Pub Date : 2017-12-01DOI: 10.1109/PIC.2017.8359545
Zhao Gang, Di Bingbing, Zhu Wenjuan, Liu Yaxu, He Hui, Zan Hui
To get rid of durance of distribution environment on Tujia Brocade dissemination and provide a more comprehensive and immersive learning environment of Tujia brocade, this paper designs a system on Tujia Brocade cultural coordinate panorama display based on touch screen, and establishes panoramic scenes which takes the birthplace of Tujia brocade named Lao Chehe village as the cultural coordinates of Tujia brocade. What's more, the system has ten panoramic scenes, which completes following functions: viewpoint control, scenes shifting, hotspot information, touch operation and voice explanation. Users can have real-time interaction with ecological panorama of Tujia Brocade Laoche village in the scene, this will contribute to the digital protection and social dissemination of Tujia brocade.
{"title":"Design and implementation for Tujia brocade cultural coordinate panorama display system based on touch screen","authors":"Zhao Gang, Di Bingbing, Zhu Wenjuan, Liu Yaxu, He Hui, Zan Hui","doi":"10.1109/PIC.2017.8359545","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359545","url":null,"abstract":"To get rid of durance of distribution environment on Tujia Brocade dissemination and provide a more comprehensive and immersive learning environment of Tujia brocade, this paper designs a system on Tujia Brocade cultural coordinate panorama display based on touch screen, and establishes panoramic scenes which takes the birthplace of Tujia brocade named Lao Chehe village as the cultural coordinates of Tujia brocade. What's more, the system has ten panoramic scenes, which completes following functions: viewpoint control, scenes shifting, hotspot information, touch operation and voice explanation. Users can have real-time interaction with ecological panorama of Tujia Brocade Laoche village in the scene, this will contribute to the digital protection and social dissemination of Tujia brocade.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116146471","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 : 2017-12-01DOI: 10.1109/PIC.2017.8359542
Kun Ren, Yuqing Yang, Lisha Meng
High-resolution (HR) image reconstruction from single low-resolution (LR) image is one of the important vision applications. Despite numerous algorithms have been successfully proposed in recent years, efficient and robust single-image super-resolution (SR) reconstruction is still challenging by several factors, such as inherent ambiguous mapping between the HR-LR images, necessary huge exemplar images, and computational load. In this paper, we proposed a new learning-based method of single-image SR. Inspired by simple mapping functions method, a mapping matrix table of HR-LR feature patches is calculated in the training phase. Each atom of dictionary learned from LR feature patches is corresponding to a mapping matrix in the mapping matrix table. Combining this mapping table with sparse coding, high quality and HR images are reconstructed in reconstruction phase. The effectiveness and efficiency of this method is validated with experiments on the training datasets. Compared with state-of-art methods, jagged and blurred artifacts are depressed effectively and high reconstruction quality is acquired with less exemplar images.
{"title":"Single image super-resolution reconstruction via combination mapping with sparse coding","authors":"Kun Ren, Yuqing Yang, Lisha Meng","doi":"10.1109/PIC.2017.8359542","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359542","url":null,"abstract":"High-resolution (HR) image reconstruction from single low-resolution (LR) image is one of the important vision applications. Despite numerous algorithms have been successfully proposed in recent years, efficient and robust single-image super-resolution (SR) reconstruction is still challenging by several factors, such as inherent ambiguous mapping between the HR-LR images, necessary huge exemplar images, and computational load. In this paper, we proposed a new learning-based method of single-image SR. Inspired by simple mapping functions method, a mapping matrix table of HR-LR feature patches is calculated in the training phase. Each atom of dictionary learned from LR feature patches is corresponding to a mapping matrix in the mapping matrix table. Combining this mapping table with sparse coding, high quality and HR images are reconstructed in reconstruction phase. The effectiveness and efficiency of this method is validated with experiments on the training datasets. Compared with state-of-art methods, jagged and blurred artifacts are depressed effectively and high reconstruction quality is acquired with less exemplar images.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125251334","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 : 2017-12-01DOI: 10.1109/PIC.2017.8359527
Chen Ke
Object detection is a very significant task for a huge range of applications. For example, the detection of military vehicles is very useful for the defense and intelligence. In recent years, hyperspectral imagery (HSI) which is generated by remote sensing systems can provide tremendous information about the spectral characteristics. Due to this characteristic, object detection using HSI becomes hot research topic. In this paper, we propose a strategy for military object detection by extracting multiple information from HSI. Firstly, we generate the superpixels from HSI by principle component analysis (PCA) and k-means clustering. Then, self-similarity method is used to calculate the correlation between each superpixel and the object spectral. At last, the shape information is extracted from the masses which have high correlation value and is used to detect the specific military objectives. Results from HSI demonstrate the benefits of the proposed strategy regarding its effectiveness at detecting specific objectives.
{"title":"Military object detection using multiple information extracted from hyperspectral imagery","authors":"Chen Ke","doi":"10.1109/PIC.2017.8359527","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359527","url":null,"abstract":"Object detection is a very significant task for a huge range of applications. For example, the detection of military vehicles is very useful for the defense and intelligence. In recent years, hyperspectral imagery (HSI) which is generated by remote sensing systems can provide tremendous information about the spectral characteristics. Due to this characteristic, object detection using HSI becomes hot research topic. In this paper, we propose a strategy for military object detection by extracting multiple information from HSI. Firstly, we generate the superpixels from HSI by principle component analysis (PCA) and k-means clustering. Then, self-similarity method is used to calculate the correlation between each superpixel and the object spectral. At last, the shape information is extracted from the masses which have high correlation value and is used to detect the specific military objectives. Results from HSI demonstrate the benefits of the proposed strategy regarding its effectiveness at detecting specific objectives.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"342 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122644508","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 : 2017-12-01DOI: 10.1109/PIC.2017.8359522
Z. Lian, Zhonggeng Liu
Recently, great progesses have been made in using discriminative classifiers in object tracking. More specifically, correlation filters (CFs) for visual tracking have been attractive due to t heir competitive performances on both accuracy and robustness. In this paper, the latest and representative approaches of CF based trackers are presented in detail. In addition, trackers used deep convolutional features are introduced and several famous tracking methods which fine-tune the pretrained deep network are presented. To evaluate the performances of different trackers, a detailed introduction of the evaluation methodology and the datasets is described, and all introduced trackers are compared based on the mentioned datasets. Finally, several promising directions as the conclusions are drawn in this paper.
{"title":"Current progress in discriminative object tracking","authors":"Z. Lian, Zhonggeng Liu","doi":"10.1109/PIC.2017.8359522","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359522","url":null,"abstract":"Recently, great progesses have been made in using discriminative classifiers in object tracking. More specifically, correlation filters (CFs) for visual tracking have been attractive due to t heir competitive performances on both accuracy and robustness. In this paper, the latest and representative approaches of CF based trackers are presented in detail. In addition, trackers used deep convolutional features are introduced and several famous tracking methods which fine-tune the pretrained deep network are presented. To evaluate the performances of different trackers, a detailed introduction of the evaluation methodology and the datasets is described, and all introduced trackers are compared based on the mentioned datasets. Finally, several promising directions as the conclusions are drawn in this paper.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127753022","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 : 2017-12-01DOI: 10.1109/PIC.2017.8359510
Zonghui Peng, Ruifang Liu, Si Li
State-of-the-art sequence labeling systems traditionally used handcrafted n-gram features and data pre-processing, but usually ignored character-level information. In this paper, we propose to apply word hashing method which can catch the morphological information of words to sequence labeling tasks. Auto-encoder is first employed to learn latent morphological representation in a pre-training stage. Our model benefits from both morphological and semantic features of words by using bidirectional LSTM structure. Experiment results show that our model achieves best result on Chunking task — 94.93% and NP-Chunking task — 95.70% on CoNLL2000 dataset and obtains competitive performance on NER task — 89.29% on CoNLL2003 dataset.
{"title":"Leveraging morphological information via employing word hashing for sequence labeling","authors":"Zonghui Peng, Ruifang Liu, Si Li","doi":"10.1109/PIC.2017.8359510","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359510","url":null,"abstract":"State-of-the-art sequence labeling systems traditionally used handcrafted n-gram features and data pre-processing, but usually ignored character-level information. In this paper, we propose to apply word hashing method which can catch the morphological information of words to sequence labeling tasks. Auto-encoder is first employed to learn latent morphological representation in a pre-training stage. Our model benefits from both morphological and semantic features of words by using bidirectional LSTM structure. Experiment results show that our model achieves best result on Chunking task — 94.93% and NP-Chunking task — 95.70% on CoNLL2000 dataset and obtains competitive performance on NER task — 89.29% on CoNLL2003 dataset.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133707798","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 : 2017-12-01DOI: 10.1109/PIC.2017.8359515
Hang Yu, Xiaoxiao Qian, Yang Yu, Jiujun Cheng, Ying Yu, Shangce Gao
By constructing a list of IF-THEN rules, the traditional ant colony optimization (ACO) has been successfully applied on data classification with not only a promising accuracy but also a user comprehensibility. However, as the collected data to be classified usually contain large volumes and redundant features, it is challenging to further improve the classification accuracy and meanwhile reduce the computational time for ACO. This paper proposes a novel hybrid mutual information based ant colony algorithm (mr2 AM+) for classification. First, a maximum relevance minimum redundancy feature selection method is used to select the most informative and discriminative attributes in a dataset. Then, we use the enhanced ACO classifier (i.e., AM+) to perform the classification. Experimental results show that the proposed mr2AM+ outperforms other seven state-of-art related classification algorithms in terms of accuracy and the size of model.
{"title":"A novel mutual information based ant colony classifier","authors":"Hang Yu, Xiaoxiao Qian, Yang Yu, Jiujun Cheng, Ying Yu, Shangce Gao","doi":"10.1109/PIC.2017.8359515","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359515","url":null,"abstract":"By constructing a list of IF-THEN rules, the traditional ant colony optimization (ACO) has been successfully applied on data classification with not only a promising accuracy but also a user comprehensibility. However, as the collected data to be classified usually contain large volumes and redundant features, it is challenging to further improve the classification accuracy and meanwhile reduce the computational time for ACO. This paper proposes a novel hybrid mutual information based ant colony algorithm (mr2 AM+) for classification. First, a maximum relevance minimum redundancy feature selection method is used to select the most informative and discriminative attributes in a dataset. Then, we use the enhanced ACO classifier (i.e., AM+) to perform the classification. Experimental results show that the proposed mr2AM+ outperforms other seven state-of-art related classification algorithms in terms of accuracy and the size of model.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"1115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116063613","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 : 2017-12-01DOI: 10.1109/PIC.2017.8359529
Haijiao Liu, Jun Zhang
Dynamic stochastic resonance (DSR) based dark and low-contrast image enhancement has attracted more and more attention in recent years. For DSR based image enhancement, noise is essential and will be enhanced simultaneously with the contrast of the image, which is undesirable for improvement of perceptual quality. Nonlinear anisotropic diffusion (NAD) is one of the most widely used denoising methods due to good performance of edge preservation, but often fails for contaminated images with high level of noise. In this paper, we propose a novel partial differential equation method for image enhancement by introducing filtering into the stochastic resonance equation, and we consider two kinds of NAD filters. Numerical results demonstrate that the improved methods can not only increase brightness and contrast of the dark and low-contrast images efficiently by optimum iterations, but also remove the noise while preserving edges well, and therefore can achieve good perceptual quality.
{"title":"Filtering combined dynamic stochastic resonance for enhancement of dark and low-contrast images","authors":"Haijiao Liu, Jun Zhang","doi":"10.1109/PIC.2017.8359529","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359529","url":null,"abstract":"Dynamic stochastic resonance (DSR) based dark and low-contrast image enhancement has attracted more and more attention in recent years. For DSR based image enhancement, noise is essential and will be enhanced simultaneously with the contrast of the image, which is undesirable for improvement of perceptual quality. Nonlinear anisotropic diffusion (NAD) is one of the most widely used denoising methods due to good performance of edge preservation, but often fails for contaminated images with high level of noise. In this paper, we propose a novel partial differential equation method for image enhancement by introducing filtering into the stochastic resonance equation, and we consider two kinds of NAD filters. Numerical results demonstrate that the improved methods can not only increase brightness and contrast of the dark and low-contrast images efficiently by optimum iterations, but also remove the noise while preserving edges well, and therefore can achieve good perceptual quality.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126215205","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 : 2017-12-01DOI: 10.1109/PIC.2017.8359544
Zhaohao Fan, Quansen Sun, Jixin Liu
Compressed sensing (CS) theory provides a new acquisition idea for sparse signals and sparsely-expressed signals. CS-based hardware design has been widely concerned. And related products have been tentatively tried in many fields. The design of remote sensing imaging based on CS mainly includes single pixel multiple exposure imaging and block focal plane coding multi — pixel single exposure imaging. In this paper, a CS asymmetric processing model, which is different from traditional image reconstruction, is proposed. And it is applied to CS hardware design for infrared (IR) remote sensing imaging. This model fully considers the global information of the image, which combines the multiple neighborhood values of the observed results in the CS process, and also combines the multiple measurement matrix blocks to form a new measurement matrix. At the same time, a sparse dictionary construction method suitable for asymmetric patterns is proposed, which can effectively compensate for the local differences caused by image segmentation. The experimental results show that the proposed method is superior to the conventional block focal plane coding compression reconstruction both in reconstruction time and in reconstruction quality.
{"title":"Infrared remote sensing imaging via asymmetric compressed sensing","authors":"Zhaohao Fan, Quansen Sun, Jixin Liu","doi":"10.1109/PIC.2017.8359544","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359544","url":null,"abstract":"Compressed sensing (CS) theory provides a new acquisition idea for sparse signals and sparsely-expressed signals. CS-based hardware design has been widely concerned. And related products have been tentatively tried in many fields. The design of remote sensing imaging based on CS mainly includes single pixel multiple exposure imaging and block focal plane coding multi — pixel single exposure imaging. In this paper, a CS asymmetric processing model, which is different from traditional image reconstruction, is proposed. And it is applied to CS hardware design for infrared (IR) remote sensing imaging. This model fully considers the global information of the image, which combines the multiple neighborhood values of the observed results in the CS process, and also combines the multiple measurement matrix blocks to form a new measurement matrix. At the same time, a sparse dictionary construction method suitable for asymmetric patterns is proposed, which can effectively compensate for the local differences caused by image segmentation. The experimental results show that the proposed method is superior to the conventional block focal plane coding compression reconstruction both in reconstruction time and in reconstruction quality.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129367268","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 : 2017-12-01DOI: 10.1109/PIC.2017.8359540
Shuyin Tao, Wen-de Dong, Zhenmin Tang, Qiong Wang
In this paper, a blind image deconvolution method which is derived from Bayesian probabilistic framework is proposed. A robust prior named Gaussian Scale Mixture Fields of Experts (GSM FoE) and a prior that is constructed with the lp-norm (p ≈ 1.5) are adopted to regularize the latent image and the point spread function (PSF) respectively. We use a two phase optimization approach to solve the resulted maximum a-posteriori (MAP) estimation problem, and a simple gradient selecting method is incorporated into the alternating minimization to improve the accuracy of the estimated PSF. Experiments on both synthetic and real world blurred images show that our method can achieve results with high quality.
本文提出了一种基于贝叶斯概率框架的盲图像反卷积方法。采用Gaussian Scale Mixture Fields of Experts (gsmfoe)鲁棒先验和lp-范数(p≈1.5)构造的先验分别对潜在图像和点扩散函数(PSF)进行正则化。我们使用两阶段优化方法来解决结果的最大后验(MAP)估计问题，并在交替最小化中加入简单的梯度选择方法以提高估计的PSF精度。在合成图像和真实世界模糊图像上的实验表明，该方法可以获得高质量的结果。
{"title":"Blind image deconvolution using the Gaussian scale mixture fields of experts prior","authors":"Shuyin Tao, Wen-de Dong, Zhenmin Tang, Qiong Wang","doi":"10.1109/PIC.2017.8359540","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359540","url":null,"abstract":"In this paper, a blind image deconvolution method which is derived from Bayesian probabilistic framework is proposed. A robust prior named Gaussian Scale Mixture Fields of Experts (GSM FoE) and a prior that is constructed with the lp-norm (p ≈ 1.5) are adopted to regularize the latent image and the point spread function (PSF) respectively. We use a two phase optimization approach to solve the resulted maximum a-posteriori (MAP) estimation problem, and a simple gradient selecting method is incorporated into the alternating minimization to improve the accuracy of the estimated PSF. Experiments on both synthetic and real world blurred images show that our method can achieve results with high quality.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"391 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124500083","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 : 2017-12-01DOI: 10.1109/PIC.2017.8359576
Dongbao Jia, Yuta Takashima, M. Hasegawa, S. Hirobayashi, T. Misawa
Recently, brain-computer interface has been applied to many fields such as steady-state visual evoked potential (SSVEP). However, in the conventional method, the frequency resolution is low due to the dependence of the short-time Fourier transform on the analysis window length. Therefore, it is not possible to analyze a non-integer multiple signal, as a side-lobe will occur. We verified the precision of non-harmonics analysis, and proposed and attempted to analyze the change and stimulus of SSVEP. We found the frequency resolution to be improved exponentially.
{"title":"Application to SSVEP of chirp stimulus using non-harmonic analysis","authors":"Dongbao Jia, Yuta Takashima, M. Hasegawa, S. Hirobayashi, T. Misawa","doi":"10.1109/PIC.2017.8359576","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359576","url":null,"abstract":"Recently, brain-computer interface has been applied to many fields such as steady-state visual evoked potential (SSVEP). However, in the conventional method, the frequency resolution is low due to the dependence of the short-time Fourier transform on the analysis window length. Therefore, it is not possible to analyze a non-integer multiple signal, as a side-lobe will occur. We verified the precision of non-harmonics analysis, and proposed and attempted to analyze the change and stimulus of SSVEP. We found the frequency resolution to be improved exponentially.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125094650","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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