2010 2nd International Workshop on Cognitive Information Processing最新文献_第7页

Musical noise controllable algorithm of channelwise spectral subtraction and beamforming based on higher-order statistics criterion 基于高阶统计量准则的信道谱减法和波束形成音乐噪声控制算法

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604226

Y. Ishikawa, H. Saruwatari, Yu Takahashi, K. Shikano, Kazunobu Kondo

引用次数: 1

The radar system and information flow 雷达系统和信息流

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604137

J. A. Malas, J. Cortese

引用次数: 1

Performance comparison for low complexity blind sensing techniques in cognitive radio systems 认知无线电系统中低复杂度盲感知技术的性能比较

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604175

B. Zayen, Wael Guibène, A. Hayar

引用次数: 5

Memory-efficient FFT architecture using R-LFSR based CORDIC common operator 基于R-LFSR的CORDIC公共算子的高效内存FFT架构

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604220

Hongzhi Wang, Y. Louët, J. Palicot, Laurent Alaus, D. Noguet

引用次数: 4

Cognitive adaptive waveform technique for HF skywave radar 高频天波雷达的认知自适应波形技术

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604247

A. L. Saverino, A. Capria, F. Berizzi, E. Mese

引用次数: 6

Improved non-parametric sparse recovery with data matched penalties 改进了数据匹配惩罚的非参数稀疏恢复

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604121

Marco Signoretto, K. Pelckmans, L. D. Lathauwer, J. Suykens

引用次数: 0

Latent semantics as cognitive components 作为认知成分的潜在语义

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604233

Michael Kai Petersen, Morten Mørup, L. K. Hansen

{"title":"Latent semantics as cognitive components","authors":"Michael Kai Petersen, Morten Mørup, L. K. Hansen","doi":"10.1109/CIP.2010.5604233","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604233","url":null,"abstract":"Cognitive component analysis, defined as an unsupervised learning of features resembling human comprehension, suggests that the sensory structures we perceive might often be modeled by reducing dimensionality and treating objects in space and time as linear mixtures incorporating sparsity and independence. In music as well as language the patterns we come across become part of our mental workspace when the bottom-up sensory input raises above the background noise of core affect, and top-down trigger distinct feelings reflecting a shift of our attention. And as both low-level semantics and our emotional responses can be encoded in words, we propose a simplified cognitive approach to model how we perceive media. Representing song lyrics in a vector space of reduced dimensionality using LSA, we combine bottom-up defined term distances with affective adjectives, that top-down constrain the latent semantics according to the psychological dimensions of valence and arousal. Subsequently we apply a Tucker tensor decomposition combined with re-weighted l1 regularization and a Bayesian ARD automatic relevance determination approach to derive a sparse representation of complementary affective mixtures, which we suggest function as cognitive components for perceiving the underlying structure in lyrics.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"34 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":"122820259","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

Game theoretical analysis of cognitive radio networks: An NCEL perspective 认知无线电网络的博弈论分析:NCEL视角

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604097

Jianwei Huang

引用次数: 2

Node localization and tracking using distance and acceleration measurements 节点定位和跟踪使用距离和加速度测量

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604256

Benjamin R. Hamilton, Xiaoli Ma, R. Baxley, B. Walkenhorst

{"title":"Node localization and tracking using distance and acceleration measurements","authors":"Benjamin R. Hamilton, Xiaoli Ma, R. Baxley, B. Walkenhorst","doi":"10.1109/CIP.2010.5604256","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604256","url":null,"abstract":"Advances in miniaturized wireless and sensing technologies have enabled the construction of cheap, low-powered, portable wireless devices capable of forming ad hoc networks. While these networks have shown enormous potential in applications such as remote sensing and target tracking, these applications require the devices to determine their own location. Additionally, devices capable of self-localization can also be used to implement location-based services or to improve coordination between first-responders to disaster sites or infantry in tactical situations. Existing techniques such as GPS may not be available due to design or environmental constraints, so other methods need to be devised. Previous works have proposed methods for wireless devices to self-localize based on received signal strength (RSS), but these methods offer limited accuracy due to the large error in RSS measurements. Recognizing the trend for these portable wireless devices to contain acceleration sensors, we propose an algorithm to combine these acceleration measurements with RSS readings to achieve accurate localization. We apply a distributed extended Kalman filter to track position based on these two measurements and a kinematic node movement model. This algorithm is able to take advantage of correlations between successive location estimates to improve estimation accuracy. We calculate the posterior Cramér-Rao bound for this algorithm and analyze it through simulation. Our analysis shows that by utilizing the acceleration information, the network is able to self-localize despite the large inaccuracy in RSS readings.","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":"131229776","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

Autonomous science platforms and question-asking machines 自主科学平台和提问机器

2010 2nd International Workshop on Cognitive Information Processing Pub Date : 2010-06-14 DOI: 10.1109/CIP.2010.5604217

K. Knuth, Julian L. Center

{"title":"Autonomous science platforms and question-asking machines","authors":"K. Knuth, Julian L. Center","doi":"10.1109/CIP.2010.5604217","DOIUrl":"https://doi.org/10.1109/CIP.2010.5604217","url":null,"abstract":"As we become increasingly reliant on remote science platforms, the ability to autonomously and intelligently perform data collection becomes critical. In this paper we view these platforms as question-asking machines and introduce a paradigm based on the scientific method, which couples the processes of inference and inquiry to form a model-based learning cycle. Unlike modern autonomous instrumentation, the system is not programmed to collect data directly, but instead, is programmed to learn based on a set of models. Computationally, this learning cycle is implemented in software consisting of a Bayesian probability-based inference engine coupled to an entropy-based inquiry engine. Operationally, a given experiment is viewed as a question, whose relevance is computed using the inquiry calculus, which is a natural order-theoretic generalization of information theory. In simple cases, the relevance is proportional to the entropy. This data is then analyzed by the inference engine, which updates the state of knowledge of the instrument. This new state of knowledge is then used as a basis for future inquiry as the system continues to learn. This paper will introduce the learning methodology, describe its implementation in software, and demonstrate the process with a robotic explorer that autonomously and intelligently performs data collection to solve a search-and-characterize problem.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"22 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":"131600420","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