Performance analysis of adaptive scheduling in integrated services UMTS networks

For an integrated services UMTS network serving speech and data calls, we propose, evaluate and compare different scheduling schemes, which dynamically adapt the shared data transport channel rates to the varying speech traffic load. within each cell, the assigned data transfer resources are distributed over the present data flows according to certain fairness objectives. The performance of the adaptive schemes is numerically evaluated by means of analytical performance optimisation methods in combination with Monte Carlo simulations.\ud \u

The Homogeneous Broadcast Problem in Narrow and Wide Strips

Let $P$ be a set of nodes in a wireless network, where each node is modeled as a point in the plane, and let $s\in P$ be a given source node. Each node $p$ can transmit information to all other nodes within unit distance, provided $p$ is activated. The (homogeneous) broadcast problem is to activate a minimum number of nodes such that in the resulting directed communication graph, the source $s$ can reach any other node. We study the complexity of the regular and the hop-bounded version of the problem (in the latter, $s$ must be able to reach every node within a specified number of hops), with the restriction that all points lie inside a strip of width $w$. We almost completely characterize the complexity of both the regular and the hop-bounded versions as a function of the strip width $w$.Comment: 50 pages, WADS 2017 submissio

Combination of satellite imagery and wind data in deep learning approach to detect oil spills

The ocean is vulnerable to oil related activities such as oil production and transport that can harm the environment. Environmental damages from oil spills can be large if not dealt with. Satellite images from radar are useful to detect oil spills because they cover both day and night and penetrates clouds. However, detecting oil spills in ocean areas from satellite images are not a trivial task due to abundance of lookalikes from other natural sources, like river inputs or geological seepage. Auxiliary data such as wind speed in the monitored area, are used to separate oil spills from natural occurring slicks in the manual oil detection process. One solution to detect oil spills is applying artificial intelligence techniques like convolutional neural networks. These convolutional neural networks have usually been a candidate to create an automatic oil detection process. However, the convolutional neural networks have problems with distinguishing between spilled oil spills and look-alikes. This project is about exploring the possibility of detecting oil spills from satellite images and distinguish between spilled oil spills and natural occurring ones by using wind speed data of the area. The convolutional neural network takes in both satellite images and auxiliary wind speed data of the area monitored. Two convolutional neural networks are designed and setup, where one includes auxiliary wind speed data and the other does not. Both CNN’s will have the same satellite images and oil spills to detect such that a direct comparison can be made between them. This work will also be a proof of concept to an automated oil spill detection process that specifically uses wind data in addition to the satellite images. To measure any difference in validation loss, precision or recall by using wind data, both convolutional neural networks are tuned to the same recall such that the false negatives are as low as possible for both neural networks. The comparison between the two neural networks shows that the neural network that includes wind data has 15 % lower validation loss and a slightly higher precision than the neural network that does not include wind data. However, this result is achieved by using wind data generated from the satellite image itself, which metrological wind data is not. A comparison test like this but with metrological wind data instead of wind data generated from the satellite image is considered future work that is worth exploring

TMB: Automatic Differentiation and Laplace Approximation

TMB is an open source R package that enables quick implementation of complex nonlinear random effect (latent variable) models in a manner similar to the established AD Model Builder package (ADMB, admb-project.org). In addition, it offers easy access to parallel computations. The user defines the joint likelihood for the data and the random effects as a C++ template function, while all the other operations are done in R; e.g., reading in the data. The package evaluates and maximizes the Laplace approximation of the marginal likelihood where the random effects are automatically integrated out. This approximation, and its derivatives, are obtained using automatic differentiation (up to order three) of the joint likelihood. The computations are designed to be fast for problems with many random effects (~10^6) and parameters (~10^3). Computation times using ADMB and TMB are compared on a suite of examples ranging from simple models to large spatial models where the random effects are a Gaussian random field. Speedups ranging from 1.5 to about 100 are obtained with increasing gains for large problems. The package and examples are available at http://tmb-project.org

Characterizing CDMA downlink feasibility via effective interference

This paper models and analyses downlink power assignment feasibility in Code Division Multiple Access (CDMA) mobile networks. By discretizing the area into small segments, the power requirements are characterized via a matrix representation that separates user and system characteristics. We obtain a closed-form analytical expression of the so-called Perron-Frobenius eigenvalue of that matrix, which provides a quick assessment of the feasibility of the power assignment for each distribution of calls over the segments. Although the obtained relation is non-linear, it basically provides an effective interference characterisation of downlink feasibility. Our results allow for a fast evaluation of outage and blocking probabilities, and enable a quick evaluation of feasibility that may be used for Call Acceptance Control. \u

An ETH-Tight Exact Algorithm for Euclidean TSP

We study exact algorithms for {\sc Euclidean TSP} in $\mathbb{R}^d$. In the early 1990s algorithms with $n^{O(\sqrt{n})}$ running time were presented for the planar case, and some years later an algorithm with $n^{O(n^{1-1/d})}$ running time was presented for any $d\geq 2$. Despite significant interest in subexponential exact algorithms over the past decade, there has been no progress on {\sc Euclidean TSP}, except for a lower bound stating that the problem admits no $2^{O(n^{1-1/d-\epsilon})}$ algorithm unless ETH fails. Up to constant factors in the exponent, we settle the complexity of {\sc Euclidean TSP} by giving a $2^{O(n^{1-1/d})}$ algorithm and by showing that a $2^{o(n^{1-1/d})}$ algorithm does not exist unless ETH fails.Comment: To appear in FOCS 201