Optical packet switching over arbitrary physical topologies using the Manhattan street network : an evolutionary approach

Published in "Towards an Optical Internet", A. Jukan (Ed.). Optical packet switching over arbitrary physical topologies typically mandates complex routing schemes and the use of buffers to resolve the likely contentions. However, the relatively immature nature of optical logic devices and the limitations with optical buffering provide significant incentive to reduce the routing complexity and avoid optical domain contentions. This paper examines how the Manhattan Street Network (MSN) and a particular routing scheme may be used to facilitate optical packet switching over arbitrary physical topologies. A novel approach, genetic algorithms (GA), is applied to the problem of deploying the MSN (near) optimally in arbitrary physical topologies. A problem encoding is proposed and different implementations of GA described. The optimum GA parameters are empirically selected and GA is successfully used to deploy the MSN in physical topologies of up to 100 nodes. Favourable results are obtained. GA are also seen to out-perform other heuristics at deploying the MSN in arbitrary physical topologies for optical packet switching

The interplay between market factors and regulation in next-generation broadband: evidence from Europe

Although many factors affect next-generation access (NGA) deployment, regulatory frameworks have the power to guide future investments, further development and, consequently, the competitiveness of a next-generation broadband market. Understanding the link between markets and regulatory requirements, therefore, is essential. Using data collected from broadband stakeholders in three markets, this paper provides an empirical analysis of this relationship. The market conditions in The Netherlands, Sweden and the United Kingdom (UK) and their roles in influencing the regulatory decisions made by the respective national regulatory authorities (NRAs) are examined. Such analysis first shows that market conditions present different priorities for regulators and policymakers. While markets with weaker incentives for investment, such as the UK, are in need of regulatory and public policy intervention, The Netherlands and Sweden require less stringent measures. Despite this, evidence shows that some level of NGA regulation is presently required in all three markets, albeit to varying degrees and with different foci. The paper then highlights the interaction of the market factors, explaining that this interrelationship is more important for policymakers than the effects of a single factor. The findings of the paper are useful for regulators in addressing the challenges of next-generation broadband deployment. --Next-generation access,Regulation,The Netherlands,Sweden,United Kingdom,Comparison

The impact of mobility models on the performance of mobile Ad Hoc network routing protocol

A mobility model represents nodes distribution and movement over the network. Several research works have shown that a selection of mobility model can affect the outcome of routing performance simulation in Mobile Ad Hoc Networks. Thus, a routing protocol may only be effective in a particular mobility model or scenario but performs inferiorly in another. As a result, analyses of routing protocol performance are often based on inadequate information leading to inaccurate argument and conclusion. In this paper, three different mobility models have been selected, where each of them is highly distinctive in terms of nodes movement behavior. In addition, a new measurement technique called probability of route connectivity is introduced. The technique is used to quantify the success rate of route established by a routing protocol. Extensive simulation runs are done and results are compared between each mobility model

The interplay between market factors and regulation in next-generation broadband: evidence from Europe

Although many factors affect next-generation access (NGA) deployment, regulatory frameworks have the power to guide future investments, further development and, consequently, the competitiveness of a next-generation broadband market. Understanding the link between markets and regulatory requirements, therefore, is essential. Using data collected from broadband stakeholders in three markets, this paper provides an empirical analysis of this relationship. The market conditions in The Netherlands, Sweden and the United Kingdom (UK) and their roles in influencing the regulatory decisions made by the respective national regulatory authorities (NRAs) are examined. Such analysis first shows that market conditions present different priorities for regulators and policymakers. While markets with weaker incentives for investment, such as the UK, are in need of regulatory and public policy intervention, The Netherlands and Sweden require less stringent measures. Despite this, evidence shows that some level of NGA regulation is presently required in all three markets, albeit to varying degrees and with different foci. The paper then highlights the interaction of the market factors, explaining that this interrelationship is more important for policymakers than the effects of a single factor. The findings of the paper are useful for regulators in addressing the challenges of next-generation broadband deployment

Geology of Babyshoe Ridge Area, Southern Cascades, Washington

The thesis area consists of approximately thirty square miles in the Southern Cascades of Washington near Mount Adams. The oldest exposed rocks are sedimentary volcanic clastics and pyroclastic flows of late-Oligocene age that form a homocline in the northwest corner of the area. A thick pile of younger Tertiary lavas, mudflows, and volcanic clastics unconformably overlie the Oligocene rocks, and contain two hydrothermally altered zones. Andesitic lavas from Mount Adams lap onto older rocks and are of Recent to Pleistocene age. Two glacial till deposits and two airfall pyroclastic layers are present throughout the area. One major fault and two broad folds are present in the area. The geothermal potential is not believed to be great, because genetic relationships indicate the hydrothermal systems that produced the two alteration zones were active only in pre- Pleistocene time. There are no external manifestations of a presently active hydrothermal system with geothermal potential

Automatic cattle location tracking using image processing

Behavioural scientists track animal behaviour patterns through the construction of ethograms which detail the activities of cattle over time. To achieve this, scientists currently view video footage from multiple cameras located in and around a pen, which houses the animals, to extract their location and determine their activity. This is a time consuming, laborious task, which could be automated. In this paper we extend the well-known Real-Time Compressive Tracking algorithm to automatically determine the location of dairy and beef cows from multiple video cameras in the pen. Several optimisations are introduced to improve algorithm accuracy. An automatic approach for updating the bounding box which discourages the algorithm from learning the background is presented. We also dynamically weight the location estimates from multiple cameras using boosting to avoid errors introduced by occlusion and by the tracked animal moving in and out of the field of view

Rice seed varietal purity inspection using hyperspectral imaging

When distributing rice seed to farmers, suppliers strive to ensure that all seeds delivered belong to the species that was ordered and that the batch is not contaminated by unhealthy seeds or seeds of a different species. A conventional method to inspect the varietal purity of rice seeds is based on manually selecting random samples of rice seed from a batch and evaluating the physical grain properties through a process of human visual inspection. This is a tedious, laborious, time consuming and extremely inefficient task where only a very small subset of the entire batch of the rice seed can be examined. There is, therefore, a need to automate this process to make it repeatable and more efficient while allowing a larger sample of rice seeds from any batch to be analysed. This paper presents an automatic rice seed inspection method which combines hyperspectral imaging and tools from machine learning to automatically detect seeds which are erroneously contained within a batch when they actually belong to a completely different species. Image data from Near-infrared (NIR) and Visible Light (VIS) hyperspectral cameras are acquired for six common rice seed varieties. Two different classifiers are applied to the data: a Support Vector Machine (SVM) and a Random Forest (RF), where each consists of six one-versus-rest binary classifiers. The results show that combining spectral and shape-based features derived from the rice seeds results in an increase in the precision (PPV) of the multi-label classification to 84% compared with 74% when only visual features are used

Spatial and spectral features utilization on a hyperspectral imaging system for rice seed varietal purity inspection

A conventional method to inspect the varietal purity of rice seeds is based on evaluating human visual inspection where a random sample is drawn from a batch. This is a tedious, laborious, time consuming and extremely inefficient task. This paper presents an automatic rice seed inspection method using Hyperspectral imaging and machine learning, to automatically detect unwanted seeds from other varieties which may be contained in a batch. Hyperspectral image data from Near-infrared (NIR) and Visible cameras are acquired for six common rice seed varieties. The results of applying two classifiers are presented, a Support Vector Machine (SVM) and a Random Forest (RF), where each consists of six one-versus-rest binary classifiers. The results show that combining spectral and shape- based features derived from the rice seeds, increase precision of the multi-label classification to 84% compared 74% when only visual features are used

Varietal classification of rice seeds using RGB and hyperspectral images.

Inspection of rice seeds is a crucial task for plant nurseries and farmers since it ensures seed quality when growing seedlings. Conventionally, this process is performed by expert inspectors who manually screen large samples of rice seeds to identify their species and assess the cleanness of the batch. In the quest to automate the screening process through machine vision, a variety of approaches utilise appearance-based features extracted from RGB images while others utilise the spectral information acquired using Hyperspectral Imaging (HSI) systems. Most of the literature on this topic benchmarks the performance of new discrimination models using only a small number of species. Hence, it is unclear whether or not model performance variance confirms the effectiveness of proposed algorithms and features, or if it can be simply attributed to the inter-class/intra-class variations of the dataset itself. In this paper, a novel method to automatically screen and classify rice seed samples is proposed using a combination of spatial and spectral features, extracted from high resolution RGB and hyperspectral images. The proposed system is evaluated using a large dataset of 8,640 rice seeds sampled from a variety of 90 different species. The dataset is made publicly available to facilitate robust comparison and benchmarking of other existing and newly proposed techniques going forward. The proposed algorithm is evaluated on this large dataset and the experimental results show the effectiveness of the algorithm to eliminate impure species by combining spatial features extracted from high spatial resolution images and spectral features from hyperspectral data cubes