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The quality of life of people is increasing together with the developing technologies. One of the most important factors affecting daily life is smart cities. The quality of life of people is positively affected by emerging this concept in recent years. Autonomous vehicles confront with the term of the smart city and have become even more popular in recent years. In this study, a system of traffic...
With he rapid development of the mobile Internet industry, information security based on trusted computing is also becoming increasingly serious. Considering that most of the existing research is based on the static security measure, and based on the server and PC side, a security operation environment measurement framework based on mobile terminal is proposed in this paper to alleviate the information...
The current focus of our research is to detect and classify the plant disease in agricultural domain, by implementing image processing techniques. We aim to propose an innovative set of statistical texture features for classification of plant diseases images of leaves. The input images are taken by various mobile cameras. The Scale-invariant feature transform (SIFT) features used as texture feature...
VGG 16 and Inception-v3 networks were trained using a texture dataset of muddied and clean cows. A new dataset with 600 images that is similar to the actual texture dataset was introduced and used to train the networks. The method used to train the networks was transfer learning. ImageNet weights were trained using the similar dataset, then the newly trained weights were trained again using the actual...
The death of the patients is an important event in the intensive care unit (ICU), mortality risk prediction thus offers much information for clinical decision making. However, Patient ICU mortality prediction faces challenges in many aspects, such as high dimensionality, imbalance distribution. In this paper, we modified the cost-sensitive principal component analysis (CSPCA), which is denoted by...
Recently, with the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a significantly important part in the clinical diagnosis of cardiovascular disease. In this paper, a 1D convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition...
In this study, we proposed an analysis method of ElectroMyoGraphic (EMG) signals in order to diagnose and to identify neuromuscular pathologies (i.e.; myopathy and neuropathy). Analysis is performed fully automatically without expert assistance and without prior segmentation of muscle contractions. The method is based on Huang-Hilbert transform (HHT) which is a data-driven algorithm that decomposes...
With the development of algorithms and computer skills, deep learning using CNN (convolutional neural network) has been applied to various fields, especially in image processing field. In this paper, we designed an improved model based on ResNet with CNN structure, and learned the database. The Chaucer Database used in the experiment consisted of 824 Chinese characters among the Chinese characters...
When a standard TCP implementation using the minimum retransmission timeout (RTOmin) of 200 ms is used in distributed file systems in data centers, a well-known throughput degradation called TCP Incast occurs, because 200 ms is too large as an RTOmin in data centers. In order to avoid TCP Incast, a TCP implementation using a much smaller RTOmin attained by a fine-grained kernel timer is proposed....
RaptorQ is the most advanced raptor code and has an overhead-failure curve close to the random fountain code over the GF(256) finite field. Theoretically, it is possible to encode and decode with linear time complexity by an inactivation decoding algorithm, which is a hybrid algorithm of belief propagation and Gaussian elimination. However, achieving linear time complexity in a real-world implementation...
Sensors in industrial systems fault frequently leading to serious consequences regarding cost and safety. The authors propose support vector machine-based classifier with diverse time- and frequency-domain feature models to detect and classify these faults. Three different kernels, i.e., linear, polynomial, and radial-basis function, are employed separately to examine classifier's performance in each...
The thriving success of the Cloud Industry greatly relies on the fact that virtual resources are as good as bare metal resources when it comes to ensuring a given level of quality of service. Thanks to the isolation provided by virtualisation techniques based on hypervisors, a big physical resource can be spatially multiplexed into smaller virtual resources which are easier to sell. Unfortunately,...
Many neural architectures including RBF, SVM, FSVC classifiers, or deep-learning solutions require the efficient implementation of neurons layers, each of them having a given number of m neurons, a specific set of parameters and operating on a training or test set of N feature vectors having each a dimension n. Herein we investigate how to allocate the computation on GPU kernels and how to better...
Twitter is a popular microblogging service that allows its users to view and share limited character messages (known as “tweets”) with the public. This paper proposes a tweet sentiment classification framework which pre-processes information from Emoticon and Emoji in such way that their textual representation is included to enrich the tweet. Once the tweets are pre-processed, a hybrid computational...
One-class support vector machines (OCSVM) have been recently applied to detect anomalies in wireless sensor networks (WSNs). Typically, OCSVM is kernelized by radial bais functions (RBF, or Gausian kernel) whereas selecting Gaussian kernel hyperparameter is based upon availability of anomalies, which is rarely applicable in practice. This article investigates the application of OCSVM to detect anomalies...
For the sake of improving the precision of speech emotion recognition, this paper proposed a novel speech emotion recognition approach based on Gaussian Kernel Nonlinear Proximal Support Vector Machine (PSVM) to recognize four basic human emotions (angry, joy, sadness, surprise). Firstly, preprocess speech signal containing sampling, quantification, pre-emphasizing, framing, adding window and endpoint...
The present status of heart sound recognition is introduced in the paper. In order to improve the performance of heart sound recognition, a new model based on SVM is proposed. Firstly, the wavelet transform is used to reduce the noise of the heart sound, and then MFCC feature is extracted from heart sound. On this basis, the Support Vector Machine is used to build the classification model. In the...
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based...
In the part of geophysics researches, the method Double Beam Forming (DBF) was found for separating the surface waves from the seismic waves. According to one of the kernels of DBF : extraction of group velocity and phase velocity, this paper benefit from this idea and concentrate on the time delay estimation (TDE) which is one of the key techniques in the array signal processing. The sound velocity...
This paper investigates the adaptive stabilization for a class of coupled parabolic PDEs. The problem remains unsolved since the presence of unknown parameters make the existing methods for coupled parabolic PDEs ineffective. By skillfully combining Lyapunov method and infinite-dimensional backstepping method, an adaptive state-feedback controller is designed to ensure that all the closed-loop system...
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