Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of

The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. As the standard DE algorithm is a fixed length optimizer, it is not suitable for solving signal de-noising problems that call for variability. A modified crossover scheme called rand-length crossover was designed to fit the proposed variable-length DE, and the new DE algorithm is referred to as the random variable-length crossover differential evolution (rvlx-DE) algorithm. The measurement results demonstrate a highly efficient capability for target detection in terms of frequency response and peak forming that was isola

LED is an ultra-lightweight block cipher that is mainly used in devices with limited resources. Currently, the software and hardware structure of this cipher utilize a complex logic operation to generate a sequence of random numbers called round constant and this causes the algorithm to slow down and record low throughput. To improve the speed and throughput of the original algorithm, the Fast Lightweight Encryption Device (FLED) has been proposed in this paper. The key size of the currently existing LED algorithm is in 64-bit & 128-bit but this article focused mainly on the 64-bit key (block size=64-bit). In the proposed FLED design, complex operations have been replaced by LFSR left feedback technology to make the algorithm perform more e

Facial identification is one of the biometrical approaches implemented for identifying any facial image with the use of the basic properties of that face. In this paper we proposes a new improved approach for face detection based on coding eyes by using Open CV's Viola-Jones algorithm which removes the falsely detected faces depending on coding eyes. The Haar training module in Open CV is an implementation of the Viola-Jones framework, the training algorithm takes as input a training group of positive and negative images, and generates strong features in the format of an XML file which is capable of subsequently being utilized for detecting the wanted face and eyes in images, the integral image is used to speed up Haar-like features calc

Often phenomena suffer from disturbances in their data as well as the difficulty of formulation, especially with a lack of clarity in the response, or the large number of essential differences plaguing the experimental units that have been taking this data from them. Thus emerged the need to include an estimation method implicit rating of these experimental units using the method of discrimination or create blocks for each item of these experimental units in the hope of controlling their responses and make it more homogeneous. Because of the development in the field of computers and taking the principle of the integration of sciences it has been found that modern algorithms used in the field of Computer Science genetic algorithm or ant colo

Data centric techniques, like data aggregation via modified algorithm based on fuzzy clustering algorithm with voronoi diagram which is called modified Voronoi Fuzzy Clustering Algorithm (VFCA) is presented in this paper. In the modified algorithm, the sensed area divided into number of voronoi cells by applying voronoi diagram, these cells are clustered by a fuzzy C-means method (FCM) to reduce the transmission distance. Then an appropriate cluster head (CH) for each cluster is elected. Three parameters are used for this election process, the energy, distance between CH and its neighbor sensors and packet loss values. Furthermore, data aggregation is employed in each CH to reduce the amount of data transmission which le

Establishing complete and reliable coverage for a long time-span is a crucial issue in densely surveillance wireless sensor networks (WSNs). Many scheduling algorithms have been proposed to model the problem as a maximum disjoint set covers (DSC) problem. The goal of DSC based algorithms is to schedule sensors into several disjoint subsets. One subset is assigned to be active, whereas, all remaining subsets are set to sleep. An extension to the maximum disjoint set covers problem has also been addressed in literature to allow for more advance sensors to adjust their sensing range. The problem, then, is extended to finding maximum number of overlapped set covers. Unlike all related works which concern with the disc sensing model, the cont

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

The three parameters distribution called modified weibull distribution (MWD) was introduced first by Sarhan and Zaindin (2009)[1]. In theis paper, we deal with interval estimation to estimate the parameters of modified weibull distribution based on singly type one censored data, using Maximum likelihood method and fisher information to obtain the estimates of the parameters for modified weibull distribution, after that applying this technique to asset of real data which taken for Leukemia disease in the hospital of central child teaching .