Article
Mathematics
Nicholas E. E. Protonotarios, George A. A. Kastis, Andreas D. D. Fotopoulos, Andreas G. G. Tzakos, Dimitrios Vlachos, Nikolaos Dikaios
Summary: This study focuses on improving motion correction algorithms in PET by using a motion-compensated image reconstruction (MCIR) algorithm based on a parabolic surrogate likelihood function. The parabolic surrogate algorithm converges faster than the expectation maximization (EM) algorithm, making it particularly useful in computationally demanding PET motion correction.
Article
Mathematical & Computational Biology
Limin Ma, Yudong Yao, Yueyang Teng
Summary: This paper introduces an image reconstruction method that combines a deep learning model with a traditional iterative algorithm. The method improves image quality while maintaining interpretability and fast reconstruction speed.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Automation & Control Systems
Mohammad S. Ramadan, Robert R. Bitmead
Summary: A Maximum Likelihood recursive state estimator is proposed for non-linear state-space models, which combines a particle filter and the Expectation Maximization algorithm. Algorithms for maximum likelihood state filtering, prediction, and smoothing are derived, and their convergence properties are examined, demonstrating the effectiveness of the method in nonlinear systems.
Article
Engineering, Electrical & Electronic
Mursel Yildiz
Summary: The expectation maximization algorithm for univariate problems often requires prior information, which can be problematic for highly dynamic environments. This study presents an EM approach based on Fourier series that can approximate the true probability distribution function and ensure tractability and closed form.
Article
Engineering, Electrical & Electronic
A. Hippert Ferrer, M. N. El Korso, A. Breloy, G. Ginolhac
Summary: This paper addresses robust mean and covariance matrix estimation in mixed effects models, proposing an expectation-conditional maximization algorithm to handle outliers, parallelized for computational efficiency and extended to deal with missing data. Numerical simulations evaluate the performance in robust regression estimators, probabilistic principal component analysis, and its robust version.
Article
Mathematics
Omar M. Abou Al-Ola, Ryosuke Kasai, Yusaku Yamaguchi, Takeshi Kojima, Tetsuya Yoshinaga
Summary: This article introduces a new algorithm that combines the advantages of different iterative schemes by combining ordered-subsets EM and MART with weighted geometric or hybrid means, achieving a decrease in the objective function with each iteration and outperforming OS-EM and OS-MART alone. The algorithm shows excellent performance in image reconstruction experiments, especially in dealing with noise issues.
Article
Biochemistry & Molecular Biology
Lei Zhao, Rasmus Nielsen, Thorfinn Sand Korneliussen
Summary: Commonly used methods for inferring phylogenies are not well-suited for handling challenges associated with noisy, diploid sequencing data. To address this problem, we introduce two new probabilistic approaches, distAngsd-geno and distAngsd-nuc, that account for uncertainty in genotype calling and are specifically designed for next-generation sequencing data.
MOLECULAR BIOLOGY AND EVOLUTION
(2022)
Article
Physics, Multidisciplinary
Jinwon Heo, Jangsun Baek
Summary: To address the difficulty in clustering matrix data, a new penalized matrix normal mixture model is proposed with regularization on both mean and covariance matrices. The method allows for parsimonious modeling and reflects the proper conditional correlation structure, showing superior clustering performance compared to conventional methods in experimental results.
Article
Automation & Control Systems
Jing Chen, Biao Huang, Feng Ding
Summary: This article develops two identification algorithms for two-dimensional causal systems, one for systems without missing data and the other for systems with missing outputs, and demonstrates the effectiveness through a simulation example.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Mechanics
Hongyi Wang, Yang Yang, Gongcheng Dou, Jipei Lou, Xinjun Zhu, Limei Song, Feng Dong
Summary: In this study, a Bi-Direction Filtering Maximum Likelihood Expectation Maximization (BDF-MLEM) algorithm based on multi-view bubble images was proposed for the 3D reconstruction of bubble flow field. The calibration method for the multi-view was studied to establish the mapping relationship between the 3D world and the projection images. The BDF-MLEM algorithm improved the iterative initial value setting method and the projection/back projection methods, resulting in significantly improved reconstruction speed and accuracy compared to SART and MLEM algorithms. Additionally, the method showed improvement in the reconstruction of overlapped bubbles.
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW
(2023)
Article
Multidisciplinary Sciences
Mohamed Kayid, Abdulrahman Abouammoh, Ghadah Alomani, Dario Ferreira, Calogero Vetro
Summary: Statistical probability models are often used to analyze real-world data in many research fields. However, data from fields such as the environment, economics, and health care may not fit traditional models. This study investigates an extension of the quasi-Lindley model that is asymmetrically distributed on the positive real number line. Various algorithms are used to estimate the parameters, and the results show that all techniques provide accurate and reliable estimates. The proposed model outperforms alternative models when analyzing a reliability dataset.
Article
Physics, Multidisciplinary
Ryosuke Kasai, Yusaku Yamaguchi, Takeshi Kojima, Omar M. Abou Al-Ola, Tetsuya Yoshinaga
Summary: The paper proposes an iterative reconstruction algorithm based on an extended class of power-divergence measures (PDMs), which include various distance and relative entropy measures. By introducing a system of nonlinear differential equations and deriving an iterative formula through discretization, the algorithm is able to reconstruct high-quality images robust to noise. The parameterized PDM family includes the Kullback-Leibler divergence, making the resulting iterative algorithm a natural extension of the maximum-likelihood expectation-maximization (MLEM) method.
Article
Green & Sustainable Science & Technology
Weipeng Feng, Zhijun Dong, Yu Jin, Hongzhi Cui
Summary: This study compared the micromechanical properties of the interfacial transition zone (ITZ) in concrete with recycled iron ore tailings (IOTs) and natural aggregates (NA). Results showed that IOT samples had higher volume fractions of different phases in the ITZ, leading to a denser microstructure and enhanced mechanical properties compared to NA samples.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Okkyun Lee
Summary: This paper introduces likelihood-based bilateral filters as a post-processing method applied to the ML-based estimates of basis sinograms. The proposed filters effectively reduce the noise in the basis images and the synthesized monochromatic CT images.
Article
Mathematics, Interdisciplinary Applications
Yang Liu, Weimeng Wang
Summary: This paper proposes a semiparametric factor model with minimal parametric assumptions for analyzing item-level response time data. Simulation studies and a real data example show the advantages of the proposed method.
Article
Optics
Vladislav Kravets, Paul Kondrashov, Adrian Stern
Article
Chemistry, Analytical
Shauli Shmilovich, Liat Revah, Yaniv Oiknine, Isaac August, Ibrahim Abdulhalim, Adrian Stern
Article
Optics
Daniel Gedalin, Yaniv Oiknine, Adrian Stern
Article
Optics
Bahram Javidi, Artur Carnicer, Jun Arai, Toshiaki Fujii, Hong Hua, Hongen Liao, Manuel Martinez-Corral, Filiberto Pla, Adrian Stern, Laura Waller, Qiong-Hua Wang, Gordon Wetzstein, Masahiro Yamaguchi, Hirotsugu Yamamoto
Article
Optics
Vladislav Kravets, Bahram Javidi, Adrian Stern
Summary: This letter introduces a novel approach using compressive sensing to defend convolutional deep neural networks (DNNs) from adversarial attacks and encode the image to prevent counterattacks. Computer simulations and optical experimental results demonstrate the effectiveness of this method in object classification tasks in adversarial images.
Article
Optics
Ofer Bar Lev, Adrian Stern, Isaac August
Summary: This paper presents a novel method for measuring the location and estimating the dynamics of fast-moving small objects in free space. The method utilizes a space-to-time optical transform and time-of-flight measurement to retrieve 3D spatial information and track the objects using a sparse approximation of the scene.
Article
Optics
Vladislav Kravets, Bahram Javidi, Adrian Stern
Summary: 3D point cloud classifiers are widely used in critical applications but susceptible to adversarial attacks. To prevent an arms race between attacker and defender, using 3D compressive sensing to recover the original label may be a solution.
Article
Optics
Bahram Javidi, Hong Hua, Adrian Stern, Manuel Martinez-Corral, Osamu Matoba, Ana Doblas, Simon Thibault
Summary: This Feature Issue of Optics Express is a collection of 31 articles presented at the 2022 Optica conference on 3D Image Acquisition, Display: Technology, Perception and Applications. The articles cover the topics and scope of the conference. This Introduction provides a summary of the published articles in this Feature Issue.
Article
Engineering, Electrical & Electronic
Or Arad, Loran Cheplanov, Yiftah Afgin, Liad Reshef, Roman Brikman, Saker Elatrash, Adrian Stern, Leah Tsror, David J. Bonfil, Iftach Klapp
Summary: A hyperspectral imaging system was developed using a point spectrometer and a double-wedge prism scanner. This system offers an inexpensive alternative to the HS camera, making it suitable for precision agriculture and environmental monitoring.
IEEE SENSORS JOURNAL
(2023)
Article
Optics
Kobi Aflalo, Moshe Ben-David, Adrian Stern, Irit Juwiler
Proceedings Paper
Computer Science, Artificial Intelligence
Daniel Gedalin, Yaron Heiser, Yaniv Oiknine, Adrian Stern
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DEFENSE APPLICATIONS
(2019)
Proceedings Paper
Optics
Adrian Stern, Vladislav Kravets, Yair Rivenson, Bahram Javidi
THREE-DIMENSIONAL IMAGING, VISUALIZATION, AND DISPLAY 2019
(2019)
Proceedings Paper
Computer Science, Information Systems
Yaron Heiser, Yaniv Oiknine, Adrian Stern
BIG DATA: LEARNING, ANALYTICS, AND APPLICATIONS
(2019)
Review
Imaging Science & Photographic Technology
Yaniv Oiknine, Isaac August, Vladimir Farber, Daniel Gedalin, Adrian Stern
JOURNAL OF IMAGING
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Yaniv Oiknine, Boaz Arad, Isaac August, Ohad Ben-Shahar, Adrian Stern
2018 9TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS)
(2018)
