Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific最新文献_第7页

Discovering and analyzing learning pattern on web based learning using social network analysis 利用社会网络分析发现和分析基于web的学习模式

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041814

P. Temdee, Wacharawan Intayoad

{"title":"Discovering and analyzing learning pattern on web based learning using social network analysis","authors":"P. Temdee, Wacharawan Intayoad","doi":"10.1109/APSIPA.2014.7041814","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041814","url":null,"abstract":"Web based learning has been promoting alternative way of learning for decades. The difficulty of web based learning is to provide the appropriate support for the learners so that the learners will not get lost and their learning achievements can be ensured. This paper thus proposes the method for discovering learning patterns of the learners on web based learning particularly for ensuring the learning achievement. The learning pattern is discovered by analyzing the interactions among the learners and the learning objects with social network analysis. Then, the achievement learning pattern is finally determined by analyzing the sets of obtained social network measurements. The interaction data is gathered from online course named introduction to Information Technology in the 2013 academic year, particularly for spreadsheet content module having 10 learning objects. The interaction patterns only of two groups of students including scientific and nonscientific background knowledge who pass the spreadsheet examination are analyzed. Finally, learning patterns ensuring learning achievement for spreadsheet content module of those students having different background knowledge is revealed.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129695065","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}

引用次数: 3

Phase detection of multi-channel SSVEPs via complex sparse spatial weighting 基于复稀疏空间加权的多通道ssvep相位检测

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041666

Keita Shimpo, Toshihisa Tanaka

引用次数: 3

Proportional feedback based rate control for intra frame of H.264/AVC high profile 基于比例反馈的H.264/AVC高清帧内速率控制

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041537

Yanping Zhou, Y. Duan, Jun Sun, Zongming Guo

引用次数: 0

Range extrapolation of Head-Related Transfer Function using improved Higher Order Ambisonics 基于改进高阶双声的头部相关传递函数范围外推

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041527

Ling-song Zhou, C. Bao, Mao-shen Jia, Bing Bu

引用次数: 6

Compressed sensing based channel estimation for uplink OFDMA systems 基于压缩感知的OFDMA上行系统信道估计

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041569

K. Hayashi, Masanori Sakai, Takuya Kamenosono, Megumi Kaneko

引用次数: 3

Analysis of customer communication by employee in restaurant and lead time estimation 分析餐厅员工与客户的沟通情况，预估交货期

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041701

Masanori Takehara, Hiroya Nojiri, S. Tamura, S. Hayamizu, T. Kurata

引用次数: 1

Denoising autoencoder and environment adaptation for distant-talking speech recognition with asynchronous speech recording 异步语音录制远程语音识别的去噪自编码器和环境自适应

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041548

Longbiao Wang, Bo Ren, Yuma Ueda, A. Kai, Shunta Teraoka, T. Fukushima

{"title":"Denoising autoencoder and environment adaptation for distant-talking speech recognition with asynchronous speech recording","authors":"Longbiao Wang, Bo Ren, Yuma Ueda, A. Kai, Shunta Teraoka, T. Fukushima","doi":"10.1109/APSIPA.2014.7041548","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041548","url":null,"abstract":"In this paper, we propose a robust distant-talking speech recognition system with asynchronous speech recording. This is implemented by combining denoising autoencoder-based cepstral-domain dereverberation, automatic asynchronous speech (microphone or mobile terminal) selection and environment adaptation. Although applications using mobile terminals have attracted increasing attention, there are few studies that focus on distant-talking speech recognition with asynchronous mobile terminals. For the system proposed in this paper, after applying a denoising autoencoder in the cepstral domain of speech to suppress reverberation and performing Large Vocabulary Continuous Speech Recognition (LVCSR), we adopted automatic asynchronous mobile terminal selection and environment adaptation using speech segments from optimal mobile terminals. The proposed method was evaluated using a reverberant WSJCAMO corpus, which was emitted by a loudspeaker and recorded in a meeting room with multiple speakers by far-field multiple mobile terminals. By integrating a cepstral-domain denoising autoencoder and automatic mobile terminal selection with environment adaptation, the average Word Error Rate (WER) was reduced from 51.8% of the baseline system to 28.8%, i.e., the relative error reduction rate was 44.4% when using multi-condition acoustic models.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128533131","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}

引用次数: 6

Contactless palmprint alignment based on intrinsic local affine-invariant feature points 基于内禀局部仿射不变特征点的非接触式掌纹对准

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041563

C. Phromsuthirak, W. Tangsuksant, A. Sanpanich, C. Pintavirooj

引用次数: 1

Support Vector Machine (SVM) based classifier for Khmer Printed Character-set Recognition 基于支持向量机的高棉印刷字符集识别分类器

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041823

Pongsametrey Sok, Nguonly Taing

{"title":"Support Vector Machine (SVM) based classifier for Khmer Printed Character-set Recognition","authors":"Pongsametrey Sok, Nguonly Taing","doi":"10.1109/APSIPA.2014.7041823","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041823","url":null,"abstract":"This paper describes on the use of Support Vector Machine (SVM) based classification method on Khmer Printed Character-set Recognition (PCR) in bitmap document. Khmer language has been identified as one of the most complex language with the total of 74 alphabets and the wording compound can has up to 5 vertical levels. This paper proposes one new method, SVM for Khmer character classification system by using 3 different SVM kernels (Gaussian, Polynomial and Linear Kernel) on data training and recognition to find out the best kernel for Khmer language. The method allows us to use small training dataset by training different pieces of character training instead of training big amount of clusters. The classification uses binary data of 0 as white space and 1 as black pixel area of the character; each training piece of character has been stretched into a matrix of the binary data in all kinds of image size. Feature extraction is extracted from the matrix to use in SVM classification. After recognition, there are some rules to combine each cluster or character by using character levels or common mistake correction. The experiment of about pure 750 Khmer words or around 3000 characters show that SVM method with Gaussian Kernel produces a good result with better performance among all kernels. The system uses one font \"Khmer OS Content\" of the training data with font size 32pt to recognize 3 different font sizes. The accuracy of 28pt font size is 98.17%, 32pt is 98.62% and 36pt is 98.54% respectively.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134026233","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}

引用次数: 9

Modeling spatial uncertainty of imprecise information in images 图像中不精确信息的空间不确定性建模

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041514

T. Pham

引用次数: 0