Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
One of the key aspects in the successful use of kernel methods such as Support Vector Machines is the proper choice of the kernel function. While there are several well known kernel functions which can produce satisfactory results for various applications (e.g. RBF), they do not take into account specific characteristics of the data sets. Moreover, they have a set of parameters to be tuned. In this...
This paper proposes a new TDOA estimation based on phase-voting cross correlation and circular standard deviation. Based on phase delay and kernel function, the proposed method generates a probability density function (PDF) of TDOA for each frequency bin. TDOA estimate is determined by voting the PDFs generated for all frequency bins. Peak positions of the bin-wise PDFs for the target signal are concentrated...
Kernel adaptive filters, a class of adaptive nonlinear time-series models, are known by their ability to learn expressive autoregressive patterns from sequential data. However, for trivial monotonic signals, they struggle to perform accurate predictions and at the same time keep computational complexity within desired boundaries. This is because new observations are incorporated to the dictionary...
We present a probabilistic framework for both (i) determining the initial settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive KAFs whereby in addition to weights and dictionaries, kernel parameters are learnt sequentially. This is achieved by formulating the estimator as a probabilistic model and defining dedicated prior distributions over the kernel parameters, weights...
In this paper we propose a cluster based version of the anomaly detection methodology based on signal reconstruction, using Auto Associative Kernel Regression (AAKR), combined with residuals analysis, using Sequential Probability Ratio Test (SPRT). We demonstrate how the proposed cluster based methodology can be successfully applied for anomaly detection on a marine diesel engine in operation. Furthermore,...
OpenCL continues to gather momentum on both desktop and mobile devices. The new features of OpenCL 2.0 provides developers better expressive power in programming heterogeneous computing environments. Currently in the experimental simulation environment, gem5-gpu only supports CUDA, but GPGPU-Sim can support OpenCL by compiling OpenCL kernel code to PTX using real GPU driver. However, this driver compilation...
Essays in different text genres have different ideas and writing method. Prediction the text genres firstly will help get a better accuracy when predicting the success of literary or finding the beautiful words and sentences in the essay. And it will help set a different standard for different text genres when scoring the writing by computer. Words and structure can be effective in discriminating...
Software defined networking (SDN) is used to provide as a centralized control with a flexible and scalable network. Also, SDN is provisioned for the communication systems of the next generation. OpenFlow is treated as a leading standard that defines the interface between the control plane and the physical network (data plane). Formaintaining the flexibility for the various network applications, software...
The new generation of virtualization is containerization. In this paper we measured the most common container techniques to determinate the performance of these techniques regarding computing power and network performance with industry standard measurement applications. We used these measurement results to compare them with native performance without any container techniques and with the Linux standard...
Dedicated Short Range Communications (DSRC) is a wireless communication standard intended for high speed secure communication between vehicles and the infrastructure. In order to overcome the limitations that vehicular environment imposes such as high mobility, IEEE published the 802.11p amendment widely known as Wireless Access in Vehicular Environment (WAVE). To the best of our knowledge, most of...
One of recent trends [31, 32, 14] in network architecture design is stacking small filters (e.g., 1x1 or 3x3) in the entire network because the stacked small filters is more efficient than a large kernel, given the same computational complexity. However, in the field of semantic segmentation, where we need to perform dense per-pixel prediction, we find that the large kernel (and effective receptive...
This paper studies visual pattern discovery in large-scale image collections via binarized mode seeking, where images can only be represented as binary codes for efficient storage and computation. We address this problem from the perspective of binary space mode seeking. First, a binary mean shift (bMS) is proposed to discover frequent patterns via mode seeking directly in binary space. The binomial-based...
This paper presents an efficient methodology for approximating kernel functions in Gaussian process classification (GPC). Two models are introduced. We first include the standard random Fourier features (RFF) approximation into GPC, which largely improves the computational efficiency and permits large scale remote sensing data classification. In addition, we develop a novel approach which avoids randomly...
Emissivity is one of the most important parameters to improve the determination of the troposphere properties (thermodynamic properties, aerosols and trace gases concentration) and it is essential to estimate the radiative budget. With the second generation of infrared sounders, we can estimate emissivity spectra at high spectral resolution, which gives us a global view and long-term monitoring of...
This note suggests some investigations about the complexity of multivariate problems based on quantized information rather than standard information. The extreme case of binary information is studied on two classical examples: the integration of multivariate Lipschitz functions, for which it is shown that adaptivity of the quantization process is beneficial, and the integration of multivariate trigonometric...
The High Efficiency Video Coding (HEVC) standard significantly saves coding bit-rate over the proceeding H.264 standard, but at the expense of extremely high encoding complexity. In fact, the coding tree unit (CTU) partition consumes a large proportion of HEVC encoding complexity, due to the brute-force search for rate-distortion optimization (RDO). Therefore, we propose in this paper a complexity...
Support vector machine (SVM) is a popular machine learning method and has been widely applied in many real-world applications. Since SVM is sensitive to noises, fuzzy SVM (FSVM) has been proposed to relieve the over-fitting problem caused by noises through assigning a fuzzy membership to each sample. Then, different samples make different contributions to the learning of classification hyperplane...
The mathematical model of nonlinear device (ND) plays an essential role in the power amplifier (PA) linearization by means of digital signal processing. In this paper we propose a model derived by the Wiener orthogonalization method. The one important feature of this method is that the resulted output decomposition depends on the statistics of the input signal, and initially it was derived by Wiener...
We introduce two generalizations of the exponential function and, hence, two generalizations of the Fourier transform and series. Each generalization depends on a real, nonnegative parameter less than or equal to one, but reduces to the standard exponential function when the parameter is equal to one. For this reason, the corresponding transforms are called fractional Fourier transforms. In this talk...
Estimating expected polynomials of density functions from samples is a basic problem with numerous applications in statistics and information theory. Although kernel density estimators are widely used in practice for such functional estimation problems, practitioners are left on their own to choose an appropriate bandwidth for each application in hand. Further, kernel density estimators suffer from...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.