2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers最新文献

Time-space-sequential distributed particle filtering with low-rate communications 低速率通信的时空序列分布粒子滤波

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers Pub Date : 2009-11-01 DOI: 10.1109/ACSSC.2009.5470131

Ondrej Hlinka, P. Djurić, F. Hlawatsch

引用次数: 21

Reconfigurable real-time MIMO detector on GPU GPU上可重构的实时MIMO检测器

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers Pub Date : 2009-11-01 DOI: 10.1109/ACSSC.2009.5469936

Michael Wu, Yang Sun, Joseph R. Cavallaro

引用次数: 20

On linear processing in AWGN channels with feedback 带反馈的AWGN通道的线性处理

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers Pub Date : 2009-11-01 DOI: 10.1109/ACSSC.2009.5470032

Zachary Chance, D. Love

引用次数: 0

Improving adaptive tracking in time-frequency plane using varying number of Adaptive Notch Filters 利用可变数量的自适应陷波滤波器改进时频平面的自适应跟踪

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers Pub Date : 2009-11-01 DOI: 10.1109/ACSSC.2009.5469893

H. Thai, M. Ta, V. DeBrunner

引用次数: 0

MIMO OFDM over underwater acoustic channels 水声信道上的MIMO OFDM

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers Pub Date : 2009-11-01 DOI: 10.1109/ACSSC.2009.5469907

M. Stojanovic

引用次数: 71

JPEG2000 ROI coding method with perfect fine-grain accuracy and lossless recovery JPEG2000 ROI编码方法具有完美的细粒精度和无损恢复

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers Pub Date : 2009-11-01 DOI: 10.1109/ACSSC.2009.5469892

Joan Bartrina-Rapesta, J. Serra-Sagristà, F. Aulí-Llinàs, J. Gómez

{"title":"JPEG2000 ROI coding method with perfect fine-grain accuracy and lossless recovery","authors":"Joan Bartrina-Rapesta, J. Serra-Sagristà, F. Aulí-Llinàs, J. Gómez","doi":"10.1109/ACSSC.2009.5469892","DOIUrl":"https://doi.org/10.1109/ACSSC.2009.5469892","url":null,"abstract":"Medical images have high spatial and high bit-depth resolution (12 bits per sample or more). These high resolutions allow computer-aided diagnosis, which are exploited by radiologists to identify relevant medical areas, known as Regions of Interest (ROI). In image compression, ROI coding allows to recover the ROI earlier than the rest of the image. In JPEG2000, ROI coding may be provided through two different mechanisms: either by modifying wavelet coefficients, or by rate-distortion optimization techniques. The former obtains an excellent accuracy to delimit ROIs, but, in some cases, the ROI and the background can not be encoded losslessly; the latter is usually not able to achieve the intended fine-grain accuracy, but it overcomes the lossless encoding shortcoming. This article introduces a ROI coding method based on rate-distortion optimization techniques that recovers the ROI and the background losslessly, regardless of the high bit-depth resolution, and that yields an accuracy equivalent to MaxShift and Scaling, the two compliant JPEG2000 ROI coding methods based on modifying wavelet coefficients. The proposed method is JPEG2000 compliant.","PeriodicalId":224492,"journal":{"name":"2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115518767","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: 10

MIMO field directionality estimation using orientation-diverse linear arrays 基于方向变化线性阵列的MIMO场方向性估计

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers Pub Date : 2009-11-01 DOI: 10.1109/ACSSC.2009.5470146

G. Hickman, J. Krolik

{"title":"MIMO field directionality estimation using orientation-diverse linear arrays","authors":"G. Hickman, J. Krolik","doi":"10.1109/ACSSC.2009.5470146","DOIUrl":"https://doi.org/10.1109/ACSSC.2009.5470146","url":null,"abstract":"This paper addresses the estimation of power versus azimuth and elevation using a sparse sensor array. In MIMO radar, clutter and noise field directionality can be measured both from the transmit array and to the receive array. In applications where sparse arrays must be used, however, often substantial ambiguities in the field directionality map occur due to spatial under-sampling and/or sidelobe leakage. In this paper, we propose an alternative field-directionality mapping approach which incoherently combines the outputs of several uniform linear arrays with different orientations. The proposed orientation-diverse multi-line array (ODMA) treats the 1-D field directionality estimate from each line array as a projection of a 2-D spatial spectrum. Two methods for reconstruction from these projections are presented: 1) least-squares estimation subject to a positivity constraint, and 2) maximum likelihood estimation using the EM algorithm. Simulation results suggest both methods achieve ODMA field directionality maps with improved ambiguity resolution and sidelobe performance versus conventional beamforming techniques.","PeriodicalId":224492,"journal":{"name":"2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122402793","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

Parametric compression of Rank-1 analog feedback in MIMO-OFDM MIMO-OFDM中Rank-1模拟反馈的参数压缩

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers Pub Date : 2009-11-01 DOI: 10.1109/ACSSC.2009.5469863

R. Porat, P. Orlik

引用次数: 0

Multi-band, multi-radio wireless LANs and PANs 多波段、多无线电无线局域网和广域网

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers Pub Date : 2009-11-01 DOI: 10.1109/ACSSC.2009.5470088

R. Stacey

引用次数: 1

Sensor scheduling with waveform design for dynamic target tracking using MIMO radar MIMO雷达动态目标跟踪的传感器调度与波形设计

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers Pub Date : 2009-11-01 DOI: 10.1109/ACSSC.2009.5470145

Bhavana B. Manjunath, J. Zhang, A. Papandreou-Suppappola, D. Morrell

{"title":"Sensor scheduling with waveform design for dynamic target tracking using MIMO radar","authors":"Bhavana B. Manjunath, J. Zhang, A. Papandreou-Suppappola, D. Morrell","doi":"10.1109/ACSSC.2009.5470145","DOIUrl":"https://doi.org/10.1109/ACSSC.2009.5470145","url":null,"abstract":"Multiple-input, multiple-output (MIMO) radar systems have gained significant attention as they can enhance target detection, identification and parameter estimation performance. In this paper, we consider the problem of optimizing the target tracking performance of a widely-separated MIMO radar system by scheduling the transmitter sensors and adaptively designing their waveforms. Specifically, for a tracking scenario consisting of a large number of MIMO radars, we propose: (a) a transmitter scheduling algorithm to achieve tracking performance gains based on resource constraints; and (b) an adaptive waveform optimization algorithm that further improves tracking performance. Under an ideal receiver assumption, we evaluate the predicted tracking mean-squared error using the derived Cramér-Rao lower bound (CRLB) on the estimation of the target states. The scheduling algorithm is then formulated as a mixed boolean-convex optimization problem to minimize the CRLB. The optimum waveform parameters are adaptively obtained using sequential quadratic programming. The effectiveness of combining the MIMO radar technology with adaptive waveform design and sensor scheduling was demonstrated with simulations.","PeriodicalId":224492,"journal":{"name":"2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122653072","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: 21