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Human activity recognition based on smart phones has been widely used in many fields including the mobile context awareness and inertial positioning. Compared to the activity recognition whose sensor location is fixed, the activity recognition based on smartphones has a new problem because the mobile direction and position are not fixed. In this paper, we study the activity recognition on the Android...
The neural networks are well known as that they have an ability of approximation of any nonlinear function, and they are applied for data prediction in many fields. The parameters of neural networks, the thresholds and the weights between nodes, are updated by using given data. The performance of a neural network, for example prediction accuracy, is evaluated by the degree of the amount of the prediction...
Background: Information in bug reports is implicit and therefore difficult to comprehend. To extract its meaning, some processes are required. Categorizing bug reports is a technique that can help in this regard. It can be used to help in the bug reports management or to understand the underlying structure of the desired project. However, most researches in this area are focusing on a supervised learning...
In Twitter, there have been various kinds of Tweets and also the total volume is extremely huge, so some kind of effectively filterable technique in response to the contents of Tweets or each user's purpose is considered to be essential in order for Twitter to be effective at the time of disaster. A framework for study aiming to effectively utilize social media at the time of disaster was proposed...
Brain-Computer Interfaces (BCIs) provide a way to communicate without movement and can offer significant clinical benefits therefore. Electrical brain activity recorded using electroencephalography (EEG) can be automatically interpreted by supervised learning classifiers according to the descriptive features of the signal. Compressive sensing paradigm commonly used for array antenna design and signal...
Existing state-of-the-art realizable RC reduction methods may not be suitable for scalable power grid reductions due to the fast growing computational complexity and the large number of ports. In this work, we present a scalable power grid reduction method for reducing large-scale flip-chip power grids based on recent spectral graph sparsification techniques. The first step of the proposed approach...
The cosine similarity measure is widely used in big data analysis to compare vectors. In this article a new set of vector similarity measures are proposed. New vector similarity measures are based on a multiplication-free operator which requires only additions and sign operations. A vector ‘product’ using the multiplication-free operator is also defined. The new vector product induces the ℓ1-norm...
Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of...
In this work we address the problem of static state estimation (SE) in distribution grids by leveraging historical meter data (pseudo-measurements) with real-time measurements from synchrophasors (PMU data). We present a Bayesian linear estimator based on a linear approximation of the power flow equations for distribution networks, which is computationally more efficient than standard nonlinear weighted...
Energy disaggregation is to discover the energy consumption of individual appliances from their aggregated energy values. To solve the problem, most existing approaches rely on either appliances' signatures or their state transition patterns, both hard to obtain in practice. Aiming at developing a simple, universal model that works without depending on sophisticated machine learning techniques or...
Despite the advent and popularity of low-cost commercial sensors (e.g., Microsoft Kinect), research in 3D vision still primarily focuses on the development of advanced algorithms geared towards high resolution data. This paper presents a comparative performance evaluation of renowned state-of-the-art 3D local surface descriptors for the task of registration of both high and low resolution range image...
By using Relevance Vector Machine (RVM) to solve the problem of sparse signal recovery, Bayesian Compressive Sensing (BCS) can obtain good performance in spectral discrete spike signal detection. However, in cognitive radio (CR) system, the spectrum of primary user's signal, which is continuous in narrowband and is block sparse in wideband, cannot be exactly recovered by BCS. In this paper, a Bayesian...
Indirect immunofluorescence (IIF) with HEp-2 cells is considered as a powerful, sensitive and comprehensive technique for analyzing antinuclear autoantibodies (ANAs). Fractal dimension can be used on the analysis of image representing and also on the property quantification like texture complexity and spatial occupation. In this study, we apply the fractal theory in the application of HEp-2 cell staining...
This paper describes the methodology for implementation of artificial neural networks with adaptable parameters (weights, connections, number of neurons) on fixed-point embedded systems. Components of neuron unit and interconnecting matrix are discussed. Particular example of implementation on PIC18F46K80 is given. Results are discussed in appropriate part.
Fingerfrinting based WiFi positioning approach needs an off-line training phase to build a radio map with received signal strength indication vector of each reference point. In existing systems, this training phase may cost a tremendous amount of workload to achieve satisfying location result. To cut down on the workload notably and guarantee the location result in the meantime, we will introduce...
The paper compares the performance of both one-dimensional (ID) and two-dimensional (2D) linear discriminant analysis (LDA) in recognizing online handwritten Kannada characters. The main difference between 1D-LDA and 2D-LDA is the way the data is presented to these tools for dimensionality reduction. While, the extracted features of a data sample are vertically cascaded to form a column vector for...
Random Forest RF is an ensemble learning approach that utilises a number of classifiers to contribute though voting to predicting the class label of any unlabelled instances. Parameters such as the size of the forest N and the number of features used at each split M, has significant impact on the performance of the RF especially on instances with very large number of attributes. In a previous work...
When packed loss caused by bit error occurs in the data transmission of IP network, one should retransmit the corrupted data to avoid information loss. However, this will take more damage for those business in which retransmission is difficult to realize or will limit the performance of system. In order to reduce this damage, this paper studied the checksum restriction mechanism of the IPv4 header...
Estimation of human activities using regression techniques has been performed in this paper. The activities considered for investigation are sitting, standing and walking. As the number of independent variable considered in the proposed work is more than two, Multiple Variate Regression (MVR) technique is applied to estimate the activities. From the performance analysis, it is found that this technique...
Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require...
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