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In particular types of Delay-Tolerant Networks (DTN) such as Opportunistic Mobile Networks, node connectivity is transient, and connections are sparse and small in length. For this reason, traditional routing mechanisms are no longer suitable. Routing algorithms designed for such networks try to maximize the probability of successful message delivery. The most popular approach is to compute the probability...
To analyze the tubular structure correctly and obtain a record of the centerlines has become significantly more challenging and infers countless applications in a large amount of fields. Hence, a robust and automated technique for extracting the centerlines of the tubular structure is required. To address complicated 3D tubular objects, a novel kernel-based modeling approach with regard to minimizing...
In the course of semiconductor manufacturing, various e-test measurements (also known as inline or kerf measurements) are collected to monitor the health-of-line and to make wafer scrap decisions preceding final test. These measurements are typically sampled spatially across the surface of the wafer from between-die scribe line sites, and include a variety of measurements that characterize the wafer's...
In this work, we are primarily concerned with robotic systems that learn online and continuously from multi-variate data-streams. Our first contribution is a new recursive kernel, which we have integrated into a sparse Gaussian Process to yield the Spatio-Temporal Online Recursive Kernel Gaussian Process (STORK-GP). This algorithm iteratively learns from time-series, providing both predictions and...
This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compared to modeling them individually. Multi-task Gaussian processes provide a powerful...
Gaussian process (GP) is well researched and used in machine learning field. Comparing with artificial neural network (ANN) and support vector regression (SVR), it provides additional covariance information for regression results. By exploiting this feature, an uncertainty based locational optimisation strategy combining with an entropy based data selection method for mobile sensor networks is presented...
We propose an online method for grasp motion learning using the Gaussian Process Dynamic Model (GPDM). Given human grasp motion data (in the form of position and orientation trajectories of the fingertips and palm), from approach to final grasp pose, a GPDM is trained with this data, and then used to generate new grasping motions even when the path to the object is partially blocked by obstacles....
Drug cocktails formed by mixing multiple drugs at various doses provide more effective cures than single-drug treatments. However, drugs interact in highly nonlinear ways making the determination of the optimal combination a difficult task. The response surface of the drug cocktail has to be estimated through expensive and time-consuming experimentation. Previous research focused on the use of space-exploratory...
This paper proposes a new method for occupancy map building using a mixture of Gaussian processes. We consider occupancy maps as a binary classification problem of positions being occupied or not, and apply Gaussian processes. Particularly, since the computational complexity of Gaussian processes grows as O(n3), where n is the number of data points, we divide the training data into small subsets and...
We present a flexible class of stochastic models that are developed for cooperative wireless relay networks systems, in which the relay processing functionality is not known at the destination. The challenge is then to perform system identification in this wireless relay network. We first construct a statistical model based on a representation of the system using Gaussian Processes. We then develop...
This paper describes a Gaussian process based method for nonlinear hyperspectral image unmixing. The proposed model assumes a nonlinear mapping from the abundance vectors to the pixel reflectances contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy physical constraints that are naturally expressed within a Bayesian framework. The proposed abundance estimation...
Music recommendation is an important aspect of many streaming services and multi-media systems, however, it is typically based on so-called collaborative filtering methods. In this paper we consider the recommendation task from a personal viewpoint and examine to which degree music preference can be elicited and predicted using simple and robust queries such as pairwise comparisons. We propose to...
Semantic interpretation and understanding of images is an important goal of visual recognition research and offers a large variety of possible applications. One step towards this goal is semantic segmentation, which aims for automatic labeling of image regions and pixels with category names. Since usual images contain several millions of pixel, the use of kernel-based methods for the task of semantic...
We present a new paradigm to perform spatial spectrum sensing in cooperative cognitive radio networks. In these networks, nodes cooperate to infer information on spectral and spatial occupancy. Following the recently proposed interference temperature metric, we formulate the problem via semi-parametric Gaussian Process modelling. This allows for a development of a flexible probabilistic framework...
Test cost reduction for RF devices has been an ongoing topic of interest to the semiconductor manufacturing industry. Automated test equipment designed to collect parametric measurements, particularly at high frequencies, can be very costly. Together with lengthy set up and test times for certain measurements, these cause amortized test cost to comprise a high percentage of the total cost of manufacturing...
In some recent works, an alternative nonparametric paradigm to linear model identification has been proposed, where the unknown system impulse response is interpreted as a realization of a Gaussian process. Its autocovariance belongs to the class of so-called stable spline kernels that incorporate the stability constraint. Within this class, the order of the kernel establishes the degree of smoothness...
Especially in times of heavy loads, cloud providers often have to outsource tasks to external clouds to fulfill service level agreements. Nevertheless, a cloud provider maximizes the company's benefit while running as many jobs as possible on the own hardware without going below a specific workload of the running processors. Since cloud providers will have to estimate the required energy in advance...
Complex control tasks involving varying or evolving system dynamics often pose a great challenge to mainstream reinforcement learning algorithms. Specifically, in most standard methods, actions are often assumed to be a concrete and fixed set that applies to the state space in a predefined manner. Consequently, without resorting to a substantial re-learning procedure, the derived policy lacks the...
Accurate prediction of load demand remains a challenge for efficient power distribution and becomes critical in the context of smart grid management when the presence of stochastic sources adds to the stochasticity of demand. Short-term load forecasting involving demand prediction in the range of hours or days is of special interest to generators and power customers. A number of methods has been developed...
This paper presents an active learning approach for recognizing human actions in videos based on multiple kernel combined method. We design the classifier based on Multiple Kernel Learning (MKL) through Gaussian Processes (GP) regression. This classifier is then trained in an active learning approach. In each iteration, one optimal sample is selected to be interactively annotated and incorporated...
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