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Real-time data-driven systems often utilize discrete valued time series data and their functionality is highly dependent on the accuracy of such data. In order to improve the performance of these systems, an important pre-processing step is the denoising of data before performing any action (e.g. forecasting or control activities). Existing algorithms have primarily focused on the offline denoising...
Noise information has a serious impact on various studies that using web pages as datasets. As a fundamental work in information retrieval, removing noise in web pages quickly and accurately received widely attention. In this paper, a noise reduction algorithm which uses DOM (Document Object Model) to preserve the original structure of web pages is proposed to the issue of low efficiency of traditional...
In this work, we employ a pair wise Markov Random Field (MRF) and a Conditional Random Field (CRF) for bi-level image segmentation and denoising. For both tasks, the Ising pair wise model and the Iterative Conditional Mode (ICM) inference method are implemented, assuming the parameters of the unary and pair wise potentials are known. Experimental results demonstrate the effectiveness of the proposed...
A limitation to accurate automatic tracking of knee motion is the noise and blurring present in low dose X-ray fluoroscopy images. For more accurate tracking, this noise should be reduced while preserving anatomical structures such as bone. Noise in low dose X-ray images is generated from different sources, however quantum noise is by far the most dominant. In this paper we present an accurate multi-modal...
In this paper, a technique to classify seven different forearm movements using surface electromyography (sEMG) data which were received from 8 able bodied subjects was proposed. A 2-channel sEMG system was used for data acquisition and recording, then this raw electromyography (EMG) signals were applied to the wavelet denoising. In the next step, time-frequency feature is extracted calculating wavelet...
This paper describes a simple and fast way to predict efficiency of DCT-based filtering of images corrupted by signal dependent noise as this often happens for hyperspectral and radar remote sensing. Such prediction allows deciding in automatic way is it worth applying denoising to a given image under condition that parameters of signal-dependent noise are known a priori or pre-estimated with appropriate...
This paper proposes a new approach to star image denoising, recognizing and centroiding for the airborne application, especially during the daytime. To extend attitude determination of aircraft to daytime, one prerequisite is to precisely obtain the centroid of the target star. To date, there has not been an adequate analytical model and experimental method to solve this problem effectively. Generally,...
In central and western regions where the topography is complex, various obstructions such as roads, rivers, and villages interfere the deployment of the mountain seismic line. In this paper, we compared the traditional operators, such as the Robert operator, the Sobel operator, and the Laplacian operator, discovering the defect in accuracy and noise resistance. Then, a computational approach using...
Increasing attention has been focused on the stability of selected features or selection stability, which is becoming a new measure in determining the effectiveness of a feature selection algorithm besides the learning performance. A recent study has shown that data characteristics play a significant role in selection stability. Hence, the solution to selection instability should begin with data....
This paper explains the issues of study that was designed to evaluate the effect of denoising algorithm to detect emotional expression through Electroencephalogram (EEG). This research led to classify the EEG features due to emotion which was induced by the facial expression stimulus include of happy and sad and neutral cases. Event-related potential (ERP) method was selected to probe the ability...
Accuracy and stability of Kinect-like depth data is limited by its generating principle. In order to serve further applications with high quality depth, the preprocessing on depth data is essential. In this paper, we analyze the characteristics of the Kinect-like depth data by examing its generation principle and propose a spatial-temporal denoising algorithm taking into account its special properties...
In order to avoid the influence on the founding of DEM, noise elimination is an important step for data processing after acquisition. Based on Least Squares theory, reference plane and partial planes are fitted separately. By comparing the relative position of each other, obvious noises are eliminated. Then, second filtering is carried out based on the distance from scattered data points to fitting...
The real-world activity data collection using simple and ubiquitous sensors is in general a passive process. During this process, a set of sensors are embedded with home appliances while the experience sampling tool (ESM) is provided to the user for acquiring self-reported activity label. No direct or active observation is provided to label the activity and to see the corresponding sensor activations...
For accurate location of a partial discharge source in the power cables, exact determination of time of arrival of initiated waves by PD source, is very important. The proposed method in this paper is based on applying mathematical morphology operators to perform this task. In this method the Opening-Closing filter of mathematical morphology is used for noise reduction of signal and gradient operator...
This paper proposes a new robust speech recognition method. Since the hidden Markov model (HMM) algorithm need a lot of training calculation, The dynamic time warping (DTW) algorithm based on median filter is used instead in our system. According to the short-term energy method, the non-speech segment can be removed. Recognition accuracy is thus improved. The cepstral mean subtraction (CMS), running...
The rich information available in hyperspectral imagery has posed significant opportunities for material classification and identification. The main problem encountered with the classification process is the high dimensionality of hyperspectral data and the low-sized training dataset. Hence, dimensionality reduction is often adopted to avoid the "curse of dimensionality" phenomenon. However,...
The important astrophysical information is hidden in spectral lines of astronomical spectra. This paper presents a method in detail extraction of spectral lines and splitting. The method consists of four main steps: First, fitting the continuum of the observed spectra by a combined method of framelet transform and spline fitting. After removing the continuum, the spectral signal is transformed by...
Image denoising is one of the important processes in the pre-processing of verification/identification systems. As a norm in verification system, Performance of feature extraction and approaches and verification depend son the input images quality. Since there is noise in the captured image via acquire/transfer sensors has direct effect on the quality of input image, insert a denoising step and enhancement...
A novel way of managing the compromise between noise reduction and speech distortion in Wiener filters is presented. It is based on adjusting the amount of noise reduced, and therefore the speech distortion introduced, on a phone-by-phone basis. We show empirically that optimal Wiener filters produce different amounts of speech distortion for different phones. Therefore we propose a phone-conditioned...
Rainfall prediction is a key question in the study field of hydrology and water resources. Point to non-linear, chaotic character and with the noise characteristics Run-off signals, we propose a new model based on empirical mode decomposition (EMD) and the RBF neural network (RBF). First, rainfall time series will be broken down into a series of different scales intrinsic mode function imf by EMD,...
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