scholarly journals The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Cundong Tang ◽  
Li Chen ◽  
Yi Wang ◽  
Wusi Yang ◽  
Rui Chen ◽  
...  
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Practical Significance ◽  
Support Vector ◽  
Detector Performance ◽  
Real Time System ◽  
Target Model ◽  
Tracking Process ◽  
5G Network

With the development of information technology in the network era and the popularization of the 5G era, UAV-related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross-validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real-time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real-time system performance, using the results of the TPD-KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.

Object Update-Based Mean Shift Tracing Algorithm Combined with LBP

2014 ◽  
Vol 548-549 ◽  
pp. 1185-1191
Author(s):  
Mou Zhong Liu ◽  
Min Sun ◽  
Ya Fen Wang
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Local Binary Pattern ◽  
Mean Shift ◽  
Experimental Result ◽  
Human Face ◽  
Target Model ◽  
Fast Acquisition ◽  
Tracking Process ◽  
Tracing Algorithm

This paper proposed a novel solution to track human face obscured largely in an image on the basis of Mean Shift Tracing Algorithm (MSTA). The improved approach aims to update the target model in real-time during the whole tracking process to avoid target losing. Local Binary Pattern (LBP) theory is chosen to improve the original MSTA here. The experimental result shows that our new solution has a better performance in target tracking under situations like face rotation and occlusion as well as in fast acquisition when faces reappear on the screen.

scholarly journals Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiongwei Zhang ◽  
Hager Saleh ◽  
Eman M. G. Younis ◽  
Radhya Sahal ◽  
Abdelmgeid A. Ali
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Sentiment Analysis ◽  
Real Time ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Support Vector ◽  
Real Time System ◽  
Online Prediction ◽  
Analysis Prediction

Twitter is a virtual social network where people share their posts and opinions about the current situation, such as the coronavirus pandemic. It is considered the most significant streaming data source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. Sentiment analysis is a text analysis method that has gained further significance due to social networks’ emergence. Therefore, this paper introduces a real-time system for sentiment prediction on Twitter streaming data for tweets about the coronavirus pandemic. The proposed system aims to find the optimal machine learning model that obtains the best performance for coronavirus sentiment analysis prediction and then uses it in real-time. The proposed system has been developed into two components: developing an offline sentiment analysis and modeling an online prediction pipeline. The system has two components: the offline and the online components. For the offline component of the system, the historical tweets’ dataset was collected in duration 23/01/2020 and 01/06/2020 and filtered by #COVID-19 and #Coronavirus hashtags. Two feature extraction methods of textual data analysis were used, n-gram and TF-ID, to extract the dataset’s essential features, collected using coronavirus hashtags. Then, five regular machine learning algorithms were performed and compared: decision tree, logistic regression, k-nearest neighbors, random forest, and support vector machine to select the best model for the online prediction component. The online prediction pipeline was developed using Twitter Streaming API, Apache Kafka, and Apache Spark. The experimental results indicate that the RF model using the unigram feature extraction method has achieved the best performance, and it is used for sentiment prediction on Twitter streaming data for coronavirus.

An adaptive SVM-based real-time scheduling mechanism and simulation for multiple-load carriers in automobile assembly lines

2017 ◽  
Vol 08 (04) ◽  
pp. 1750048 ◽  
Author(s):  
Binghai Zhou ◽  
Jiahui Xu
Keyword(s):  
Real Time ◽  
System Performance ◽  
Manufacturing Systems ◽  
Batch Size ◽  
Support Vector ◽  
Real Time System ◽  
Real Time Scheduling ◽  
Automobile Assembly ◽  
Time Scheduling ◽  
Multiple Load

Multiple-load carriers are widely introduced for material delivery in manufacturing systems. The real-time scheduling of multiple-load carriers is so complex that it deserves attention to pursue higher productivity and better system performance. In this paper, a support vector machine (SVM)-based real-time scheduling mechanism was proposed to tackle the scheduling problem of parts replenishment with multiple-load carriers in automobile assembly plants under dynamic environment. The SVM-based scheduling mechanism was trained first and then used to make the optimal real-time decisions between “wait” and “deliver” on the basis of real-time system states. An objective function considering throughput and delivery distances was established to evaluate the system performance. Moreover, a simulation model in eM-Plant software was developed to validate and compare the proposed SVM-based scheduling mechanism with the classic minimum batch size (MBS) heuristic. It simulated both the steady and dynamic environments which are characterized by the uncertainty of demands or scheduling criteria. The simulation results demonstrated that the SVM-based scheduling mechanism could dynamically make optimal real-time decisions for multiple-load carriers and outperform the MBS heuristic as well.

scholarly journals The Research and Application of Visual Saliency and Adaptive Support Vector Machine in Target Tracking Field

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yuantao Chen ◽  
Weihong Xu ◽  
Fangjun Kuang ◽  
Shangbing Gao
Keyword(s):  
Support Vector Machine ◽  
Target Tracking ◽  
Visual Saliency ◽  
Image Features ◽  
Tracking Algorithm ◽  
Support Vector ◽  
Intelligent Robots ◽  
Tracking Process ◽  
Adaptive Support ◽  
Novel Target

The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking’s accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper’s algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target’s saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination.

Vehicle-Classification Based on Edge Extraction and Background Difference

2011 ◽  
Vol 128-129 ◽  
pp. 1109-1113
Author(s):  
Chan Yang ◽  
Zhong Jian Dai
Keyword(s):  
Real Time ◽  
Intelligent Transportation System ◽  
Weather Conditions ◽  
Extraction Process ◽  
Detection Algorithm ◽  
Vehicle Classification ◽  
Experimental Result ◽  
Support Vector ◽  
Real Time System ◽  
Background Difference

The real-time vehicle classification plays an important role in Intelligent Transportation System (ITS). How to effectively improve the accuracy rate and the speed of the vehicle classification is still a hot research issue, the classification algorithm has to be effective but simple. In this paper, a vehicle detection algorithm based on edge-based background difference and region-based background difference is proposed. This algorithm can extract the moving vehicle completely, eliminate vehicle shadow effectively, and it is still significant despite the variations of illumination and weather conditions. The algorithm is simple with low computation quantity and suitable for real-time system. In the feature extraction process, the feature vector can be obtained in short time. Support vector machine (SVM) is also discussed in the classification process. The experimental result shows that the system can accurately recognize the vehicles.

Real Time Acoustic Feature Analysis for Gender Identification

2020 ◽  
Vol 13 (2) ◽  
pp. 275-282
Author(s):  
Vanita Jain ◽  
Paras Chhabra ◽  
Mansi Bhardwaj
Keyword(s):  
Real Time ◽  
Search Space ◽  
Support Vector ◽  
Accuracy Score ◽  
Decision Tree Classifier ◽  
Acoustic Feature ◽  
Real Time System ◽  
Authentication Mechanism ◽  
Tree Classifier ◽  
Optimal Accuracy

Objective: Humans with their developed senses can easily ascertain a person’s gender just by listening to a few uttered words and it does not take any conscious additional effort to do so, however, a machine cannot do the same unless trained. This research proposes a real time system to identify a person’s gender from their voice. Methods: Features are extracted from the dataset and checked for outliers. Then a baseline classifier is constructed to measure performance of the different models. Next the dataset is prepared for training and five machine learning models, Decision Tree Classifier, Random Forest Classifier, K Nearest Neighbours, Support Vector Machine and Gaussian Naive Bayes Classifier are applied. Finally, real time prediction is done by taking speech input and analysing it against the trained model, after input of speech the gender along with accuracy of prediction is displayed within 1.37s. Results: A maximum accuracy score of 88.19% is obtained using SVM. Additionally, the juxtaposition of the feature importance graph highlights the two most important features which fuel this classification. A combination of these features is then studied to design a less complex system and it is observed that using just MFCCs and Chroma Vector a near optimal accuracy score of 87.78% is obtained. Conclusion: Identification of gender prior to applying speech recognition and emotion recognition algorithm can help in reduction of the search space. Further, using only MFCC and Chroma Vector can make the system memory efficient and yet provide near optimal accuracy. The system can be used as an authentication mechanism and can be installed in public places.

scholarly journals ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3069 ◽  
Author(s):  
Beom-Hun Kim ◽  
Jae-Young Pyun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Support Vector ◽  
Time System ◽  
Real Time System ◽  
Deep Recurrent Neural Network ◽  
Personal Authentication

Securing personal authentication is an important study in the field of security. Particularly, fingerprinting and face recognition have been used for personal authentication. However, these systems suffer from certain issues, such as fingerprinting forgery, or environmental obstacles. To address forgery or spoofing identification problems, various approaches have been considered, including electrocardiogram (ECG). For ECG identification, linear discriminant analysis (LDA), support vector machine (SVM), principal component analysis (PCA), deep recurrent neural network (DRNN), and recurrent neural network (RNN) have been conventionally used. Certain studies have shown that the RNN model yields the best performance in ECG identification as compared with the other models. However, these methods require a lengthy input signal for high accuracy. Thus, these methods may not be applied to a real-time system. In this study, we propose using bidirectional long short-term memory (LSTM)-based deep recurrent neural networks (DRNN) through late-fusion to develop a real-time system for ECG-based biometrics identification and classification. We suggest a preprocessing procedure for the quick identification and noise reduction, such as a derivative filter, moving average filter, and normalization. We experimentally evaluated the proposed method using two public datasets: MIT-BIH Normal Sinus Rhythm (NSRDB) and MIT-BIH Arrhythmia (MITDB). The proposed LSTM-based DRNN model shows that in NSRDB, the overall precision was 100%, recall was 100%, accuracy was 100%, and F1-score was 1. For MITDB, the overall precision was 99.8%, recall was 99.8%, accuracy was 99.8%, and F1-score was 0.99. Our experiments demonstrate that the proposed model achieves an overall higher classification accuracy and efficiency compared with the conventional LSTM approach.

Interaction of the Decision Maker in the Process of Production Scheduling

2014 ◽  
Vol 1036 ◽  
pp. 830-833 ◽  
Author(s):  
Krzysztof Kalinowski ◽  
Cezary Grabowik ◽  
Iwona Paprocka ◽  
Wojciech Kempa
Keyword(s):  
Real Time ◽  
Decision Maker ◽  
Production Scheduling ◽  
General Procedure ◽  
Time System ◽  
Real Time System ◽  
Scheduling System ◽  
Advantages And Disadvantages

In the paper the role of the decision maker in the process of production scheduling is discussed. A general procedure specifying the activities carried out within the framework of particular steps of scheduling is shown. The scope of the interactions of the decision maker in the scheduling process under conditions of real time system is commented. The purpose of this work is to present a range of knowledge and information which should have a decision maker and also the advantages and disadvantages of each method of participation. The work in this area is one of the most important steps when designing a new, dedicated scheduling system as well as during the analysis, selection and adaptation of an external one.

scholarly journals Reduction of Time for Speech Recognition System by Shrinking Adaptive Layers of ADAG for Real Time System using ADAT SVM

2019 ◽  
Vol 9 (1) ◽  
pp. 4367-4371
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Current System ◽  
Test Word ◽  
Recognition System ◽  
Support Vector ◽  
Testing Time ◽  
Real Time System ◽  
Large Vocabulary ◽  
Data Process

Recently speech recognition becomes very major area for large vocabulary real time applications. In the existing work research was formulated for large number of words spoken by same speaker using Adaptive Directed Acyclic Graph (ADAG) with support vector machines. In today’s era the emphasis is given to processing large vocabulary data process and recognition. In existing system, when spoken words are recognizing by number of adaptive layers causes increase in testing time for recognition of data occur. The aim of the proposed Reduced Adaptive Directed Acyclic technique (RADAT) is to develop a system to recognize test word in less amount of time than existing System [1]. In our current system it is possible to remove unnecessary finding distance of test word with number of times with training word. The proposed RADAT system handles this by applying reduction in number of adaptive layers to recognize any testing word and which result reduction in time. The experiment results of current system reduce time complexity without loss of recognition accuracy for any data of speaker dependent system. The results are obtained for various dataset like large vocabulary speech record as well for small size data to large vocabulary dataset. In our paper we use same feature extracted samples for training and testing. This method is work for various speech dataset like machine operation command in English or required language to operate computer system in real time application as well applied for offline application such as start, logoff, login, notepad, open, close, shutdown etc [5].

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