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2010 2nd International Workshop on Cognitive Information Processing最新文献

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Vessel detection and classification: An integrated maritime surveillance system in the Tyrrhenian sea 船舶探测与分类:第勒尼安海综合海上监视系统
2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604209
S. Maresca, M. Greco, F. Gini, R. Grasso, S. Coraluppi, J. Horstmann
{"title":"Vessel detection and classification: An integrated maritime surveillance system in the Tyrrhenian sea","authors":"S. Maresca, M. Greco, F. Gini, R. Grasso, S. Coraluppi, J. Horstmann","doi":"10.1109/CIP.2010.5604209","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604209","url":null,"abstract":"In recent years a number of organizations, both national and international, have put significant efforts in developing knowledge-based integrated maritime surveillance (IMS) systems. The final aim is to have a clear picture of the position, classification, identification and movement of cooperative and non-cooperative targets entering and leaving the 200 nautical miles limit of the Exclusive Economic Zone (EEZ). Each sensor (i.e. satellite-based, ground-based, shipborne or airborne) has its own task and, in such a context, high frequency (HF) surface wave (SW) radars are inexpensive tools for long range early warning applications in open waters. They allow maximizing the effectiveness in dealing with fisheries protection, drug interdiction, illegal immigration, terrorist threats, search and rescue tasks. This paper focuses on the possibility of combining automatic identification system (AIS) data with HFSWR data for vessel detection and classification purposes. Three algorithms for target detection in compound Gaussian HF sea clutter are presented and their performance evaluated. The combined use of AIS plots provided by cooperative targets can allow the operator to discriminate non-cooperative targets and possible threats. The concurrent exploitation of AIS and HFSWR data is presented and discussed by means of real data recorded during the NURC experiment in the northern Tyrrhenian Sea in May 2009.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128208193","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: 20
Overview of spectrum sensing for cognitive radio 认知无线电频谱感知概述
2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604136
E. Axell, G. Leus, E. Larsson
{"title":"Overview of spectrum sensing for cognitive radio","authors":"E. Axell, G. Leus, E. Larsson","doi":"10.1109/CIP.2010.5604136","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604136","url":null,"abstract":"We present a survey of state-of-the-art algorithms for spectrum sensing in cognitive radio. The algorithms discussed range from energy detection to sophisticated feature detectors. The feature detectors that we present all have in common that they exploit some known structure of the transmitted signal. In particular we treat detectors that exploit cyclostationarity properties of the signal, and detectors that exploit a known eigenvalue structure of the signal covariance matrix. We also consider cooperative detection. Specifically we present data fusion rules for soft and hard combining, and discuss the energy efficiency of several different sensing, sleeping and censoring schemes in detail.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128259096","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: 65
Mental fatigue analysis by measuring synchronization of brain rhythms incorporating enhanced empirical mode decomposition 采用增强的经验模式分解测量脑节律同步的精神疲劳分析
2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604127
D. Jarchi, B. Makkiabadi, S. Sanei
{"title":"Mental fatigue analysis by measuring synchronization of brain rhythms incorporating enhanced empirical mode decomposition","authors":"D. Jarchi, B. Makkiabadi, S. Sanei","doi":"10.1109/CIP.2010.5604127","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604127","url":null,"abstract":"A new and effective approach for mental fatigue analysis is presented here. Empirical mode decomposition (EMD), as a fully adaptive and data-driven method for analyzing nonlinear and nonstationary time series, is presented for measuring the synchronization of the brain rhythms from different brain lobes. The EMD algorithm is applied to a desired channel and each time one of the extracted intrinsic mode functions (IMFs) is considered as one of the brain rhythms. This IMF can be filtered by an adaptive line enhancement (ALE) algorithm. The superiority of using ALE to conventional filtering has been tested using simulated signals. Then, by applying Hilbert transform to several enhanced IMFs from different parts of the brain, the changes in linear and non linear synchronization levels are estimated for determination of the fatigue state.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116490065","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
Adaptive radar detection: A subspace identification approach 自适应雷达探测:一种子空间识别方法
2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604250
F. Bandiera, D. Orlando, G. Ricci, L. Scharf
{"title":"Adaptive radar detection: A subspace identification approach","authors":"F. Bandiera, D. Orlando, G. Ricci, L. Scharf","doi":"10.1109/CIP.2010.5604250","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604250","url":null,"abstract":"We address adaptive detection of Swerling 2 pulse trains by an array of antennas. The disturbance is modeled in terms of a state space model and the ideas of subspace identification are used to come up with a GLRT-based detector. Such detector is compared by Monte Carlo simulation with a Kelly's detector derived assuming that returns are temporally uncorrelated (but spatially correlated) and that a proper set of secondary data is available.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116536340","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
Bayesian fault diagnosis: Common approaches and challenges 贝叶斯故障诊断:常用方法与挑战
2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604215
R. Dearden
{"title":"Bayesian fault diagnosis: Common approaches and challenges","authors":"R. Dearden","doi":"10.1109/CIP.2010.5604215","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604215","url":null,"abstract":"In this paper we describe a Bayesian approach to fault diagnosis based on Markov chain Monte Carlo algorithms. These approaches are largely applied to hybrid diagnosis problems in which the system being diagnosed is modelled with a mixture of discrete and continuous state variables. We describe the probabilistic hybrid automaton model typically used, and an algorithm based on particle filtering that can be applied to these models. Diagnosis provides some particular challenges for Monte Carlo approaches, including large dimensional state spaces, and low probability transitions in the Markov chain. We discuss these and some proposed solutions to them. Finally, we examine some open challenges for the Bayesian approach.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115935982","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
From differential to information geometry 从微分几何到信息几何
2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604248
F. Opitz
{"title":"From differential to information geometry","authors":"F. Opitz","doi":"10.1109/CIP.2010.5604248","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604248","url":null,"abstract":"Information geometry is a new and increasing topic between statistics, estimation and differential geometry. Many amazing relationships between these domains were established through the last years. Unfortunately, it is not easy to find an easy approach to information geometry, which requires a deep understanding of differential geometry and statistics. The paper presents an easy readable introduction to information geometry, adapted from the analogies of surfaces embedded in the three dimensional space. It is shown, how well known structures like hyperbolic geometry and affine spaces occurs again in information geometry. Finally, some applications are given.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122795637","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
Adaptive transmitter weight design for clutter suppression 杂波抑制的自适应发射机重量设计
2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604206
S. Pillai, K. Li, B. Himed
{"title":"Adaptive transmitter weight design for clutter suppression","authors":"S. Pillai, K. Li, B. Himed","doi":"10.1109/CIP.2010.5604206","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604206","url":null,"abstract":"The objective of this paper is to develop methods for enhanced clutter/interference suppression by redesigning the transmit pattern in the spatio-temporal domain to emphasize the target response and de-emphasize the clutter response. In this context, traditional adaptive transmitter weight design in the spatial domain to generate multiple spatial nulls is extended to both the spatio-temporal domains to suppress certain Doppler frequencies as well. Analysis of this approach and preliminary simulation results are included in this paper.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129892164","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
Maximum likelihood blind deconvolution for sparse systems 稀疏系统的最大似然盲反卷积
2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604139
S. Barembruch, A. Scaglione, É. Moulines
{"title":"Maximum likelihood blind deconvolution for sparse systems","authors":"S. Barembruch, A. Scaglione, É. Moulines","doi":"10.1109/CIP.2010.5604139","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604139","url":null,"abstract":"In recent years many sparse estimation methods, also known as compressed sensing, have been developed for channel identification problems in digital communications. However, all these methods presume the transmitted sequence of symbols to be known at the receiver, i.e. in form of a training sequence. We consider blind identification of the channel based on maximum likelihood (ML) estimation via the EM algorithm incorporating a sparsity constraint in the maximization step. We apply this algorithm to a linear modulation scheme on a doubly-selective channel model.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121577112","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
Building a bayesian factor tree from examples 从示例中构建贝叶斯因子树
2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604232
F. Palmieri, G. Romano, P. Rossi, D. Mattera
{"title":"Building a bayesian factor tree from examples","authors":"F. Palmieri, G. Romano, P. Rossi, D. Mattera","doi":"10.1109/CIP.2010.5604232","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604232","url":null,"abstract":"A criterion based on mutual information among variables is proposed for building a bayesian tree from a finite number of examples. The factor graph, in Forney-style form, can be used as an associative memory that performs probabilistic inference in data fusion applications. The procedure is explained with the aid of a fully-described example.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121770566","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
Metacognition in radar 雷达中的元认知
2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604113
G. Capraro, M. Wicks, R. Schneible
{"title":"Metacognition in radar","authors":"G. Capraro, M. Wicks, R. Schneible","doi":"10.1109/CIP.2010.5604113","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604113","url":null,"abstract":"An airborne ground looking radar sensor's performance may be enhanced by selecting algorithms adaptively as the environment changes. A short description of an airborne intelligent radar system (AIRS) is presented with an in-depth description of the knowledge based filter and detection portions. A second level of artificial intelligence (AI) processing is presented that monitors, tests, and learns how to improve and control the first level. This approach is based upon metacognition.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129792553","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
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