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The relative radiometric calibration is essential to get high-quality remote sensing images. An efficient way to bypass the quest of uniformity is to use the satellite agility in order to align the ground projection of the scanline on the ground velocity. This weird viewing principle(side-slither) allows all the detectors to view the same landscape. A relative radiometric calibration model based on...
Regularized Tyler Estimator's (RTE) have raised attention over the past years due to their attractive performance over a wide range of noise distributions and their natural robustness to outliers. Developing adaptive methods for the selection of the regularisation parameter α is currently an active topic of research. Indeed, the bias-performance compromise of RTEs highly depends on the considered...
Audio tagging aims to assign one or several tags to an audio clip. Most of the datasets are weakly labelled, which means only the tags of the clip are known, without knowing the occurrence time of the tags. The labeling of an audio clip is often based on the audio events in the clip and no event level label is provided to the user. Previous works have used the bag of frames model assume the tags occur...
Outlier detection algorithms are often computationally intensive because of their need to score each point in the data. Even simple distance-based algorithms have quadratic complexity. High-dimensional outlier detection algorithms such as subspace methods are often even more computationally intensive because of their need to explore different subspaces of the data. In this paper, we propose an exceedingly...
Proper feature selection for unsupervised outlier detection can improve detection performance but is very challenging due to complex feature interactions, the mixture of relevant features with noisy/redundant features in imbalanced data, and the unavailability of class labels. Little work has been done on this challenge. This paper proposes a novel Coupled Unsupervised Feature Selection framework...
This paper discusses utilizing sparse autoencoders for building regression models in order to predict real-valued timeseries data. The focus of this research is on exploiting and learning from the determining features of continuous data via stacked autoencoders, thus increasing the prediction accuracy of regression method. Archi-tecture comprising different layers of sparse autoencod-ers, where each...
Multi-label learning is widely applied in many tasks, where an object possesses multiple concepts with each represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is open and new concepts may emerge with previously...
Ensemble methods for classification have been effectively used for decades, while for outlier detection it has only been studied recently. In this work, we design a new ensemble approach for outlier detection in multi-dimensional point data, which provides improved accuracy by reducing error through both bias and variance by considering outlier detection as a binary classification task with unobserved...
Unsupervised anomaly detection algorithms search for outliers and then predict that these outliers are the anomalies. When deployed, however, these algorithms are often criticized for high false positive and high false negative rates. One cause of poor performance is that not all outliers are anomalies and not all anomalies are outliers. In this paper, we describe an Active Anomaly Discovery (AAD)...
Traffic state estimation plays a significant role in intelligent transportation systems. For a specific road, traffic state varies at different times of a day. Therefore, to estimate real-time traffic state is difficult. This paper presents a probabilistic approach for traffic state estimation. In this approach, traffic state distribution and data point distribution are used to describe the pattern...
Vehicle detection is an essential task in an intelligent vehicle. Despite being a well-studied vision problem, it is unclear how well vehicle detectors generalize to new settings. Specifically, this paper studies the generalization capability of vehicle detectors on a U.S. highway dataset. Two types of models are employed in the experimental analysis, a subcategory aggregate channel features model...
In order to meet the requirements of traffic data fusion for real-time urban traffic state estimation, a new kind of fusion structure model is proposed. This fusion model consists of both spatial fusion and temporal fusion. First we use the power average operator as spatial fusion method. Then we propose a temporal correlation based data compression (TCDC) algorithm, based on segment linear regression...
A detailed study of hadronic interactions is presented using data recorded with the highly granular CALICE silicon-tungsten electromagnetic calorimeter. The predictions of several Monte Carlo GEANT4 models are compared with experimental data, taken at FNAL in 2008. The contribution recaps results published in 2015 and a set of new results available since the beginning of 2016. The published results...
With increasingly sophisticated experiment, online Data Quality Monitoring (DQM) is of a significant importance for the detector and operation efficiency. Most experiments use their own Event Data Model (EDM) for data taking and built a dedicated monitoring system on top of it. This leads to a strong dependency to the data format and storage, making the reusability of the system for another experiment...
The X-ray observatory Athena (Advanced Telescope for High Energy Astrophysics) is an ESA satellite to be launched in 2028. One of the two focal plane instruments is the Wide Field Imager (WFI). WFI is an X-ray camera for imaging and spectroscopic applications. Its detector consists of active pixel DEPFET (Depleted P-channel Field Effect Transistors) sensors with front-end electronics. Mathematical...
The alignment of muon chambers relative to each other and to the inner tracker is crucial to achieve the optimal performance of muon reconstruction at high momentum, in particular the best possible momentum measurement. With the energy and luminosity increase in LHC Run2, the high momentum muons have become more important for the searches of new particles with masses around the TeV scale. The muon...
For evaluating the electromagnetic emission (EME) of an integrated circuit (IC) during the design phase, a precise transient simulation of the disturbing signal is required. This usually takes a lot of time, so that simulating a signal trace longer than a couple of ms is not feasible. In this paper an extension to an existing electromagnetic interference (EMI) receiver model is introduced, which enables...
Crowd simulation technologies and systems can show and tell a lot of insights on massive crowds' movement behavior. Benefiting from such characteristics, they have found themselves useful in many application fields. On the other hand, a typical educational institution, such as university, has a large body of student population, whose course schedules dominate their daily movement patterns. How to...
This contribution illustrates the benefit of incorporating real-time environmental data into highway traffic information systems and describes the modelling and integration of weather conditions into a complex microscopic traffic simulation. Using stationary measured weather data as an example, the achieved results show the potential extended Floating Car Data (xFCD) - submitted to the traffic information...
This paper presents a detection scheme for determining the number of signals that are correlated across multiple data sets when the sample size is small compared to the dimensions of the data sets. To accommodate the sample-poor regime, we decouple the problem into several independent two-channel order-estimation problems that may be solved separately by a combination of principal component analysis...
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