Maximum Selection Method Research Articles

The recital assessment of image fusion based on multi-resolution transforms using smoothness approach proach

Combining the information from the registered source images is the process involved in the image-fusion. In this present manuscript, two fusion rules are explored. The first one is based on the weightage-based rule. The second one is the Smoothness and weightage-based algorithm. Smoothness is used to reduce the noise from the source images. These two methods are independent of the selection of the transform. In this work, Discrete Wavelet Transform is considered to perform the experimentation. The recital comparison was made between multiresolution transforms using maximum selection method, weightage method, and smoothness along with weightage. The source images are generally multi-focused, satellite, panchromatic, and clinical medical images. The experimental results show that more smoothened (in addition with weightage) images (including edges and curves) provide high visual information. The advantage of this approach is proven using the performance metrics such as PSNR, NCC, MI, ESOP, and FSIM. The blocking artifacts are reduced by decomposing the transforms and the high frequency noise in the source image is smoothened by proposed approaches.

A Deep Neural Network-Based Approach to Media Hotspot Discovery

In recent years, with the rapid development of social network media, it has become a valuable research direction to quickly analyze these texts and find out the current hotspots from them in real time. To address this problem, this paper proposes a method to discover current hotspots by combining deep neural networks with text data. First, the text data features are extracted based on the graphical convolutional neural network, and the temporal correlation of numerical information is modeled using gated recurrent units, and the numerical feature vectors are fused with the text feature vectors. Then, the K-means algorithm is optimized for the initial point selection problem, and a clustering algorithm based on the maximum density selection method in the moving range is proposed. Finally, the text feature representation method based on graph convolutional neural network is combined with the clustering algorithm based on the moving range density maximum selection method to build a deep learning-based media hotspot discovery framework. The accuracy of the proposed media hotspot discovery method and the comprehensive evaluation of the computing time have been verified experimentally.

Межканальная корреляция полезных сигналов и сравнительный анализ эффективности различных способов объединения каналов

The comparative efficiency analysis of two ways of multiplexing based on minimum and maximum selection methods has been carried out. It has been shown that minimum selection method is less effective than the maximum one for independent channels. Minimum selection method approaches efficiency of the maximum selection one when considering interchannel desired signal correlation. However this consideration implies the efficiency decrease of maximum selection method, while improving the efficiency of minimum selection one.

A joint sparse wavelet coefficient extraction and adaptive noise reduction method in recovery of weak bearing fault features from a multi-component signal mixture

Rolling element bearings are widely used to support rotating components of a machine. Due to close space locations of components in the machine, a vibration signal caused by bearing localized defects is easily overwhelmed by other strong vibration signals. Extracting the bearing fault signal from a multi-component signal mixture is thus significant to detect early bearing fault features and prevent machine breakdown. In this paper, a bearing fault diagnosis method, named cyclic spike detection method, is proposed to extract the weak bearing fault features from a multi-component signal mixture. Firstly, the optimal center frequency and bandwidth of a complex Morlet wavelet filter are determined by a simplex-simulated annealing algorithm along with a maximum sparsity objective function. The filtered signal is then obtained by applying the optimal wavelet filter to the multi-component signal mixture. After that, a new adaptive local maximum selection method is proposed to make the filtered signal succinct. Only a few spikes are retained to reveal potential cyclic intervals caused by bearing localized defects. Two multi-component signal mixtures, including a simulated signal and a real vibration signal collected from an industrial machine, are used to validate the effectiveness of the proposed cyclic spike detection method. The results demonstrate that the proposed method can extract the weak bearing fault features from other strong masking vibration signals and noise.

On the threshold setting principles in code acquisition of DS-SS signals

This paper compares the meaning of different threshold setting principles in the code acquisition process of a direct sequence (DS) spread spectrum (SS) receiver. The consideration is made mainly in one-path additive white Gaussian noise (AWGN) channel. A consideration to a fixed multipath channel is given to see its effect on the results. Also, a consideration for a certain type of fading is given in a case where the signal power is assumed to be considerably lower, i.e., faded, part of the time. For the possible performance measures of code acquisition, the main interest is in the mean acquisition time T/sub MA/. The probability of the acquisition in a given observation interval P/sub acq/ is also considered to see if different measures have different demands. A matched filter (MF) acquisition is used with and without a verification mode using an active integration. In the comparisons, fixed thresholds, thresholds based on constant false alarm rate (CFAR) criteria, and optimal thresholds in the sense to give either the minimum T/sub MA/ or the maximum P/sub acq/ are used. The results, which are obtained by using a method of selecting the maximum value at the output of the MF, are compared to the threshold cases. The results can be summarized as follows: when the performance measure is T/sub MA/, the best results are obtained by using CFAR-based threshold comparison. By a proper selection of the probability of a false alarm, the same performance is obtained as by using the optimal thresholds. When the performance measure is P/sub acq/, the maximum-selection method is the best choice.