Showing posts with label special issue. Show all posts
Showing posts with label special issue. Show all posts

Thursday, 26 March 2026

Upcoming Journal Special Issues

Submission deadline: 31 March 2026

Submission deadline: 1 September 2026

Submission deadline: 31 July 2026

Submission deadline: 31 August 2026

IEEE Transactions on Cognitive and Developmental Systems: Special Issue on Brain-Inspired Computing for Embodied AI 
Submission deadline: 31 August 2026

Wednesday, 24 December 2025

Journal Special Issues

Journal special issues with upcoming submission deadlines:

Wednesday, 24 September 2025

Journal Special Issues

Journal special issues with upcoming submission deadlines:

Tuesday, 21 January 2025

Journal Special Issues

Journal special issues with upcoming submission deadlines:

Wednesday, 13 November 2024

Special Journal Issues

IEEE Transactions on Emerging Topics in Computational IntelligenceSpecial Issue on Digital Trust for Artificial Intelligence 
Submission deadline: 1 December 2024

Submission deadline: 1 December 2024

IEEE Transactions on Emerging Topics in Computational IntelligenceSpecial Issue on Neural Architecture Search and Large Machine Learning Models 
Submission deadline: 31 December 2024

Submission deadline: 31 December 2024

IEEE Transactions on Cognitive and Developmental SystemsSpecial Issue on Bridging the Gap between Machine and Brain in Speech Processing 
Submission deadline: 31 December 2024

IEEE Transactions on Fuzzy SystemsSpecial Issue on Fuzzy Affective Computing Systems 
Submission deadline: 31 January 2025

IEEE Transactions on Evolutionary ComputationSpecial Issue on Evolutionary Computation Meets Large Language Models 
Submission deadline: 31 January 2025

Monday, 19 August 2024

Upcoming Journal Special Issue Deadlines

IEEE Transactions on Evolutionary Computation - Special Issue on Evolutionary Dynamic Optimization.

Paper submission deadline: 1 September, 2024


IEEE Transactions on Emerging Topics in Computational Intelligence - Special Issue on Advances in Methodologies for Metaheuristic Algorithms.

Paper submission deadline: 30 September, 2024


IEEE Transactions on Cognitive and Developmental Systems - Special Issue on Bridging the Gap between Machine and Brain in Speech Processing.

Paper submission deadline: 30 September, 2024


IEEE Transaction on Emerging Topics in Computational Intelligence - Special Issue on Digital Trust for Artificial Intelligence.

Paper submission deadline: 1 December, 2024



Paper submission deadline: 31 December, 2024


IEEE Transactions on Fuzzy Systems - Special Issue on Fuzzy Affective Computing Systems.

Paper submission deadline: 31 January, 2025

Tuesday, 5 March 2024

Upcoming Journal Special Issues

Upcoming submission deadlines for journal special issues:
    IEEE Transactions on Fuzzy Systems - Special Issue on Deep Neuro-Fuzzy Approaches for Intelligent Big Data Processing - 30 March 2024
        IEEE Transactions on Evolutionary Computation - Special Issue on Machine Learning Assisted Evolutionary Computation - 1 April 2024
          IEEE Transactions on Evolutionary Computation - Special Issue on Evolutionary Bilevel Optimization - 1 August 2024
            IEEE Transactions on Evolutionary Computation - Special Issue on Evolutionary Dynamic Optimization - 1 September 2024

            Friday, 8 December 2023

            Upcoming Journal Special Issue Deadlines

            Some upcoming journal special issues and deadlines:

            Monday, 11 September 2023

            Upcoming Journal Special Issues

            Some upcoming journal special issues and deadlines:


            Wednesday, 14 December 2022

            Upcoming Journal Special Issues


            Wednesday, 20 July 2022

            Upcoming Special Issues

             

            Thursday, 26 August 2021

            IEEE Transactions on Evolutionary Computation: Special Issues

            Current Special Issues: IEEE TEVC Special Issue on "Benchmarking Sampling-Based Optimization Heuristics: Methodology and Software," Guest Editors: Thomas Bäck, Leiden University, The Netherlands; Carola Doerr, CNRS researcher at LIP6, Sorbonne Université, France; Bernhard Sendhoff, Honda Research Institute Japan Co.; Thomas Stützle, Université libre de Bruxelles (ULB), Belgium. Submission Deadline: September 15, 2021. https://cis.ieee.org/publications/t-evolutionary-computation/tevc-special-issues

            Saturday, 20 July 2019

            IEEE TFS CALL FOR PAPERS – SPECIAL ISSUE ON TYPE-2 FUZZY-MODEL-BASED CONTROL AND ITS APPLICATIONS

            I. AIM AND SCOPE
            Nonlinear systems are difficult to analyze and control due to their intrinsic complexity. During the past decades, Fuzzy-Model-Based (FMB) control strategy has been recognized as one of the most effective control approaches for nonlinear systems. Takagi–Sugeno (T–S) fuzzy model plays an important role in FMB control systems and it has demonstrated a wide range of successful industrial applications. Thanks to its rigorous mathematical foundation, the stability analysis and control synthesis of T-S FMB control systems can be conducted in a systematic way. Prof. Kazuo Tanaka has made pioneering significant contribution to investigate the stability issues of T-S FMB control systems and relaxed the stability conditions by proposing the well-known parallel distributed compensation (PDC) method, which is the most popular method adopted to deal with (type-1) T-S FMB control systems.

            Considering the ability of dealing with uncertainty directly, the importance and development of type-2 fuzzy sets and theory have been highly noticed and promoted by Prof. Jerry M. Mendel. Many researchers have devoted to contributing to the field of type-2 fuzzy set and its control applications. Just name a few, Prof. Jerry M. Mendel and Prof. Robert I. Bob John made significant contribution to advertize the necessity of (interval) type-2 fuzzy set; Prof. Dongrui Wu and Prof. Jerry M. Mendel developed the enhanced Karnik-Mendel algorithms for type-reduction; Prof. Woei Wan Tan utilized type-2 fuzzy logic to design the practical controllers; Prof. Tufan Kumbasar and Prof. Hani Hagras successfully applied the type-2 fuzzy set in control of mobile robots subject to uncertainty.

            Introducing type-2 fuzzy sets into control strategies is a promising way to push the FMB control techniques to a new frontier. Beginning with the first attempt on the stability analysis and control synthesis of (interval) type-2 FMB control system by Dr. H.K. Lam in 2008 (Lam, H.K. and Seneviratne, L.D., 2008. Stability analysis of interval type-2 fuzzy-model-based control systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(3), pp.617-628), recently, the research on interval type-2 FMB control systems has drawn the attention of researchers. It is also worth mentioning that Prof. William Melek and Prof. Hao Ying have contributed excellent research works to the type-2 T-S/TSK FMB control field. Nonlinearity and uncertainty are generally considered as challenging components to be addressed during the system analysis and control design. The merit of type-2 FMB control techniques is to deal with nonlinearity/uncertainty in the system through type-2 fuzzy sets. Recent research outcomes of type-2 FMB have verified that type-2 FMB control strategy can be successfully applied to real-world nonlinear systems subject to uncertainty. Although there were already some seminal works on type-2 FMB control systems that can be found in the literature, there are still many interesting related topics await.

            The potential research topics for type-2 FMB control systems can be the relaxation of stability/stabilization conditions, control methodologies, design of robust type-2 fuzzy controller, performance realization of type-2 fuzzy controller, applications of type-2 fuzzy control theory, etc. Inspired by the great research potential of type-2 FMB control systems, we believe that further efforts are worth to be devoted into these areas, which can promote the development of type-2 FMB control research and take it into next level.

            This special issue is to serve as a vehicle to promote some focused frontier topics in the field of type-2 FMB control systems. A collection of high-quality type-2 fuzzy control related papers in a special issue will lead to the long-term impact that the accepted papers will serve as an indicator of the most influential topics, highlight the unique advantages of type-2 fuzzy control techniques, and provide reference and driving force in support of the research development.

            II. TOPICS COVERED

            The topics cover a broad range of research on the control issues of type-2 fuzzy logic systems. The solicited contributions involve the following applications (but not limited to):
            • Membership-function-dependent (MFD) analysis;
            • Type-2 fuzzy modeling;
            • Design of robust type-2 controller subject to the external disturbance;
            • Combination of type-2 fuzzy logic to polynomial FMB control systems;
            • Model reduction of type-2 FMB control systems;
            • New type-reduction methods of type-2 membership functions in FMB control systems;
            • Adaptive control of type-2 FMB systems;
            • Optimal control of type-2 FMB systems;
            • Stability/performance/robustness analysis of type-2 FMB control systems
            • Type-2 fuzzy neural-network control systems;
            • Networked type-2 FMB control systems;
            • Industrial applications of type-2 fuzzy systems.

            Advanced type-2 methods involve the following technologies (but not limited to):
            • Type-2 FMB control with reinforcement learning;
            • Type-2 FMB control with medical robotics;
            • Type-2 FMB control with mobile robots;
            • Type-2 FMB control of bio-systems;
            • Type-2 FMB control of continuum robotic manipulator;
            • Type-2 FMB control with bio-inspired robotics;
            • Type-2 FMB control with evolutionary algorithms;
            • Type-2 FMB control with machine learning;
            • Type-2 FMB control with visual servo.

            III. SUBMISSION GUIDELINES

            All authors should read ‘Information for Authors’ before submitting a manuscript at https://cis.ieee.org/publications/t-fuzzy-systems/tfs-information-for-authors

            Submissions should be through the IEEE TFS journal website http://mc.manuscriptcentral.com/tfs-ieee.

            Submissions should also be in the correct format https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/

            It is essential that your manuscript is identified as a Special Issue contribution:
             Ensure you choose ‘Special Issue’ when submitting.
             A cover letter must be included which includes the title ‘Special Issue on Type-2 Fuzzy-Model-Based Control and its Applications’

            IV. IMPORTANT DATES


            1 February 2020 – submission deadline
            April 2020 – notification of the first-round review (for guidance)
            May 2020 – revised submission due
            July 2020 – final notification of acceptance/rejection

            V. GUEST EDITORS

            Dr. Bo Xiao
            Imperial College London, London, UK
            Email: b.xiao@imperial.ac.uk

            Dr. H.K. Lam
            King’s College London, London, UK
            Email: hak-keung.lam@kcl.ac.uk

            Prof. Kazuo Tanaka
            University of Electro-Communications, Tokyo, Japan
            Email: ktanaka@mce.uec.ac.jp

            Prof. Jerry M. Mendel
            University of Southern California, Los Angeles, USA
            Email: mendel@sipi.usc.edu

            Wednesday, 10 April 2019

            IEEE Transactions on Fuzzy Systems - Call for Papers

            Special Issue on SMART FUZZY OPTIMIZATION IN OPERATIONAL RESEARCH AND RENEWABLE ENERGY: MODELLING, SIMULATION, AND APPLICATION

            Deadline for Submissions 1.11.19

            1. AIMS AND SCOPE
            Over the last five decades fuzzy optimization have found numerous successful applications in diverse fields including operational research, manufacturing, information technology, energy optimization, data science and smart cities, big data analytics and the list goes on. Fuzzy optimization has strongly influenced research and development in other areas of intelligent computing leading to many hybrid and deep learning systems. It has opened up new horizons in thinking, research and development and it will certainly guide us into another half century of progress. Actually, fuzzy optimization is one kind of the approximation of nonlinear optimization techniques, which has basically formed some systematic but not unified theory of fuzzy systems and other fuzzy-sets based methodologies. Fuzzy Optimization and Decision Making is an interdisciplinary area focusing upon methodologies for extracting useful knowledge and experiences from data technology. Specific topics include fuzzy sets, rough sets, statistical methods, parallel/distributed data mining, hybrid fuzzy optimization such as hybrid evolutionary and swarm intelligence methods and human interaction, big data optimization, IoTs, flexibility, reliability and robustness, smart systems in specific domains, high-dimensional data, energy optimization and software engineering, data science, analytics, etc.

            The high complexity of neural systems and the large number of constituents with an often unknown functional interconnections lead to significant computational challenges. An important consideration is the development of mathematical models incorporating the many influences of randomness, noise, uncertainty and fuzziness in both their static and time-dependent versions. The quality of prediction by a model highly depends on the integration of imprecise data and the quantification of uncertain structural parameters. A sophisticated analysis of the influence of all types of randomness, uncertainty and noise is indispensable for the development of reliable computational and neural models and programs.
            Objective of this special issue is to explore latest modeling, simulation and fuzzy optimization, related with and renewable energy, electronics and electricity, and various related subjects. This offers a concentrative venue for researchers to make rapid exchange of ideas and innovative research findings in fuzzy optimization and Operational Research. In particular, new interdisciplinary approaches in fuzzy optimization applications, computer science and engineering applications, or strong conceptual foundations in newly evolving topics are especially welcomed. We invite researchers and experts worldwide to submit high-quality innovative research papers and critical review articles on the subsequent potential topics.

            2. TOPICS COVERED
            The topics include but are not limited to:

            Energy optimization
            • Power supply reliability
            • Power systems
            • Hybrid renewable energy
            • Photovoltaic
            • Energy efficient
            Operational research
            • System reliability
            • Combinatorial problems
            • Parameters optimization
            • Autonomous vehicle
            • Dynamic programming
            Fuzzy modeling and simulation
            • Fuzzy cybercrime
            • Fuzzy visualization
            • Fuzzy detection
            • Fuzzy computing
            • Fuzzy regulation
            • Fuzzy vehicular networks
            • Fuzzy failure diagnosis
            • Non-Fuzzy approaches to uncertainty handling 
            • Comparison: Fuzzy vs. Non-Fuzzy approaches
            Big Data Analytics and IoTs
            • Cloud computing
            • Territorial planning
            • Link prediction
            • Smart vehicles
            • Deep learning
            • Online social networks
            • Remote sensing                                                        
            • Sensor technologies                                                                                                                 
            • Electricity and commodity markets

            3. SUBMISSION GUIDELINES
            All authors should read ‘Information for Authors’ before submitting a manuscript:

            http://cis.ieee.org/ieee-transactions-on-fuzzy-systems.html

            Submissions should be through the IEEE TFS journal website:

            http://mc.manuscriptcentral.com/tfs-ieee

            It is essential that your manuscript is identified as a Special Issue contribution:
            • Ensure you choose ‘Special Issue’ when submitting.
            • A cover letter must be included which includes the title ‘Special Issue on Smart Fuzzy Optimization in Operational Research and Renewable Energy: Modelling, Simulation, and Applications’

            4. IMPORTANT DATES

            1 November 2019 Submission deadline

            For guidance only:
            January 2020            Notification of the first round review
            April 2020                      Revised submission due
            July 2020               Final notice of acceptance/reject


            5. GUEST EDITORS

            Prof. Dr. Gerhard-Wilhelm Weber
            Poznan University of Technology, Poland
            Email: gerhard.weber@put.poznan.pl                                                                                                                                      Affiliation:  IAM, METU, Ankara

            Dr. Pandian Vasant
            Universiti Teknologi PETRONAS, Malaysia
            Email: pandian_m@utp.edu.my

            Prof. Dr. Gilberto Perez Lechuga
            Universidad Autonoma del Estado de Hidalgo, Mexico
            Email: glechuga@uaeh.edu.mx

            Tuesday, 22 September 2015

            Call for Papers to IEEE TFS 2015 Special Issue on Brain Computer Interface (BCI)

            The submission deadline has been extended from October 1, 2015 to November 1, 2015

            I. AIMS AND SCOPE

            Brain computer interfaces (BCIs) have attracted rapidly increasing research interest in the last decade, thanks to recent advances in neurosciences, wearable/mobile biosensors, and analytics. However, there are many challenges in the transition from their laboratory settings to real-life applications, including the reliability and convenience of the sensing hardware, the availability of high-performance and robust algorithms for signal analysis and interpretation, and fundamental advances in automated reasoning that enable the reasoning and generalization across individuals.

            Computational intelligence techniques, particularly fuzzy sets and systems, have demonstrated superior performance in handling uncertainties in many real-world applications. They have also started attracting more attentions in the BCI domain. More specifically, fuzzy sets and systems have been used in electroencephalogram (EEG) feature extraction (e.g., self-organizing fuzzy neural networks, fuzzy region of interest, fuzzy wavelet packet), pattern recognition (e.g., fuzzy ARTMAP, type-1 and type-2 fuzzy logic systems, fuzzy-neural systems, fuzzy c-means clustering, fuzzy integrals, fuzzy SVM, fuzzy similarity, rough sets), optimization (e.g., fuzzy particle swarm optimization), etc..

            This special issue aims at showcasing the most exciting and recent advances in fuzzy sets and systems for BCI and related topics. It welcomes survey, position, research, and application papers.

            II. TOPICS COVERED

            The topics include but are not limited to:
            • Fuzzy control for BCI
            • Fuzzy signal processing for BCI/EEG/ ECoG/ MEG/MRI
            • Fuzzy feature extraction for BCI/EEG/ ECoG/ MEG/MRI
            • Fuzzy pattern recognition (classification, regression) for BCI/EEG/ ECoG/MEG/MRI
            • Fuzzy sets and systems for handling uncertainties and individual differences in BCI
            • Fuzzy approaches applied to data fusion of EEG with other physiological and contextual sensing modalities
            • Hybrid approaches that combine fuzzy sets and systems with machine learning, data mining, or other computational intelligence techniques for BCI

            III. IMPORTANT DATES

            • November 1, 2015: Submission deadline
            • January 1, 2016: Notification of 1st round review
            • February 1, 2015: Revised submission due
            • April 1, 2016: Final notice of acceptance/reject
            • August 2016: Special issue publication

            IV. SUBMISSION GUIDELINES

            Manuscripts should be prepared according to the instruction of the “Information for Authors” section of the journal found and submission should be done through the IEEE TFS journal website: http://mc.manuscriptcentral.com/tfs-ieee.  Clearly mark “Special Issue on Brain Computer Interfaces” in your cover letter to the Editor-in-Chief.  All submitted manuscripts will be reviewed using the standard procedure that is followed for regular submissions.

            V. GUEST EDITORS

            Dr. Dongrui Wu
            DataNova, USA
            drwu09@gmail.com

            Dr. Brent Lance
            Translational Neuroscience Branch
            Army Research Laboratory, USA
            brent.j.lance.civ@mail.mil

            Dr. Vernon Lawhern
            Translational Neuroscience Branch
            Army Research Laboratory, USA
            vernon.j.lawhern.civ@mail.mil

            Call for Papers IEEE Computational Intelligence Magazine Special Issue on Model Complexity, Regularization and Sparsity

            http://home.deib.polimi.it/boracchi/events/ModelComplexity.html

            Aims and Scope

            The effective management of solution complexity is one of the most important issues in addressing Computational Intelligence problems. Regularization techniques control model complexity by taking advantage of some prior information regarding the problem at hand, represented as penalty expressions that impose these properties on the solution. Over the past few years, one of the most prominent and successful types of regularization has been based on the sparsity prior, which promotes solutions that can be expressed as a linear combination of a few atoms belonging to a dictionary. Sparsity can in some sense be considered a measure of simplicity and, as such, is compatible with many nature-inspired principles of Computational Intelligence. Nowadays, sparsity has become one of the leading approaches for learning adaptive representations for both descriptive and discriminative tasks, and has been shown to be particularly effective when dealing with structured, complex and high-dimensional data.

            Regularization, including sparsity and other priors to control the model complexity, is often the key ingredient in the successful solution of difficult problems; it is therefore not surprising that these aspects have also recently gained a lot of attention in big-data processing, due to unprecedented challenges associated with the need to handle massive datastreams that are possibly high-dimensional and organized in complex structures.

            This special issue aims at presenting the most relevant regularization techniques and approaches to control model complexity in Computational Intelligence. Submissions of papers presenting regularization methods for Neural Networks, Evolutionary Computation or Fuzzy Systems, are welcome. Submissions of papers presenting advanced regularization techniques in specific, but relevant, application fields such as data/datastream-mining, classification, big-data analytics, image/signal analysis, natural-language processing, are also encouraged.

            Topics of Interest

            • Regularization methods for big and high-dimensional data;
            • Regularization methods for supervised and unsupervised learning;
            • Regularization methods for ill-posed problems in Computational Intelligence;
            • Techniques to control model complexity;
            • Sparse representations in Computational Intelligence;
            • Managing model complexity in data analytics;
            • Effective priors for solving Computational Intelligence problems;
            • Multiple prior integration;
            • Regularization in kernel methods and support vector machines.

            Important Dates

            • 22nd January, 2016: Submission of Manuscripts
            • 30th March, 2016: Notification of Review Results
            • 30th April, 2016: Submission of Revised Manuscripts
            • 15th June, 2016: Submission of Final Manuscripts
            • November, 2016: Special Issue Publication

            Submission Process

            The maximum length for the manuscript is typically 20 pages in single column with double-spacing, including figures and references. Authors of papers should specify in the first page of their manuscripts the corresponding author's contact and up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website.

            Submission should be made via at https://easychair.org/conferences/?conf=ieeecim1116

            Guest Editors

            Prof. Cesare Alippi,
            Dipartimento di Elettronica, Informazione e Biongegneria, Politecnico di Milano,
            via Ponzio 34/5, Milano, 20133, Italy
            email: cesare.alippi@polimi.it

            Dr. Giacomo Boracchi,
            Dipartimento di Elettronica, Informazione e Biongegneria, Politecnico di Milano,
            via Ponzio 34/5, Milano, 20133, Italy
            email: giacomo.boracchi@polimi.it

            Dr. Brendt Wohlberg,
            Theoretical Division, Los Alamos National Laboratory,
            Los Alamos NM 87545, USA
            email: brendt@lanl.gov

            Friday, 24 July 2015

            Call for Papers IEEE CIM Special Issue "Model Complexity, Regularization and Sparsity"

            IEEE Computational Intelligence Magazine Special Issue on “Model Complexity, Regularization and Sparsity” http://home.deib.polimi.it/boracchi/events/ModelComplexity.html

             

            Aims and Scope

            The effective management of solution complexity is one of the most important issues in addressing Computational Intelligence problems. Regularization techniques control model complexity by taking advantage of some prior information regarding the problem at hand, represented as penalty expressions that impose these properties on the solution. Over the past few years, one of the most prominent and successful types of regularization has been based on the sparsity prior, which promotes solutions that can be expressed as a linear combination of a few atoms belonging to a dictionary. Sparsity can in some sense be considered a "measure of simplicity" and, as such, is compatible with many nature-inspired principles of Computational Intelligence. Nowadays, sparsity has become one of the leading approaches for learning adaptive representations for both descriptive and discriminative tasks, and has been shown to be particularly effective when dealing with structured, complex and high-dimensional data.

            Regularization, including sparsity and other priors to control the model complexity, is often the key ingredient in the successful solution of difficult problems; it is therefore not surprising that these aspects have also recently gained a lot of attention in big-data processing, due to unprecedented challenges associated with the need to handle massive datastreams that are possibly high-dimensional and organized in complex structures.

            This special issue aims at presenting the most relevant regularization techniques and approaches to control model complexity in Computational Intelligence. Submissions of papers presenting regularization methods for Neural Networks, Evolutionary Computation or Fuzzy Systems, are welcome. Submissions of papers presenting advanced regularization techniques in specific, but relevant, application fields such as data/datastream-mining, classification, big-data analytics, image/signal analysis, natural-language processing, are also encouraged.

            Topics of Interest

            • Regularization methods for big and high-dimensional data;
            • Regularization methods for supervised and unsupervised learning;
            • Regularization methods for ill-posed problems in Computational Intelligence;
            • Techniques to control model complexity;
            • Sparse representations in Computational Intelligence;
            • Managing model complexity in data analytics;
            • Effective priors for solving Computational Intelligence problems;
            • Multiple prior integration;
            • Regularization in kernel methods and support vector machines.

            Important Dates

            • 22nd January, 2016: Submission of Manuscripts
            • 30th March, 2016: Notification of Review Results
            • 30th April, 2016: Submission of Revised Manuscripts
            • 15th June, 2016: Submission of Final Manuscripts
            • November, 2016: Special Issue Publication

            Submission Process

            The maximum length for the manuscript is typically 20 pages in single column with double-spacing, including figures and references. Authors of papers should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website.
            Submission should be made via at https://easychair.org/conferences/?conf=ieeecim1116

             

            Guest Editors

            Prof. Cesare Alippi,
            Dipartimento di Elettronica, Informazione e Biongegneria, Politecnico di Milano,
            via Ponzio 34/5, Milano, 20133, Italy
            email: cesare.alippi@polimi.it

            Dr. Giacomo Boracchi,
            Dipartimento di Elettronica, Informazione e Biongegneria, Politecnico di Milano,
            via Ponzio 34/5, Milano, 20133, Italy
            email: giacomo.boracchi@polimi.it

            Dr. Brendt Wohlberg,
            Theoretical Division, Los Alamos National Laboratory,
            Los Alamos NM 87545, USA
            email: brendt@lanl.gov