, comprising an array of thirty-two polymer carbon black composite sensors has been used to identify six species of bacteria responsible for eye infections when present at a range of concentrations in saline solutions. Readings were taken from the headspace of the samples by manually introducing the portable e-nose system into a sterile glass containing a fixed volume of bacteria in suspension. Gathered data were a very complex mixture of different chemical compounds. Method Linear Principal Component Analysis (PCA) method was able to classify four classes of bacteria out of six classes though in reality other two classes were not better evident from PCA analysis and we got 74% classification accuracy from PCA. An innovative data clustering approach was investigated for these bacteria data by combining the 3-dimensional scatter plot, Fuzzy C Means (FCM) and Self Organizing Map (SOM) network. Using these three data clustering algorithms simultaneously better 'classification' of six eye bacteria classes were represented. Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes. Results A [6 × 1] SOM network gave 96% accuracy for bacteria classification which was best accuracy. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to predict six classes of bacteria with up to 98% accuracy with the application of the RBF network. Conclusion This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this combined use of three nonlinear methods can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320

Fault Tolerant Dense Wavelength Division Multiplexing Optical Transport Networks, Journal of Telecommunications and Information Technology, 2009, nr 1

Design of fault tolerant dense wavelength division multiplexing (DWDM) backbones is a major issue for service provision in the presence of failures. The problem is an NP-hard problem. This paper presents a genetic algorithm based approach for designing fault tolerant DWDM optical networks in the presence of a single link failure. The working and spare lightpaths are encoded into variable length chromosomes. Then the best lightpaths are found by use of a fitness function and these are assigned the minimum number of wavelengths according to the problem constraints using first-fit (FF) algorithm. The proposed approach has been evaluated for dedicated path protection architecture. The results, obtained from the ARPA2 test bench network, show that the method is well suited to tackling this complex and multi-constraint problem

Network Topology Effecton QoS Delivering in Survivable DWDM Optical Networks, Journal of Telecommunications and Information Technology, 2009, nr 1

The quality of service (QoS) is an important and considerable issue in designing survivable dense wavelength division multiplexing (DWDM) backbones for IP networks. This paper investigates the effect of network topology on QoS delivering in survivable DWDM optical transport networks using bandwidth/load ratio and design flexibility metrics. The dedicated path protection architecture is employed to establish diverse working and spare lightpaths between each node pair in demand matrix for covering a single link failure model. The simulation results, obtained for the Pan-European and ARPA2 test bench networks, demonstrate that the network topology has a great influence on QoS delivering by network at optical layer for different applications. The Pan-European network, a more connected network, displays better performance than ARPA2 network for both bandwidth/load ratio and design flexibility metrics

An effective genetic algorithm for network coding

The network coding problem (NCP), which aims to minimize network coding resources such as nodes and links, is a relatively new application of genetic algorithms (GAs) and hence little work has so far been reported in this area. Most of the existing literature on NCP has concentrated primarily on the static network coding problem (SNCP). There is a common assumption in work to date that a target rate is always achievable at every sink as long as coding is allowed at all nodes. In most real-world networks, such as wireless networks, any link could be disconnected at any time. This implies that every time a change occurs in the network topology, a new target rate must be determined. The SNCP software implementation then has to be re-run to try to optimize the coding based on the new target rate. In contrast, the GA proposed in this paper is designed with the dynamic network coding problem (DNCP) as the major concern. To this end, a more general formulation of the NCP is described. The new NCP model considers not only the minimization of network coding resources but also the maximization of the rate actually achieved at sinks. This is particularly important to the DNCP, where the target rate may become unachievable due to network topology changes. Based on the new NCP model, an effective GA is designed by integrating selected new problem-specific heuristic rules into the evolutionary process in order to better diversify chromosomes. In dynamic environments, the new GA does not need to recalculate target rate and also exhibits some degree of robustness against network topology changes. Comparative experiments on both SNCP and DNCP illustrate the effectiveness of our new model and algorithm

Food security risk level assessment : a fuzzy logic-based approach

A fuzzy logic (FL)-based food security risk level assessment system is designed and is presented in this article. Three inputs—yield, production, and economic growth—are used to predict the level of risk associated with food supply. A number of previous studies have related food supply with risk assessment for particular types of food, but none of the work was specifically concerned with how the wider food chain might be affected. The system we describe here uses the Mamdani method. The resulting system can assess risk level against three grades: severe, acceptable, and good. The method is tested with UK (United Kingdom) cereal data for the period from 1988 to 2008. The approach is discussed on the basis that it could be used as a starting point in developing tools that may either assess current food security risk or predict periods or regions of impending pressure on food supply

Meta-heuristic algorithms for optimized network flow wavelet-based image coding

Optimal multipath selection to maximize the received multiple description coding (MDCs) in a lossy network model is proposed. Multiple description scalar quantization (MDSQ) has been applied to the wavelet coefficients of a color image to generate the MDCs which are combating transmission loss over lossy networks. In the networks, each received description raises the reconstruction quality of an MDC-coded signal (image, audio or video). In terms of maximizing the received descriptions, a greater number of optimal routings between source and destination must be obtained. The rainbow network flow (RNF) collaborated with effective meta-heuristic algorithms is a good approach to resolve it. Two meta-heuristic algorithms which are genetic algorithm (GA) and particle swarm optimization (PSO) have been utilized to solve the multi-objective optimization routing problem for finding optimal routings each of which is assigned as a distinct color by RNF to maximize the coded descriptions in a network model. By employing a local search based priority encoding method, each individual in GA and particle in PSO is represented as a potential solution. The proposed algorithms are compared with the multipath Dijkstra algorithm (MDA) for both finding optimal paths and providing reliable multimedia communication. The simulations run over various random network topologies and the results show that the PSO algorithm finds optimal routings effectively and maximizes the received MDCs with assistance of RNF, leading to reduce packet loss and increase throughput

Neural network based electronic nose for classification of tea aroma

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