发布求助
文献互助 智能选刊 最新文献

2018 4th International Conference on Frontiers of Signal Processing (ICFSP)最新文献

筛选
英文 中文
Quasi-Likelihood Estimates of the Time of Arrival and the Duration of the Signal with the Unknown Amplitude 未知振幅信号到达时间和持续时间的拟似然估计
2018 4th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2018-09-01 DOI: 10.1109/ICFSP.2018.8552063
O. Chernoyarov, A. Faulgaber, Y. Korchagin, A. Makarov
{"title":"Quasi-Likelihood Estimates of the Time of Arrival and the Duration of the Signal with the Unknown Amplitude","authors":"O. Chernoyarov, A. Faulgaber, Y. Korchagin, A. Makarov","doi":"10.1109/ICFSP.2018.8552063","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552063","url":null,"abstract":"We found the structure and the asymptotic characteristics of the quasi-likelihood estimates of the time of arrival and the duration of the video pulse with the unknown amplitude. We determined the losses in accuracy of the produced estimates of the time parameters due to the difference of the true amplitude of the received signal from the expected one. We also established the usefulness of the introduced quasi-likelihood measurer depending on the available prior information.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"123 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116378131","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
Hand Gesture Feature Extraction Using Deep Convolutional Neural Network for Recognizing American Sign Language 基于深度卷积神经网络的手势特征提取与美国手语识别
2018 4th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2018-09-01 DOI: 10.1109/ICFSP.2018.8552044
Md. Rashedul Islam, Umme Kulsum Mitu, R. Bhuiyan, Jungpil Shin
{"title":"Hand Gesture Feature Extraction Using Deep Convolutional Neural Network for Recognizing American Sign Language","authors":"Md. Rashedul Islam, Umme Kulsum Mitu, R. Bhuiyan, Jungpil Shin","doi":"10.1109/ICFSP.2018.8552044","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552044","url":null,"abstract":"In this era, Human-Computer Interaction (HCI) is a fascinating field about the interaction between humans and computers. Interacting with computers, human Hand Gesture Recognition (HGR) is the most significant way and the major part of HCI. Extracting features and detecting hand gesture from inputted color videos is more challenging because of the huge variation in the hands. For resolving this issue, this paper introduces an effective HGR system for low-cost color video using webcam. In this proposed model, Deep Convolutional Neural Network (DCNN) is used for extracting efficient hand features to recognize the American Sign Language (ASL) using hand gestures. Finally, the Multi-class Support Vector Machine (MCSVM) is used for identifying the hand sign, where CNN extracted features are used to train up the machine. Distinct person hand gesture is used for validation in this paper. The proposed model shows satisfactory performance in terms of classification accuracy, i.e., 94.57%","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116553284","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: 37
Fetal QRS Detection Based on Convolutional Neural Networks in Noninvasive Fetal Electrocardiogram 基于卷积神经网络的无创胎儿心电图QRS检测
2018 4th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2018-09-01 DOI: 10.1109/ICFSP.2018.8552074
Jun Seong Lee, M. Seo, S. W. Kim, Minho Choi
{"title":"Fetal QRS Detection Based on Convolutional Neural Networks in Noninvasive Fetal Electrocardiogram","authors":"Jun Seong Lee, M. Seo, S. W. Kim, Minho Choi","doi":"10.1109/ICFSP.2018.8552074","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552074","url":null,"abstract":"Detection of fetal QRS complexes in a noninvasive fetal electrocardiogram (NI-FECG) signal is an important task to check fetal conditions and to prevent birth defects. However, the detection is not easy because the NI-FECG signal contains a maternal ECG signal that has greater amplitude than that of a fetal ECG signal. This paper proposes an algorithm to detect the fetal QRS complexes in the NI-FECG signal. The proposed algorithm is based on convolutional neural networks (CNN) and can reliably detect the fetal QRS complexes without separating the maternal ECG signal. To verify the algorithm, NI-FECG data (PhysioNet/computing in cardiology challenge 2013) were used. The proposed algorithm showed the average sensitivity of 89.06 % and positive predictive value of 92.77 %. The proposed algorithm can help to check fetal conditions and to prevent birth defects.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133577062","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: 25
Classification of Metaphase Chromosomes Using Deep Learning Neural Network 基于深度学习神经网络的中期染色体分类
2018 4th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2018-09-01 DOI: 10.1109/ICFSP.2018.8552042
P. Kiruthika, K B Jayanthi, Madian Nirmala
{"title":"Classification of Metaphase Chromosomes Using Deep Learning Neural Network","authors":"P. Kiruthika, K B Jayanthi, Madian Nirmala","doi":"10.1109/ICFSP.2018.8552042","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552042","url":null,"abstract":"Karyotyping of Banded Metaphase Chromosomes is one of the preliminary steps used in cytogenetics to analyze the chromosomes for diagnostic purposes. Deep learning is a subfield of machine learning concerned with structure and function of brain. It exploits a way to automate predictive analysis. The key aspect of deep learning is that the layers of features are not designed by human engineers. They are learned from data using a general purpose learning procedure. This paper proposes a convolution based deep learning to classify the chromosomes for automated karyotyping. The developed architecture allows us to train and test images that helps in predicting the chromosome abnormality. The performance analysis is based on loss and accuracy curves and the graphical representation clearly exhibits better classification results for this architecture.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131305230","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
Development of Technology for the Identification of Model Parameters for Dendritic Structures Images 树突结构图像模型参数识别技术的发展
2018 4th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2018-09-01 DOI: 10.1109/ICFSP.2018.8552052
R. Paringer, M. Boori, Y. Donon, A. Kupriyanov, D. Kirsh, Kravtsova Natalia
{"title":"Development of Technology for the Identification of Model Parameters for Dendritic Structures Images","authors":"R. Paringer, M. Boori, Y. Donon, A. Kupriyanov, D. Kirsh, Kravtsova Natalia","doi":"10.1109/ICFSP.2018.8552052","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552052","url":null,"abstract":"This work aims to increase the reliability of dendritic crystallogram’s images classification. Crystallographic methods are used for medical diagnosis and we propose here to improve the reliability of their classification through an improved description of de dendritic structures’ features. In this paper, we use the parameters of the mathematical model describing objects with dendritic structure. We developed a technology of parameters identification from a model image of dendritic structures, that was then implemented through the use of geometric and statistical features, together with a nearest neighbor classification algorithm.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122416083","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: 0
Statistical Compressive Sensing for Efficient Signal Reconstruction and Classification 基于统计压缩感知的高效信号重构与分类
2018 4th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2018-09-01 DOI: 10.1109/ICFSP.2018.8552059
Ivan Ralašić, A. Tafro, D. Seršić
{"title":"Statistical Compressive Sensing for Efficient Signal Reconstruction and Classification","authors":"Ivan Ralašić, A. Tafro, D. Seršić","doi":"10.1109/ICFSP.2018.8552059","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552059","url":null,"abstract":"Compressive sensing (CS) represents a signal processing technique for simultaneous signal acquisition and compression that relies on signal dimensionality reduction. Statistical compressive sensing (SCS) uses statistical models to develop an efficient sampling strategy for signals that follow some statistical distribution. In this paper, statistical model based on Gaussian mixtures is employed to design an efficient framework for the CS signal reconstruction and classification. A robust classification method based on sparse signal representation using overcomplete eigenvector dictionaries andl1-norm is presented. Optimal non-adaptive measurement matrix for observed Gaussian mixture model is discussed. A series of experiments to analyze the performance of the proposed method has been performed and presented in the experimental results section.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114879425","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
Photonic Generation of Millimeter-Wave Signals With Frequency-Multiplying and Tunable Phase Shift 具有倍频和可调相移的毫米波信号的光子产生
2018 4th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2018-09-01 DOI: 10.1109/ICFSP.2018.8552069
Conghui Zhang, Ruiying He, Xiaoyu Zhang, Yongfeng Wei
{"title":"Photonic Generation of Millimeter-Wave Signals With Frequency-Multiplying and Tunable Phase Shift","authors":"Conghui Zhang, Ruiying He, Xiaoyu Zhang, Yongfeng Wei","doi":"10.1109/ICFSP.2018.8552069","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552069","url":null,"abstract":"A novel photonic approach for generating a frequency-septupling or frequency-nonupling millimeter-wave (mm-wave) signal with tunable phase shift is proposed. Two fourth-order sidebands and an optical carrier are generated by using a dual-parallel Mach-Zehnder modulator (DPMZM). An optical bandstop filter (OBSF) is used to filter out optical carrier and a Mach-Zehnder interferometer (MZI) is employed to separate the +4th-order sideband and the −4th-order sideband. Then the −4th-order sideband is modulated by an optical phase modulator (PM), and the phase of the −4th-order sideband can be controlled by changing the dc voltage that drives the PM. The +4th-order sideband is modulated by the second DPMZM, then a +3rd-order sideband or +5th-order sideband is generated by controlling the dc voltage that drives the main-MZM of the second DPMZM. After an optical coupler and a photodiode (PD), a frequency-septupling or frequency-nonupling mm-wave signal with tunable phase shift is gotten. A simulation experiment is performed, and tunable 360-degree phase shift is realized, and the amplitude variation of the generated mm-wave signal is less than 0.2dB.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116996845","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
An Improved Macroscopic Modeling for Highway Traffic Density Estimation 一种用于公路交通密度估算的改进宏观模型
2018 4th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2018-09-01 DOI: 10.1109/ICFSP.2018.8552077
A. Zeroual, F. Harrou, Ying Sun
{"title":"An Improved Macroscopic Modeling for Highway Traffic Density Estimation","authors":"A. Zeroual, F. Harrou, Ying Sun","doi":"10.1109/ICFSP.2018.8552077","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552077","url":null,"abstract":"Efficient and accurate estimation of traffic density plays an important role in the development of intelligent transportation systems by providing relevant information for rapid decision-making. The purpose of this study is to design a model-based procedure to estimate traffic density. Here, we design an innovative observer that combines the benefits of piecewise switched linear traffic model with Luenberger observer estimator for improving road traffic density estimation. We evaluated the proposed estimator by using traffic data from the four-lane SR-60 freeway in southern California.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134167895","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
Application of Perfectly Undetectable Network Steganography Method for Malware Hidden Communication 完全不可检测网络隐写法在恶意软件隐藏通信中的应用
2018 4th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2018-09-01 DOI: 10.1109/ICFSP.2018.8552057
Patryk Bąk, Jȩdrzej Bieniasz, M. Krzemiński, K. Szczypiorski
{"title":"Application of Perfectly Undetectable Network Steganography Method for Malware Hidden Communication","authors":"Patryk Bąk, Jȩdrzej Bieniasz, M. Krzemiński, K. Szczypiorski","doi":"10.1109/ICFSP.2018.8552057","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552057","url":null,"abstract":"Currently designed malware utilizes various mechanisms allowing to increase the level of its undetectability through static and dynamic analysis. One of such mechanisms may be hiding in overt network traffic proper communication between the attacker and an active malware application on the infected terminal side. In this paper, a design of such a covert channel of communication is proposed, using a StegBlocks method, which is characterized by a proven feature of perfectly undetectable network steganography. An environment was implemented to test the proof of concept of the designed system of covert transmission. Characteristics and limitations of the method were discussed and directions for development were proposed.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129556010","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
Energy Detection Characteristics of the Quasi-Deterministic Signal with the Nakagami Amplitude 具有中上振幅的准确定性信号的能量检测特性
2018 4th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2018-09-01 DOI: 10.1109/ICFSP.2018.8552049
O. Chernoyarov, A. Salnikova, V. Kostylev, A. Faulgaber
{"title":"Energy Detection Characteristics of the Quasi-Deterministic Signal with the Nakagami Amplitude","authors":"O. Chernoyarov, A. Salnikova, V. Kostylev, A. Faulgaber","doi":"10.1109/ICFSP.2018.8552049","DOIUrl":"https://doi.org/10.1109/ICFSP.2018.8552049","url":null,"abstract":"We studied the possibility of applying power method for the radio signal detection against Gaussian white noise. It is presupposed that the signal amplitude is random and distributed by the Nakagami law. For this case, we found the distribution of the decision statistics of the energy detector. We obtained the expressions for the probability of correct detection under the discrete processing of the observable data realization within the limited time interval. We also analyzed the influence of the average power signal-to-noise ratio value and the time-bandwidth product upon the detection characteristics.","PeriodicalId":355222,"journal":{"name":"2018 4th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125112988","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信