{"title":"Ultranarrow-band synthetic aperture radar imaging for arbitrary flight trajectories","authors":"Ling Wang, B. Yazıcı","doi":"10.1109/ICDSP.2011.6004956","DOIUrl":"https://doi.org/10.1109/ICDSP.2011.6004956","url":null,"abstract":"We present a novel image formation method for synthetic aperture radar (SAR) using ultra-narrowband continuous waveforms. Considering the high Doppler resolution nature of the ultra-narrowband continuous wave (CW) signals, we refer to the SAR system using ultra-narrowband CW signals as Doppler Synthetic Aperture Radar (DSAR). We first correlate the translated version of the received signal with a scaled or frequency-shifted version of the transmitted signal over a finite time window, and then use microlocal analysis to reconstruct the scene by a filtered-backprojection (FBP) of the correlated signals. We show that the resolution of the image is directly related to the length of the support of the windowing function, the carrier-frequency of the transmitted waveform, and the sampling rate of the aperture. Unlike the previous approaches in the literature, our approach backprojects the correlated received signal onto iso-Doppler curves as opposed to iso-range curves, and takes advantage of the velocity, as well as the acceleration of the antennas in a certain direction to form a high resolution SAR image. Furthermore, it can accommodate arbitrary flight trajectories. We present numerical experiments to demonstrate the performance of the new image formation method.","PeriodicalId":360702,"journal":{"name":"2011 17th International Conference on Digital Signal Processing (DSP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116653319","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}
Y. Ebrahimdoost, S. Qanadli, A. Nikravanshalmani, T. Ellis, Z. Shojaee, J. Dehmeshki
{"title":"Automatic segmentation of Pulmonary Artery (PA) in 3D pulmonary CTA images","authors":"Y. Ebrahimdoost, S. Qanadli, A. Nikravanshalmani, T. Ellis, Z. Shojaee, J. Dehmeshki","doi":"10.1109/ICDSP.2011.6004964","DOIUrl":"https://doi.org/10.1109/ICDSP.2011.6004964","url":null,"abstract":"This paper proposes an efficient algorithm for segmenting the Pulmonary Artery (PA) tree in 3D pulmonary Computed Tomography Angiography (CTA) images. In this algorithm, to reduce the search area the lung regions from the original image are first segmented and the heart region is extracted by selecting the regions between the lungs. A pre-processing algorithm based on Hessian matrix and its eigenvalues is used to remove the connectivity between the pulmonary artery and other nearby pulmonary organs. To extract the pulmonary artery tree, we first use a region growing method initialized by a seed point which is automatically selected within the pulmonary artery trunk in the heart region. In the second step, the segmentation of the pulmonary artery is performed using a 3D level set algorithm, using the output of region grower as the initial contour. We use a new stopping criterion for the used level set algorithm, a consideration often neglected in many level set implementations. To validate and assess the robustness of the method, 20 CT angiography datasets were used (10 free pulmonary embolism scans and 10 CT with pulmonary emboli). A very good agreement with the visual judgment was obtained in both normal and positive pulmonary emboli CT scans.","PeriodicalId":360702,"journal":{"name":"2011 17th International Conference on Digital Signal Processing (DSP)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116765600","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}
{"title":"MMSE transmit diversity selection for multi-relay cooperative MIMO systems using discrete stochastic gradient algorithms","authors":"P. Clarke, R. D. Lamare","doi":"10.1109/ICDSP.2011.6004898","DOIUrl":"https://doi.org/10.1109/ICDSP.2011.6004898","url":null,"abstract":"This paper presents a set of transmit diversity selection algorithms based on discrete stochastic optimization for a two-phase, decode-and-forward, multi-relay cooperative MIMO system with a non-negligible direct path. Transmit diversity selection is performed jointly with channel estimation using discrete stochastic and continuous least squares optimization, respectively. Linear minimum mean square error receivers are used at the relay and destination nodes whilst no forward channel knowledge, precoding or inter-relay communication is required. Sets of candidate transmit diversity selections are generated and methods to optimize the selection whilst avoiding exhaustive searching are presented. The benefits of reducing the cardinality of these sets is shown and the performance of the proposed schemes are assessed via mean square error, bit-error rate and complexity comparisons. The performance and diversity achieved is shown to exceed that of standard multi-relay cooperative MIMO systems and random transmit diversity selection, and closely match that of the exhaustive solution.","PeriodicalId":360702,"journal":{"name":"2011 17th International Conference on Digital Signal Processing (DSP)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115030623","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}
{"title":"Advanced statistical and adaptive threshold techniques for moving object detection and segmentation","authors":"L. Christodoulou, T. Kasparis, Oge Marques","doi":"10.1109/ICDSP.2011.6004875","DOIUrl":"https://doi.org/10.1109/ICDSP.2011.6004875","url":null,"abstract":"The current research project proposes advanced statistical and adaptive threshold techniques for video object detection and segmentation. We present new statistical adaptive threshold techniques to show the advantages, and how these algorithms overcome the limitations and the technical challenges for object motion detection. The algorithm utilizes statistical quantities such as mean, standard deviation, and variance to define a new adaptive and automatic threshold based on two-frame and three-frame differencing. The proposed algorithms were compared with classic statistical thresholding methods on a testing video for human motion detection, and the experimental results show the effectiveness of the algorithms. Furthermore this research shows an evaluation and comparison among all statistical and adaptive algorithms and proves the benefits of the proposed algorithm.","PeriodicalId":360702,"journal":{"name":"2011 17th International Conference on Digital Signal Processing (DSP)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123410435","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}
{"title":"Morphological wavelets for 3D volume image decorrelation","authors":"Dragana D. Sandić-Stanković","doi":"10.1109/ICDSP.2011.6004948","DOIUrl":"https://doi.org/10.1109/ICDSP.2011.6004948","url":null,"abstract":"The performances of morphological wavelets for 3D volume image decorrelation in lossless image compression are presented. Nonlinear morphological wavelets used in non-redundant multiresolution signal decomposition schemes with perfect reconstruction are very fast for implementation as only integer arithmetic is used. The lifting schemes implementing morphological wavelet transforms avoid quantizers which is attractive property for lossless data compression. The performances of both separable and non-separable 3D morphological wavelet transforms using lifting scheme is evaluated. The computation time of 3D decomposition implemented by morphological wavelets is 3 times shorter than the decomposition time with 5/3 wavelet filters used in JPEG2000 standard with similar decorrelation efficiency for lossless compression.","PeriodicalId":360702,"journal":{"name":"2011 17th International Conference on Digital Signal Processing (DSP)","volume":"1 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125274264","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}
Foteini Agrafioti, Jiexin Gao, H. Mohammadzade, D. Hatzinakos
{"title":"A 2D bivariate EMD algorithm for image fusion","authors":"Foteini Agrafioti, Jiexin Gao, H. Mohammadzade, D. Hatzinakos","doi":"10.1109/ICDSP.2011.6004923","DOIUrl":"https://doi.org/10.1109/ICDSP.2011.6004923","url":null,"abstract":"Although the benefits of the empirical mode decomposition, in analyzing stochastic signals have been reported, and the algorithm has been established for fusion applications, there is currently no solution to the problem of the simultaneous decomposition of 2D data. This paper proposes an extension of the bivariate EMD (BEMD) [1] algorithm for 2D sources, which retains spatial information while addressing the uniqueness problem of the intrinsic mode functions. The performance of the algorithm is tested on partially blurred and defocused images. The fused images are compared against 1D-BEMD solutions, to demonstrate increased visual quality of the result.","PeriodicalId":360702,"journal":{"name":"2011 17th International Conference on Digital Signal Processing (DSP)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129144878","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}
Y. Kopsinis, K. Slavakis, S. Theodoridis, S. McLaughlin
{"title":"Reduced complexity online sparse signal reconstruction using projections onto weighted ℓ1 balls","authors":"Y. Kopsinis, K. Slavakis, S. Theodoridis, S. McLaughlin","doi":"10.1109/ICDSP.2011.6005005","DOIUrl":"https://doi.org/10.1109/ICDSP.2011.6005005","url":null,"abstract":"This paper presents a novel online method for sparse signal reconstruction. In particular, the notion of sub-dimensional projections is introduced, which allows a significant complexity reduction in the Adaptive Projection-based Algorithm using Weighted ℓ1 balls (APWL1). This is achieved without sacrificing performance. The proposed method is evaluated in both stationary and time-varying environments and its performance is compared with state-of-the-art online and batch LASSO-based methods.","PeriodicalId":360702,"journal":{"name":"2011 17th International Conference on Digital Signal Processing (DSP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127781167","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}
{"title":"Compressed channel sensing: Is the Restricted Isometry Property the right metric?","authors":"A. Scaglione, Xiao Li","doi":"10.1109/ICDSP.2011.6005010","DOIUrl":"https://doi.org/10.1109/ICDSP.2011.6005010","url":null,"abstract":"In this paper we are concerned with the estimation of doubly-selective multi-path communication channels trough methods referred to as compressed channel sensing. Many authors have used the Restricted Isometry Property (RIP) as a guiding principle to select training to ensure good estimation performance. In this paper we discuss why this approach can be restrictive and why its entanglement with modeling aspects can be misleading. More importantly, we provide an alternative approach to classify inputs based on a new metric that we call localized coherence.","PeriodicalId":360702,"journal":{"name":"2011 17th International Conference on Digital Signal Processing (DSP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117048507","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}
{"title":"Maximum a posteriori estimation approach to sparse recovery","authors":"Mashud Hyder, K. Mahata","doi":"10.1109/ICDSP.2011.6004892","DOIUrl":"https://doi.org/10.1109/ICDSP.2011.6004892","url":null,"abstract":"We adopt a maximum a posteriori (MAP) estimation based approach for recovering sparse signals from a small number of measurements formed by computing the inner products of the signal with rows of a matrix. We assume that each component of the sparse signal is independent and identically distributed (i.i.d) random variable drawn from a Gaussian mixture model. We then develop a suitable MAP formulation which results in an iterative algorithm. Simulations are performed to study the performance of the algorithm. We observe that our approach has a number of advantages over other sparse recovery techniques, including robustness to noise, increased performance with limited measurements and lower computation time.","PeriodicalId":360702,"journal":{"name":"2011 17th International Conference on Digital Signal Processing (DSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127112256","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}
P. Sattigeri, K. Ramamurthy, Jayaraman J. Thiagarajan, M. Goryll, A. Spanias, T. Thornton
{"title":"Analyte detection using an ion-channel sensor array","authors":"P. Sattigeri, K. Ramamurthy, Jayaraman J. Thiagarajan, M. Goryll, A. Spanias, T. Thornton","doi":"10.1109/ICDSP.2011.6004913","DOIUrl":"https://doi.org/10.1109/ICDSP.2011.6004913","url":null,"abstract":"Ion-channel sensors can be used for detecting small metal ions and organic molecules. The sensor consists of a chamber with a lipid bilayer hosting ion channels produced by protein insertion. These channels allow selective transport and produce a characteristic signal across the chamber for each analyte. A four chamber ion channel sensor array is built for accurate analyte detection. In this paper, we address the case in which non-uniform number of channels formed in each chamber. The power distribution information in the transform domain is used as features for each chamber signal. We employ support vector regression to estimate the number of channels inserted in each chamber and normalize the chamber signal features. The change observed in the normalized features of the chamber containing the analyte with respect to other chambers is used for detection. Results show high accuracy rates for detection of analyte using simulated data and experimental data.","PeriodicalId":360702,"journal":{"name":"2011 17th International Conference on Digital Signal Processing (DSP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131496164","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}