Author |
Session |
Start page |
Title |
A A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Addabbo, Pia |
A.6.5 |
|
High Resolution Range Profiling for Stepped Radar via Sparsity Exploitation |
Anitori, Laura |
A.3.1 |
|
Recovery Guarantees for Slow Time Phase Coded Waveforms in MIMO radar |
Askarpour, Amirnader |
A.7.3 |
|
A low cost non-imaging system for standoff threat detection |
Aubry, Augusto |
A.6.5 |
|
High Resolution Range Profiling for Stepped Radar via Sparsity Exploitation |
B A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Bagheri-Korani, Ebrahim |
A.7.3 |
|
A low cost non-imaging system for standoff threat detection |
Bah, Bubacarr |
A.8.4 |
|
Weighted sparse recovery with expanders |
Becker, John |
A.1.3 |
|
Expansion of Dropped-Channel PolSAR CS to include a Spatial Dictionary |
Bi, Hui |
A.1.1 |
|
Comparison of raw data based and complex image based sparse SAR imaging methods |
Biondi, Filippo |
A.1.2 |
|
Low-Rank Plus Sparse Decomposition, Multi-Chromatic Analysis and Generalized Likelihood Ratio Test for Ship Weak Detection, (L+S)-MCA-GLRT |
A.1.5 |
|
Synthetic Aperture Radar Image filtering by Unbiased Risk Estimates for Singular Value Thresholding and Multi-Chromatic-Analysis |
Bosworth, Bryan |
A.4.3 |
|
Compressive Nonlinear Frequency Modulated CW LIDAR |
Brajovic, Milos |
A.2.4 |
|
Reconstruction of Rigid Body with Noncompensated Acceleration After Micro-Doppler Removal |
A.2.1 |
|
Analysis of Initial Estimate Noise in the Sparse Randomly Sampled ISAR Signals |
Buxbaum, Bernd |
A.7.5 |
|
Fast Multipath Estimation for PMD Sensors |
C A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Condat, Laurent |
A.4.4 |
|
Analysis of masks for compressed acquisitions in variational-based pansharpening |
Czajkowski, Krzysztof |
A.4.1 |
|
Single-pixel real-time video imaging with closed-form single-step image reconstruction |
D A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Daković, Miloš |
A.2.4 |
|
Reconstruction of Rigid Body with Noncompensated Acceleration After Micro-Doppler Removal |
A.2.1 |
|
Analysis of Initial Estimate Noise in the Sparse Randomly Sampled ISAR Signals |
Dalla Mura, Mauro |
A.4.4 |
|
Analysis of masks for compressed acquisitions in variational-based pansharpening |
De Maio, Antonio |
A.6.5 |
|
High Resolution Range Profiling for Stepped Radar via Sparsity Exploitation |
E A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Eldar, Yonina |
A.3.2 |
|
Multiple Carrier Agile Radar via Compressed Sensing |
Ender, Joachim |
A.5.1 |
|
Hybrid Aperture Modulation for THz Imaging with Compressive Sensing |
A.1.4 |
|
Dictionary learning for multiplicative distortions with applications to SAR autofocus |
Ertin, Emre |
A.3.1 |
|
Recovery Guarantees for Slow Time Phase Coded Waveforms in MIMO radar |
Escande, Paul |
A.8.1 |
|
Learning and Exploiting Physics of Degradations |
F A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Feuillen, Thomas |
A.7.6 |
|
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing |
A.3.3 |
|
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing |
Foster, Mark |
A.4.3 |
|
Compressive Nonlinear Frequency Modulated CW LIDAR |
Fritzen, Claus-Peter |
A.5.3 |
|
Sparsity-constrained Kalman Filter concept for damage identification in mechanical structures |
G A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Gabbouj, Moncef |
A.6.1 |
|
Through the wall Target Detection/Monitoring from Compressively Sensed Signals via Structural Sparsity |
Gall, Juergen |
A.9.2 |
|
Are good local minima wide in sparse recovery? |
Genzel, Martin |
A.8.3 |
|
A New Perspective on the Sample Complexity of the Analysis Basis Pursuit |
Ginsberg, Daniel |
A.5.3 |
|
Sparsity-constrained Kalman Filter concept for damage identification in mechanical structures |
Giovanneschi, Fabio |
A.1.4 |
|
Dictionary learning for multiplicative distortions with applications to SAR autofocus |
Guicquero, William |
A.3.4 |
|
Experimental results of Analog-to-Information converter using Non Uniform Wavelet Bandpass Sampling for RF application |
H A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Hage, Dunja |
A.9.3 |
|
Convergence Accelerator for l1-Minimizing Kalman Filter |
Heredia Conde, Miguel |
A.7.5 |
|
Fast Multipath Estimation for PMD Sensors |
A.9.1 |
|
Iterative Hard Thresholding with Optimal Measurement Matrices |
A.9.3 |
|
Convergence Accelerator for l1-Minimizing Kalman Filter |
Hlubucek, Jiri |
A.4.2 |
|
Evaluation of using coded aperture imaging in the mid- and far-infrared region |
Hu, Chang yu |
A.7.7 |
|
A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks |
A.2.3 |
|
A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks |
Huang, Tianyao |
A.3.2 |
|
Multiple Carrier Agile Radar via Compressed Sensing |
I A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Ioana, Cornel |
A.2.4 |
|
Reconstruction of Rigid Body with Noncompensated Acceleration After Micro-Doppler Removal |
A.2.1 |
|
Analysis of Initial Estimate Noise in the Sparse Randomly Sampled ISAR Signals |
J A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Jackson, Julie |
A.1.3 |
|
Expansion of Dropped-Channel PolSAR CS to include a Spatial Dictionary |
Jacques, Laurent |
A.7.6 |
|
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing |
A.3.3 |
|
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing |
Jung, Hans |
A.8.2 |
|
One-bit compressed sensing with partial Gaussian circulant matrices |
K A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Kerstein, Thomas |
A.7.5 |
|
Fast Multipath Estimation for PMD Sensors |
Kexel, Christian |
A.5.2 |
|
Sparse Damage Imaging for Guided-Wave Structural Health Monitoring |
Knott, Peter |
A.5.1 |
|
Hybrid Aperture Modulation for THz Imaging with Compressive Sensing |
Kotyński, Rafał |
A.4.1 |
|
Single-pixel real-time video imaging with closed-form single-step image reconstruction |
Kozlov, Dmitrii |
A.6.4 |
|
Reducing Radar Energy Consumption in Classification Tasks through the use of Compressed Sensing |
Krahmer, Felix |
A.9.2 |
|
Are good local minima wide in sparse recovery? |
Kutyniok, Gitta |
A.8.3 |
|
A New Perspective on the Sample Complexity of the Analysis Basis Pursuit |
L A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Lee, Dae Gwan |
A.7.1 |
|
Fast Binary Compressive Sensing via $\ell_0$ Gradient Descent |
Li, Xiaobin |
A.2.2 |
|
Wide Angle SAR imaging based on LS-CS-Residual |
Li, Ze |
A.7.7 |
|
A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks |
A.2.3 |
|
A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks |
Liu, Tianlin |
A.7.1 |
|
Fast Binary Compressive Sensing via $\ell_0$ Gradient Descent |
Liu, Yimin |
A.3.2 |
|
Multiple Carrier Agile Radar via Compressed Sensing |
Loffeld, Otmar |
A.9.1 |
|
Iterative Hard Thresholding with Optimal Measurement Matrices |
A.7.5 |
|
Fast Multipath Estimation for PMD Sensors |
A.5.3 |
|
Sparsity-constrained Kalman Filter concept for damage identification in mechanical structures |
A.1.4 |
|
Dictionary learning for multiplicative distortions with applications to SAR autofocus |
A.9.4 |
|
A Deep Learning Framework for Compressed Learning and Signal Reconstruction |
A.7.7 |
|
A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks |
A.9.2 |
|
Are good local minima wide in sparse recovery? |
A.9.3 |
|
Convergence Accelerator for l1-Minimizing Kalman Filter |
A.2.3 |
|
A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks |
Lu, Yun |
A.6.2 |
|
Relevant Vector Identification using Matrix Extension for Anisotropic SFCW Radar |
M A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Maggioni, Mauro |
A.8.1 |
|
Learning and Exploiting Physics of Degradations |
März, Maximilian |
A.8.3 |
|
A New Perspective on the Sample Complexity of the Analysis Basis Pursuit |
MESNARD, Philippe |
A.7.4 |
|
Ground Clutter Processing for Airborne Radar in a Compressed Sensing Context |
Moeller, Michael |
A.9.2 |
|
Are good local minima wide in sparse recovery? |
|
T.1.1 |
|
Minimization Algorithms for ℓ1 Regularized Problems Lecturemotivation |
|
T.1.2 |
|
Minimization Algorithms for ℓ1 Regularized Problems I |
|
T.2.1 |
|
Minimization Algorithms for ℓ1 Regularized Problems II |
Mohammadpour-Aghdam, Karim |
A.7.3 |
|
A low cost non-imaging system for standoff threat detection |
Moll, Jochen |
A.5.2 |
|
Sparse Damage Imaging for Guided-Wave Structural Health Monitoring |
N A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Nguyen, Xuan Vinh |
A.9.4 |
|
A Deep Learning Framework for Compressed Learning and Signal Reconstruction |
O A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Orhan, Melek |
A.6.1 |
|
Through the wall Target Detection/Monitoring from Compressively Sensed Signals via Structural Sparsity |
Ott, Peter |
A.6.4 |
|
Reducing Radar Energy Consumption in Classification Tasks through the use of Compressed Sensing |
Ouvry, Laurent |
A.3.4 |
|
Experimental results of Analog-to-Information converter using Non Uniform Wavelet Bandpass Sampling for RF application |
P A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Pallotta, Luca |
A.6.5 |
|
High Resolution Range Profiling for Stepped Radar via Sparsity Exploitation |
Pastuszczak, Anna |
A.4.1 |
|
Single-pixel real-time video imaging with closed-form single-step image reconstruction |
Pelissier, Michael |
A.3.4 |
|
Experimental results of Analog-to-Information converter using Non Uniform Wavelet Bandpass Sampling for RF application |
Picone, Daniele |
A.4.4 |
|
Analysis of masks for compressed acquisitions in variational-based pansharpening |
Plettemeier, Dirk |
A.6.2 |
|
Relevant Vector Identification using Matrix Extension for Anisotropic SFCW Radar |
Pribić, Radmila |
A.6.3 |
|
Resolution Analysis of Compressive Data Acquisition |
R A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Rizk, Charbel |
A.4.3 |
|
Compressive Nonlinear Frequency Modulated CW LIDAR |
S A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Sankur, Bulent |
A.6.1 |
|
Through the wall Target Detection/Monitoring from Compressively Sensed Signals via Structural Sparsity |
Scofield, Adam |
A.4.5 |
|
Compressive sensing analog-to-information system based on optical speckle |
Sefler, George |
A.4.5 |
|
Compressive sensing analog-to-information system based on optical speckle |
Shaw, Justin |
A.4.5 |
|
Compressive sensing analog-to-information system based on optical speckle |
Stankovic, Isidora |
A.2.4 |
|
Reconstruction of Rigid Body with Noncompensated Acceleration After Micro-Doppler Removal |
A.2.1 |
|
Analysis of Initial Estimate Noise in the Sparse Randomly Sampled ISAR Signals |
Stankovic, Ljubisa |
A.2.4 |
|
Reconstruction of Rigid Body with Noncompensated Acceleration After Micro-Doppler Removal |
A.2.1 |
|
Analysis of Initial Estimate Noise in the Sparse Randomly Sampled ISAR Signals |
Sugavanam, Nithin |
A.3.1 |
|
Recovery Guarantees for Slow Time Phase Coded Waveforms in MIMO radar |
T A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Tian, Jing |
A.2.2 |
|
Wide Angle SAR imaging based on LS-CS-Residual |
Turk, Ahmet |
A.6.1 |
|
Through the wall Target Detection/Monitoring from Compressively Sensed Signals via Structural Sparsity |
U A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Ullo, Silvia |
A.6.5 |
|
High Resolution Range Profiling for Stepped Radar via Sparsity Exploitation |
V A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Valley, George |
A.4.5 |
|
Compressive sensing analog-to-information system based on optical speckle |
van Rossum, Wim |
A.3.1 |
|
Recovery Guarantees for Slow Time Phase Coded Waveforms in MIMO radar |
Vandendorpe, Luc |
A.7.6 |
|
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing |
A.3.3 |
|
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing |
Vogl, Christian |
A.5.2 |
|
Sparse Damage Imaging for Guided-Wave Structural Health Monitoring |
W A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Wang, Ling |
A.7.7 |
|
A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks |
A.2.3 |
|
A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks |
Wang, Xiqin |
A.3.2 |
|
Multiple Carrier Agile Radar via Compressed Sensing |
Wei, Zhonghao |
A.1.1 |
|
Comparison of raw data based and complex image based sparse SAR imaging methods |
A.2.2 |
|
Wide Angle SAR imaging based on LS-CS-Residual |
Wu, Chenyang |
A.1.1 |
|
Comparison of raw data based and complex image based sparse SAR imaging methods |
Wu, YiRong |
A.1.1 |
|
Comparison of raw data based and complex image based sparse SAR imaging methods |
X A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Xu, Chunlei |
A.7.6 |
|
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing |
A.3.3 |
|
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing |
Xu, Zhilin |
A.1.1 |
|
Comparison of raw data based and complex image based sparse SAR imaging methods |
Y A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Yamac, Mehmet |
A.6.1 |
|
Through the wall Target Detection/Monitoring from Compressively Sensed Signals via Structural Sparsity |
Yousef-Zamanian, Abolfazl |
A.7.3 |
|
A low cost non-imaging system for standoff threat detection |
Z A B C D E F G H I J K L M N O P R S T U V W X Y Z |
Zhang, Bingchen |
A.2.2 |
|
Wide Angle SAR imaging based on LS-CS-Residual |
A.1.1 |
|
Comparison of raw data based and complex image based sparse SAR imaging methods |
Zidek, Karel |
A.4.2 |
|
Evaluation of using coded aperture imaging in the mid- and far-infrared region |
A.7.2 |
|
Focal Plane Speckle Patterns for Compressive Microscopic Imaging in Laser Spectroscopy |