This leads to the curse of dimensionality . In order to combat this problem , some researchers suggest using prior knowledge to enhance feature selection for supervised learning algorithms . We propose an enhancement to the LASSO , a shrinkage and selection technique that induces parameter sparsity by penalizing a modelâs objective function . Abstract Data mining techniques are traditionally divided into two distinct disciplines depending on the task to be performed by the algorithm : supervised learning and unsupervised learning . While the former aims at making accurate predictions after deeming an underlying structure in data — which requires the presence of a teacher during the learning phase — the latter aims at discovering regular-occurring patterns beneath the data without making any a priori assumptions concerning their underlying structure . We conducted experiments to support this claim by collecting data sets from diff?erent geographical environments and then comparing classification accuracies . Our results show that our self-supervised learning approach is able to outperform state of the art supervised visual learning techniques . While deep learning has been incredibly successful in modeling tasks with large , carefully curated labeled datasets , its application to problems with limited labeled data remains a challenge . The aim of the present work is to improve the label efficiency of large neural networks operating on audio data through a combination of multitask learning and self-supervised learning on unlabeled data . We trained an end-to-end audio feature extractor based on WaveNet that feeds into simple , yet versatile task-specific neural networks . This paper presented an unsupervised self-learning method to detect shadows without any priori scene information or threshold parameters . According to the statistical techniques , the method learned scene shadow characteristics actively , so as to achieve the purpose of shadow detection . According to the non-stationary and non-liner characteristics of bearing vibration signals , a large number of approaches for feature extraction and fault classification have been developed . An effective unsupervised self-learning method is proposed to achieve the complicated fault diagnosis of rolling bearing in this paper , which uses stacked denoising autoencoder ( SDA ) to learn useful feature representations and improve fault pattern classification robustness by corrupting the input data , meanwhile employs “ dropout ” to prevent the overfitting of hidden units . Finally the high-level feature representations extracted are set as the inputs of softmax classifier to achieve fault classification . We therefore propose Cross - View Training ( CVT ) , a semi - supervised learning algorithm that improves the representations of a Bi - LSTM sentence encoder using a mix of labeled and unlabeled data . On labeled examples , standard supervised learning is used . On unlabeled examples , CVT teaches auxiliary prediction modules that see restricted views of the input ( e.g. , only part of a sentence ) to match the predictions of the full model seeing the whole input . Momentum methods such as Polyak 's heavy ball ( HB ) method , Nesterov 's accelerated gradient ( AG ) as well as accelerated projected gradient ( APG ) method have been commonly used in machine learning practice , but their performance is quite sensitive to noise in the gradients . We study these methods under a first-order stochastic oracle model where noisy estimates of the gradients are available . Unexpected collisions may occur anywhere along the manipulator structure . Their fast detection is realized using our previous momentum-based method , which does not require any external sensing . The reaction torque applied to the joints reduces the effective robot inertia seen at the contact and lets the robot safely move away from the collision area . [ 5 ] In this paper , we investigate the effects of these methods on the performance , of federated training of artificial neural networks . 1.2 Momentum techniques Momentum based techniques [ 6 ] use a velocity term during learning , that is an exponentially weighted average over the gradients of the past . v← βv−∇w ( 1 m m ∑ i=1 fi(w ) ) With respect to a kind of missile with morphing wings , six-degree-of-freedom dynamics mathematical model including morphing wing dynamic was established using momentum principle , and guidance system model was presented , at the same time the whole attitude control system for missile with morphing wings was designed based on variable structure control theory . In order to prove the correctness of the validity of the altitude control strategy , under the environment of MATLAB6.5/ SIMULINK , the six-degree-of-freedom digital simulation system was studied and developed for the kind of missile with morphing wings . This paper deals with solutions of the Saint-Venant equations as presented by Cunge , Holly and Verwey [ 2 ] . These equations rely on momentum analysis as discussed by the authors , and have been widely used as the basis for unsteady flow modelling . However steady flow analysis in channels has historically followed a different route , starting from Bernoulli analysis . ABSTRACT Mechanism Technique is a lesson taught in many universities especially faculty of mechancial engineering . When viewed from the contents of this course , the basic mechanisms used in the design and manufacture , these assemblies include operational features , speed , position and momentum analysis . The visual analysis and expressions have improved the quality of education . The automatic monitoring system with the nursing function is a device installed at a fixed point and is applicable to monitoring nursed people such as babies or pregnant women by preference within the range of a camera . The automatic monitoring system is characterized in that the automatic monitoring system comprises an image processor for receiving images shot by the camera device and processing the digital images ; the image processor comprises an abnormality unit , a momentum analysis unit , a human face identification unit and a life information processing area , wherein the abnormality unit is used for determining whether a monitored site corresponding to the shot images is normal ; the human face identification unit can be used for identifying human faces , specific human faces and relevant data are stored in the human face identification unit , and some specific human faces are set as caution persons by the human face identification unit ; the life information processing area comprises logic blocks corresponding to at least one biosensing assembly and at least one environment sensing assembly . On the basis of the processing of the image monitor , the biosensing assemblies , the environment sensing assemblies and software , the automatic monitoring system can be used for monitoring nursed people instead of a lot of manual monitoring operation . However , since movements in binary search spaces and continuous search spaces are different , it is not appropriate to apply directly the momentum concept of CPSO to BPSO . This paper introduces a new momentum concept to BPSO , which leads to a novel BPSO algorithm , named SBPSO . SBPSO is compared with a recent BPSO algorithm , named PBPSO , in two well-known binary problems : knapsack and feature selection . This paper proposes a Back Propagation ( BP ) neural network with momentum enhancement aiming to achieving the smooth convergence for aggregate volumetric estimation purpose . Network inputs are first selected by optically measuring the eight geometry-related parameters from the given particle image . At each level , the outputs of multiple networks are fused for robust and accurate estimation . It used to realize edge detection task it takes the advantage of momentum features extraction , it can process any input image of any size with no more training required , the results are very promising when compared to both classical methods and other ANN based methods . Furthermore , we employ CNNs to estimate the scale through the accurate localization of some key points . This paper presents a momentum modification of two RLS algoritms : momentum RLS and UD momentum RLS , each in classical and linear version . All methods are tested on two standart benchmarks . A novel low-pass filter ( LPF ) with harmonic-suppressed , sharp-rejection slop , wide and deep stop-band attenuation properties is proposed , which is composed of coplanar waveguide ( CPW ) short-circuited stubs and microstrip shunt open-end stubs as series and shunt components in the pass-band . Based on this composite microstrip/CPW structure , the low-pass filter with excellent electrical performances is simulated and optimized by the momentum of method ( MoM ) . The obtained frequency responses show that the harmonics are not only suppressed effectively , for most attenuation is more than 40 dB from 4 GHz to 16 GHz , but the transition response exhibiting sharp slop varieties from -3 dB to -70 dB , corresponding to frequency range from 3.8 GHz to 4.5 GHz . Training details . The network is trained using stochastic gradient descend with momentum , implemented in MatConvNet [ reference ] . On the latent representation of size , we impose a Gaussian distribution with standard deviation of . We used the gradient descent with momentum algorithm for optimizing all the cost functions of the network . For the PixelCNN reconstruction cost , we used the learning rate of 0.001 and the momentum value of 0.9 . In practice , we implement this approach by creating a dataset of mini - batches of smaller brain image patches , paired with the corresponding center segmentation label as the target . To further improve optimization , we implemented a so - called momentum strategy which has been shown successful in the past . The idea of momentum is to use a temporally averaged gradient in order to damp the optimization velocity : where stands for the CNNs parameters at iteration , the gradient of the loss function at , V is the integrated velocity initialized at zero , is the learning rate , and the momentum coefficient . Then a modified sub-aperture and sub-band nonlinear chirp scaling ( NCS ) algorithm is used to divide the SAR signal processing into pipelined parallel steps . To improve the image quality , motion compensation ( MoCo ) and radio frequency interference ( RFI ) suppression are introduced in real time processing . Using a distributed-memory structure , the design and implementation based on digital signal processors ( DSPs ) are proposed finally . In this note , we verify the effectiveness of two of SimCLR 's design improvements by implementing them in the MoCo framework . With simple modifications to MoCo --- namely , using an MLP projection head and more data augmentation --- we establish stronger baselines that outperform SimCLR and do not require large training batches . We hope this will make state-of-the-art unsupervised learning research more accessible . Our method is able to reanimate patient data virtually . In combination with the MVFs it serves as a digital ground truth and provides an improved framework to assess MoCo algorithms . In one-step MoCo strategy , the second-order MoCo used for compensating the range-variant phase error is implemented before the range cell migration correction ( RCMC ) processing , instead of after the RCMC processing . As compared to the traditional two-step MoCo strategy , the one-step MoCo strategy has better performance on compensating the high frequency motion error . This paper deducts the proposed MoCo method in detail , and proves its validity with simulation experiment . This letter presents a comparison between three Fourier-based motion compensation ( MoCo ) algorithms for airborne synthetic aperture radar ( SAR ) systems . These algorithms circumvent the limitations of conventional MoCo , namely the assumption of a reference height and the beam-center approximation . An extended omega-K ( EOK ) algorithm was proposed in which the two-step MoCo could be integrated . However , the second-order MoCo is inaccurate due to the wavelength-dependent scaling of the azimuth signal after the modified Stolt mapping . Although a third-step MoCo was introduced to make up for this drawback , it is limited in dealing with high-frequency motion errors because it is applied block-wise in the time domain . The approach entails correcting the navigation data of each slave image of a one-to-many interferometric network with a cumulative correction . The summation is over the results of a precursory application of a general data-driven residual MoCo algorithm ( e.g. , multisquint ) to interferometric pairs for which the impact of interferometric decorrelation is marginal ( i.e. , small temporal and/or spatial baselines ) . Compared to other RME correction strategies , the main appeal of the proposed approach lies in the simplicity of its implementation . Although we demonstrated this approach to be superior to 3D DWC and 3D MLT , we did not account for artifacts induced by motion . The aim of this study was therefore to propose a motion compensation ( MoCo ) method to further improve the performance of MPT beamforming . Various motion compensation ( MoCo ) procedures have been used in the airborne DInSAR processing to acquire reliable deformation mapping . In this paper , we present the use of time-domain backprojection ( BP ) algorithm for SAR focusing in an airborne DInSAR survey : No MoCo procedure is needed because the BP algorithm is inherently able to compensate for platform motion . In this study , we present the results of a pilot study aimed at demonstrating the feasibility of deformation mapping with an airborne SAR system based on the monitoring of the Slumgullion landslide in Colorado , USA between July 3 and 10 of 2015 . Purpose : In order to mitigate motion‐induced artifacts , several motion compensation ( MoCo ) methods have been developed , which are either able to ( a ) compensate for severe artifacts , but utilize the data for the reconstruction of several cardiac phases , or ( b ) improve image quality of a single reconstruction with only moderate motion artifacts . We propose a method combining both benefits : dose efficiency by utilizing only the data needed for a single short‐scan reconstruction while still being able to compensate for severe artifacts . We propose a family of contrastive learning methods , CLOCS , that encourages representations across time , leads , and patients to be similar to one another . We show that CLOCS consistently outperforms the state-of-the-art approach , SimCLR , on both linear evaluation and fine-tuning downstream tasks . We also show that CLOCS achieves strong generalization performance with only 25 % of labelled training data . Contrastive unsupervised learning has recently shown encouraging progress , e.g. , in Momentum Contrast ( MoCo ) and SimCLR . In this note , we verify the effectiveness of two of SimCLR 's design improvements by implementing them in the MoCo framework . With simple modifications to MoCo --- namely , using an MLP projection head and more data augmentation --- we establish stronger baselines that outperform SimCLR and do not require large training batches .
This leads to the curse of dimensionality . In order to combat this problem , some researchers suggest using prior knowledge to enhance feature selection for supervised learning algorithms . We propose an enhancement to the LASSO , a shrinkage and selection technique that induces parameter sparsity by penalizing a modelâs objective function .
Abstract Data mining techniques are traditionally divided into two distinct disciplines depending on the task to be performed by the algorithm : supervised learning and unsupervised learning . While the former aims at making accurate predictions after deeming an underlying structure in data — which requires the presence of a teacher during the learning phase — the latter aims at discovering regular-occurring patterns beneath the data without making any a priori assumptions concerning their underlying structure .
We conducted experiments to support this claim by collecting data sets from diff?erent geographical environments and then comparing classification accuracies . Our results show that our self-supervised learning approach is able to outperform state of the art supervised visual learning techniques .
While deep learning has been incredibly successful in modeling tasks with large , carefully curated labeled datasets , its application to problems with limited labeled data remains a challenge . The aim of the present work is to improve the label efficiency of large neural networks operating on audio data through a combination of multitask learning and self-supervised learning on unlabeled data . We trained an end-to-end audio feature extractor based on WaveNet that feeds into simple , yet versatile task-specific neural networks .
This paper presented an unsupervised self-learning method to detect shadows without any priori scene information or threshold parameters . According to the statistical techniques , the method learned scene shadow characteristics actively , so as to achieve the purpose of shadow detection .
According to the non-stationary and non-liner characteristics of bearing vibration signals , a large number of approaches for feature extraction and fault classification have been developed . An effective unsupervised self-learning method is proposed to achieve the complicated fault diagnosis of rolling bearing in this paper , which uses stacked denoising autoencoder ( SDA ) to learn useful feature representations and improve fault pattern classification robustness by corrupting the input data , meanwhile employs “ dropout ” to prevent the overfitting of hidden units . Finally the high-level feature representations extracted are set as the inputs of softmax classifier to achieve fault classification .
We therefore propose Cross - View Training ( CVT ) , a semi - supervised learning algorithm that improves the representations of a Bi - LSTM sentence encoder using a mix of labeled and unlabeled data . On labeled examples , standard supervised learning is used . On unlabeled examples , CVT teaches auxiliary prediction modules that see restricted views of the input ( e.g. , only part of a sentence ) to match the predictions of the full model seeing the whole input .
Momentum methods such as Polyak 's heavy ball ( HB ) method , Nesterov 's accelerated gradient ( AG ) as well as accelerated projected gradient ( APG ) method have been commonly used in machine learning practice , but their performance is quite sensitive to noise in the gradients . We study these methods under a first-order stochastic oracle model where noisy estimates of the gradients are available .
Unexpected collisions may occur anywhere along the manipulator structure . Their fast detection is realized using our previous momentum-based method , which does not require any external sensing . The reaction torque applied to the joints reduces the effective robot inertia seen at the contact and lets the robot safely move away from the collision area .
[ 5 ] In this paper , we investigate the effects of these methods on the performance , of federated training of artificial neural networks . 1.2 Momentum techniques Momentum based techniques [ 6 ] use a velocity term during learning , that is an exponentially weighted average over the gradients of the past . v← βv−∇w ( 1 m m ∑ i=1 fi(w ) )
With respect to a kind of missile with morphing wings , six-degree-of-freedom dynamics mathematical model including morphing wing dynamic was established using momentum principle , and guidance system model was presented , at the same time the whole attitude control system for missile with morphing wings was designed based on variable structure control theory . In order to prove the correctness of the validity of the altitude control strategy , under the environment of MATLAB6.5/ SIMULINK , the six-degree-of-freedom digital simulation system was studied and developed for the kind of missile with morphing wings .
This paper deals with solutions of the Saint-Venant equations as presented by Cunge , Holly and Verwey [ 2 ] . These equations rely on momentum analysis as discussed by the authors , and have been widely used as the basis for unsteady flow modelling . However steady flow analysis in channels has historically followed a different route , starting from Bernoulli analysis .
ABSTRACT Mechanism Technique is a lesson taught in many universities especially faculty of mechancial engineering . When viewed from the contents of this course , the basic mechanisms used in the design and manufacture , these assemblies include operational features , speed , position and momentum analysis . The visual analysis and expressions have improved the quality of education .
The automatic monitoring system with the nursing function is a device installed at a fixed point and is applicable to monitoring nursed people such as babies or pregnant women by preference within the range of a camera . The automatic monitoring system is characterized in that the automatic monitoring system comprises an image processor for receiving images shot by the camera device and processing the digital images ; the image processor comprises an abnormality unit , a momentum analysis unit , a human face identification unit and a life information processing area , wherein the abnormality unit is used for determining whether a monitored site corresponding to the shot images is normal ; the human face identification unit can be used for identifying human faces , specific human faces and relevant data are stored in the human face identification unit , and some specific human faces are set as caution persons by the human face identification unit ; the life information processing area comprises logic blocks corresponding to at least one biosensing assembly and at least one environment sensing assembly . On the basis of the processing of the image monitor , the biosensing assemblies , the environment sensing assemblies and software , the automatic monitoring system can be used for monitoring nursed people instead of a lot of manual monitoring operation .
However , since movements in binary search spaces and continuous search spaces are different , it is not appropriate to apply directly the momentum concept of CPSO to BPSO . This paper introduces a new momentum concept to BPSO , which leads to a novel BPSO algorithm , named SBPSO . SBPSO is compared with a recent BPSO algorithm , named PBPSO , in two well-known binary problems : knapsack and feature selection .
This paper proposes a Back Propagation ( BP ) neural network with momentum enhancement aiming to achieving the smooth convergence for aggregate volumetric estimation purpose . Network inputs are first selected by optically measuring the eight geometry-related parameters from the given particle image .
At each level , the outputs of multiple networks are fused for robust and accurate estimation . It used to realize edge detection task it takes the advantage of momentum features extraction , it can process any input image of any size with no more training required , the results are very promising when compared to both classical methods and other ANN based methods . Furthermore , we employ CNNs to estimate the scale through the accurate localization of some key points .
This paper presents a momentum modification of two RLS algoritms : momentum RLS and UD momentum RLS , each in classical and linear version . All methods are tested on two standart benchmarks .
A novel low-pass filter ( LPF ) with harmonic-suppressed , sharp-rejection slop , wide and deep stop-band attenuation properties is proposed , which is composed of coplanar waveguide ( CPW ) short-circuited stubs and microstrip shunt open-end stubs as series and shunt components in the pass-band . Based on this composite microstrip/CPW structure , the low-pass filter with excellent electrical performances is simulated and optimized by the momentum of method ( MoM ) . The obtained frequency responses show that the harmonics are not only suppressed effectively , for most attenuation is more than 40 dB from 4 GHz to 16 GHz , but the transition response exhibiting sharp slop varieties from -3 dB to -70 dB , corresponding to frequency range from 3.8 GHz to 4.5 GHz .
Training details . The network is trained using stochastic gradient descend with momentum , implemented in MatConvNet [ reference ] .
On the latent representation of size , we impose a Gaussian distribution with standard deviation of . We used the gradient descent with momentum algorithm for optimizing all the cost functions of the network . For the PixelCNN reconstruction cost , we used the learning rate of 0.001 and the momentum value of 0.9 .
In practice , we implement this approach by creating a dataset of mini - batches of smaller brain image patches , paired with the corresponding center segmentation label as the target . To further improve optimization , we implemented a so - called momentum strategy which has been shown successful in the past . The idea of momentum is to use a temporally averaged gradient in order to damp the optimization velocity : where stands for the CNNs parameters at iteration , the gradient of the loss function at , V is the integrated velocity initialized at zero , is the learning rate , and the momentum coefficient .
Then a modified sub-aperture and sub-band nonlinear chirp scaling ( NCS ) algorithm is used to divide the SAR signal processing into pipelined parallel steps . To improve the image quality , motion compensation ( MoCo ) and radio frequency interference ( RFI ) suppression are introduced in real time processing . Using a distributed-memory structure , the design and implementation based on digital signal processors ( DSPs ) are proposed finally .
In this note , we verify the effectiveness of two of SimCLR 's design improvements by implementing them in the MoCo framework . With simple modifications to MoCo --- namely , using an MLP projection head and more data augmentation --- we establish stronger baselines that outperform SimCLR and do not require large training batches . We hope this will make state-of-the-art unsupervised learning research more accessible .
Our method is able to reanimate patient data virtually . In combination with the MVFs it serves as a digital ground truth and provides an improved framework to assess MoCo algorithms .
In one-step MoCo strategy , the second-order MoCo used for compensating the range-variant phase error is implemented before the range cell migration correction ( RCMC ) processing , instead of after the RCMC processing . As compared to the traditional two-step MoCo strategy , the one-step MoCo strategy has better performance on compensating the high frequency motion error . This paper deducts the proposed MoCo method in detail , and proves its validity with simulation experiment .
This letter presents a comparison between three Fourier-based motion compensation ( MoCo ) algorithms for airborne synthetic aperture radar ( SAR ) systems . These algorithms circumvent the limitations of conventional MoCo , namely the assumption of a reference height and the beam-center approximation .
An extended omega-K ( EOK ) algorithm was proposed in which the two-step MoCo could be integrated . However , the second-order MoCo is inaccurate due to the wavelength-dependent scaling of the azimuth signal after the modified Stolt mapping . Although a third-step MoCo was introduced to make up for this drawback , it is limited in dealing with high-frequency motion errors because it is applied block-wise in the time domain .
The approach entails correcting the navigation data of each slave image of a one-to-many interferometric network with a cumulative correction . The summation is over the results of a precursory application of a general data-driven residual MoCo algorithm ( e.g. , multisquint ) to interferometric pairs for which the impact of interferometric decorrelation is marginal ( i.e. , small temporal and/or spatial baselines ) . Compared to other RME correction strategies , the main appeal of the proposed approach lies in the simplicity of its implementation .
Although we demonstrated this approach to be superior to 3D DWC and 3D MLT , we did not account for artifacts induced by motion . The aim of this study was therefore to propose a motion compensation ( MoCo ) method to further improve the performance of MPT beamforming .
Various motion compensation ( MoCo ) procedures have been used in the airborne DInSAR processing to acquire reliable deformation mapping . In this paper , we present the use of time-domain backprojection ( BP ) algorithm for SAR focusing in an airborne DInSAR survey : No MoCo procedure is needed because the BP algorithm is inherently able to compensate for platform motion . In this study , we present the results of a pilot study aimed at demonstrating the feasibility of deformation mapping with an airborne SAR system based on the monitoring of the Slumgullion landslide in Colorado , USA between July 3 and 10 of 2015 .
Purpose : In order to mitigate motion‐induced artifacts , several motion compensation ( MoCo ) methods have been developed , which are either able to ( a ) compensate for severe artifacts , but utilize the data for the reconstruction of several cardiac phases , or ( b ) improve image quality of a single reconstruction with only moderate motion artifacts . We propose a method combining both benefits : dose efficiency by utilizing only the data needed for a single short‐scan reconstruction while still being able to compensate for severe artifacts .
We propose a family of contrastive learning methods , CLOCS , that encourages representations across time , leads , and patients to be similar to one another . We show that CLOCS consistently outperforms the state-of-the-art approach , SimCLR , on both linear evaluation and fine-tuning downstream tasks . We also show that CLOCS achieves strong generalization performance with only 25 % of labelled training data .
Contrastive unsupervised learning has recently shown encouraging progress , e.g. , in Momentum Contrast ( MoCo ) and SimCLR . In this note , we verify the effectiveness of two of SimCLR 's design improvements by implementing them in the MoCo framework . With simple modifications to MoCo --- namely , using an MLP projection head and more data augmentation --- we establish stronger baselines that outperform SimCLR and do not require large training batches .
Improved Feature Selection by Incorporating Gene Similarity Into the LASSO Improving data partition schemes in Smart Grids via clustering data streams Self-Supervised Learning on Mobile Robots Using Acoustics, Vibration, and Visual Models to Build Rich Semantic Terrain Maps Label-efficient audio classification through multitask learning and self-supervision Self-learning method for shadow detection A deep learning method using SDA combined with dropout for bearing fault diagnosis Semi-Supervised Sequence Modeling with Cross-View Training Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances Exploiting robot redundancy in collision detection and reaction Optimization in Federated Learning Modeling and Simulation Research of Missile with Morphing Wings Implicit high-resolution methods for modelling one-dimensional open channel flow PREPARATION AND APPLICATION MODULE OF DISTANCE EDUCATION FOR MECHANISMS COURSE Automatic monitoring system with nursing function A Novel Binary Particle Swarm Optimization Algorithm and Its Applications on Knapsack and Feature Selection Problems Aggregate volumetric estimation based on PCA and momentum-enhanced BP neural network Convolutional Neural Network for Edge Detection in SAR Grayscale Images Momentum Modification of the RLS Algorithms Design of a wide stop-band LPF using microstrip/CPW composite structure GhostVLAD for set-based face recognition PixelGAN Autoencoders Brain tumor segmentation with Deep Neural Networks Ultra wide band synthetic aperture radar real time processing with a subaperture nonlinear chirp scaling algorithm Improved Baselines with Momentum Contrastive Learning Reanimating patients: cardio-respiratory CT and MR motion phantoms based on clinical CT patient data Motion Compensation Method of Airborne Ultra-WideBand SAR Comparison of Topography- and Aperture-Dependent Motion Compensation Algorithms for Airborne SAR A motion compensation approach integrated in the omega-K algorithm for airborne SAR A Motion Compensation Strategy for Airborne Repeat-Pass SAR Data Motion correction for multi-plane-transmit beamforming: A simulation study Airborne DInSAR Results Using Time-Domain Backprojection Algorithm: A Case Study Over the Slumgullion Landslide in Colorado With Validation Using Spaceborne SAR, Airborne LiDAR, and Ground-Based Observations Motion compensation in the region of the coronary arteries based on partial angle reconstructions from short‐scan CT data CLOCS: Contrastive Learning of Cardiac Signals Improved Baselines with Momentum Contrastive Learning
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ICDM Workshops EMNLP ICML IROS ITAT ICAISC ACCV NIPS Medical Image Anal.
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