Article
Engineering, Electrical & Electronic
Sahar Sadrizadeh, Nematollah Zarmehi, Ehsan Asadi Kangarshahi, Hamidreza Abin, Farokh Marvasti
Summary: The paper presents a low complexity algorithm for reconstructing signals corrupted by noise, with superior reconstruction quality compared to other state-of-the-art methods. The algorithm is versatile, effective for different types of noise, and has been validated through experimental applications in various scenarios.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Sahar Sadrizadeh, Hatef Otroshi-Shahreza, Farokh Marvasti
Summary: This study presents a deep learning approach using a fully convolutional neural network to remove impulsive noise from images. Through training with a customized dataset and a multi-term loss function, the approach achieves superior reconstruction quality and speed. Additionally, the introduction of a fast iterative method as a post-processing stage significantly improves the reconstruction quality of the neural network.
Article
Computer Science, Artificial Intelligence
Chuang Li, Zhizhong Mao
Summary: The quality of training data is crucial for the establishment of intelligent models. This paper proposes a novel noise filter for cleaning noisy instances in regression tasks. The method addresses the deficiency of the existing noise determination criterion and combines ensemble filtering and iterative filtering to detect potential noisy samples. The proposed method improves prediction accuracy and achieves better noise filtering performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Lixun Huang, Xinyang Guo, Lijun Sun, Qiuwen Zhang, Weihua Liu, Zhe Zhang
Summary: Instead of designing new ILC controllers, this paper focuses on designing a filter on the side of objects to calculate the updated input of ILC controllers under the effects of communication delays and noises in both links. The filter is designed based on the orthogonality projection principle using the knowledge of ILC controllers and the developed transmission model. Theoretical analysis and simulation results demonstrate that the calculated input effectively improves the convergence of objects controlled by the P-type ILC controller with communication delays and noises.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Environmental Sciences
Pei Nie, Zhenqi Cui, Yaping Wan
Summary: This paper introduces a rapid parallel remote sensing image mosaicking algorithm utilizing read filtering. By dividing the target images into blocks and storing them in a distributed file system, and using asynchronous reading and processing methods, the algorithm reduces data I/O and computing overhead and improves the efficiency of parallel computing.
Article
Engineering, Electrical & Electronic
Yongjiang Luo, Jiali Yang, Qiang Zhang, Changlong Wang
Summary: In this paper, a fractional-order LMP algorithm (FOLMP) is proposed, which optimizes LMP with a fractional-order gradient to ensure that the cost function is fractionally differentiable everywhere. The stability of FOLMP is analyzed to determine the ranges of fractional order and step size required for stability. Experimental results demonstrate that the proposed algorithm outperforms previous algorithms in terms of convergence speed and tracking performance in impulsive noise environments, regardless of the values of the characteristic exponent alpha.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2021)
Article
Energy & Fuels
Xingyu Wang, Anna Wang, Dazhi Wang, Wenhui Wang
Summary: An improved spline adaptive filtering algorithm is proposed to address the negative impact of impulse noise in conventional SAF algorithm for nonlinear system identification. The algorithm utilizes a cost function constructed with a hyperbolic tangent function to enhance filter robustness and introduces a pre-filtering observation strategy to improve convergence speed and reduce steady-state error. Analysis of noise in time and frequency domains informs weight update rule, demonstrating superior performance compared to existing SAF algorithm.
Article
Computer Science, Artificial Intelligence
Joel Jonsson, Bevan L. Cheeseman, Suryanarayana Maddu, Krzysztof Gonciarz, Ivo F. Sbalzarini
Summary: This paper presents data structures and algorithms for native implementations of discrete convolution operators over Adaptive Particle Representations (APR) of images on parallel computer architectures. The APR is a content-adaptive image representation that reduces memory and runtime costs. The study provides efficient algorithmic building blocks for processing APR images and demonstrates the speedup achieved with APR convolution compared to pixel-based algorithms.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Automation & Control Systems
Lixun Huang, Lijun Sun, Tao Wang, Qiuwen Zhang, Jianyong Li, Zhe Zhang, Weihua Liu
Summary: This article investigates the convergence performance of wireless networked iterative learning control (ILC) systems under data dropouts and channel noises in both sensor-to-controller and controller-to-actuator channels. In order to improve the convergence performance, an optimal input filter is developed at the actuator side to estimate the controller updated input with the effect of network uncertainties. The convergence performance of the filtering error covariance matrix is analyzed theoretically and simulation results demonstrate the effectiveness of the proposed filtering method.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Engineering, Electrical & Electronic
Yizhong Pan, Chao Ren, Xiaohong Wu, Jie Huang, Xiaohai He
Summary: Deep learning-based methods have shown superior performance in image denoising, but most of them assume known noise levels, which is not realistic for real-world noise. Introducing noise levels is crucial for effective denoising. Noise level mismatch can introduce artifacts, and an iterative correction scheme using intermediate denoised images is proposed to address this issue. Experimental results demonstrate the effectiveness of the proposed method for real-world denoising tasks.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Hua Li, Yuanyuan Dong, Caihong Gong, Zhenyu Zhang, Xiyuan Wang, Xiaoming Dai
Summary: This paper discusses the impact of impulsive noise on underwater acoustic communication systems and the effect of ICI on system performance. It proposes a non-Gaussianity-aware iterative ICI cancelation scheme to mitigate the adverse effects of PB. Incorporating a companding transformation scheme helps alleviate the non-Gaussianity caused by residual ICI and impulsive noise, leading to significant performance gains with lower computational complexity.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Optics
Dang N. H. Thanh, V. B. Surya Prasath, Thai Kim Phung, Nguyen Quoc Hung
Summary: The article introduces a robust impulse denoising method, NAHAT filter, based on noise accumulation and harmonic analysis techniques. This method effectively removes impulse noise even under high-density levels and preserves image structures. Experimental results demonstrate its good performance under various noise levels, outperforming similar state-of-the-art methods.
Article
Chemistry, Multidisciplinary
Muhammad Tahir Akhtar
Summary: This paper proposes a new recursive least squares (RLS) algorithm for active noise control (ANC) systems to control impulsive sources. The algorithm improves robustness against impulse type sources by designing an objective function, employs smoothing and a step size in the adaptive procedure to address stability and convergence speed issues, and introduces a convex combined step size (CCSS) strategy to achieve a trade-off between convergence speed and steady-state performance.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Xueman Sun, Xiaoguang Lv, Guoliang Zhu, Biao Fang, Le Jiang
Summary: In many real-world applications, it is important to remove insignificant image details while preserving significant structures. This paper investigates the image smoothing problem using the lp-lq optimization model with 0<= p,q <= 1. The authors utilize the fast additive half-quadratic (AHQ) iterative minimization algorithm to solve the lp-lq optimization model. They discuss the convergence of the AQH iterative minimization algorithm and provide experimental results and comparisons to demonstrate the efficiency and flexibility of their proposed method.
IET IMAGE PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Xinqi Huang, Yingsong Li, Xiao Han, Wanlu Shi, Huawei Tu
Summary: In this paper, an adaptive estimation algorithm called affine-projection q-Renyi (APQR) algorithm is proposed, analyzed, and simulated for channel estimation in the impulsive noise environment. The APQR algorithm minimizes the q-Renyi kernel function with past errors to achieve robustness to impulsive noise and improve convergence speed for colored input signals. The paper also provides step-size boundary and steady-state analysis. Simulation results confirm the correctness of theoretical analysis and demonstrate the superiority of the proposed algorithm under the conditions of colored input and impulsive noise.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Computer Science, Software Engineering
Charles M. Stein, Dinei A. Rockenbach, Dalvan Griebler, Massimo Torquati, Gabriele Mencagli, Marco Danelutto, Luiz G. Fernandes
Summary: The study aims to implement latency-aware adaptive microbatching techniques and algorithms for streaming compression applications targeting GPUs. The evaluation is conducted using the Lempel-Ziv-Storer-Szymanski compression application considering different input workloads. It was found that algorithms with elastic adaptation factors respond better for stable workloads, while algorithms with narrower targets respond better for highly unbalanced workloads.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Biochemical Research Methods
Daniele D'Agostino, Pietro Lio, Marco Aldinucci, Ivan Merelli
Summary: Hi-C technology allows the study of DNA interactions and chromosome folding, and using a graph database can effectively manage and visualize the data, helping to compare similarities and differences between different experiments and identify changes in functional domains.
BMC BIOINFORMATICS
(2021)
Article
Computer Science, Theory & Methods
Gabriele Mencagli, Massimo Torquati, Andrea Cardaci, Alessandra Fais, Luca Rinaldi, Marco Danelutto
Summary: Nowadays, there is a growing focus on Stream Processing Systems (SPSs) for scale-up machines, where some systems still use the continuous model for low latency while others optimize throughput with batching approaches. The approach presented in the text aims to design a runtime system of SPSs targeting multicores to optimize throughput and latency, using building blocks for easy and compositional optimizations.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2021)
Article
Computer Science, Theory & Methods
Marco Aldinucci, Valentina Cesare, Iacopo Colonnelli, Alberto Riccardo Martinelli, Gianluca Mittone, Barbara Cantalupo, Carlo Cavazzoni, Maurizio Drocco
Summary: The work presents a systematic methodology to modernize existing sequential scientific codes with little re-designing effort, turning them into parallel and robust code. The proposed semi-automatic methodology can parallelize scientific applications designed with purely sequential programming mindset, and it has been successfully demonstrated in shared memory, message passing, and GPU computing models. The method has been applied to parallelize real-world sequential codes in physics and material science domains.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2021)
Article
Computer Science, Information Systems
Iacopo Colonnelli, Barbara Cantalupo, Ivan Merelli, Marco Aldinucci
Summary: Workflows are commonly used in various execution environments, but few allow execution in different environments; StreamFlow complements workflow graph with declarative descriptions of complex execution environments for multi-site execution.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2021)
Editorial Material
Computer Science, Information Systems
Daniele D'Agostino, Francesco Leporati, Massimo Torquati, Jingling Xue
Summary: The papers in this special section focus on collecting high-quality scientific contributions from the research community working in the fields of parallel and distributed computing, data analytics algorithms and big data frameworks, with a main focus on emerging new computing trends that affect concrete human life, the so-called Human Sensible Applications. Problems related to parallel computing, such as precision medicine, biomedical IoT, computational biology, and human body organ modeling, as well as emerging computing systems for Human Sustainability, including weather and climate changes monitoring/prediction, resources management, safety, disaster prediction and prevention, are included in this scenario.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2021)
Article
Computer Science, Theory & Methods
Iacopo Colonnelli, Marco Aldinucci, Barbara Cantalupo, Luca Padovani, Sergio Rabellino, Concetto Spampinato, Roberto Morelli, Rosario Di Carlo, Nicolo Magini, Carlo Cavazzoni
Summary: The article discusses the choice between language-independent and language-dependent approaches in designing new coordination interfaces, highlighting the unique role of Jupyter Notebooks. By combining language-independent and language-dependent methods, Jupyter Notebooks have the potential to express complex distributed workflows and show promise in lowering barriers between prototypical and production-ready implementations in HPC and Cloud scenarios.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Astronomy & Astrophysics
Valentina Cesare, Ugo Becciani, Alberto Vecchiato, Mario Gilberto Lattanzi, Fabio Pitari, Marco Aldinucci, Beatrice Bucciarelli
Summary: We optimized and ported the Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) Parallel Solver to the GPU using CUDA. The code solves a system of linear equations to find the astrometric parameters of billions of stars, attitude and instrumental settings of the Gaia satellite, and the global parameter of the parametrized Post-Newtonian formalism. The CUDA code achieved a speedup of up to 14x compared to the original AVU-GSR solver parallelized on the CPU with MPI + OpenMP.
PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC
(2023)
Review
Cardiac & Cardiovascular Systems
Yasir Arfat, Gianluca Mittone, Roberto Esposito, Barbara Cantalupo, Gaetano M. De Ferrari, Marco Aldinucci
Summary: This paper reviews the use of artificial intelligence tools, specifically machine learning techniques, in the field of cardiology. It focuses on the application of machine learning based risk scores in cardiovascular research and discusses the merits and shortcomings of these techniques. The paper also compares these techniques with corresponding statistical approaches and highlights the main open issues and future directions in applying machine learning tools in cardiology.
MINERVA CARDIOLOGY AND ANGIOLOGY
(2022)
Proceedings Paper
Engineering, Biomedical
C. Pino, S. Palazzo, F. Trenta, F. Cordero, U. Bagci, F. Rundo, S. Battiato, D. Giordano, M. Aldinucci, C. Spampinato
Summary: The study presents a deep learning model for automated lesion segmentation and classification based on CT data to identify oncogene-addicted lung tumors. They trained the model with adversarial samples and built a dataset of 73 CT scans for testing the model performance.
2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
(2021)
Proceedings Paper
Engineering, Biomedical
C. Pino, G. Vecchio, M. Fronda, M. Calandri, M. Aldinucci, C. Spampinato
Summary: The article introduces TwinLiverNet, a deep neural network that predicts the effectiveness of TACE treatment for HCC by learning visual cues from CT scans. Experimental results show an average accuracy of 82% in predicting complete response to TACE treatment for HCC lesions, with a performance increase of over 12% by combining multiple CT phases. The introduction of capsule layers in the model prevents overfitting and enhances accuracy.
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
(2021)
Article
Ethics
Gianluca Bontempi, Ricardo Chavarriaga, Hans Ed Canck, Emanuela Girardi, Holger Hoos, Iarla Kilbane-Dawe, Tonio Ball, Ann Nowe, Jose Sousa, Davide Bacciu, Marco Aldinucci, Manlio Ed Domenico, Alessandro Saffiotti, Marco Maratea
Summary: The volunteer effort by AI researchers has shown the potential to deliver significant research outcomes rapidly to help tackle COVID-19, but has also faced challenges. They offer seven recommendations to guide how to best leverage such efforts and collaborations in managing future crises.
ETHICS AND INFORMATION TECHNOLOGY
(2021)
Article
Medicine, General & Internal
Fabrizio D'Ascenzo, Ovidio De Filippo, Guglielmo Gallone, Gianluca Mittone, Marco Agostino Deriu, Mario Iannaccone, Albert Ariza-Sole, Christoph Liebetrau, Sergio Manzano-Fernandez, Giorgio Quadri, Tim Kinnaird, Gianluca Campo, Jose Paulo Simao Henriques, James M. Hughes, Alberto Dominguez-Rodriguez, Marco Aldinucci, Umberto Morbiducci, Giuseppe Patti, Sergio Raposeiras-Roubin, Emad Abu-Assi, Gaetano Maria De Ferrari
Summary: A machine learning-based risk stratification model was developed to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after acute coronary syndrome (ACS), showing accurate discriminative capabilities in internal and external validation cohorts.
Proceedings Paper
Computer Science, Theory & Methods
Marco Aldinucci, Giovanni Agosta, Antonio Andreini, Claudio A. Ardagna, Andrea Bartolini, Alessandro Cilardo, Biagio Cosenza, Marco Danelutto, Roberto Esposito, William Fornaciari, Roberto Giorgi, Davide Lengani, Raffaele Montella, Mauro Olivieri, Sergio Saponara, Daniele Simoni, Massimo Torquati
Summary: High-Performance Computing (HPC) is considered a strategic priority for research and innovation globally. Italian researchers have established a specialized laboratory to address HPC challenges and have successfully participated in the EuroHPC Joint Undertaking, securing funding for five ongoing projects.
PROCEEDINGS OF THE 18TH ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS 2021 (CF 2021)
(2021)
