Converging Game Theory and Reinforcement Learning For Industrial Internet-of-Things
The fifth-generation (5G) wireless network provides high-rate, ultra-low latency, and high-reliability connections that can meet the Industrial Internet of Things (IIoT) requirements in factory automation, especially for robot motion control. In this paper, we address 5G service provisioning in an automated warehouse scenario, where swarm robotics is controlled by an industrial controller that provides routing and job instructions over the 5G network. Leveraging the coordinated multipoint (CoMP), we formulate a [...]
WINNER of the The 2022 Administration Council Excellence Award to doctorates
Synchromedia est ravi d'annoncer que Abderrahmane Rahiche a été nommé lauréat du Council Excellence Award d'administration au doctorat de l'année 2022. Le concours auquel il a participé vise à mettre en lumière qualité exceptionnelle des travaux de recherche des doctorants et de reconnaître leur formation de spécialistes de haut niveau. Ce prix est accompagné d'une bourse de 2 500 $. Synchromedia is thrilled to announce that Abderrahmane Rahiche has been [...]
Monitoring and Measurement System for Green Operation of Geographically Distributed ICT Services
Despite recent efforts and important results already achieved, the reduction of energy consumption and carbon emissions by Information and Communication Technologies is still far from the expected goals. As the annual growth in traffic is doubling every two years with more and more connections to the Internet, to be energy and carbon-aware it is paramount to implement a Monitoring and Measurement System which supports green strategies in a geographically distributed [...]
Historical documents dating using multispectral imaging and ordinal classification
The estimation of the age of undated old manuscripts is one of the most challenging and controversial tasks in the field of historical document analysis. Several dating methods have been proposed, but most of them either use destructive techniques or rely on the textual information of documents. In this work, we rather focus our attention on the discoloration and the changes in the optical proprieties of their writing materials, which [...]
KFBin: Kalman Filter-Based Approach for Document Image Binarization
In this paper, we propose a novel two-step approach, called KFBin, for the binarization of document images based on the Kalman filtering (KF) technique. In the first step, a state space model is developed as a new document image representation, and then the Kalman filter is applied to track the positions of the foreground and background information and generate two corresponding outputs, which allows the enhancement of the foreground content [...]
Kernel Orthogonal Nonnegative Matrix Factorization: Application to Multispectral Document Image Decomposition
As a nonlinear extension of the standard nonnegative matrix factorization (NMF), kernel-based variants have demonstrated to be more effective for discovering meaningful latent features from raw data. However, many existing kernel methods allow only obtaining the basis matrix in the projected feature space, which prevents its inverse mapping back to the original space as requested in many applications. In this work, we propose a new kernel orthogonal NMF method that [...]
Blind Decomposition of Multispectral Document Images Using Orthogonal Nonnegative Matrix Factorization
This paper addresses the challenge of Multispectral (MS) document image segmentation, which is an essential step for subsequent document image analysis. Most previous studies have focused only on binary (text/non-text) separation. They also rely on handcrafted features and techniques dedicated to conventional images that do not take advantage of MS images' spectral richness. In this work, we reformulate this task as a source separation problem, whereby we target the blind [...]
Forgery Detection in Hyperspectral Document Images using Graph Orthogonal Nonnegative Matrix Factorization
The analysis of inks plays a crucial role in the examination process of questioned documents. To address this issue, we propose a new approach for ink mismatch detection in Hyperspectral document (HSD) images based on a new orthogonal and graph regularized Nonnegative Matrix Factorization (NMF) model. Although some previous works have proposed orthogonality constraints to solve clustering problems in different contexts, the application of such constraints is not straightforward due [...]
Feature learning for footnote-based document image classification
Classifying document images is a challenging problem that is confronted by many obstacles; specifically, the pivotal need of hand-designed features and the scarcity of labeled data. In this paper, a new approach for classifying document images, based on the availability of footnotes in them, is presented. Our proposed approach depends mainly on a Deep Belief Network (DBN) that consists of two phases, unsupervised pre-training and supervised fine-tuning. The main advantage [...]
Unsupervised exemplar-based learning for improved document image classification
Many recent state-of-the-art approaches for document image classification are based on supervised feature learning that requires a large amount of labeled training data. In real-world problem of document image classification, the available amount of labeled data is limited and scarce while a large amount of unlabeled data is often available at almost no cost. In this paper, we present an approach for learning visual features for document analysis in an [...]
Weakly supervised bounding box extraction for unlabeled data in table detection
Most of the table detection tasks are using existing off- the-shelf methods for their detection algorithm. However, datasets that are used for evaluation are not challenging enough due to the lack of quantity and diversity. To have a better comparison between proposed methods we introduce the NAS dataset in this paper for historical digitized images. Tables in historic scientific documents vary widely in their characteristics. They also appear alongside visually [...]
Towards More Reliable Deep Learning-Based Link Adaptation for WiFi 6
The problem of selecting the modulation and coding scheme (MCS) that maximizes the system throughput, known as link adaptation, has been investigated extensively, especially for IEEE 802.11 (WiFi) standards. Recently, deep learning has widely been adopted as an efficient solution to this problem. However, in failure cases, predicting a higher-rate MCS can result in a failed transmission. In this case, a retransmission is required, which largely degrades the system throughput. [...]
Game Theoretic Reinforcement Learning Framework For Industrial Internet of Things
The fifth-generation (5G) wireless network provides high-rate, ultra-low latency, and high-reliability connections that can meet the industrial IoT requirements in factory automation, especially for swarm robotics communication. In this paper, we address 5G service provisioning in an automated warehouse scenario where swarm robotics is controlled by an industrial controller that provides routing and job instructions over the 5G network. Leveraging the coordinated multipoint (CoMP), we formulate a joint CoMP clustering [...]
Computing on Wheels: A Deep Reinforcement Learning-Based Approach
Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation vehicular networks can be enhanced by incorporating vehicular edge or fog computing paradigm. However, the growing popularity and massive adoption of novel services make the edge resources insufficient. A possible solution to overcome this challenge is to employ the [...]
Energy-aware Control Of UAV-based Wireless Service Provisioning
Unmanned aerial vehicle (UAV)-assisted communications have several promising advantages, such as the ability to facilitate on-demand deployment, high flexibility in network reconfiguration, and high chance of having line-of-sight (LoS) communication links. In this paper, we aim to optimize the UAV control for maximizing the UAV's energy efficiency, in which both aerodynamic energy and communication energy are considered while ensuring the communication requirements for each ground terminal (GT) and backhaul link [...]
Deep Reinforcement Learning for URLLC in 5G Mission-Critical Cloud Robotic Application
In this paper, we investigate the problem of robot swarm control in 5G mission-critical robotic applications, i.e., in an automated grid-based warehouse scenario. Such application requires both the kinematic energy consumption of the robots and the ultra-reliable and low latency communication (URLLC) between the central controller and the robot swarm to be jointly optimized in real-time. The problem is formulated as a nonconvex optimization problem since the achievable rate and [...]
Deep Q-Learning for Joint Server Selection, Offloading, and Handover in Multi-access Edge Computing
In this paper, we propose a deep reinforcement learning (DRL) based approach to solving the problem of joint server selection, task offloading and handover in a multi-access edge computing (MEC) wireless network. The 5G networks tend to have a large number of users and MEC servers involving large numbers of different states and actions (both continuous and discrete), in which evaluating every possible combination becomes very challenging for traditional DRL [...]
UAV Control for Wireless Service Provisioning in Critical Demand Areas: A Deep Reinforcement Learning Approach
In this paper, we investigate the problem of wireless service provisioning through a rotary-wing UAV which can serve as an aerial base station (BS) to communicate with multiple ground terminals (GTs) in a boost demand area. Our objective is to optimize the UAV control for maximizing the UAV.s energy efficiency, in which both aerodynamic energy and communication energy are considered while ensuring the communication requirements for each GT and backhaul [...]
Joint Server Selection, Cooperative Offloading and Handover in Multi-access Edge Computing Wireless Network: A Deep Reinforcement Learning Approach
In this paper, we consider a multi-user MEC wireless network in which multiple mobile devices can associate and perform computation offloading via wireless channels to MEC servers attached to the base stations. The decision whether the computation task is executed locally at the user device or to be offloaded for MEC server execution should be adaptive to the time-varying network dynamics. Taking into account the dynamic of the environment, we [...]
Energy disaggregation using variational autoencoders
Non-intrusive load monitoring (NILM) is a technique that uses a single sensor to measure the total power consumption of a building. Using an energy disaggregation method, the consumption of individual appliances can be estimated from the aggregate measurement. Recent disaggregation algorithms have significantly improved the performance of NILM systems. However, the generalization capability of these methods to different houses as well as the disaggregation of multi-state appliances are still major [...]
Crosstalk Suppression in Semi-Intrusive Load Monitoring Systems Using Hall Effect Sensors
Semi-intrusive load monitoring (SILM) is an appliance load monitoring approach using multiple meters, each meter measuring power for a subgroup of appliances. As an effective solution for demand response programs, SILM is used to get granular power measurements at the level of individual appliances in buildings. Hall effect sensors (HES) on each wire attached to a circuit breaker in distribution panels are one means of providing SILM. However, HES are [...]
OpenFlow Rule Placement In Carrier Networks For Augmented Reality Applications
Today, mobile consumers increasingly use Augmented Reality (AR) devices to stream personal video through carrier networks. Thanks to its flexibility, Software-Defined Networking (SDN) is deployed in many carrier networks to support end-to-end network-slicing, which is substantial for these AR applications. In an OpenFlow-enabled SDN network, a controller must decide the rules to be placed into the switches in the network, subject to multiple constraints such as memory capacity, link bandwidth [...]
TaijiGNN: A New Cycle-Consistent Generative Neural Network for High-Quality Bidirectional Transformation between RGB and Multispectral Domains
Since multispectral images (MSIs) and RGB images (RGBs) have significantly different definitions and severely imbalanced information entropies, the spectrum transformation between them, especially reconstructing MSIs from RGBs, is a big challenge. We propose a new approach, the Taiji Generative Neural Network (TaijiGNN), to address the above-mentioned problems. TaijiGNN consists of two generators, G_MSI, and G_RGB. These two generators establish two cycles by connecting one generator’s output with the other’s input. [...]
Model-based Approach to Data Center Design and Power Usage Effectiveness Assessment
Data Centers (DC) are complex systems, which provide an environment to host Information and Technologies (IT) equipment. DCs are typically composed of a variety of components including servers, storage systems, networking infrastructures in addition to non-IT equipment such as power distribution and cooling systems. In order for a DC to function properly, all of its components need to be correctly configured and integrated. However, the variety of configurations and the [...]
Multispectral Image Reconstruction From Color Images Using Enhanced Variational Autoencoder and Generative Adversarial Network
Since multispectral images (MSIs) have much more sufficient spectral information than RGB images (RGBs), reconstructing MS images from RGB images is a severely underconstrained problem. We have to generate colossally different information between the two scopes. Almost all previous approaches are based on static and dependent neural networks, which fail to explain how to supplement the massive lost information. This paper presents a low-cost and high-efficiency approach, “VAE-GAN”, based on [...]
Multi features and multi-time steps LSTM based methodology for bike sharing availability prediction
Most cities in the world promote bike-sharing services to encourage people to decrease carbon exhausting and to enhance their health. However, it is a big challenge for a bike-sharing service supplying corporation to re-balance bikes efficiently among different bike-sharing dockers without a forecasting ability. For solving this problem, we contribute two new approaches based on standard Long short-term memory (LSTM), which can not only take advantages of multi features inputs [...]
A hybrid GPU-FPGA based design methodology for enhancing machine learning applications performance
The high-density computing requirements of machine learning (ML) is a challenging performance bottleneck. Limited by the sequential instruction execution system, traditional general purpose processors are not suitable for efficient ML. In this work, we present an ML system design methodology based on GPU and FPGA to tackle this problem. The core idea of our proposal is when designing an ML platform, we leverage the graphics processing unit (GPU)’s high-density computing [...]
A Hybrid GPU-FPGA-based Computing Platform for Machine Learning
We present a hybrid GPU-FPGA based computing platform to tackle the high-density computing problem of machine learning. In our platform, the training part of a machine learning application is implemented on GPU and the inferencing part is implemented on FPGA. It should also include a model transplantation part which can transplant the model from the training part to the inferencing part. For evaluating this design methodology, we selected the LeNet-5 [...]
A Two-Stage Unsupervised Deep Learning Framework for Degradation Removal in Ancient Documents
Processing historical documents is a complicated task in computer vision due to the presence of degradation, which decreases the performance of Machine Learning models. Recently, Deep Learning (DL) models have achieved state-of-the-art accomplishments in processing historical documents. However, these performances do not match the results obtained in other computer vision tasks, and the reason is that such models require large datasets to perform well. In the case of historical documents, [...]
Simultaneous Detection of Regular Patterns in Ancient Manuscripts Using GAN-Based Deep Unsupervised Segmentation
Document Information Retrieval has attracted researchers’ attention when discovering secrets behind ancient manuscripts. To understand such documents, analyzing their layouts and segmenting their relevant features are fundamental tasks. Recent efforts represent unsupervised document segmentation, and its importance in ancient manuscripts has provided a unique opportunity to study the said problem. This paper proposes a novel collaborative deep learning architecture in an unsupervised mode that can generate synthetic data to avoid [...]
Federated Imitation Learning: A Cross-Domain Knowledge Sharing Framework for Traffic Scheduling in 6G Ubiquitous IoT
The ubiquitous Internet of Things (IoT) system is a key component of future 6G networks to realize a fully connected world. Extensive efforts have been made to provide on-demand traffic scheduling in IoT networks through machine learning algorithms. However, the current learning approaches are hindered by the heterogeneous information in ubiquitous IoT systems since the data are collected from different domains (e.g., space, air, ground, and ocean). To uncover the [...]
Routing and Packet Scheduling For Virtualized Disaggregate Functions in 5G O-RAN Fronthaul
Abstract: Open Radio Access Network (O-RAN) is an innovative RAN architecture designed to revolutionize 5G and beyond mobile networks. O-RAN virtualizes the fronthaul network functions into O-RAN Centralized Unit (O-CU), O-RAN Distributed Unit (O-DU) and O-RAN Radio Unit (O-RU). Unfortunately, no standard data communication mechanism has been defined for the communication between these elements. Therefore, O-DUs may not work efficiently in O-DU pool, limiting the RAN performance. This paper investigates [...]
Graduation Announcement and Honor for Ph.D. student, RAHICHE Abderrahmane
Congratulations! We are so glad and proud to announce the Honor being deserved to our colleague RAHICHE Abderrahmane following his PhD graduation in January 2022. RAHICHE was supervised by Prof. Mohamed Cheriet from System Engineering Department. We are very pleased to let you know that, as a result of his outstanding academic results, his name appears on the 2021 ÉTS Honour List. The Honour List is posted on the ÉTS website. His name [...]
Graduation Announcement and Honor for Ph.D. student, XU LIU
Congratulations! We are so glad and proud to announce the Honor being deserved to our colleague XU LIU following his PhD graduation in November 2021. XU was supervised by Prof. Abdelouahed Gherbi from Software and IT Dept and co-supervised by Prof. Mohamed Cheriet. We are very pleased to let you know that, as a result of his outstanding academic results, his name appears on the 2021 ÉTS Honour List. The Honour List is [...]
TV show following the MoU signature between ÉTS and University of Bari (Italy), Jan 2022.
TV show Speciale Antena Sud Italia, MoU signature and partnership ÉTS and University of Bari, Jan 2022. The TV shows following the signature and partnership between the École de technologie supérieure (ÉTS) and The University of Bari Aldo Moro is a higher education institution in Bari, Apulia, in Southern Italy. https://www.youtube.com/watch?v=ojo_Nuj2jNY https://youtu.be/ojo_Nuj2jNY : TV show Speciale Antena Sud Italia, MoU signature and partnership ÉTS/University of Bari, Jan 2022.
CEOS Net: Intelligent Cyber Value Chain Network
Project Title: Intelligent Cyber Value Chain Network (CEOSNet) Back Project Investigators: Prof. Mohamed Cheriet (ÉTS), Soumaya Yacout (Polytechnique Montréal) Partner: Ecole Polytechnique de Montreal, Concordia University, Laval University, University of Sherbrooke, UQAM, Jacobb College, Productique Quebec College Project Funder: Canada Foundation for Innovation (CFI) Description: What problem are we solving? Recently, Canada’s manufacturing sector has experienced a heavy decline in employment and overall output, which results in a sizable adverse effect on our [...]
Ph.D. and M.Sc internship offers in Applied Artificial Intelligence (AAI) for Corporate Data
University of Sherbrooke in partnership with Synchromedia Lab at École de Technologie Supérieure (ÉTS) is carrying out research on AAI for business assessment. The aim is to research and develop a state-of-the-art Machine Learning models and Data Analytics methodologies targeting corporate data. Students who are already enrolled in a master or a PhD in their universities can benefit from these internships, given a co-supervision agreement setup. This is a call [...]
Ph.D. and M.Sc internship offers in Applied Artificial Intelligence (AAI) for Sustainability
University of Québec à Chicoutimi (UQAC) with Synchromedia Lab at École de Technologie Supérieure (ÉTS) is carrying out research in Applied data science and AI in sustainability transition. Students who are already enrolled in a master or a PhD in their universities can benefit from these internships, given a co-supervision agreement setup. This is a call for applications under interuniversity partnership. For more additional details, please see the description and [...]
Canada Research Chair In Sustainable Smart Eco-Cloud
Project Title: Canada Research Chair In Sustainable Smart Eco-Cloud Project Investigators: Partner: Project Funder: Social Sciences and Humanities Research Council of Canada (SSHRC) Description: What problem are we solving? Chairholder Mohamed Cheriet is addressing issues related to a sustainable smart eco-cloud platform, which is a virtual and analytical system capable of deep, complex computation and intelligent behaviors performed in an energy-efficient and eco-friendly manner. By means of smart meters, [...]
Routing and Packet Scheduling in LoRaWANs-EPC Integration Network
Abstract: Recently, Mobile Network Operators are considering the integration of LoRaWAN in their Evolved Packet Core (EPC) to expand their business, and to improve the interoperability and multi-vendor integration in their networks. In such integration, a LoRa gateway can implement the virtual base station function of eNodeB protocol stacks to forward LoRa packets through EPC to the application servers. Unfortunately, the current integration of LoRa and the mobile access according [...]
Self-Optimization Fabric (SOF) for ENCQOR Network
Project Title: Self-Optimization Fabric (SOF) for ENCQOR Network Project Investigators: Prof. Mohamed Cheriet, Prof. Kim Khoa Nguyen Partner: Ciena Project Funder: MITACS Description: What problem are we solving? 5G is revolutionary in many ways but what makes it unique is that it is the first generation of networking that makes connectivity as pervasive as the air we breathe in. Imagine the user landscape that would emerge from the confluence of robotics and [...]
CANARIE – Green Star Network
Project Title: CANARIE – Green Star Network Project Investigators: Partner: Project Funder: Description: What problem are we solving? The goal of the GreenStar Network Project is to initiate a Canadian consortium of industry, universities and government agencies with the common goal of reducing greenhouse gas (GHG) emissions arising from information & communication technology (ICT) services. The expected result is the creation [...]
CIENA – “Smart V-WAN Hypervisor for Inter Data Centre Network”
Project Title: CIENA – “Smart V-WAN Hypervisor for Inter Data Centre Network” Project Investigators: Partner: Ciena Project Funder: NSERC,PROMPT Québec, Ciena and Inocybe Technologies Description: What problem are we solving? Along with the advent of the ever-increasing cloud computing applications on the Internet, WAN-based services are contributing significantly to the overall environmental footprint of the Information and Communications Technologies (ICT) industry. With an increase in traffic [...]
Sustainable and Green Telco Cloud
Project Title: Sustainable and Green Telco Cloud Project Investigators: Partner: Ericsson, Inocybe Technologies, NSERC-CRD, MITACS, University of Toronto, CIRAIG (ÉcolePolytechnique de Montréal) Project Funder: NSERC-CRD, Ericsson, Inocybe Technologies and MITACS Description: What problem are we solving? Reducing greenhouse gas (GHG) emissions caused by the extremely widespread use of electronic devices and telecommunications services in the Information and Communications Technologies (ICT) industry is emerging in Canada and throughout the world. Cloud computing, [...]
Sustainable Cloud-based M2M Smart Home
Project Title: Sustainable Cloud-based M2M Smart Home Project Investigators: Partner: The project will strengthen the academia-industry-government collaboration involving the ÉTS, CIRODD, CIRAIG (École Polytechnique de Montréal), Ericsson, Videotron, Homebeaver and Varitron. Project Funder: Description: What problem are we solving? The Machine-to-Machine (M2M) communications paradigm, a novel automated exchange of data among autonomous devices without human intervention, is crucial for the networked society and is shaping the evolution of [...]
Sustainable Smart ÉTS Residence Testbed
Project Title: Sustainable Smart ÉTS Residence Testbed Project Investigators: Partner: CFI, CRC, ÉTS, Quartier de l’innovation, Ericsson Canada, Videotron. Project Funder: Canadian Foundation for Innovation (CFI) Description: What problem are we solving? The Sustainable Smart ÉTS Residence (StarÉTS) project is aimed at building a platform for supporting the research program of the Canada Research Chair (CRC) Tier 1 on Sustainable Smart Eco-Cloud held by Professor Mohamed Cheriet. It is [...]
PANLAB I Pan European Laboratory for Next Generation Networks and Services (FP6)
Project Title: PANLAB I Pan European Laboratory for Next Generation Networks and Services (FP6) Project Investigators: Partner: Synchomedia consortium Project Funder: Description: What problem are we solving? The Synchomedia consortium is a partner in the European project entitled PANLAB 1 (2006-2008). This is a specific research project that is part of a larger set of European activities: The PANLAB (Pan European Laboratory) for Next Generation Networks and Services research [...]
PANLAB II – Implementation of Panlab I Project (FP7)
Project Title: Implementation of Panlab I Project (FP7) Project Investigators: Partner: Project Funder: Description: What problem are we solving? In order to assure the leading role of Europe in the area of Telecommunications, the European Commission renewed its support to the PANLAB project as part of the FIRE initiative (Future Internet Research and Experimentation) of the 7th European Frame Program (FP7). Its core mission is the implementation of the PANLAB [...]
Visibility of knowledge
Project Title: Visibility of knowledge Project Investigators: Partner: Project Funder: Description: What problem are we solving? Bringing together a team of humanists and computer scientists, we are interested in understanding how visual techniques, such as the use of footnotes, diagrams, tables and mimetic representations of objects, were used to engage the public and make new ideas accessible. Rather than focus exclusively on language, we want to know more about [...]
Global Currents: Cultures of Literary Networks, 1050-1900
Project Title: Cultures of Literary Networks, 1050-1900 Project Investigators: Andrew Piper, McGill University, Canada; Mohamed Cheriet, École de technologie supérieure, Canada; Elaine Treharne, Stanford University, United States; Lambert Schomaker, University of Groningen, The Netherlands Partner: Project Funder: Description: What problem are we solving? In what ways have different cultures in different periods approached the page? Regardless of material, size, or the word used to designate it (page itself only [...]
Indian Ocean World MCRI – (Major Collaborative Research Project) 2010-2017
Project Title: Indian Ocean World MCRI Project Investigators: Prof. Mohamed Cheriet Partner: Project Funder: Social Sciences and Humanities Research Council of Canada (SSHRC) Description: What problem are we solving? This project investigates the rise and development of the world’s first “global economy” in the context of human-environment interaction from early times to the present day. The region under study is the Indian Ocean world (IOW), an arena of primary geopolitical [...]
MDEIE Project with IMADOC, Rennes
Project Title: MDEIE Project with IMADOC, Rennes Project Investigators: Partner: Project Funder: Description: What problem are we solving? An MDEIE (Quebec’s Ministry of Economic Development, Innovation and Export Trade) funding was recently awarded to Prof. Cheriet as Project Leader of SynchroMedia Consortium, under a unified international research initiative. This project deals with the editing and understanding of online handwriting collaborative annotations. The aim of this research is to [...]
ENIT Project AUF
Project Title: Agence Universitaire de la Francophonie Project Investigators: Partner: ENIT from Tunis, University of Paris 5, and Synchromedia Project Funder: Description: What problem are we solving? An AUF funding was awarded to Prof. Cheriet as Project Leader of SynchroMedia Consortium, under a unified international research initiative. In the same line, we joined a Francophone effort supported by AUF to support eLearning or distance learning and [...]
Sustainable Cloud-based M2M Smart Home
Project Title: Self-Optimization Fabric (SOF) for ENCQOR Network Project Investigators: Partner: ÉTS, CIRODD, CIRAIG (École Polytechnique de Montréal), Ericsson, Videotron, Homebeaver and Varitron Project Funder: NSERC-RDC Description: What problem are we solving? The Machine-to-Machine (M2M) communications paradigm, a novel automated exchange of data among autonomous devices without human intervention, is crucial for the networked society and is shaping the evolution of the future Internet and the Internet of Things (IoT) [...]
Sustainable and Green Telco Cloud
Project Title: Sustainable and Green Telco Cloud Project Investigators: Partner: Ericsson, Inocybe Technologies, NSERC-CRD, MITACS, University of Toronto, CIRAIG (ÉcolePolytechnique de Montréal). Project Funder: NSERC-CRD, Ericsson, Inocybe Technologies and MITACS Description: What problem are we solving? Reducing greenhouse gas (GHG) emissions caused by the extremely widespread use of electronic devices and telecommunications services in the Information and Communications Technologies (ICT) industry is emerging in [...]
Sustainable Smart ÉTS Residence testbed
Project Title: Sustainable Smart ÉTS Residence testbed Project Investigators: Partner: CFI, CRC, ÉTS, Quartier de l’innovation, Ericsson Canada, Videotron. Project Funder: Canadian Foundation for Innovation (CFI) Description: What problem are we solving? The Sustainable Smart ÉTS Residence (StarÉTS) project is aimed at building a platform for supporting the research program of the Canada Research Chair (CRC) Tier 1 on Sustainable [...]
CIENA – “Smart V-WAN Hypervisor for Inter Data Centre Network”
Project Title: Smart V-WAN Hypervisor for Inter Data Centre Network Project Investigators: Partner: Project Funder: NSERC,PROMPT Québec, Ciena and Inocybe Technologies Description: What problem are we solving? Along with the advent of the ever-increasing cloud computing applications on Internet, WAN-based services are contributing significantly to the overall environmental footprint of the Information and Communications Technologies (ICT) industry. With an increase in traffic demand and heterogeneity of [...]
Global Currents: Cultures of Literary Networks, 1050 -1900
Project Title: Digging into Data - DIDC Project 2013 Project Investigators: Partner: Project Funder: Social Sciences and Humanities Research Council of Canada (SSHRC), Natural Sciences and Engineering Research (NSERC) and Canadian Foundation for Innovation (CFI) Description: What problem are we solving? The International, multi-disciplinary project is based on the Indian Ocean World (IOW) project with cross-cultural study of literary networks in a global context by integrating new [...]
Indian Ocean World
Project Title: The Indian Ocean World: The Making of the First Global Economy in the Context of Human-Environment Interaction Project Investigators: Partner: Project Funder: Social Sciences and Humanities Research Council of Canada (SSHRC) Description: What problem are we solving? This innovative project is a large, international and multi-disciplinary program of collaborative research that aims to examine the history of human-environment interaction in the Indian Ocean World—an arena of [...]
Green Star Network
The goal of the GreenStar Network Project is to initiate a Canadian consortium of industry, universities, and government agencies with the common goal of reducing greenhouse gas (GHG) emissions arising from information & communication technology (ICT) services. The expected result is the creation of tools, protocols, procedures, use cases for a growing network of ICT service providers that offers customers the lowest price and greenest services. The project is innovative [...]
PANLAB I – Pan European Laboratory for Next Generation Networks and Services (FP6)
Project Title: Pan European Laboratory for Next Generation Networks and Services Project Investigators: Partner: Eurescom, Alcatel, Thomson, Nokia, Telefonica, France Telecom, Italtel, DIMES Association, Fraunhofer/Focus, RAD and École de Technologie Supérieure (ÉTS) Project Funder: Description: What problem are we solving? The Synchomedia consortium is a partner in the European project entitled PANLAB 1 (2006-2008). This is a specific research project that is part of a larger set [...]
PANLAB II – Implementation of Panlab I Project (FP7)
Project Title: Implementation of Panlab I Project (FP7) Project Investigators: Partner: Project Funder: Description: What problem are we solving? In order to assure the leading role of Europe in the area of Telecommunications, the European Commission renewed its support to the PANLABproject aspart of the FIRE initiative (Future Internet Research and Experimentation) of the 7th European Frame Progamme (FP7). Its core mission is the implementation of the [...]
ENIT Project AUF
Project Title: Agence Universitaire de la Francophonie Project Investigators: Partner: Project Funder: Description: What problem are we solving? An AUF funding was awarded to Prof. Cheriet as Project Leader of SynchroMedia Consortium, under a unified international research initiative. In the same line, we joined a Francophone effort supported by AUF to support eLearning or distance learning and collaborative research. Three partners are involved: ENIT from Tunis, University [...]
Telepresence
Remote collaborative working environments aim at reducing time and space constraints by exploiting existing computer and network infrastructures. Such environments become popular in business, education and research institutions. Several integrated environments for supporting collaborative work exist, but there are very few solutions that consider the hardware as well as the software sharing dimension of collaborative workspaces. Research conducted at Synchromedia ultimately resulted in a highly integrated working platform supporting remote [...]
Intelligent interfaces
The bottleneck in human-machine interaction is exactly to make the human user and a computer system interact. The system must adapt to the user and the user to the system. Usually, user interfaces shift the entire burden of adaptation to the user. Typically, intelligent interfaces will attempt to adapt to users and model knowledge about them. Ongoing research at Synchromedia addresses the development of intelligent interfaces integrating new perceptual and [...]
Biomedical image processing
Information processing is now one of the main frontiers in medical imaging. Modern medical imaging modalities revolutionize many aspect of the practice of medicine. Whereas radiology was previously associated with diagnostic only-application, interventional tools are emerging, thanks to more accurate, more manageable technologies. Advanced methodologies, such as computational anatomy and image-guided procedures, are now reality. They promise a better understanding of the human body and better, less invasive treatments. Those [...]
Document Processing and Understanding
Processing of huge volumes of unprocessed, handwritten, and historical documents is a critical challenge in front of many heritage and cultural institutes and organizations. Our main objective in the field of document image processing and understanding is the development and implementation of novel models and techniques which may help in generating, enhancing, presenting and understanding of handwritten document images. Direct involvement of scholars and researchers from various institutes and universities, [...]
Databases and Contests
Machine learning for document understanding In order to provide training data for optical shape recognition (OSR), two databases of different sizes have been created in collaboration with Prof. Robert Wisnovsky (Institute of Islamic Studies, McGill University): 1. IBN SINA database: A database of 22720 shapes (fast access and fast access). 2. Avicenna database: A database of 123,007 labeled and not labeled shapes (fast access). 3. IBN SINA Ext database (with images): A database of more [...]
Cloud Computing
Synchromedia leads pilot research projects developing various cloud computing platforms driving state-of-the-art next-generation data centers and networks. Cloud computing links all resources together in a common architecture that can be virtualized. In other words, the compute and storage platform is architecturally "unified" with the network and the virtualization platform, making no distinction between the network and the edge devices connected to it. Cloud computing eliminates manual integration, in favor of [...]
Network Virtualization
Network virtualization research at Synchromedia is aimed at creating new models providing a logical software-based view of the underlying hardware and software networking resources (switches, routers, etc.). This results in an intelligent abstraction that makes it easy to deploy and manage network services and resources. We are pioneering the NFV (Network Function Virtualization) concept and very active in SDN (Software-Defined Networking) solutions, especially with respect to intra and inter-data center [...]
Sustainable and Environment Awareness
Relying heavily on the computing power of data centers, the Internet is enabling the establishment of entirely new industries and unlocking the vast potential for innovation in existing ones, like education, health care, energy, the public sectors, and knowledge dissemination. Advanced countries, like the US, have proposed national plans to ensure that every citizen will have access to ultra-broadband Internet. As a result, larger data centers are required to host [...]
Network and Cloud Security
The potentially revolutionary cloud computing and networking paradigms could not be achieved in reality without appropriate security and privacy solutions. Designing trustworthy clouds or interoperable secure clouds is very challenging, because of the heterogeneity and diversity of the services provided, as well as the diverse access requirements of domains in cloud environments, demand fine-grained access control policies. Synchromedia developed new security services for cloud platforms by integrating privacy-preserving protocols to [...]
Traffic Modeling and Big Data Analytics
Synchromedia research has a strong focus on smart traffic analyzer and QoS provisioning based on big data mining. Various models have been built for both intra and inter-data center network and a framework for optimally allocating resources according to each traffic model has been developed, in particular with respect to Telco and HPC applications. Based on advanced learning algorithms, the relationship between application requirements and underlying traffic is discovered. These patents [...]
Wireless Communication
Synchromedia has recently conducted large-scale research projects on wireless communications in collaboration with Ericsson. A cloud-based platform has been developed to support virtual wireless communications across heterogeneous access networks and extent mobile content distribution across cloud and mobile devices. The research framework covers various types of wireless technologies, such as WiFi, ZigBee, LTE, and 5G, as well as ubiquitous multiple dimensional communications between wireless and optical networks. Synchromedia research outcome [...]
Databases and Contests
Machine learning for document understanding In order to provide training data for optical shape recognition (OSR), two databases of different sizes have been created in collaboration with Prof. Robert Wisnovsky (Institute of Islamic Studies, McGill University): 1. IBN SINA database: A database of 22720 shapes (fast access and fast access). 2. Avicenna database: A database of 123,007 labeled and not labeled shapes (fast access). 3. IBN SINA Ext database (with images): A database of more [...]
Training LS-SVM Classifier in semi-supervised mode
The learning process typically assumes some form of a priori knowledge of the contextual problem at hand in the form of examplar data associated with labels. These data, called the training set, are used to design a classifier, the performance of which is measured on a separate dataset, called the testing set. This is supervised learning in which the performance of the classifier on the test set is viewed as [...]
Manifold learning (dimensionality reduction)
In machine learning, the data are usually represented in a high dimensional feature space. Nevertheless in practice, the data are restricted to a limited area of the feature space. This leads to the well known problem of the curse of dimensionality. The manifold learning techniques, also known as dimensionality reduction aim to find a mapping of the data from the high dimensional feature space to a new space of lower [...]
Classification and Machine Learning
Machine learning is a field of artificial intelligence, which use empirical data to extract characteristic of interest of their unknown underlying probability distribution. Knowledge about a given problem is learnt from examples (training data). This knowledge is implicit, unlike explicit knowledge that can be given by formal definitions or rules. A machine learning algorithm is evaluated on its generalization capability, which is its ability to apply successfully the learnt [...]
Experiemental Results
Experimental results of the published papers, including the full-size images, could be found in this section. Usually, there is a permanent link in each paper that points directly to its experimental results. You can also browse this section. Articles in this category Synchro Super-Resolution Method for Text Images Here, the code and also a few examples of the Synchro Super-Resolution (SR) method is presented. The Synchro SR method was submitted [...]
Behavioral Learning
Novel cognitive sciences and machine learning methodologies are developed by Synchromedia team dealing with efficient management in cloud computing environment. Cognitive radio network models have also been proposed for dynamically selecting transmission bands and routing paths, helping to maximize network throughput by performing joint routing, dynamic spectrum allocation, scheduling and power transmission control. Cognitive techniques allow mobile users to select a nearby data centre, thereby reducing delays and jitter. In [...]
ENIT Project AUF
Project Title: Agence Universitaire de la Francophonie Project Investigators: Prof. Mohamed Cheriet Partner: Project Funder: Description: What problem are we solving? An AUF funding was awarded to Prof. Cheriet as Project Leader of SynchroMedia Consortium, under a unified international research initiative. In the same line, we joined a Francophone effort supported by AUF to support eLearning or distance learning and collaborative research. Three partners are involved: ENIT [...]
Canadian Foundation for Innovation Grant
Project Title: Canadian Foundation for Innovation Grant Project Investigators: Partner: Project Funder: Description: What problem are we solving? A CFI grant was awarded to Prof. Cheriet as Project Leader of SynchroMedia Consortium, under a unified international research initiative. SynchroMedia is a pan-Canadian collaborative effort which is active in the emerging field of IST technologies, and, more specifically, in Intelligent Distributed Cooperative. Environments in Telepresence, which [...]
Learning Framework for IoT Services Chain Implementation in Edge Cloud Platform
Abstract: As an emerging solution to latency requirements of Internet of Things (IoT) services, edge computing can bring powerful processing capacity closer to data sources. However, with the limited resources at edge nodes, a major challenge is finding optimal resources in distributed edges to reduce the operational costs of service deployment. Prior works focus mainly on static optimization which may not work efficiently with the time-varying workloads and resource constraints. [...]
TCO Game in 5G Multi-Tenant Virtualized Mobile BackHaul (V-MBH) Network
Abstract: Raising density and ever-increasing traffic demand within future 5G Heterogeneous Networks (HetNets) will result in huge deployment, expansion and operating costs for upcoming Mobile Backhaul (MBH) networks. Multi-tenancy and network slicing based on virtualized resources are promising solutions to satisfy MBH network greediness while reducing related expenditures. Nevertheless, there is no appropriate model that fairly distributes costs over multiple Mobile Network Operators (MNO), and also optimizes physical resource planning. [...]
Machine-Learning-Based Cognitive Spectrum Assignment for 5G URLLC Applications
Abstract: As one of the main scenarios in 5G mobile networks, ultra-reliable low-latency communication (URLLC) can satisfy the stringent requirements of many emerging applications. To ensure end-to-end secure delivery of critical data, 5G URLLC needs an efficient hybrid access scheme for licensed and unlicensed spectrum in mmWave bands. This article introduces machine learning (ML) and fountain codes into mmWave hybrid access, and proposes an adaptive channel assignment method. The proactively [...]
Optimized Flow Assignment in a Multi-Interface IoT Gateway
Abstract: The last few years have witnessed a significant increase in the deployment of heterogeneous Internet of Things (IoT) networks. IoT devices send data with different requirements such as tolerated delay and data rates. Emerging multi-interface IoT devices bring the flexibility of connecting to multiple heterogeneous access networks, which thus improves the network capacity. However, each network interface has its own constraints in terms of network coverage, capacity, packet loss [...]
Embedding Multiple-Step-Ahead Traffic Prediction in Network Energy Efficiency Problem
Abstract: Adaptive Link Rate (ALR) is widely used to save energy consumption of network by adjusting the link rate according to the carried traffic through a network-level optimization of the flow allocation process. Existing ALR solution is mainly reactive, in which link speed is changed only when new traffic demand is requested. Also, they focus on energy consumption, and do not consider the cost of changes in the network (e.g., [...]
Dynamic QoS-aware Queuing for Heterogeneous Traffic in Smart Home
Abstract: Smart home gateways have to forward multi-sourced network traffic generated with different distributions and with different quality-of-service (QoS) requirements. The state-of-the-art QoS-aware scheduling methods consider only the conventional priority metrics based on the IP type of service (ToS) field to make a decision for bandwidth allocation. Such priority-based scheduling methods are not optimal to provide both QoS and quality of experience (QoE), since higher priority traffic may not require [...]
TCO Planning Game for 5G Multi-Tenant Virtualized Mobile BackHaul (V-MBH) Network
Abstract: Raising density and ever-increasing traffic demand within future 5G Heterogeneous Networks (HetNets) will result in huge deployment, expansion and operating costs for upcoming Mobile Backhaul (MBH) networks. Multitenancy and network slicing based on virtualized resources are promising solutions to satisfy MBH network greediness while reducing related expenditures. Nevertheless, there is no appropriate model that fairly distributes costs over multiple virtual operators, and also optimizes physical resource planning. In this [...]
SACO: A Service Chain Aware SDN Controller-Switch Mapping Framework
Abstract—The emerging paradigm of Software Defined Network (SDN) and virtualization technology promises an efficient solution for network providers to deploy services. Adopting them not only facilitates network management but also helps reduce the cost of maintaining network infrastructure. However, despite these advantages, there are still obstacles that must be overcome before SDN and virtualization can advance to reality in industrial deployments. In this paper, we focus on two well-researched issues, [...]
Placement and Chaining for Run-time IoT Service Deployment in Edge-Cloud
Abstract: This paper investigates an efficient placement and chaining of Virtual Network Functions (VNFs) to provide cloud based IoT services with minimal resource usage cost. We take into account bandwidth capacity and link delay of network connection between clouds where VNFs are allocated and underlying IoT networks where sensors and IoT gateways are deployed. Regarding the constantly changing network dynamics, input traffic of service components is considered at the lower [...]
Concurrent Traffic Queuing Game in Smart Home
Abstract: Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their QoS requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in the smart home networks do not consider concurrent traffic in their scheduling solutions. This paper presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network [...]
Energy Efficient Software Update Mechanism for Networked IoT Devices
Abstract—Due to security issues and incremental user requirements, software in IoT devices needs to be changed frequently. Recently, advanced IoT devices employ the component-based software architecture in which components can be updated at run-time. In such IoT networks, devices can download updated components from neighbor nodes, enabling quick deployment of updates in the entire network. A key operation which consumes a significant amount of energy in the update process is [...]
A Survey on Adaptive Multi-channel MAC Protocols in VANETs Using Markov Models
Abstract: A medium access control (MAC) protocol is designed to disseminate safety messages reliably and rapidly to improve the safety and efficiency of vehicles on the road in vehicular ad-hoc networks (VANETs). VANETs, which are created by moving vehicles, have specific properties, such as high node mobility with constrained movements and quick topology changes. Hence, MAC protocols should be designed to adapt to the changing data traffic patterns due to [...]
Subjective and objective quality assessment of degraded document images
Abstract The huge amount of degraded documents stored in libraries and archives around the world needs automatic procedures of enhancement, classification, transliteration, etc. While high-quality images of these documents are in general easy to be captured, the amount of damage these documents contain before imaging is unknown. It is highly desirable to measure the severity of degradation that each document image contains. The degradation assessment can be used in tuning parameters [...]
SDN-based fault-tolerant on-demand and in-advance bandwidth reservation in data center interconnects
Summary Geographically distributed data centers are interconnected through provisioned dedicated WAN links, realized by circuit/wavelength–switching that support large‐scale data transfer between data centers. These dedicated WAN links are typically shared by multiple services through on‐demand and in‐advance resource reservations, resulting in varying bandwidth availability in future time periods. Such an inter‐data center network provides a dynamic and virtualized environment when augmented with cloud infrastructure supporting end‐host migration. In such an [...]
Phishing-Aware: A Neuro-Fuzzy Approach for Anti-Phishing on Fog Networks
Abstract—Phishing detection is recognized as a criminal issue of Internet security. By deploying a gateway anti-phishing in the networks, these current hardware-based approaches provide an additional layer of defense against phishing attacks. However, such hardware devices are expensive and inefficient in operation due to the diversity of phishing attacks. With promising technologies of virtualization in fog networks, an anti-phishing gateway can be implemented as software at the edge of the [...]
NFV-based Architecture for the Interworking between WebRTC and IMS
Abstract: The emerging paradigm of network function virtualization (NFV) technology promises an efficient solution for optimized service deployment in the cloud computing environment thanks to its ability to dynamically add or remove virtual resources when there is a change in workload. Nevertheless, telecom providers are still facing a challenging issue in efficiently adopting NFV to deploy Web real-time communication (WebRTC) service on top of IP multimedia subsystem (IMS). Providing WebRTC [...]
Multiple-Step-Ahead Traffic Prediction in High-Speed Networks
Abstract: Traffic in high-speed networks shows distinct patterns at different timescales. This characteristic should be taken into account to address the error propagation in the multiple-step-ahead traffic prediction. Based on this idea, we proposed an algorithm in which traffic is modeled at different timescales using Gaussian process regression (GPR). The prediction at a timescale is made using the data of that timescale as well as the prediction results at larger [...]
MAC Protocols with Dynamic Interval Schemes for VANETs
Abstract Under the dynamically changing topology of Vehicular Ad-hoc NETworks (VANETs), the restricted intervals in Medium Access Control (MAC) protocols cannot provide sufficient capacity to carry both safety and non-safety applications. One approach which can solve these issues is a dynamic MAC protocol that can adapt itself to the vehicle density or traffic conditions. In this survey, we, therefore, study various techniques for dynamic intervals used in MAC protocols, their advantages, and disadvantages. First, [...]
Dynamic Controller/Switch Mapping in Virtual Networks Service Chains
Abstract: Accelerated Software Defined Networking (SDN) adoption makes SDN paradigm emerging in the state of the art. Especially, the combination of SDN and network functions virtualization (NFV) becomes a promising trend in deploying virtual network services for network operators. Although SDN can decouple networks into the control plane and the data plane to obtain flexible operation and programmability, there are many open issues that need to be addressed for SDN [...]
Backhauling-as-a-Service (BHaaS) for 5G Optical Sliced Networks: An Optimized TCO Approach
Keywords: 5G, BHaaS, CAPEX, OPEX, Optical Mobile Backhaul, ROI, TCO, TPaaS, Traffic Profiles., Voronoi
Energy and connectivity aware resource optimization of nodes traffic distribution in smart home networks
Keywords: Smart home network Energy optimization Software defined networks Coverage and connectivity Lagrangian decomposition method
Virtual Edge-Based Smart Community Network Management
Abstract: Providing multitenant, multiaccess, and multiservice solutions in communities is a key enabler to sustain innovations and economic development in society. This article investigates a solution for rearchitecting a telecommunications company's (telco's) central office to offer services in a smart community, enabled by virtual network function elements running on a smart edge. At the same time, these elements manage a multiaccess underlying infrastructure and deploy telco services with minimal resource [...]
Real-time optimized NFV architecture for internetworking WebRTC and IMS
Abstract: Network Function Virtualization (NFV) technology has emerged as a promising solution to optimize the deployment of network elements in cloud computing environment, both in terms of user Quality of Service (QoS) and resource allocation. To deliver IP Multimedia Subsystem (IMS) advanced services across multiple access networks, a cloud-based model likely improves not only flexibility in network management but also in service invocation. This model is particularly suitable for the [...]
MUG: A Parameterless No-Reference JPEG Quality Evaluator Robust to Block Size and Misalignment
Abstract: In this letter, a very simple no-reference image quality assessment (NR-IQA) model for JPEG compressed images is proposed. The proposed metric called median of unique gradients (MUG) is based on the very simple facts of unique gradient magnitudes of JPEG compressed images. MUG is a parameterless metric and does not need training. Unlike other NR-IQAs, MUG is independent to block size and cropping. A more stable index called MUG + is [...]
Iterative classifiers combination model for change detection in remote sensing imagery
Abstract: In this paper, we propose a new unsupervised change detection method designed to analyze multispectral remotely sensed image pairs. It is formulated as a segmentation problem to discriminate the changed class from the unchanged class in the difference images. The proposed method is in the category of the committee machine learning model that utilizes an ensemble of classifiers (i.e., the set of segmentation results obtained by several thresholding methods) [...]
Gaussian Process Regression based Traffic Modeling and Prediction in High-Speed Networks
Abstract: Evolving nature of network traffic challenges existing models to fit and predict its behavior. In particular, real traffic modeling requires more flexible design that can adapt to long-range and short-range dependent traffic with dynamic patterns. Unfortunately, existing models cannot handle such requirements because various traffic behaviors such as periodic and self-similar are not taken into account. In this paper, Gaussian process regression (GPR) is adapted for traffic modeling and [...]
Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator
Keywords: chrominance similarity, deviation pooling, gradient similarity, Image quality assessment
Toward an architectural model for highly-dynamic multi-tenant multi-services cloud-oriented platforms
Abstract - The characteristics of the Cloud Computing paradigm make it attractive to be used along with other paradigms like mobile and pervasive computing, smart cities, etc. There is a need to develop new platforms in order to take advantage of those converged infrastructures, and to abstract its high heterogeneities and complexities. The design and development of such robust and efficient platforms is challenging, because of the high heterogeneities, complexities [...]
Taxonomy of Information Security Risk Assessment (ISRA)
Abstract Information is a perennially significant business asset in all organizations. Therefore, it must be protected as any other valuable asset. This is the objective of information security, and an information security program provides this kind of protection for a company's information assets and for the company as a whole. One of the best ways to address information security problems in the corporate world is through a risk-based approach. In [...]
Taxonomy of Distributed Denial of Service Mitigation Approaches for Cloud Computing
Abstract Cloud computing has a central role to play in meeting today׳s business requirements. However, Distributed Denial-of-Service (DDoS) attacks can threaten the availability of cloud functionalities. In recent years, many effort has been expended to detect the various DDoS attack types. In this survey paper, our concentration is on how to mitigate these attacks. We believe that cloud computing technology can substantially change the way we respond to a DDoS attack, based [...]
SO-ARTIST: Self-Organized ART-2A inspired clustering for online Takagi-Sugeno fuzzy models.
Abstract In this paper we introduce a novel online self-organized clustering method based on the ART-2A network for Takagi–Sugeno fuzzy models. To accomplish the self-organization, we introduce an automatic decision algorithm along with solutions for merging and splitting of rules as well as the parameters they operate with, such as our novel incremental distance measurement and competitive recursive least squares. We emphasize the learning algorithm's having an impact for initial [...]
Optimal Placement of Sequentially Ordered Virtual Security Appliances in the Cloud
Abstract: Traditional enterprise network security is based on the deployment of security appliances placed on some specific locations filtering, monitoring the traffic going through them. In this perspective, security appliances are chained in specific order to perform different security functions on the traffic. In the cloud, the same approach is often adopted using virtual security appliances to protect traffic for different virtual applications with the challenge of dealing with the [...]
OpenFlow-based In-Network Layer-2 Adaptive Multipath Aggregation in Data Centers
Keywords: Aggregated path capacity, Forwarding, Multipath, OpenFlow, Routing
Carbon-aware distributed cloud: multi-level grouping genetic algorithm
Keywords: Carbon footprint, Cloud computing, Distributed cloud, Genetic algorithm, Green IT, Grouping genetic algorithm, Multi-level
Taxonomy of intrusion risk assessment and response system
Abstract In recent years, we have seen notable changes in the way attackers infiltrate computer systems compromising their functionality. Research in intrusion detection systems aims to reduce the impact of these attacks. In this paper, we present a taxonomy of Intrusion Response Systems (IRS) and Intrusion Risk Assessment (IRA), two important components of an intrusion detection solution. We achieve this by classifying a number of studies published during the last [...]
Phase-based binarization of historical manuscripts: Model and application
Abstract—In this paper, a phase-based binarization model for ancient document images is proposed, as well as a post-processing method that can improve any binarization method and a ground truth generation tool. Three feature maps derived from the phase information of an input document image constitute the core of this binarization model. These features are: the maximum moment of phase congruency covariance, a locally weighted mean phase angle, and a phase [...]
Challenges and complexities in application of LCA approaches in the case of ICT for a sustainable future
Abstract In this work, three of many ICT-specific challenges of LCA are discussed. First, the inconsistency versus uncertainty is reviewed with regard to the meta-technological nature of ICT. As an example, the semiconductor technologies are used to highlight the complexities especially with respect to energy and water consumption. The need for specific representations and metric to separately assess products and technologies is discussed. It is highlighted that applying product-oriented approaches [...]
Environment-aware Virtual Slice Provisioning in Green Cloud Environment
Keywords: Cloud computing, Energy-aware cloud, multi-tenant cloud, virtual network embedding, virtual slice allocation
Multi-tenancy Isolation and Self-Management in the cloud using Autonomic SDN Architecture
Abstract Systems and methods for ensuring multi-tenant isolation in a data center are provided. A switch, or virtualized switch, can be used to de-multiplex incoming traffic between a number of data centers tenants and to direct traffic to the appropriate virtual slice for an identified tenant. The switch can store tenant identifying information received from a master controller and packet forwarding rules received from at least one tenant controller. The [...]
Manifold Learning for the Shape-Based Recognition of Historical Arabic Documents
Abstract In this work, a recognition approach applicable at the letter block (subword) level for Arabic manuscripts is introduced. The approach starts with the binary images of the letter block to build their input representation, which makes it highly objective and independent of the designer. Then, using two different manifold learning techniques, the representations are reduced and learned. In order to decrease the computational complexity, PCA is applied to the [...]
Historical document image restoration using multispectral imaging system
Keywords: Multispectral image in-painting Abstract Thousands of valuable historical documents stored on the shelves of national libraries throughout the world are waiting to be scanned in order to facilitate access to the information they contain. The first major problem faced is degradation, which renders the visual quality of the document very poor, and in most cases, difficult to decipher. This work is part of our collaboration with the BAnQ (Bibliotheque [...]
Enabling infrastructure as a service (IaaS) on IP networks: from distributed to virtualized control plane
Abstract: Infrastructure as a Service (IaaS) is considered a prominent model for IP based service delivery. As grid and cloud computing have become a stringent demand for today's Internet services, IaaS is required for providing services, particularly "private cloud," regardless of physical infrastructure locations. However, enabling IaaS on traditional Internet Service Provider (ISP) network infrastructures is challenging because IaaS requires a high abstraction level of network architectures, protocols, and devices. [...]
Unsupervised Ensemble of Experts (EoE) Framework for Automatic Binarization of Document Images
Keywords: automatic binarization, Binarization, Confidentness, Consolidation, Decision making, Document image processing, Educational institutions, Endorsement, endorsement graph, endorsement-dependent weights, Ensemble of Experts, EoE framework, Expert Selection, Feature extraction, graph theory, grid-based Sauvola method, input document image, Laplace equations, PSNR, Robustness, School of Experts, unsupervised ensemble of experts framework, unsupervised learning, Visualization
An Efficient Ground Truthing Tool for Binarization of Historical Manuscripts
Keywords: Binarization, binarization method, Data preprocessing, Degradation, document image degradation type, Document image processing, Feature extraction, Groundtruthing, historical document image, historical manuscript binarization, history, Image edge detection, image ground truthing tool, Ink, learning (artificial intelligence), learning approach, Manuals, Noise, Persian heritage dataset, Persian heritage image binarization dataset, phase congruency features, PhaseGT fast ground truthing approach, PHIBD 2012 dataset, Training
Visual language processing (VLP) of ancient manuscripts: Converting collections to windows on the past
Keywords: Adaptation models, ancient manuscript, computational pattern analysis, cultural heritage, data mining, data-driven mining, Degradation, directed graphical model, Feature extraction, feature vector extraction, graph-based representation, Hidden Markov models, history, HMM, image document restoration, image restoration, natural language processing, social network, sparse-based representation, spatial relation model, spectral-based representation, transliteration, undirected random field, visual language processing, visual object, Visualization, VLP, Writing
W-TSV: Weighted topological signature vector for lexicon reduction in handwritten Arabic documents
Abstract This paper proposes a holistic lexicon-reduction method for ancient and modern handwritten Arabic documents. The word shape is represented by the weighted topological signature vector (W-TSV), which encodes graph data into a low-dimensional vector space. Three directed acyclic graph (DAG) representations are proposed for Arabic word shapes, based on topological and geometrical features. Lexicon reduction is achieved by a nearest neighbors search in the W-TSV space. The proposed framework [...]
Sparse Descriptor for Lexicon Reduction in Handwritten Arabic Documents
Abstract Arabic words have a rich structure. They are made of subwords (groups of connected letters) and diacritical marks (dots). This paper proposes a sparse descriptor specifically designed for lexicon reduction in handwritten Arabic documents. The topological and geometrical features of subwords are extracted from the skeleton image, based on the concept of local density. The sparse descriptor is then formed as a 3-bins histogram, describing the distribution of the [...]
A Robust Word Spotting System for Historical Arabic Manuscripts
Abstract A novel system for word spotting in old Arabic manuscripts is developed. The system has a complete chain of operations and consists of three major steps: pre-processing, data preparation, and word spotting. In the pre-processing step, using multi-level classifiers, clean binarization is obtained from the input degraded document images. In the second step, the smallest units of data, i.e., the connected components, are processed and clustered in a robust [...]
Image patches analysis for text block identification
Abstract: In this paper, we propose a novel text block identification method for ancient document understanding. Unlike traditional top-down and bottom-up approaches, our method is based on supervised learning on the patches of document images, which can be considered as an intermediate level method but integrates essential advantages of both the top-down and the bottom-up strategies. In our method, the document images are firstly partitioned into small patches, and then [...]
Hyperspectral band selection based on graph clustering
Abstract: In this paper we present a new method for hyperspectral band selection problem. The principle is to create a band adjacency graph (BAG) where the nodes represent the bands and the edges represent the similarity weights between the bands. The Markov Clustering Process (abbreviated MCL process) defines a sequence of stochastic matrices by alternation of two operators on the associated affinity matrix to form distinct clusters of high correlated [...]
Environmental-aware virtual data center network
Abstract: Cloud computing services have recently become a ubiquitous service delivery model, cov- 25 ering a wide range of applications from personal file sharing to being an enterprise data 26 warehouse. Building green data center networks providing cloud computing services is 27 an emerging trend in the Information and Communication Technology (ICT) industry, 28 because of Global Warming and the potential GHG emissions resulting from cloud services. 29 As one [...]
Real-Time Knowledge-Based Processing of Images: Application of the Online NLPM} Method to Perceptual Visual Analysis
Keywords: convergence, hidden chain series, human vision, image processing, input image, knowledge based systems, knowledge images, online NLPM method, online nonlocal patch means method, perceptual image, perceptual observations, perceptual visual analysis, real-time knowledge-based image processing, real-time systems, series (mathematics), shape perception, shape recognition, visual perception
Unsupervised MRI Segmentation of Brain Tissues Using a Local Linear Model and Level Set
Abstract Real-world magnetic resonance imaging of the brain is affected by intensity nonuniformity (INU) phenomena which makes it difficult to fully automate the segmentation process. This difficult task is accomplished in this work by using a new method with two original features: (1) each brain tissue class is locally modeled using a local linear region representative, which allows us to account for the INU in an implicit way and to more accurately [...]
TSV-LR: topological signature vector-based lexicon reduction for fast recognition of pre-modern Arabic subwords
Abstract Automatic recognition of Arabic words is a challenging task and its complexity increases as the lexicon grows. In pre-modern documents, the vocabulary is unconstrained; therefore a lexicon-reduction strategy is needed to reduce the recognition computational complexity. This paper proposes a novel lexicon-reduction method for Arabic subwords based on their shapes' topology and geometry. First the sub-word shape's topological and geometrical information is extracted from its skeleton and encoded into [...]
Renewable Energy Provisioning for ICT Services in a Future Internet
Abstract As one of the first worldwide initiatives provisioning ICT (Information and Communication Technologies) services entirely based on renewable energy such as solar, wind and hydroelectricity across Canada and around the world, the GreenStar Network (GSN) is developed to dynamically transport user services to be processed in data centers built in proximity to green energy sources, reducing GHG (Greenhouse Gas) emissions of ICT equipments. Regarding the current approach, which focuses [...]
Novel data representation for text extraction from multispectral historical document images
Abstract: The extraction and analysis of useful information from old document images is very important into cultural heritage preservation. In advanced research, where the goal is to separate the foreground (in general, text) from the background, image restoration and pattern classification techniques are used. Most of these methods consist of classifying the pixels based on their gray-scale value. In this paper, we propose to perform foreground pattern extraction using regions-of-interest [...]
Lecture Notes in Computer ScienceThe Future InternetRenewable Energy Provisioning for ICT Services in a Future Internet
Renewable Energy Provisioning for ICT Services in a Future Internet Springer Berlin Heidelberg, Volume 6656, Berlin, Heidelberg, p.419 - 429 (2011) ISBN: 978-3-642-20898-0
Indexing On-Line Handwritten Texts Using Word Confusion Networks
Abstract: In the context of handwriting recognition, word confusion networks (WCN) are convenient representations of alternative recognition candidates. They provide alignment for mutually exclusive words along with the posterior probability of each word. In this paper, we present a method for indexing on-line handwriting based on WCN. The proposed method exploits the information provided by WCN in order to enhance relevant keyword extraction. In addition, querying the index for a [...]
Non-local adaptive structure tensors. Application to anisotropic diffusion and shock filtering
Keywords: Adaptive tensor regularization, Anisotropic diffusion, PDEs, shock filter, Structure tensor
Leveraging Green Communications for Carbon Emission Reductions: Techniques, Testbeds and Emerging Carbon Footprint Standards
Abstract: Green communication systems and, in broader terms, green information and communications technologies have the potential to significantly reduce greenhouse gas emissions worldwide. This article provides an overview of two issues related to achieving the full carbon abatement potential of ICT. First, green communications research challenges are discussed, notably as they pertain to networking issues. Various initiatives regarding green ICT testbeds are presented in the same realm in order to [...]
A local linear level set method for the binarization of degraded historical document images
Keywords: Computer Science
A spatially adaptive statistical method for the binarization of historical manuscripts and degraded document images
Keywords: Adaptive local document image classification, Document images binarization, Historical and degraded documents
Semi-Supervised Learning for Weighted LS-SVM
Abstract: The least squares support vector machine (LS-SVM) is an interesting variant of the SVM. It performs structural risk through margin-maximization and has excellent power of generalization. For some applications, it is more interesting to use the weighted LS-SVM where the impact of each training sample is controlled by weighting factors. In this paper, we consider the use of the weighted LS-SVM in semi-supervised learning. We propose an algorithm to [...]
Evolving Fuzzy Classifiers: Application to Incremental Learning Handwritten Gesture Recognition Systems
Abstract: In this paper, we present a new method to design customizable self-evolving fuzzy rule-based classifiers. The presented approach combines an incremental clustering algorithm with a fuzzy adaptation method in order to learn and maintain the model. We use this method to build an evolving handwritten gesture recognition system. The self-adaptive nature of this system allows it to start its learning process with few learning data, to continuously adapt and [...]
Degraded Color Document Image Enhancement Based on NRCIR
Abstract: An automatic algorithm of degraded color document image enhancement is proposed based on our previous work on Natural Rendering of Color Image using Retinex (NRCIR) with respects to document image characteristics. In the proposed work, an adaptive workflow is designed to enhance both luminance and chrominance contrast of document image while maintaining degradations within tolerance and hue-shift minimized. Tests with degraded document image databases are effectuated, and the results [...]
Semi-automatic segmentation of major aorto-pulmonary collateral arteries for image guided procedures
Abstract Manual segmentation of pre-operative volumetric dataset is generally time consuming and results are subject to large inter-user variabilities. Level-set methods have been proposed to improve segmentation consistency by finding interactively the segmentation boundaries with respect to some priors. However, in thin and elongated structures, such as major aorto-pulmonary collateral arteries (MAPCAs), edge-based level set methods might be subject to flooding whereas region-based level set methods may not be selective [...]
A New PCA-based Face Authentication Approach for Smart-Card Implementation
Abstract: In this paper, we present a new PCA-based face verification approach for smart cards implementation. In fact, our scheme deals with the reduced storage space of smart cards. First of all the DCT2 and then the self Eigen face, are respectively applied for the training step and in the decision step, a new similarity index based on the weighted distance by the representation quality of individuals is used. Experimental [...]
Decreasing live virtual machine migration down-time using a memory page selection based on memory change PDF
Keywords: ICT infrastructures, information and communication technology, information management, live data center migration, live virtual machine, mathematical model, memory change PDF, memory page selection, resource allocation, service level agreements, virtual machines
Ontology-Based Resource Description and Discovery Framework for Low Carbon Grid Networks
Keywords: belief networks, computer centres, energy conservation, energy consumption, energy management systems, ontologies (artificial intelligence), power engineering computing, smart power grids
Le contrôle d’accès dans les environnements fédérés – Problématique et approches techniques
Keywords: contrôle d’accès, environnements fédérés, federated environments, grids, grilles, GSI, Information Cards, Information Cards. access control, Shibboleth
A multi-scale framework for adaptive binarization of degraded document images
Keywords: Adaptive methods, Binarization, Document image processing, Multi-scale framework
A Variational Approach to Degraded Document Enhancement
Keywords: bleed-through, variational, wavelet
Unified Framework for SVM model Selection
Abstract Model selection for support vector machines (SVMs) involves tuning SVM hyperparameters, such as C, which controls the amount of overlap, and the kernel parameters. Several criteria developed for doing so do not take C into account. In this paper, we propose a unified framework for SVM model selection which makes it possible to include C in the definition of the kernel parameters. This makes tuning hyperparameters for SVMs equivalent [...]
AEG, un cadre conceptuel d’Architecture d’Entreprise pour les Gestionnaires
Keywords: Enterprise Architecture, Framework
AEG, un cadre conceptuel d’Architecture d’Entreprise pour les Gestionnaires
Keywords: Enterprise Architecture, Framework
Gray-level Texture Characterization Based on a New Adaptive Nonlinear Auto-Regressive Filter
Keywords: Image Analysis; 2-D nonlinear filter; 2-D adaptive filter; texture characterization Abstract: In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced [...]
Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities
Index Terms—Bayes classification, Gibbs density parameter estimation, histograms, online handwritten Arabic character recognition.
DIAR: Advances in Degradation Modelling and Processing
Abstract: State-of-the-art OCR/ICR algorithms and software are the result of large-scale experiments on the accuracy of OCR systems and proper selection of the size and distribution of training sets. The key factor in improving OCR technology is the degradation models. While it is a leading-edge tool for processing conventional printed materials, the degradation model now faces additional challenges as a result of the appearance in recent years of new imaging [...]
Towards Segmentation of Pedicles on Posteroanterior X-Ray Views of Scoliotic Patients
Key words: X-ray images, idiopathic scoliosis, active contour, vertebral orientation.
Shock Filters for Character Image Enhancement and Peeling
Keywords: Electric shock , Filters , Image enhancement , Image restoration , Image recognition , Character recognition , Image processing , Degradation , Shape , Indexing.