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This paper presents two new modelling algorithms that was recently added to the commercially available GRASP software package for electrically large antenna and scattering problems. In particular, a new higher-order multilevel fast multipole solver (HO-MLFMM) provides very high simulation accuracy while requiring significantly less memory and CPU time than the commonly available low-order MLFMM. At...
This paper proposes a digital image watermarking method in homomorphic domain. In the proposed method, a homomorphic filter is applied to the host color image to extract a low variational illumination component. This component is used to embed a binary watermark image with the same size as host image. The watermark extraction is achieved by using an original illumination component prediction technique...
There has been an exponential growth in brain mapping studies in the past decade using functional MRI (fMRI). Apart from simple fMRI studies from a single site (scanner), multi-site studies are gaining great attention, as it has the potential to provide more data for brain mapping studies, thereby increasing the statistical power of the brain mapping studies. Major limitations with the multi-center...
People like listening music primarily due to the emotion it evokes. Any activity or work that a person performs also generates emotions. Considering the above two statements it can assume that people tend to associate music with certain activity if it induces emotions that are in sync with it. In today's world of infinite storage, the number of songs that a user has is ever increasing. With the increased...
Social media data and other web-based network data are large and dynamic rendering the identification of structural changes in such systems a hard problem. Typically, online data is constantly streaming and results in data that is incomplete thus necessitating the need to understand the robustness of network metrics on partial or sampled network data. In this paper, we examine the effects of sampling...
A real-time surveillance system for IP network cameras is presented. Motion, part-body, and whole-body detectors are efficiently combined to generate robust and fast detections, which feed multiple compressive trackers. The generated trajectories are then improved using a re-identification strategy for long term operation.
Imbalanced classification is one of the most challenging machine learning problem. Recent studies show, that often the uneven ratio of objects in classes is not the biggest factor, determining the drop of classification accuracy. It is also related to some difficulties embedded in the nature of the data. In this paper we study the different types of minority class examples and distinguish four groups...
Phonemes are the smallest units of sound produced by a human being. Automatic classification of phonemes is a well-researched topic in linguistics due to its potential for robust speech recognition. With the recent advancement of phonetic segmentation algorithms, it is now possible to generate datasets of millions of phonemes automatically. Phoneme classification on such datasets is a challenging...
In rough set approaches, decision rules are induced from a given data table showing the relation between attribute values and classes of objects. The induced decision rules are used for the classification of new objects by their attribute values. However, some of new objects do not match any decision rule conditions because the given data table does not always include all possible patterns. In those...
Rules inferring the memberships to single decision classes have been induced in rough set approaches and used to build a classifier system. Rules inferring the memberships to unions of multiple decision classes can be also induced in the same manner. In this paper, we show the classifier system with rules about the union of multiple decision classes has an advantage in the accuracy of classification...
Multibiometrics aims at improving biometric security in presence of spoofing attempts, but exposes a larger availability of points of attack. Standard fusion rules have been shown to be highly sensitive to spoofing attempts - even in case of a single fake instance only. This paper presents a novel spoofing-resistant fusion scheme proposing the detection and elimination of anomalous fusion input in...
This paper introduces a novel method for forming efficient one-class classifier ensembles. A common problem in one-class classification is a complex structure of the target class, which often leads to creation of a too expanded decision boundary. We propose to employ a clustering step in order to partition the target class into atomic subsets and using these as input for one-class classifiers. By...
Methods based on Local Binary Patterns have been used successfully in a wide range of texture classification tasks. A restriction shared by all methods based on Local Binary Patterns is the high sensitivity to signal scale. In recent work we presented a general framework for scale-adaptive computation of Local Binary Patterns, improving the accuracy in texture classification scenarios involving varying...
This paper deals with the fusion of TanDEM-X raw DEMs in ascending and descending pass over Mumbai test area and enhance its quality. Before applying fusion method, a robust layover and shadow map has been calculated in ITP using TanDEM-X DEM and the corresponding slant range image. The selection of optimum weights for fusion has been based on height error map calculated from interferometric coherence...
A new modification of the popular finite-time-convergent robust exact sliding-mode-based differentiator is proposed. Such nth-order differentiator provides for the fast global convergence of its outputs to the first n exact derivatives of its input, provided a time-variable local Lipschitz constant of the input's nth derivative is available and has a bounded logarithmic derivative. It features the...
Linear discriminant analysis (LDA) is the most commonly used classification method for single trial data in a brain-computer interface (BCI) framework. The popularity of LDA arises from its robustness, simplicity and high accuracy. However, the standard LDA approach is not capable to exploit sublabel information (such as stimulus identity), which is accessible in data from event related potentials...
Detecting changes in data streams is an important area of research in many applications. The challenging issue is to know how to monitor, update and diagnose these changes so that the accuracy of the learner will be improved whatever the nature of the encountered drifts. In this paper a new error distance based approach for drift detection and monitoring, namely EDIST, is proposed. In EDIST, a difference...
Reliable detection of the mid-sagittal plane is the key for brain image registration, asymmetry analysis, and group studies. Although the brain presents most of the time a regular structure, outliers in the data consisting of brain tumors or various deformations pose challenges to the existing approaches. We propose in this paper a robust approach for mid-sagittal plane extraction based on hierarchical...
Although during the last decade considerable efforts have been invested in the integration of different wireless technologies, a new surge of interest is arising due to the upcoming internet of things (IoT) in which many relevant application scenarios rely on location information. However, due to the heterogeneity of the devices, ergo the heterogeneity of information available, novel indoor positioning...
In this paper, we propose a novel robust and efficient minutia-based fingerprint matching algorithm. There are two key contributions. First, we apply a set of global level minutia dependent features, i.e., the qualities that measure the reliabilities of the extracted minutiae and the area of overlapping regions between the query and template images of fingerprints. The implementation of these easy-to-get...
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