2016 Twenty Second National Conference on Communication (NCC)最新文献

Performance evaluation of satellite-to-earth FSO link in presence of turbulence and weather conditions for different IM schemes 湍流和天气条件下星地FSO链路性能评价

2016 Twenty Second National Conference on Communication (NCC) Pub Date : 2016-03-04 DOI: 10.1109/NCC.2016.7561092

Anjitha Viswanath, V. Jain, Subrat Kar

{"title":"Performance evaluation of satellite-to-earth FSO link in presence of turbulence and weather conditions for different IM schemes","authors":"Anjitha Viswanath, V. Jain, Subrat Kar","doi":"10.1109/NCC.2016.7561092","DOIUrl":"https://doi.org/10.1109/NCC.2016.7561092","url":null,"abstract":"We evaluate the bit error rate (BER) performance of a geostationary earth orbit (GEO) satellite to earth free space optical (FSO) link using intensity modulated/direct detection (IM/DD) system. The analysis is carried out in presence of turbulence and various weather conditions. The combined channel state probability density function (pdf) is obtained by using gamma-gamma distribution to model the turbulence and Beer-Lambert law to incorporate the weather effects. Owing to the fact that IM schemes are simple in implementation and cost effective, we use three IM schemes, viz., on-off keying (OOK), M-ary pulse position modulation (M-PPM) and M-ary differential PPM (M-DPPM) for our analysis. The closed from hBERi expressions are derived for the above schemes using the combined channel state pdf. Results show that hBERi increases with increase in the strength of ground level turbulence. Also, the presence of various weather conditions such as moderate, light and thin fog can cause additional degradation. In case of thick or dense fog, the degradation becomes quite high and may lead to link failure. Among the IM schemes, with the same average received power, M-PPM scheme performs the best followed by M-DPPM and OOK schemes in presence of turbulence and different weather conditions.","PeriodicalId":279637,"journal":{"name":"2016 Twenty Second National Conference on Communication (NCC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123006780","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: 4

Low complexity in-loop skin tone detection for ROI coding in the HEVC encoder HEVC编码器中ROI编码的低复杂度环内肤色检测

2016 Twenty Second National Conference on Communication (NCC) Pub Date : 2016-03-04 DOI: 10.1109/NCC.2016.7561096

P. Goswami, P. Srikanth, Jasmin Rahiman

引用次数: 0

E-Braille-a self-learning Braille device e-盲文——一种自学盲文设备

2016 Twenty Second National Conference on Communication (NCC) Pub Date : 2016-03-04 DOI: 10.1109/NCC.2016.7561162

P. M. Wagh, U. Prajapati, Mayuresh Shinde, Prafulla M. Salunke, Vinayak A. Chaskar, Sanchit Telavane, Vijaypal Yadav

引用次数: 13

Indigenous design and development of underwater wireless power transfer system 水下无线电力传输系统的自主设计与研制

2016 Twenty Second National Conference on Communication (NCC) Pub Date : 2016-03-04 DOI: 10.1109/NCC.2016.7561099

J. Manikandan, A. Vishwanath, V. K. Agrawal, M. Korulla

引用次数: 9

On the development of a dynamic virtual reality system using audio and visual scenes 基于视听场景的动态虚拟现实系统的开发

2016 Twenty Second National Conference on Communication (NCC) Pub Date : 2016-03-04 DOI: 10.1109/NCC.2016.7561204

S. Reddy, G. S. S. Srinivas Rao, R. Hegde

引用次数: 0

Landscaping of random forests through controlled deforestation 通过控制森林砍伐来美化随机森林

2016 Twenty Second National Conference on Communication (NCC) Pub Date : 2016-03-04 DOI: 10.1109/NCC.2016.7561112

Kausik Das, Abhijit Guha Roy, J. Chatterjee, D. Sheet

{"title":"Landscaping of random forests through controlled deforestation","authors":"Kausik Das, Abhijit Guha Roy, J. Chatterjee, D. Sheet","doi":"10.1109/NCC.2016.7561112","DOIUrl":"https://doi.org/10.1109/NCC.2016.7561112","url":null,"abstract":"Random forest (RF) is an ensemble learner constructed using a set of decision trees, where each tree is trained using randomly bootstrapped samples and aggregated to provide a decision. While the generalization error is reduced by increasing the number of trees in a RF, it substantially increases the testing time complexity, inhibiting its fast deployment in practical applications. In this paper, we propose a post-training optimization technique termed landscaping of RF for reducing computational complexity by compensating for trees associated with similar decision boundary. This allows faster deployment of the RF without compromising its performance. Landscaping is achieved through a two stage mechanism: (i) computation of decision similarity between all pairs of trees in the RF, and (ii) deletion of the computationally expensive tree in the RF with decision bias compensation for the removed tree. Performance of the proposed methodology was evaluated using three publicly available datasets. The RF performance before and after landscaping over the datasets was observed to have an error of 0.1084 ± 0.03 and 0.1087 ± 0.03, respectively, while testing times of the RF before landscaping was 2.5508 ± 0.08 sec. and 0.9066 ± 0.19 sec. after landscaping with 32 - 76% reduction in execution time. These results strongly substantiates our claim of achieving deployment speedup without compromising the decision quality with landscaping of RF through controlled deforestation.","PeriodicalId":279637,"journal":{"name":"2016 Twenty Second National Conference on Communication (NCC)","volume":"478 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131988328","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: 1

A deep learning approach to fetal-ECG signal reconstruction 胎儿心电信号重构的深度学习方法

2016 Twenty Second National Conference on Communication (NCC) Pub Date : 2016-03-04 DOI: 10.1109/NCC.2016.7561206

P. R. Muduli, Rakesh Reddy Gunukula, A. Mukherjee

引用次数: 30

TDoA based EKF localization for LTE 基于TDoA的LTE EKF定位

2016 Twenty Second National Conference on Communication (NCC) Pub Date : 2016-03-04 DOI: 10.1109/NCC.2016.7561192

R. Sriram, D. Jalihal

引用次数: 5

Sonority rise: Aiding backoff in syllable-based speech synthesis 声音上升:在基于音节的语音合成中帮助后退

2016 Twenty Second National Conference on Communication (NCC) Pub Date : 2016-03-04 DOI: 10.1109/NCC.2016.7561189

S. Rallabandi, Ayushi Pandey, Sai Krishna Rallabandi, Tejas Godambe, S. Gangashetty

{"title":"Sonority rise: Aiding backoff in syllable-based speech synthesis","authors":"S. Rallabandi, Ayushi Pandey, Sai Krishna Rallabandi, Tejas Godambe, S. Gangashetty","doi":"10.1109/NCC.2016.7561189","DOIUrl":"https://doi.org/10.1109/NCC.2016.7561189","url":null,"abstract":"Back off techniques are employed in syllable based unit selection speech synthesis systems to maintain the naturalness of the speech in spite of the missing syllables. In synthesizing the missing complex consonant clusters syllables of Telugu, we introduced reduced vowel epenthesis as a rule-based backoff strategy[1]. In this paper, we refine the scope of the approach in selectively applying vowel epenthesis only in cases of sonority rise between adjacent consonants. When the sonority does not rise (stop-stop, liquid-stop clusters), we increase the duration of the consonant. Owing to specific patterns of vowel epenthesis observed in languages, we conduct a subjective evaluation to determine the identity of the epenthetic vowel in Hindi. From the inferences of the listening test, we devise a class based rule to perform epenthesis. Further, to evaluate the performance of the designed system, we perform both subjective as well as an objective evaluation based on confidence measures from an ASR system. We conduct a phone level automatic speech recognition task on the intelligibility of the words synthesized using epenthesis as a cluster-repair strategy. The results show that the proposed back off method helps in producing more natural-sounding speech compared to the conventional backoffs.","PeriodicalId":279637,"journal":{"name":"2016 Twenty Second National Conference on Communication (NCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132930266","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: 2

Emotion-specific features for classifying emotions in story text 基于情感特征的故事文本情感分类

2016 Twenty Second National Conference on Communication (NCC) Pub Date : 2016-03-04 DOI: 10.1109/NCC.2016.7561205

D. M. Harikrishna, K. S. Rao

{"title":"Emotion-specific features for classifying emotions in story text","authors":"D. M. Harikrishna, K. S. Rao","doi":"10.1109/NCC.2016.7561205","DOIUrl":"https://doi.org/10.1109/NCC.2016.7561205","url":null,"abstract":"In this work, we are attempting emotion classification in view of synthesizing story speech. We are proposing emotion-specific text features (ESF) for classifying sentences from children stories into five different emotion categories: happy, sad, anger, fear and neutral. ESF is a five dimensional feature vector, where each dimension corresponds to weight of the sentence according to each emotion class. The dataset consists of 780 Hindi emotional sentences collected from children stories belonging to three genres: fable, folk-tale and legend. Part-of-speech (POS) and proposed ESF are used as features for emotion classification. Emotion classification performance is analysed using various combinations of features with three classifiers: Naive Bayes (NB), k-nearest neighbour (KNN) and support vector machine (SVM). The effectiveness of classifiers is analysed using precision, recall, F-measure and accuracy. The classification performance of 67.9% and 67.2% is achieved using POS and ESF respectively. The fusion of both features resulted an accuracy of 71.1%. Further, the importance of story genre information in emotion classification was observed from the experiments conducted on classifying emotions within story genre. An accuracy of 73.7% was observed after adding story genre information to the fusion of POS and ESF. SVM models outperformed other models in terms of classification accuracy.","PeriodicalId":279637,"journal":{"name":"2016 Twenty Second National Conference on Communication (NCC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123497792","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: 5