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An efficient and flexible dictionary designing algorithm is proposed for sparse and redundant signal representation. The proposed Augmented Dictionary (AD) is based on a new dictionary model with an augmented form compared to the conventional model. With this model, we can bridge the gap between the classic dictionary learning approaches, which have general structure yet lack computational efficiency,...
This paper presents a new data-driven classification pipeline for discriminating two groups of individuals based on the medical images of their brain. The algorithm combines deformation-based morphometry and penalised linear discriminant analysis with resampling. The method is based on sparse representation of the original brain images using deformation logarithms reflecting the differences in the...
The paper presents the results of research related to the efficiency of the so called rule quality measures which are used to evaluate the quality of rules at each stage of the rule induction. The stages of rule growing and pruning were considered along with the issue of conflicts resolution which may occur during the classification. The work is the continuation of research on the efficiency of quality...
Stereo matching, as many problems in computer vision, has been addressed by a multitude of algorithms, each with its own strengths and weaknesses. Instead of following the conventional approach and trying to tune or enhance one of the algorithms so that it dominates the competition, we resign to the idea that a truly optimal algorithm may not be discovered soon and take a different approach. We present...
Approximate computing is a paradigm for trading off program accuracy to save energy in memory or computational resources. However, determining feasible program approximations is difficult to achieve. Popular solutions involve programmer in annotating instructions or data that can be approximated. Recently, program testing based techniques have also been explored. But these are computationally expensive...
When conventional techniques run of steam, it is time for extreme creativity. Approximate computing provides one possible path forward by relaxing the tradition abstraction of full accuracy across the computing stack.
In this paper, we propose a novel range-free localization algorithm tailored for mobile wireless sensor networks (MWSN)s. In contrast to the most existing range- free algorithms, the nodes mobility is taken into accounts when designing our algorithm. We show that nodes are able to estimate their positions using solely their locally-available information, thereby avoiding any unnecessary overhead and...
In this paper, a novel localization algorithm tailored for underground mines is proposed. Using the proposed algorithm, each regular (i.e position-unaware) node estimates its distances to the anchor (i.e., position-aware) nodes exploiting only its locally available information. Furthermore, a new hop count adjustment scheme, which complies with the labyrinthic nature of underground mines, is developed...
The Zernike moments can achieve high accuracy and strong robustness for the classification and retrieval of images, but involve huge amount of computation caused by its complex definition. It has limited its exploitation in online real-time applications or big data processing. So researches on how to improve the computation speed of Zernike moments are carried out. One of the existing high-accuracy...
machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and...
Wireless location is one of the core technologies of Wireless Sensor Network. In many applications, the accuracy of the location is the precondition of the useful of data information the node collected. Under the premise of cost limits, improving the accuracy of wireless sensor node position has crucial significance. After analyzing reasons of the location weakness of Dv-Distance algorithm due to...
Conventional approaches for photovoltaic maximum power point tracking (PV MPPT) design based on rule-of-thumb assumptions might not result in the optimal performance in the field. To improve the field performance of practical MPPT designs, this paper proposes a comprehensive approach to MPPT design driven by experimentally measured field data. The data on the dynamic behavior of PV panel I-V characteristics...
The paper presents a technique based on the use of single-variable Kalman filters (KFs) to track the frequency variation of signal components in multifrequency phasor analysis. KFbased tracking is employed for accurate frequency estimation of both harmonic and inter-harmonic components in a Compressive Sensing Taylor Fourier Multifrequency (CSTFM) algorithm. This novel approach improves robustness...
Feature selection algorithm has a great influence on the accuracy of text categorization. The traditional information gain (IG) feature selection algorithm usually selects the features that rarely appear in the specified categories, but frequently appear in other categories. To overcome this drawback, on the basis of in-depth analysis of the related algorithms, an improved IG feature selection method...
Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary -- also known as hyperplane -- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute...
A novel method is introduced for label propagation on similarity tensors. The proposed method operates on data with multiple representations. A higher order similarity matrix is constructed for describing the relationship between the data representations in different modalities. Then, label propagation is performed on the above mentioned similarity matrix, by extending the state of the art label propagation...
We present a parallel hierarchical graph clustering algorithm that uses modularity as clustering criteria to effectively extract community structures in large graphs of different types. In order to process a large complex graph (whose vertex number and edge number are around 1 billion), we design our algorithm based on the Louvain method by investigating graph partitioning and distribution schemes...
With the rapid development of information society, intricate relationship between objects establish huge heterogeneous networks. The linkage is affected by multiple factors, which makes community detection on heterogeneous network a difficult task. Traditional clustering algorithms focus on divided factors, ignoring the combination of them. If the structure of multi-dimensional information is taken...
In order to solve the problem of poor localization accuracy of DV-Hop algorithm, an improved DV-Hop algorithm is proposed in this paper. The proposed algorithm is based on RSSI technology to modify the hop count between nodes. The concept of basic signal strength of node which will be used to modify hop count is proposed in this paper. The hop coefficient and hopsize coefficient are set up to modify...
It is important to match vehicle GPS points onto a map. Many map-matching algorithms, which use heading, speed and position to complete the map-matching process, are not suitable for the low frequency GPS data. The distance between two adjacent GPS points in the low frequency GPS data can range from 500m to 1000m, and the vehicle can cross several streets. This paper puts forward a modified weight-based...
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