Depending on the observation time and the sampling frequency used in the experiments we may be interested in analyzing the transient or the stationary stage of the wave propagation in impact echo tests . Usually with high resolution in time , analyzes of wave propagation velocity can give useful information , for instance , to build a tomography of a material inspected from different locations . Considering the sampling frequency that we used in the experiments ( 100 kHz ) , a feature extracted from the signal as the wave propagation velocity is not accurate enough to discern between homogeneous and different kind of defective materials . In this paper we review the tomographic reconstruction algorithm and present the implementation of multiple-angle tomography . In addition to the projecdon phase correction required for multiple-frequency tomography [ 1 ] , multiple-angle tomography also requires registration and alignment of the tomographic projections . By addressing the projection phase error and alignment problems , we have successfully implemented the STAM for multiple-angle tomography , and experimental reconstructions of a two-layer specimen are presented to demonstrate the feasibility and capability of the STAM . One challenging problem in network tomography is reconstructing available bandwidth along all links during multiple source / multiple destination transmissions . The traditional measurement procedures used for bandwidth tomography are extremely time consuming . We propose a novel solution to this problem . One challenging problem in network tomography is reconstructing available bandwidth along all links during multiple source / multiple destination transmissions . The traditional measurement procedures used for bandwidth tomography are extremely time consuming . We propose a novel solution to this problem . Abstract We investigate the reconstruction problem of limited angle tomography . Such problems arise naturally in applications like digital breast tomosynthesis , dental tomography , electron microscopy , etc . Since the acquired tomographic data is highly incomplete , the reconstruction problem is severely ill-posed and the traditional reconstruction methods , e.g. filtered backprojection ( FBP ) , do not perform well in such situations . Computed Tomography ( CT ) is commonly used in clinical procedures and limited angle tomography reconstruction has important applications in diagnostic CT , breast tomography , dental tomography , etc . However , CT images reconstructed from limited angle acquisitions suffer from severe artifacts due to incomplete sinogram data . Historically , region growing , clustering , and edge tracing have been used and while significant steps have been made in the first two , research into automatic , recursive , edge following has not kept pace . In this thesis , a new , advanced , edge tracing strategy based on recursive , target tracking algorithms and suitable for use in segmenting magnetic resonance ( MR ) and computed tomography ( CT ) medical images is presented . This work represents the first application of recursive , target-tracking-based , edge tracing to the segmentation of MR and CT images of the head . A 32-electrode data collection system for electrical impedance tomography ( ETT ) is presented . In this system , the demodulator is a multiplexed sample and hold ( S&H ) circuit followed by a voltage difference stage . In order to eliminate the affects of beamhardening which causes the cuplike artifacts in ICT(Industrial Computed Tomography ) reconstruction , a method of beamhardening correction is introduced . This method is based on Beer 's law . Abstract Motivation Since 2017 , an increasing amount of attention has been paid to the supervised deep learning-based macromolecule in situ structural classification ( i.e. subtomogram classification ) in cellular electron cryo-tomography ( CECT ) due to the substantially higher scalability of deep learning . However , the success of such supervised approach relies heavily on the availability of large amounts of labeled training data . In noninvasive tomography , subcutaneous abnormal tissue can change the absorption coefficient of light . This paper explores diffusion optical tomography . Recent events showed that dose administration has become a major concern regarding clinical computed tomography ( CT ) examinations . A simple solution to lower this dose is the use of more appropriate CT image reconstruction methods . Abstract The combination of positron emission tomography ( PET ) with magnetic resonance ( MR ) imaging opens the way to more accurate diagnosis and improved patient management . At present , the data acquired by PET-MR scanners are essentially processed separately , but the opportunity to improve accuracy of the tomographic reconstruction via synergy of the two imaging techniques is an active area of research . In this paper , we present Release 2.1.0 of the CCP-PETMR Synergistic Image Reconstruction Framework ( SIRF ) software suite , providing an open-source software platform for efficient implementation and validation of novel reconstruction algorithms . The projection orientation information is unknown . The scope of this paper is the tomographic reconstruction of the observed object in the ab-initio case where the volume has to be estimated only from a raw projection dataset . A new approach based on a parametric model of the volume is presented . We consider detection of a nodule signal profile in noisy images meant to roughly simulate the statistical properties of tomographic image reconstructions in nuclear medicine . The images have two sources of variability arising from quantum noise from the imaging process and anatomical variability in the ensemble of objects being imaged . Magnification digital breast tomosynthesis was also performed to test the calibration method further . Tomographic image reconstructions successfully demonstrated isotropic super-resolution and magnified super-resolution . In this paper , we develop a new approach to tomographic reconstruction problems based on geometric curve evolution techniques . We use a small set of texture coefficients to represent the object and background inhomogeneities and a contour to represent the boundary of multiple connected or unconnected objects . Discrete tomography refers to tomographic reconstruction of images that are known to contain only a few intensity levels . We propose a new reconstruction technique for discrete tomography that uses a relaxed partial segmentation and a refinement update in each iteration . I. Theoretical analysis and fundamental limitations . The authors consider the tomographic reconstruction of objects with spatially localized temporal variation , such as a thorax cross section with a beating heart . The conventional scan format , in which projections are taken progressively around the object , requires high and sometimes infeasible scan rates to avoid motion artifacts in the reconstructed images . Computed tomography ( CT ) has great impact in many fields such as medical applications , industrial inspection , etc ... Low dose constraints and Limited projection are common problems in a variety of tomographic reconstruction examples which lead to wrong data . In this work , we propose a method of CT reconstruction based on the simultaneous iterative reconstruction techniques SIRT improved by imposing positivity constraint in the total variation ( TVcim-p ) . A preclinical Single Photon Emission Computed Tomography ( SPECT ) system design is presented , that will be used in rabbit myocardial perfusion and related cardiac studies . The system includes digital-waveform capture of all PMT signals , which allows for optimal maximum-likelihood estimation in the gamma-ray event parameter estimation and tomographic reconstruction processes . In a typical gamma ray camera , only the integrated signal of a gamma ray event is recorded . However , information missing in electron tomography due to limited angular sampling is still the bottleneck in high-resolution electron tomography application . Here , based on the understanding of smooth nature of biological specimen , we present a new iterative image reconstruction algorithm , FIRT ( filtered iterative reconstruction technique ) for electron tomography by combining the algebra reconstruction technique ( ART ) and the nonlinear diffusion ( ND ) filter technique . Using both simulated and experimental data , in comparison to ART and weight back projection method , we proved that FIRT could generate a better reconstruction with reduced ray artifacts and significant improved correlation with the ground truth and partially restore the information at the non-sampled angular region , which was proved by investigating the 90 ° re-projection and by the cross-validation method . Electrical Tomography ( ET ) is an advanced visualization technique , which can reconstruct all targets in an investigated field based on boundary measurements . Since the spatial resolution in the ET process can be greatly affected by the selected similarity norm , different norms may result in different ET time and spatial resolutions . I confidently expect both of these limitations to yield to energetic attack over the next two decades . The recent advent of super-resolution microscopy and electron microscopic tomography are important steps in bridging the resolution gaps between cell and structural biology . These and other developments have led to a new approach , sometimes called cellular structural biology , that integrates across scales to move toward a detailed , structure-based understanding of cellular organization and function . It has become a powerful tool for elucidating 3D architectures of biological samples at resolution of about 4 - 6 nm and is the only method suitable for investigating polymorphic structures such as organelles , cells and tissues . However , in addition to the relatively low resolution , electron tomograms inevitably suffer from a low signal-to-noise ratio and some data-collection artifacts . These factors significantly hamper development of algorithms for reliable detection of structural features , which poses a severe barrier to progress in the field . Electron density maps at moderate resolution are often difficult to interpret due to the lack of recognizable features . This is especially true for electron tomograms that suffer in addition to the resolution limitation from low signal-to-noise ratios . Reliable segmentation of such maps into smaller , manageable units can greatly facilitate interpretation . Tomo3D 2.0 allows huge reconstructions to be calculated in standard computers in a matter of minutes . Thus , it will be a valuable tool for electron tomography studies with increasing resolution needs . Ice embedment has brought a breakthrough in the degree of preservation of specimens under close-to-native conditions . The current challenge is to push the resolution of electron tomographic imaging to a point where macromolecular signatures can be recognized within the cellular context . We show first progress toward this goal by examples in two areas of application : the structure of the muscle triad junction and the architecture and fine structure of mitochondria . With the aim of addressing the issue of sample damage during electron tomography data acquisition , we propose a number of new reconstruction strategies based on subsampling ( which uses only a subset of a full image ) and inpainting ( recovery of a full image from subsampled one ) . We point out that the total-variation ( TV ) inpainting model commonly used to inpaint subsampled images may be inappropriate for 2D projection images of typical TEM specimens . In electron tomography , noise attenuation is critical for high-resolution structural analysis . Though numerous denoising methods have been introduced into electron tomography ( ET ) scope , attenuating noise as well as preserving signal details remains a challenge . Here , we propose an adaptive 3-D transform-Domain collaborative filtering ( A3DTCF ) for denoising of ET data . The main motivation for the proposed work is the development of mathematically sound and computationally robust algorithms for the reduction of noise and enhancement of structural information in mitochondrial images . Our research efforts will initially focus on the understanding of the sources and mechanisms which introduce noise ( image degradation ) in electron tomography process . Correct modeling of the degradation , will allow us to design effective , correct , algorithms for the removal of the noise without negatively impacting fine-detail structural information , and will allow for a posteriori reconstruction of more accurate three-dimensional mitochondrion images . Such a set is called a “ symmetric , informationally complete ” POVM ( SIC – POVM ) and is equivalent to a set of d2 equiangular lines in Cd . SIC – POVMs are relevant for quantum state tomography , quantum cryptography , and foundational issues in quantum mechanics . We construct SIC – POVMs in dimensions two , three , and four . We then develop a novel purification protocol which enhances the degree of entanglement in a noisy channel that includes combined errors of bit-flip , phase-flip and phase-change error . We perform quantum state tomography to verify the entanglement swapping between the two pairs of qubits and working of the purification protocol . These experimental requirements are compounded by the complexity of processing the collected data , which can take several orders of magnitude longer than the experiment itself . In this paper we propose an alternative self-guided algorithm for quantum process tomography , tuned for the task of finding an unknown unitary process . Our algorithm is a fully automated and adaptive process characterization technique . However , as these processors grow in size , standard process tomography becomes an almost impossible task . Here , we present an approach for efficient quantum process tomography that uses a physically motivated ansatz for an unknown quantum process . Our ansatz bootstraps to an effective description for an unknown process on a multi-qubit processor from pairwise two-qubit tomographic data . Paindaveine and Šiman ( Comput Stat Data Anal 2011b ) described a first elegant solution relying on linear programming techniques . The present paper provides another solution based on the fact that the quantile regions can also be computed from a competing concept of projection regression quantiles , elaborated in Kong and Mizera ( Quantile tomography : using quantiles with multivariate data 2008 ) and Paindaveine and Šiman ( J Multivar Anal 2011a ) . As a by-product , this alternative solution further provides various characteristics useful for statistical inference . In addition , other famous complexity measures like covering numbers and Rademacher complexities are derived explicitly . We are able to connect measures of sample complexity with various areas in quantum information science , e.g. quantum state/measurement tomography , quantum state discrimination and quantum random access codes , which may be of independent interest . Lastly , with the assistance of general Bloch-sphere representation , we show that learning quantum measurements/states can be mathematically formulated as a neural network .
Depending on the observation time and the sampling frequency used in the experiments we may be interested in analyzing the transient or the stationary stage of the wave propagation in impact echo tests . Usually with high resolution in time , analyzes of wave propagation velocity can give useful information , for instance , to build a tomography of a material inspected from different locations . Considering the sampling frequency that we used in the experiments ( 100 kHz ) , a feature extracted from the signal as the wave propagation velocity is not accurate enough to discern between homogeneous and different kind of defective materials .
In this paper we review the tomographic reconstruction algorithm and present the implementation of multiple-angle tomography . In addition to the projecdon phase correction required for multiple-frequency tomography [ 1 ] , multiple-angle tomography also requires registration and alignment of the tomographic projections . By addressing the projection phase error and alignment problems , we have successfully implemented the STAM for multiple-angle tomography , and experimental reconstructions of a two-layer specimen are presented to demonstrate the feasibility and capability of the STAM .
One challenging problem in network tomography is reconstructing available bandwidth along all links during multiple source / multiple destination transmissions . The traditional measurement procedures used for bandwidth tomography are extremely time consuming . We propose a novel solution to this problem .
One challenging problem in network tomography is reconstructing available bandwidth along all links during multiple source / multiple destination transmissions . The traditional measurement procedures used for bandwidth tomography are extremely time consuming . We propose a novel solution to this problem .
Abstract We investigate the reconstruction problem of limited angle tomography . Such problems arise naturally in applications like digital breast tomosynthesis , dental tomography , electron microscopy , etc . Since the acquired tomographic data is highly incomplete , the reconstruction problem is severely ill-posed and the traditional reconstruction methods , e.g. filtered backprojection ( FBP ) , do not perform well in such situations .
Computed Tomography ( CT ) is commonly used in clinical procedures and limited angle tomography reconstruction has important applications in diagnostic CT , breast tomography , dental tomography , etc . However , CT images reconstructed from limited angle acquisitions suffer from severe artifacts due to incomplete sinogram data .
Historically , region growing , clustering , and edge tracing have been used and while significant steps have been made in the first two , research into automatic , recursive , edge following has not kept pace . In this thesis , a new , advanced , edge tracing strategy based on recursive , target tracking algorithms and suitable for use in segmenting magnetic resonance ( MR ) and computed tomography ( CT ) medical images is presented . This work represents the first application of recursive , target-tracking-based , edge tracing to the segmentation of MR and CT images of the head .
A 32-electrode data collection system for electrical impedance tomography ( ETT ) is presented . In this system , the demodulator is a multiplexed sample and hold ( S&H ) circuit followed by a voltage difference stage .
In order to eliminate the affects of beamhardening which causes the cuplike artifacts in ICT(Industrial Computed Tomography ) reconstruction , a method of beamhardening correction is introduced . This method is based on Beer 's law .
Abstract Motivation Since 2017 , an increasing amount of attention has been paid to the supervised deep learning-based macromolecule in situ structural classification ( i.e. subtomogram classification ) in cellular electron cryo-tomography ( CECT ) due to the substantially higher scalability of deep learning . However , the success of such supervised approach relies heavily on the availability of large amounts of labeled training data .
In noninvasive tomography , subcutaneous abnormal tissue can change the absorption coefficient of light . This paper explores diffusion optical tomography .
Recent events showed that dose administration has become a major concern regarding clinical computed tomography ( CT ) examinations . A simple solution to lower this dose is the use of more appropriate CT image reconstruction methods .
Abstract The combination of positron emission tomography ( PET ) with magnetic resonance ( MR ) imaging opens the way to more accurate diagnosis and improved patient management . At present , the data acquired by PET-MR scanners are essentially processed separately , but the opportunity to improve accuracy of the tomographic reconstruction via synergy of the two imaging techniques is an active area of research . In this paper , we present Release 2.1.0 of the CCP-PETMR Synergistic Image Reconstruction Framework ( SIRF ) software suite , providing an open-source software platform for efficient implementation and validation of novel reconstruction algorithms .
The projection orientation information is unknown . The scope of this paper is the tomographic reconstruction of the observed object in the ab-initio case where the volume has to be estimated only from a raw projection dataset . A new approach based on a parametric model of the volume is presented .
We consider detection of a nodule signal profile in noisy images meant to roughly simulate the statistical properties of tomographic image reconstructions in nuclear medicine . The images have two sources of variability arising from quantum noise from the imaging process and anatomical variability in the ensemble of objects being imaged .
Magnification digital breast tomosynthesis was also performed to test the calibration method further . Tomographic image reconstructions successfully demonstrated isotropic super-resolution and magnified super-resolution .
In this paper , we develop a new approach to tomographic reconstruction problems based on geometric curve evolution techniques . We use a small set of texture coefficients to represent the object and background inhomogeneities and a contour to represent the boundary of multiple connected or unconnected objects .
Discrete tomography refers to tomographic reconstruction of images that are known to contain only a few intensity levels . We propose a new reconstruction technique for discrete tomography that uses a relaxed partial segmentation and a refinement update in each iteration .
I. Theoretical analysis and fundamental limitations . The authors consider the tomographic reconstruction of objects with spatially localized temporal variation , such as a thorax cross section with a beating heart . The conventional scan format , in which projections are taken progressively around the object , requires high and sometimes infeasible scan rates to avoid motion artifacts in the reconstructed images .
Computed tomography ( CT ) has great impact in many fields such as medical applications , industrial inspection , etc ... Low dose constraints and Limited projection are common problems in a variety of tomographic reconstruction examples which lead to wrong data . In this work , we propose a method of CT reconstruction based on the simultaneous iterative reconstruction techniques SIRT improved by imposing positivity constraint in the total variation ( TVcim-p ) .
A preclinical Single Photon Emission Computed Tomography ( SPECT ) system design is presented , that will be used in rabbit myocardial perfusion and related cardiac studies . The system includes digital-waveform capture of all PMT signals , which allows for optimal maximum-likelihood estimation in the gamma-ray event parameter estimation and tomographic reconstruction processes . In a typical gamma ray camera , only the integrated signal of a gamma ray event is recorded .
However , information missing in electron tomography due to limited angular sampling is still the bottleneck in high-resolution electron tomography application . Here , based on the understanding of smooth nature of biological specimen , we present a new iterative image reconstruction algorithm , FIRT ( filtered iterative reconstruction technique ) for electron tomography by combining the algebra reconstruction technique ( ART ) and the nonlinear diffusion ( ND ) filter technique . Using both simulated and experimental data , in comparison to ART and weight back projection method , we proved that FIRT could generate a better reconstruction with reduced ray artifacts and significant improved correlation with the ground truth and partially restore the information at the non-sampled angular region , which was proved by investigating the 90 ° re-projection and by the cross-validation method .
Electrical Tomography ( ET ) is an advanced visualization technique , which can reconstruct all targets in an investigated field based on boundary measurements . Since the spatial resolution in the ET process can be greatly affected by the selected similarity norm , different norms may result in different ET time and spatial resolutions .
I confidently expect both of these limitations to yield to energetic attack over the next two decades . The recent advent of super-resolution microscopy and electron microscopic tomography are important steps in bridging the resolution gaps between cell and structural biology . These and other developments have led to a new approach , sometimes called cellular structural biology , that integrates across scales to move toward a detailed , structure-based understanding of cellular organization and function .
It has become a powerful tool for elucidating 3D architectures of biological samples at resolution of about 4 - 6 nm and is the only method suitable for investigating polymorphic structures such as organelles , cells and tissues . However , in addition to the relatively low resolution , electron tomograms inevitably suffer from a low signal-to-noise ratio and some data-collection artifacts . These factors significantly hamper development of algorithms for reliable detection of structural features , which poses a severe barrier to progress in the field .
Electron density maps at moderate resolution are often difficult to interpret due to the lack of recognizable features . This is especially true for electron tomograms that suffer in addition to the resolution limitation from low signal-to-noise ratios . Reliable segmentation of such maps into smaller , manageable units can greatly facilitate interpretation .
Tomo3D 2.0 allows huge reconstructions to be calculated in standard computers in a matter of minutes . Thus , it will be a valuable tool for electron tomography studies with increasing resolution needs .
Ice embedment has brought a breakthrough in the degree of preservation of specimens under close-to-native conditions . The current challenge is to push the resolution of electron tomographic imaging to a point where macromolecular signatures can be recognized within the cellular context . We show first progress toward this goal by examples in two areas of application : the structure of the muscle triad junction and the architecture and fine structure of mitochondria .
With the aim of addressing the issue of sample damage during electron tomography data acquisition , we propose a number of new reconstruction strategies based on subsampling ( which uses only a subset of a full image ) and inpainting ( recovery of a full image from subsampled one ) . We point out that the total-variation ( TV ) inpainting model commonly used to inpaint subsampled images may be inappropriate for 2D projection images of typical TEM specimens .
In electron tomography , noise attenuation is critical for high-resolution structural analysis . Though numerous denoising methods have been introduced into electron tomography ( ET ) scope , attenuating noise as well as preserving signal details remains a challenge . Here , we propose an adaptive 3-D transform-Domain collaborative filtering ( A3DTCF ) for denoising of ET data .
The main motivation for the proposed work is the development of mathematically sound and computationally robust algorithms for the reduction of noise and enhancement of structural information in mitochondrial images . Our research efforts will initially focus on the understanding of the sources and mechanisms which introduce noise ( image degradation ) in electron tomography process . Correct modeling of the degradation , will allow us to design effective , correct , algorithms for the removal of the noise without negatively impacting fine-detail structural information , and will allow for a posteriori reconstruction of more accurate three-dimensional mitochondrion images .
Such a set is called a “ symmetric , informationally complete ” POVM ( SIC – POVM ) and is equivalent to a set of d2 equiangular lines in Cd . SIC – POVMs are relevant for quantum state tomography , quantum cryptography , and foundational issues in quantum mechanics . We construct SIC – POVMs in dimensions two , three , and four .
We then develop a novel purification protocol which enhances the degree of entanglement in a noisy channel that includes combined errors of bit-flip , phase-flip and phase-change error . We perform quantum state tomography to verify the entanglement swapping between the two pairs of qubits and working of the purification protocol .
These experimental requirements are compounded by the complexity of processing the collected data , which can take several orders of magnitude longer than the experiment itself . In this paper we propose an alternative self-guided algorithm for quantum process tomography , tuned for the task of finding an unknown unitary process . Our algorithm is a fully automated and adaptive process characterization technique .
However , as these processors grow in size , standard process tomography becomes an almost impossible task . Here , we present an approach for efficient quantum process tomography that uses a physically motivated ansatz for an unknown quantum process . Our ansatz bootstraps to an effective description for an unknown process on a multi-qubit processor from pairwise two-qubit tomographic data .
Paindaveine and Šiman ( Comput Stat Data Anal 2011b ) described a first elegant solution relying on linear programming techniques . The present paper provides another solution based on the fact that the quantile regions can also be computed from a competing concept of projection regression quantiles , elaborated in Kong and Mizera ( Quantile tomography : using quantiles with multivariate data 2008 ) and Paindaveine and Šiman ( J Multivar Anal 2011a ) . As a by-product , this alternative solution further provides various characteristics useful for statistical inference .
In addition , other famous complexity measures like covering numbers and Rademacher complexities are derived explicitly . We are able to connect measures of sample complexity with various areas in quantum information science , e.g. quantum state/measurement tomography , quantum state discrimination and quantum random access codes , which may be of independent interest . Lastly , with the assistance of general Bloch-sphere representation , we show that learning quantum measurements/states can be mathematically formulated as a neural network .
Automatic Classification of Impact-Echo Spectra II Implementation of the Scanning Tomographic Acoustic Microscope for Multiple-Angle Tomography Efficient and reliable network tomography in heterogeneous networks using BitTorrent broadcasts and clustering algorithms Efficient and reliable network tomography in heterogeneous networks using BitTorrent broadcasts and clustering algorithms Sparse regularization in limited angle tomography Limited Angle Tomography Reconstruction: Synthetic Reconstruction via Unsupervised Sinogram Adaptation Dynamic edge tracing: recursive methods for medical image segmentation A 32-electrode data collection system for electrical impedance tomography Method of beam hardening correction for CT reconstruction Adversarial domain adaptation for cross data source macromolecule in situ structural classification in cellular electron cryo-tomograms An effective forward model based on accelerated Monte Carlo method for diffusion optical tomography imaging systems Cylindrical and spherical ray-tracing for CT iterative reconstruction SIRF: Synergistic Image Reconstruction Framework Radial Function Based Ab-Initio Tomographic Reconstruction for Cryo Electron Microscopy Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. Geometric calibration for a next-generation digital breast tomosynthesis system using virtual line segments A curve evolution approach to object-based tomographic reconstruction Discrete Iterative Partial Segmentation Technique (DIPS) for Tomographic Reconstruction Optimal scan for time-varying tomography. I. Theoretical analysis and fundamental limitations Reconstruction of Tomographic Images From Limited Projections Using TVcim-p Algorithm Maximum-Likelihood Event Parameter Estimation from Digital Waveform Capture FIRT: Filtered iterative reconstruction technique with information restoration. Optimal similarity norm for electrical tomography based on Bregman divergence. It's the Technology, Stupid Efficient Automatic Detection of Filaments in Cellular Electron Tomograms based on Reduced Representation Templates A novel three-dimensional variant of the watershed transform for segmentation of electron density maps. Tomo3D 2.0--exploitation of advanced vector extensions (AVX) for 3D reconstruction. Three-dimensional imaging of biological complexity. Subsampling and inpainting approaches for electron tomography. An adaptive 3-D collaborative filter for denoising of electron tomography Total Variation Based Image and Structure Enhancement for Electron Tomography Symmetric informationally complete quantum measurements Demonstration of entanglement purification and swapping protocol to design quantum repeater in IBM quantum computer Experimental realization of self-guided quantum process tomography Bootstrapping quantum process tomography via a perturbative ansatz Computing multiple-output regression quantile regions from projection quantiles The learnability of unknown quantum measurements
1b5292b56daaa9496c51e33bbc7ed2256d14348f cf204463076bf69c8b56fa88e0d914a5347f642f 8b95a19c294522ced081c2dd9f68bb531b074c46 c208f2299ed3a515dbd1988194c1d2627373cd20 8c328a50a0591e662052876fa3c58631a3bd8d13 2f6bbdcec53c9fe4dfdc553552a2ee9b4da8cebc 0f3ef836a1366e880dfea38b8174624a2a15ede2 2dfa0beb0ba9909df1dd66ee0b70d6282380bd05 47abb2313f4c4f55a0a09c16705bed9722b1a32e 5f548aa535877b2b9c6e32b59255bdf1a75c9028 38e9506395c52e313424bbcc0cb0adfcf06dbe76 0396281f76b96c100242b5c8d21fd4821d36b011 e6588d11b748dc73b66aa44e9b304709290d698a 0f280e06e9e89987f2cc45c75b78953d76e15324 0427a871eb8a6080dd6966e240220936c7967592 642720f07b7636eab6a9f00cc5e2c429964ea762 a85ccce202a9fa5994150cfc75ac149a249b1178 9eaf0ab99e0e510ceb54f58be44592a1adbb2d1b 6f564fb0fecfaf8f98c4131f979ca2c784abe448 b2e13c8858823cd7bb06846352146a21fbec52a3 85287b699de84a6bd674df0f9fb07f8619d33706 96b5b31a842553e43467bfa493d0f217b704be85 a0415cc6caf481ab87a17862bfd44d51d288d68d 9abaea92a076c6e35b3bb284a3c3c0243093a5bb a81f1c6b066fa10d87daf64f77d61479f44fee03 09397eaf87072bb428d2f7e3e4d45074d91ff87f 733ca15aa21244004765c8f1900e7c901b95fad5 b577268e5941029552ca491b88cca51bdcc937b8 6ecf683bbfa89c3c790f800b81a027ba92cf15c2 2e78011c50dcddb58b1811c54ef025d372b2e3ff 9e566658ea73aa507bb5123e76eaf7bf0d15a78d d06ebdbcab48d34b1a486e71d2ea285faf7154c7 48d97a8e680e11bf23d852ebc4ef7c99eac07961 3d798292b345093393e22512708dd79888d7732b 98b26b030e8356dec8f3a001fbf3c51ae5ae0161 7a2f3d48b754a33802eeb16b63bcfb39e9e2e090 86d275c48dcc72bc56248e2ebd0cc845137068df
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Physics","Medicine"] ["Engineering","Computer Science"] ["Computer Science","Mathematics","Medicine"] ["Mathematics","Computer Science"] ["Mathematics","Medicine","Computer Science"] ["Computer Science"] ["Computer Science"] ["Medicine","Biology"] ["Physics","Medicine"] ["Medicine","Computer Science"] ["Biology"] ["Medicine","Biology"] ["Medicine","Computer Science"] ["Medicine","Biology"] ["Chemistry","Medicine"] ["Computer Science"] ["Computer Science"] ["Physics","Computer Science","Mathematics"] ["Computer Science","Physics"] ["Mathematics","Physics"] ["Physics","Medicine","Mathematics","Biology"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science","Physics"]
2009 1992 2012 2012 2011 2019 2006 1995 2003 2019 2012 2011 2020 2018 2001 2018 2003 2016 1995 2018 2017 2016 2020 2010 2012 2002 2015 2002 2017 2016 2007 2004 2019 2019 2020 2012 2015
Encyclopedia of Artificial Intelligence SC IPMI ICIP
https://api.semanticscholar.org/1b5292b56daaa9496c51e33bbc7ed2256d14348f https://api.semanticscholar.org/cf204463076bf69c8b56fa88e0d914a5347f642f https://api.semanticscholar.org/8b95a19c294522ced081c2dd9f68bb531b074c46 https://api.semanticscholar.org/c208f2299ed3a515dbd1988194c1d2627373cd20 https://api.semanticscholar.org/8c328a50a0591e662052876fa3c58631a3bd8d13 https://api.semanticscholar.org/2f6bbdcec53c9fe4dfdc553552a2ee9b4da8cebc https://api.semanticscholar.org/0f3ef836a1366e880dfea38b8174624a2a15ede2 https://api.semanticscholar.org/2dfa0beb0ba9909df1dd66ee0b70d6282380bd05 https://api.semanticscholar.org/47abb2313f4c4f55a0a09c16705bed9722b1a32e https://api.semanticscholar.org/5f548aa535877b2b9c6e32b59255bdf1a75c9028 https://api.semanticscholar.org/38e9506395c52e313424bbcc0cb0adfcf06dbe76 https://api.semanticscholar.org/0396281f76b96c100242b5c8d21fd4821d36b011 https://api.semanticscholar.org/e6588d11b748dc73b66aa44e9b304709290d698a https://api.semanticscholar.org/0f280e06e9e89987f2cc45c75b78953d76e15324 https://api.semanticscholar.org/0427a871eb8a6080dd6966e240220936c7967592 https://api.semanticscholar.org/642720f07b7636eab6a9f00cc5e2c429964ea762 https://api.semanticscholar.org/a85ccce202a9fa5994150cfc75ac149a249b1178 https://api.semanticscholar.org/9eaf0ab99e0e510ceb54f58be44592a1adbb2d1b https://api.semanticscholar.org/6f564fb0fecfaf8f98c4131f979ca2c784abe448 https://api.semanticscholar.org/b2e13c8858823cd7bb06846352146a21fbec52a3 https://api.semanticscholar.org/85287b699de84a6bd674df0f9fb07f8619d33706 https://api.semanticscholar.org/96b5b31a842553e43467bfa493d0f217b704be85 https://api.semanticscholar.org/a0415cc6caf481ab87a17862bfd44d51d288d68d https://api.semanticscholar.org/9abaea92a076c6e35b3bb284a3c3c0243093a5bb https://api.semanticscholar.org/a81f1c6b066fa10d87daf64f77d61479f44fee03 https://api.semanticscholar.org/09397eaf87072bb428d2f7e3e4d45074d91ff87f https://api.semanticscholar.org/733ca15aa21244004765c8f1900e7c901b95fad5 https://api.semanticscholar.org/b577268e5941029552ca491b88cca51bdcc937b8 https://api.semanticscholar.org/6ecf683bbfa89c3c790f800b81a027ba92cf15c2 https://api.semanticscholar.org/2e78011c50dcddb58b1811c54ef025d372b2e3ff https://api.semanticscholar.org/9e566658ea73aa507bb5123e76eaf7bf0d15a78d https://api.semanticscholar.org/d06ebdbcab48d34b1a486e71d2ea285faf7154c7 https://api.semanticscholar.org/48d97a8e680e11bf23d852ebc4ef7c99eac07961 https://api.semanticscholar.org/3d798292b345093393e22512708dd79888d7732b https://api.semanticscholar.org/98b26b030e8356dec8f3a001fbf3c51ae5ae0161 https://api.semanticscholar.org/7a2f3d48b754a33802eeb16b63bcfb39e9e2e090 https://api.semanticscholar.org/86d275c48dcc72bc56248e2ebd0cc845137068df