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

Performance Evaluation of Spectrum Sensing Using Recovered Secondary Frames With Decoding Errors

The performance of spectrum sensing using the recovered secondary frames is analyzed. Unlike the previous work that assumes perfect decoding of the secondary signal, the new analysis takes the decoding errors into account and therefore provides a more realistic comparison between the new model and the conventional model. Both the receiver operating characteristics curves for spectrum sensing and the achievable throughput for data transmission are derived. Effects of fading and error control codes are also investigated. Numerical results show that the new model that considers the decoding error outperforms the conventional model when the number of transmitted secondary frames is below a certain threshold. An upper bound performance can also be obtained by ignoring the decoding error. The threshold is determined by the primary user traffic, the spectrum sensing technique and the secondary signal modulation scheme

Identification of Staphylococcus aureus infections in hospital environment : electronic nose based approach

An electronic nose (e-nose), the Cyrano Sciences’ Cyranose 320 (C-320), comprising an array of 32 polymer carbon black composite sensors has been used to identify two species of Staphylococcus aureus bacteria, namely methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus (MSSA) responsible for ear nose and throat (ENT) infections when present in standard agar solution in the hospital environment. C-320 e-nose has also been used to identify coagulase-negative staphylococci (C-NS) in the hospital environment. Swab samples were collected from the infected areas of the ENT patients’ ear, nose and throat regions. Gathered data were a very complex mixture of different chemical compounds. An innovative object-oriented data clustering approach was investigated for these groups of S. aureus data by combining the principal component analysis (PCA) based three-dimensional scatter plot, Fuzzy C Means (FCM) and self-organizing map (SOM) network. Using these three data clustering algorithms simultaneously better ‘classification’ of three bacteria subclasses 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 three classes. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to identify three bacteria subclasses with up to 99.69% accuracy with the application of the RBF network along with C-320. This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this preliminary study proves that e-nose based approach can provide very strong solution for identifying S. aureus infections in hospital environment and early detection