Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492745
Tserennadmid Tumurbaatar, Taejung Kim
3D tracking plays a vital role in 3D applications by enhancing interaction between real and virtual world. We present various real-time 3D motion estimation approaches developed in photogrammetry and computer vision fields and compare their performance. The methods developed in both fields estimates 3D motion of a moving object relative to a camera or equivalently moving camera relative to the object in an image sequence when its corresponding features are known at different times. We reviewed 3D motion models formulated by different methods related to their geometric properties. We implemented four different methods and analyzed their performance results. Comparison from test datasets from image sequences demonstrated that homography based approaches in both fields were more accurate than relative orientation or essential matrix based approaches under noisy situations.
{"title":"Comparision of Computer Vision and Photogrammetric Approaches for Motion Estimation of Object in an Image Sequence","authors":"Tserennadmid Tumurbaatar, Taejung Kim","doi":"10.1109/ICIVC.2018.8492745","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492745","url":null,"abstract":"3D tracking plays a vital role in 3D applications by enhancing interaction between real and virtual world. We present various real-time 3D motion estimation approaches developed in photogrammetry and computer vision fields and compare their performance. The methods developed in both fields estimates 3D motion of a moving object relative to a camera or equivalently moving camera relative to the object in an image sequence when its corresponding features are known at different times. We reviewed 3D motion models formulated by different methods related to their geometric properties. We implemented four different methods and analyzed their performance results. Comparison from test datasets from image sequences demonstrated that homography based approaches in both fields were more accurate than relative orientation or essential matrix based approaches under noisy situations.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128083203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492837
Chengtao Cai, Boyu Wang, Yue Liu, Yongjie Yan
In machine vision, image processing technology is the basis of target recognition and positioning. When the background of the image is complex, especially when the background feature is similar to the target feature, the accuracy of the target recognition by traditional image processing methods cannot be guaranteed. In this paper, based on the background of automatic welding technology, proposing a new method of combining the neural networks and machine vision. Specifically, the image is preprocessed by using an improved convolutional auto-encoder to enhance the target features and remove the characteristics of the main interferers. Then, use the improved traditional image processing technology to extract the target and complete the processing of the featureless image. Finally, use a binocular camera to achieve accurate positioning of the target. This paper provides a new idea for the identification and positioning of the target.
{"title":"Unfeatured Weld Positioning Technology Based on Neural Network and Machine Vision","authors":"Chengtao Cai, Boyu Wang, Yue Liu, Yongjie Yan","doi":"10.1109/ICIVC.2018.8492837","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492837","url":null,"abstract":"In machine vision, image processing technology is the basis of target recognition and positioning. When the background of the image is complex, especially when the background feature is similar to the target feature, the accuracy of the target recognition by traditional image processing methods cannot be guaranteed. In this paper, based on the background of automatic welding technology, proposing a new method of combining the neural networks and machine vision. Specifically, the image is preprocessed by using an improved convolutional auto-encoder to enhance the target features and remove the characteristics of the main interferers. Then, use the improved traditional image processing technology to extract the target and complete the processing of the featureless image. Finally, use a binocular camera to achieve accurate positioning of the target. This paper provides a new idea for the identification and positioning of the target.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133033297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492908
Qidan Zhu, X. Ai
For current domestic and international tire detection systems, the software operation of them is complex and poor to be put into application. In reality, it is necessary to do the task of defect detection by observing the X-ray image of the tire with the help of human eyes. This practice is affected by some subjective factors and both the accuracy and efficiency vary from person to person without strong robustness. To tackle this issue, one detection algorithm for tire defects based on deep learning is proposed. In this case, the model is trained, learnt and tested using the collected defect samples preprocessed from tire X-ray images. The designed algorithm was verified by the developed automatic tire defect detection software, in which the desired results were obtained.
{"title":"The Defect Detection Algorithm for Tire X-Ray Images Based on Deep Learning","authors":"Qidan Zhu, X. Ai","doi":"10.1109/ICIVC.2018.8492908","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492908","url":null,"abstract":"For current domestic and international tire detection systems, the software operation of them is complex and poor to be put into application. In reality, it is necessary to do the task of defect detection by observing the X-ray image of the tire with the help of human eyes. This practice is affected by some subjective factors and both the accuracy and efficiency vary from person to person without strong robustness. To tackle this issue, one detection algorithm for tire defects based on deep learning is proposed. In this case, the model is trained, learnt and tested using the collected defect samples preprocessed from tire X-ray images. The designed algorithm was verified by the developed automatic tire defect detection software, in which the desired results were obtained.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133541283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492907
Peng Hu, X. Hao, Jiansheng Li, Chuanqi Cheng, Anran Wang
In this paper, we present a baseline-adjustable and highly synchronous real-time binocular image acquisition system which is easy to realize. Binocular stereo vision is gradually applied to the field of robot navigation, industrial detection and virtual reality etc. However, most designs are not flexible in practice due to fixed baseline and they have longer production cycle and higher costs. We propose a novel solution by using an adjustable baseline to acquire binocular image with robust hardware part and parallel software part. Cameras can capture binocular images triggered by external trigger pulse generated via Raspberry Pi in 20 Hz with synchronization error in microsecond level. Since our system is highly synchronized and portable, easy to deploy and low-cost. It is more convenient to generate binocular datasets for SLAM experiments. Finally, we testify the design via ORB-SLAM2 system.
{"title":"Design and Implementation of Binocular Vision System with an Adjustable Baseline and High Synchronization","authors":"Peng Hu, X. Hao, Jiansheng Li, Chuanqi Cheng, Anran Wang","doi":"10.1109/ICIVC.2018.8492907","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492907","url":null,"abstract":"In this paper, we present a baseline-adjustable and highly synchronous real-time binocular image acquisition system which is easy to realize. Binocular stereo vision is gradually applied to the field of robot navigation, industrial detection and virtual reality etc. However, most designs are not flexible in practice due to fixed baseline and they have longer production cycle and higher costs. We propose a novel solution by using an adjustable baseline to acquire binocular image with robust hardware part and parallel software part. Cameras can capture binocular images triggered by external trigger pulse generated via Raspberry Pi in 20 Hz with synchronization error in microsecond level. Since our system is highly synchronized and portable, easy to deploy and low-cost. It is more convenient to generate binocular datasets for SLAM experiments. Finally, we testify the design via ORB-SLAM2 system.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129362949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492862
Nursultan Taubaldy, Zexuan Ji
Automatically and accurately segment neurosensory retinal detachment (NRD) associated subretinal fluid in spectral-domain optical coherence tomography (SD-OCT) is vital for the evaluation of central serous chorioretinopathy (CSC). A two-stage unsupervised fluid segmentation algorithm is proposed. In the first stage, the candidate fluid region is automatically estimated to obtain the initial curve of the fluid area for the level set method. In the second stage, the local Gaussian pre-fitting energy model is proposed to segment subretinal fluid. The testing data set with 23 longitudinal SD-OCT cube scans from 12 eyes of 12 patients are used to evaluate the proposed algorithm. Comparing with two independent experts' manual segmentations, our algorithm obtained a mean positive predicative value 94.0% and dice similarity coefficient 94.4%, respectively. Without retinal layer segmentation, the proposed algorithm can obtain high segmentation accuracy. Our model may provide reliable subretinal fluid segmentations for NRD from SD-OCT images and shows the potential to improve clinical therapy for CSC.
{"title":"Beyond Retinal Layers: An Automatic Active Contour Model with Pre-Fitting Energy for Subretinal Fluid Segmentation in SD-OCT Images","authors":"Nursultan Taubaldy, Zexuan Ji","doi":"10.1109/ICIVC.2018.8492862","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492862","url":null,"abstract":"Automatically and accurately segment neurosensory retinal detachment (NRD) associated subretinal fluid in spectral-domain optical coherence tomography (SD-OCT) is vital for the evaluation of central serous chorioretinopathy (CSC). A two-stage unsupervised fluid segmentation algorithm is proposed. In the first stage, the candidate fluid region is automatically estimated to obtain the initial curve of the fluid area for the level set method. In the second stage, the local Gaussian pre-fitting energy model is proposed to segment subretinal fluid. The testing data set with 23 longitudinal SD-OCT cube scans from 12 eyes of 12 patients are used to evaluate the proposed algorithm. Comparing with two independent experts' manual segmentations, our algorithm obtained a mean positive predicative value 94.0% and dice similarity coefficient 94.4%, respectively. Without retinal layer segmentation, the proposed algorithm can obtain high segmentation accuracy. Our model may provide reliable subretinal fluid segmentations for NRD from SD-OCT images and shows the potential to improve clinical therapy for CSC.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116080443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492903
Chenn-Jung Huang, Kai-Wen Hu, Yu-Kang Huang
Recently, the architecture of the Internet of Energy (IoE) has been proposed to replace the current smart grid in the future. However, the large volume of energy produced, the copious amounts of accompanying consumption data, and the uncertainties germane to intermittent energy sources will result in the real-time energy management of the IoE in the future being much more complicated than the energy management of traditional power generation systems. We thus propose a real-time power scheduling system to tackle these complex energy management problems. The whole power system is divided into different geographical regional grids under a hierarchical framework, and the scheduling process is activated at the regional grid if a power shortage is estimated to happen during a fixed time period ahead. The experimental results show that the proposed work can mitigate the dependency on traditional power plants effectively and balance peak and off-peak period loads in an electricity market.
{"title":"An Intelligent Real-Time Renewables-Based Power Scheduling System for the Internet of Energy","authors":"Chenn-Jung Huang, Kai-Wen Hu, Yu-Kang Huang","doi":"10.1109/ICIVC.2018.8492903","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492903","url":null,"abstract":"Recently, the architecture of the Internet of Energy (IoE) has been proposed to replace the current smart grid in the future. However, the large volume of energy produced, the copious amounts of accompanying consumption data, and the uncertainties germane to intermittent energy sources will result in the real-time energy management of the IoE in the future being much more complicated than the energy management of traditional power generation systems. We thus propose a real-time power scheduling system to tackle these complex energy management problems. The whole power system is divided into different geographical regional grids under a hierarchical framework, and the scheduling process is activated at the regional grid if a power shortage is estimated to happen during a fixed time period ahead. The experimental results show that the proposed work can mitigate the dependency on traditional power plants effectively and balance peak and off-peak period loads in an electricity market.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"512 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123424721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492747
Bin Zhu, Hongmin Gao, Xin Wang, Mengxi Xu, Xiaobin Zhu
Through the analysis of satellite remote sensing image data, the identification of newly added buildings in the same area can be realized to judge the use of land. The identification of newly added buildings based on remote sensing images, involving image object extraction, semantic segmentation and change detection. The difficulty is not only to identify the changes of remote sensing images in different periods, but also to identify the newly added buildings with the original buildings. Both of the recognition effect and the detection precision of the traditional method based on mathematical modeling need to be improved. SegNet neural network is a kind of deep convolution neural network. It shows good performance in dealing with the task of semantic segmentation of single image, but it is directly applied to building change detection with low accuracy. The simulation results show that the improved SegNet neural network method improves the accuracy of the quantitative evaluation index F1 score by 8.6% compared with the conventional SegNet network in the newly added building detection effect in the same area in 2015 and 2017. In addition, the situation that the change detection result will produce a large number of noise, a combination of improved SegNet network and image morphological method is adopted to eliminate the noise and reduce the misjudgment. The simulation results show that the F1 index increased further by 1.4% on the basis of 8.6%.
{"title":"Change Detection Based on the Combination of Improved SegNet Neural Network and Morphology","authors":"Bin Zhu, Hongmin Gao, Xin Wang, Mengxi Xu, Xiaobin Zhu","doi":"10.1109/ICIVC.2018.8492747","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492747","url":null,"abstract":"Through the analysis of satellite remote sensing image data, the identification of newly added buildings in the same area can be realized to judge the use of land. The identification of newly added buildings based on remote sensing images, involving image object extraction, semantic segmentation and change detection. The difficulty is not only to identify the changes of remote sensing images in different periods, but also to identify the newly added buildings with the original buildings. Both of the recognition effect and the detection precision of the traditional method based on mathematical modeling need to be improved. SegNet neural network is a kind of deep convolution neural network. It shows good performance in dealing with the task of semantic segmentation of single image, but it is directly applied to building change detection with low accuracy. The simulation results show that the improved SegNet neural network method improves the accuracy of the quantitative evaluation index F1 score by 8.6% compared with the conventional SegNet network in the newly added building detection effect in the same area in 2015 and 2017. In addition, the situation that the change detection result will produce a large number of noise, a combination of improved SegNet network and image morphological method is adopted to eliminate the noise and reduce the misjudgment. The simulation results show that the F1 index increased further by 1.4% on the basis of 8.6%.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123839650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to effectively extract the characteristic information of microscopic image feature to Chinese herbal medicines (CHM), and improve the recognition accuracy automatically, a novel algorithm using Pulse Coupled Neural Networks (PCNN) is put forward. Firstly, the PCNN model is introduced from suitable for processing image of biological tissue. Secondly, the characteristic of time series with PCNN image processing is formed, and transformed into the feature of one dimensional entropy series, which can behalf the image inherent characteristics. Finally, the automatic identification is taken to the extracted image entropy sequence feature. The experimental results show that the entropy sequence feature has the ability of anti-geometric distortions, the novel method have characteristics of simple extraction approach, little extraction parameter, easy implementation, higher accurate recognition ratio and strong robustness.
{"title":"Image Feature Extraction and Recognition of Chinese Herbal Medicine Based on Pulse Coupled Neural Networks","authors":"Qing Liu, Xiao-Long Zha, Xiao-ping Yang, Weijun Ling, Fei-Ping Lu, Yu-Xiang Zhao","doi":"10.1109/ICIVC.2018.8492851","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492851","url":null,"abstract":"In order to effectively extract the characteristic information of microscopic image feature to Chinese herbal medicines (CHM), and improve the recognition accuracy automatically, a novel algorithm using Pulse Coupled Neural Networks (PCNN) is put forward. Firstly, the PCNN model is introduced from suitable for processing image of biological tissue. Secondly, the characteristic of time series with PCNN image processing is formed, and transformed into the feature of one dimensional entropy series, which can behalf the image inherent characteristics. Finally, the automatic identification is taken to the extracted image entropy sequence feature. The experimental results show that the entropy sequence feature has the ability of anti-geometric distortions, the novel method have characteristics of simple extraction approach, little extraction parameter, easy implementation, higher accurate recognition ratio and strong robustness.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122345338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492870
Ximei Xu, Daqing Huang
In the autonomous flight of unmanned aerial vehicles (UAVs), real-time acquisition of its pose information is the basis for navigation and control. For the pose estimation of UAVs, this paper proposes a RANSAN algorithm based on prior information to implement the pose estimate of UAVs. This method firstly uses the SURF algorithm to process the sequence images acquired by the UA V in different angles of the same target area in the actual flight to achieve the extraction and matching of feature points between the images. With the assistance of the pose information provided by the GPS and IMU systems, the RANSAC algorithm combined with the five-point algorithm is used to obtain the corresponding pose information of the UA V at each moment. Experiments show that this method is more accurate than simply using visual information or GPS and IMU system to realize the pose estimation of UA V. It can meet the needs of the actual projects within the allowable range of error, and can enrich the pose estimation theory of UA V to some extent.
{"title":"UAV Pose Estimation Based on Prior Information and RANSAC Algorithm","authors":"Ximei Xu, Daqing Huang","doi":"10.1109/ICIVC.2018.8492870","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492870","url":null,"abstract":"In the autonomous flight of unmanned aerial vehicles (UAVs), real-time acquisition of its pose information is the basis for navigation and control. For the pose estimation of UAVs, this paper proposes a RANSAN algorithm based on prior information to implement the pose estimate of UAVs. This method firstly uses the SURF algorithm to process the sequence images acquired by the UA V in different angles of the same target area in the actual flight to achieve the extraction and matching of feature points between the images. With the assistance of the pose information provided by the GPS and IMU systems, the RANSAC algorithm combined with the five-point algorithm is used to obtain the corresponding pose information of the UA V at each moment. Experiments show that this method is more accurate than simply using visual information or GPS and IMU system to realize the pose estimation of UA V. It can meet the needs of the actual projects within the allowable range of error, and can enrich the pose estimation theory of UA V to some extent.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124814892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492812
Zhaoxia Zhang, Xiu Hu, Xiaopeng Ma, Lingzhen Yang, Junfen Wang
In this paper, the dynamics and nonlinear performance of novel improved two-stage Colpitts oscillators designed to operate in ultrahigh frequency range are proposed. The novel two-stage Colpitts oscillators model is described by transfering the inductance of the standard two-stage Colpitts circuit to the base of the second transistor and connect a resistor in series with the base of the two transistors in this paper. The novel two-stage Colpitts circuit not only can effectively reduce the influence of the parasitic capacitance on the resonant frequency of the circuit, but also can greatly reduce the total capacitance of the resonant network, which not only increase the fundamental frequency of the chaotic signal, but also eliminate the periodicity and make the spectrum flatter and smoother. The numerical analysis is used to solve the graph and numerical solution of the sixth order differential equation of the novel improved two-stage Colpitts circuit. The basic information of the system such as bifurcation diagram, Lyapunov exponent, phase diagram and time traces diagram, spectrum diagram and auto-correlation are obtained by numerical simulation and circuit simulation. The simulation results show that the novel improved two-stage Colpitts oscillators can work in the periodic and chaotic states, and its fundamental frequency can be increased to 5GHz., about 0.6 times the cutoff frequency of the transistor, which is 2GHz higher than that of the standard two-stage Colpitts oscillator. The spectrum of novel improved two-stage Colpitts oscillators circuit is flatter and smoother, and has no periodicity. Numerical simulations are performed to demonstrate the effectiveness and feasibility of the proposed novel two-stage Colpitts oscillators.
{"title":"Design and Nonlinear Dynamical Performance Analysis of Novel Improved Two-Stage Colpitts Wideband Chaotic Oscillator","authors":"Zhaoxia Zhang, Xiu Hu, Xiaopeng Ma, Lingzhen Yang, Junfen Wang","doi":"10.1109/ICIVC.2018.8492812","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492812","url":null,"abstract":"In this paper, the dynamics and nonlinear performance of novel improved two-stage Colpitts oscillators designed to operate in ultrahigh frequency range are proposed. The novel two-stage Colpitts oscillators model is described by transfering the inductance of the standard two-stage Colpitts circuit to the base of the second transistor and connect a resistor in series with the base of the two transistors in this paper. The novel two-stage Colpitts circuit not only can effectively reduce the influence of the parasitic capacitance on the resonant frequency of the circuit, but also can greatly reduce the total capacitance of the resonant network, which not only increase the fundamental frequency of the chaotic signal, but also eliminate the periodicity and make the spectrum flatter and smoother. The numerical analysis is used to solve the graph and numerical solution of the sixth order differential equation of the novel improved two-stage Colpitts circuit. The basic information of the system such as bifurcation diagram, Lyapunov exponent, phase diagram and time traces diagram, spectrum diagram and auto-correlation are obtained by numerical simulation and circuit simulation. The simulation results show that the novel improved two-stage Colpitts oscillators can work in the periodic and chaotic states, and its fundamental frequency can be increased to 5GHz., about 0.6 times the cutoff frequency of the transistor, which is 2GHz higher than that of the standard two-stage Colpitts oscillator. The spectrum of novel improved two-stage Colpitts oscillators circuit is flatter and smoother, and has no periodicity. Numerical simulations are performed to demonstrate the effectiveness and feasibility of the proposed novel two-stage Colpitts oscillators.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124061795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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