Abdeljalil ABBAS-TURKI

Abdeljalil ABBAS-TURKI Information

University

Université de Technologie de Belfort-Montbéliard

Position

___

Citations(all)

1205

Citations(since 2020)

657

Cited By

762

hIndex(all)

16

hIndex(since 2020)

13

i10Index(all)

32

i10Index(since 2020)

18

Email

University Profile Page

Université de Technologie de Belfort-Montbéliard

Abdeljalil ABBAS-TURKI Skills & Research Interests

DEDS

Urban Mobility

Cooperative Driving

Combinatorial Optimization

Train Timetabling

Top articles of Abdeljalil ABBAS-TURKI

Review of Traffic Assignment and Future Challenges

The problem of traffic assignment consists of determining the routes taken by the users of transportation infrastructure. This problem has been the subject of numerous studies, particularly in analyzing scenarios for developing road infrastructure and pricing strategies. This paper reviews the major progress in the field. Accordingly, it shows that the evolution of intelligent transportation systems and the emergence of connected and autonomous vehicles present new challenges to classical approaches for solving the traffic assignment problem. It addresses two major perspectives: digital twins coupled with artificial intelligence to help decision-makers, and rule-based policy to offer users fair and efficient itineraries while respecting infrastructure capacity.

Authors

Manal Elimadi,Abdeljalil Abbas-Turki,Abder Koukam,Mahjoub Dridi,Yazan Mualla

Published Date

2024/1/13

Hybrid Deep Reinforcement Learning Model for Safe and Efficient Autonomous Vehicles at Pedestrian Crossings

The autonomous vehicle is a particularly promising area of application for machine learning algorithms. Autonomous driving is challenging due to the complexity of the road network, the hardly predictable human behaviors, either pedestrians or drivers and the imperative need to ensure the safety of all road users. Deep Reinforcement Learning algorithms have garnered significant interest as a viable option for controlling autonomous vehicles. They allow defining a control policy by solely describing the observation space, the action space, and a reward function. Yet, the most common solutions face difficulties when the action space is hybrid (simultaneously continuous and discrete), and the reward is sparse, which is the case when a selection between choices must be made (like yielding or not), and the trajectory of the vehicle must be adapted according to this decision. This paper addresses these challenges by …

Authors

Alexandre Brunoud,Alexandre Lombard,Abdeljalil Abbas-Turki,Nicolas Gaud,Jo Kang-Hyun

Published Date

2023/8/9

Distributed artificial intelligence for traffic assignment in smart cities

This paper aims to contribute to the challenging issue of one microscopic simulation round for dynamic traffic assignment. It relays on the selfish behaviour of the vehicle agent that benefits from a more accurate estimation of its travel time. The main novelty is that, rather than considering the average travel times in the network links according to the present vehicles, the vehicle must first know when it can cross the nodes located at both extremities of the road. This is achieved through a negotiation between the vehicle agent and the node agents to book the crossing time. In order to assess this new paradigm, this paper compares it with well-known approaches in an elementary network. The result invites us to extend the approach to more general cases. A discussion of the opportunities and limitations of the approach extension is provided in this paper. One of the notable opportunities is that the proposed approach …

Authors

Manal Elimadi,Abdeljalil Abbas-Turki,Abderrafiaa Koukam

Published Date

2023/7/3

A Systematic Approach for Automotive Privacy Management

As of today, car manufacturers are currently addressing privacy goals primarily from a legal perspective. However, with the common acceptance of privacy by design, it is important to also address the technical perspective. As of today there is no systematic understanding or even approach how to address privacy requirements. Our contribution is twofold: (i) We propose a system model for the automotive domain to model and analyse a use case for suitable locations of adding privacy enhancing technologies. (ii) As a generic solution, we propose the privacy manager, a generic entity which supports applications in the implementation of privacy enhancing technologies or enforces a certain data flow avoiding that information is leaked in an avoidable way. To evaluate our approach, we apply our system model at two automotive scenarios, platooning and silent testing, and describe how the privacy manager can be …

Authors

Sebastian Pape,Sarah Syed-Winkler,Armando Miguel Garcia,Badreddine Chah,Anis Bkakria,Matthias Hiller,Tobias Walcher,Alexandre Lombard,Abdeljalil Abbas-Turki,Reda Yaich

Published Date

2023/12/5

Deep reinforcement learning approach for V2X managed intersections of connected vehicles

Intersections are major bottlenecks for road traffic, as well as the origin of many accidents. Efficient management of traffic at intersections is required to ensure both safety and efficiency. Yet, the traditional solutions (static signs, traffic lights) are limited in their efficiency as they consider the flow of vehicles and not the vehicles at the microscopic level. By using inter-vehicular communication of connected vehicles, recent works have shown the possibility to have a great increase in the number of evacuated vehicles thanks to the possibility to give an individual right-of-way directly to each vehicle. In this context of intersections of cooperative vehicles, the scheduling of this right-of-way in order to maximize the throughput of the intersection is still a challenging task, with regard to the hybrid and dynamic aspects of the problem. In this paper, we propose an approach based on Deep Reinforcement Learning (DRL) to …

Authors

Alexandre Lombard,Ahmed Noubli,Abdeljalil Abbas-Turki,Nicolas Gaud,Stéphane Galland

Journal

IEEE Transactions on Intelligent Transportation Systems

Published Date

2023/3/16

2023 Index IEEE Transactions on Intellitent Transportation Systems Vol. 24

2023 Index IEEE Transactions on Intelligent Transportation Systems Vol. 24 Page 1 1 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 24, NO. 12, DECEMBER 2023 This index covers all technical items—papers, correspondence, reviews, etc.—that appeared in this periodical during 2023, and items from previous years that were commented upon or corrected in 2023. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author’s name. The primary entry includes the coauthors’ names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author’s name, the publication …

Authors

F Aadil,S Abadal,LR Abbade,G Abbas,H Abbas,Q Abbas,T Abbas,ZH Abbas,A Abbas-Turki,R Abbasi,S Abd-Elhaleem,M Abdar,M Abdel-Aty,BM Abdel-Karim,A Abdelhakim,A Abdelraouf

Journal

IEEE Transactions on Intelligent Transportation Systems

Published Date

2023/12

Agent-based model and service-oriented architecture for shifting from consumer to prosumer e-mobility behaviors in flex community

Agent-based model and service-oriented architecture for shifting from consumer to prosumer e-mobility behaviors in flex community

Authors

Abdeljalil Abbas-Turki,Stéphane Galland,Thomas Martinet,Yazan Mualla

Published Date

2023/2/2

Addressing hazardous weather conditions on Middle East highways with smart infrastructure and connected vehicles using agent-based simulation

The lack of visibility due to foggy conditions is known to cause of a lot of accidents every year in the United Arab Emirates, eventually leading to fatal injuries. Yet, today’s technology can help to overcome these visibility issues by providing dynamic information to the driver about the current weather and an appropriate speed limit. This paper explores four strategies, ranging from static road signs to advanced inter-vehicular communication, to better warn the drivers and make them adapt their speed depending on the weather. To evaluate the impact of each policy, agent-based simulations are designed and performed. The results show that a dynamic communication about the weather conditions, supported by either an infrastructure-to-vehicle or a vehicle-to-vehicle protocol, can reduce the probability of occurrence of accidents.

Authors

Fatma Outay,Stéphane Galland,Abdeljalil Abbas-Turki,Thomas Martinet,Alexandre Lombard,Nicolas Gaud

Journal

Personal and Ubiquitous Computing

Published Date

2023/10

Autonomous intersection management: Optimal trajectories and efficient scheduling

Intersections are at the core of congestion in urban areas. After the end of the Second World War, the problem of intersection management has benefited from a growing body of advances to address the optimization of the traffic lights’ phase splits, timing, and offset. These contributions have significantly improved traffic safety and efficiency in urban areas. However, with the growth of transportation demand and motorization, traffic lights show their limits. At the end of the 1990s, the perspective of autonomous and connected driving systems motivated researchers to introduce a paradigm shift for controlling intersections. This new paradigm is well known today as autonomous intersection management (AIM). It harnesses the self-organization ability of future vehicles to provide more accurate control approaches that use the smallest available time window to reach unprecedented traffic performances. This is achieved by optimizing two main points of the interaction of connected and autonomous vehicles at intersections: the motion control of vehicles and the schedule of their accesses. Considering the great potential of AIM and the complexity of the problem, the proposed approaches are very different, starting from various assumptions. With the increasing popularity of AIM, this paper provides readers with a comprehensive vision of noticeable advances toward enhancing traffic efficiency. It shows that it is possible to tailor vehicles’ speed and schedule according to the traffic demand by using distributed particle swarm optimization. Moreover, it brings the most relevant contributions in the light of traffic engineering, where flow–speed diagrams are used …

Authors

Abdeljalil Abbas-Turki,Yazan Mualla,Nicolas Gaud,Davide Calvaresi,Wendan Du,Alexandre Lombard,Mahjoub Dridi,Abder Koukam

Journal

Sensors

Published Date

2023/1/29

H3PC: Enhanced Security and Privacy-Preserving Platoon Construction Based on Fully Homomorphic Encryption

With the increasing adoption of connected and autonomous vehicles, platooning services have emerged as a promising solution to enhance road traffic efficiency. However, the widespread deployment of platooning services raises concerns about the privacy and security of sensitive vehicle data. This paper proposes a novel privacy-preserving framework for platoon formation, named H3PC (Homomorphic Privacy-Preserving Platooning Construction), to address privacy and security challenges. The proposed approach is based on fully homomorphic encryption and order-preserving encryption, to enable secure and private operations within the platoon construction. In addition, the H3PC framework incorporates a safe vehicle control method that adheres to established norms in the literature. By striking a balance between security and computational efficiency, H3PC enables effective platooning construction …

Authors

Badreddine Chah,Alexandre Lombard,Anis Bkakria,Abdeljalil Abbas-Turki,Reda Yaich

Published Date

2023/9/24

Cooperative Behaviors of Connected Autonomous Vehicles and Pedestrians to Provide Safe and Efficient Traffic in Industrial Sites

The technology of Connected and Autonomous Vehicles (CAV) is a hot topic of transportation systems, especially regarding platooning and the interaction with other road users. Considering traffic safety, many studies have been devoted to the exchange of information among various road users, such as CAVs and pedestrians. In a platooning scenario, when a pedestrian is detected by a CAV, the leader CAV shares the information with its followers to provide a safe and courteous environment thanks to its connectivity. However, the possibility to improve traffic efficiency while meeting the safety requirements has rarely been addressed in current research. Yet, in industrial areas, where automated vehicles and pedestrians frequently interact, combining safety and efficiency is crucial. The present paper addresses this challenge by first analyzing the intersection of CAVs and pedestrians in no-traffic-signal scenarios …

Authors

Meng Zhang,Alexandre Brunoud,Alexandre Lombard,Yazan Mualla,Abdeljalil Abbas-Turki,Abderrafiaa Koukam

Published Date

2022/10/9

Comparison of reaction time-based collaborative velocity control and intelligent driver model for agent-based simulation of autonomous car

Based on historical records, driving in hazardous weather conditions is one of the most serious causes that lead to fatal accidents on roads in general and in United Arab Emirates (UAE) highways in particular. One solution to improve road safety is to equip vehicles and infrastructure with connected and smart devices and convert them into autonomous vehicles. Before deploying a concrete solution to the field, it must be validated by simulation, and more specifically by agent-based simulation. In this paper, we propose to implement the Reaction Time-Based Collaborative Velocity Control (RT-CVC) model that was implemented in autonomous cars into an agent-based simulator. This model is compared to the Intelligent Driver Model (IDM), which is one of the standard longitudinal driving behaviors in simulation environments. The experimental results show that RT-CVC generates traffic flows with fewer vehicle …

Authors

Fatma Outay,Abdeljalil Abbas-Turki,Stéphane Galland,Alexandre Lombard,Nicolas Gaud

Journal

Procedia Computer Science

Published Date

2022/1/1

Comparison of deep reinforcement learning methods for safe and efficient autonomous vehicles at pedestrian crossings

These past years, the domain of Connected and Autonomous Vehicles (CAV) has been extremely flourishing, with fully autonomous self-driving cars being an active research area. The most challenging aspect of the driving task is the interaction between the driver agent and the environment, especially to handle conflicts with the other road users. The most vulnerable ones are the pedestrians. To ensure better safety at crosswalks, this paper focuses on planning a vehicle trajectory, to pass without risks, while cooperating with the pedestrian. According to recent works, Deep Reinforcement Learning can be used to efficiently control the vehicle in a complex environment. This paper assesses both widely held continuous action space approaches for controlling the vehicle: Deep Deterministic Policy Gradient and Proximal Policy Optimization. Both approaches are used to allow the vehicle to safely and efficiently …

Authors

Alexandre Brunoud,Alexandre Lombard,Meng Zhang,Abdeljalil Abbas-Turki,Nicolas Gaud,Abder Koukam

Published Date

2022

The quest of parsimonious XAI: A human-agent architecture for explanation formulation

With the widespread use of Artificial Intelligence (AI), understanding the behavior of intelligent agents and robots is crucial to guarantee successful human-agent collaboration since it is not straightforward for humans to understand an agent's state of mind. Recent empirical studies have confirmed that explaining a system's behavior to human users fosters the latter's acceptance of the system. However, providing overwhelming or unnecessary information may also confuse the users and cause failure. For these reasons, parsimony has been outlined as one of the key features allowing successful human-agent interaction with parsimonious explanation defined as the simplest explanation (i.e. least complex) that describes the situation adequately (i.e. descriptive adequacy). While parsimony is receiving growing attention in the literature, most of the works are carried out on the conceptual front. This paper proposes a …

Authors

Yazan Mualla,Igor Tchappi,Timotheus Kampik,Amro Najjar,Davide Calvaresi,Abdeljalil Abbas-Turki,Stéphane Galland,Christophe Nicolle

Journal

Artificial intelligence

Published Date

2022/1/1

Safe Cooperative Intersection of Autonomous and Connected Robots: Trajectory and Schedule Optimization

Conventional intersection managements, such as signalized intersections, may not necessarily be the optimal strategies when it comes to connected and automated vehicles (CAV) environment. Cooperative intersection management (CIM) is tailored for CAVs aiming at replacing the conventional traffic control strategies. This paper focuses on the safety constraint in the case of conflicting movements. The constraint is needed for both optimization: robot trajectory (speed profile) and sequence (which robot crosses the intersection first, which is the second one and so on). The main objective is to safely minimize the lost time between two conflicting robots while saving energy. The paper studies the safety condition of the cyber-physical system and derives the optimal control point that saves time. Quadratic optimization is used to derive the speed trajectory of the robot in order to save energy. Simulations show that the …

Authors

Wendan Du,Abdeljalil Abbas-Turki,Abder Koukam,Kang-Hyun Jo

Published Date

2022/8/17

Coordination Between Connected Automated Vehicles and Pedestrians to Improve Traffic Safety and Efficiency at Industrial Sites

Transportation in controlled industrial sites provides a conducive environment for technologies of Connected Automated Vehicles (CAV). Recent studies show that safe and efficient road sharing between CAVs and pedestrians is challenging. Besides safety issues, a significant loss of time occurs when pedestrians cross a stream of CAVs. Currently, many techniques have been employed to improve the coordination between CAVs and pedestrians. They focus on pedestrian detection, display of the intention of CAVs, and cooperative collision avoidance. However, one of the most significant sources of information that the pedestrian uses for her/his decision-making is the speed profile of CAVs. This paper aims to provide a safe and efficient pedestrian crossing at industrial sites through communicative crossing behavior. To this end, a suitable speed profile of the CAV is designed by assuming that pedestrians and CAV …

Authors

Meng Zhang,Abdeljalil Abbas-Turki,Yazan Mualla,Abderrafiaa Koukam,Xiaowei Tu

Journal

IEEE Access

Published Date

2022/6/23

Privacy Threat Analysis for connected and autonomous vehicles

Connected and autonomous vehicles produce, store and communicate a large amount of personal data (the route taken, the stop points, home and work addresses, etc.). The development of this type of vehicles brings the opportunity to offer new services to road users, with more software and hardware components on the vehicle. Many of these components suffer from weaknesses that can be exploited. The issue is that a single vulnerability in one element of the system will threaten the privacy of the vehicle's users (The weakest link principle of IT security). In this paper, we present a privacy threat analysis on the general architecture of connected and autonomous vehicles, to point out privacy risks according to formal privacy requirements. We present a use case modularization and its analysis that helps us to discern the privacy requirements of this use case, which can be enabled by manufacturers. To meet the …

Authors

Badreddine Chah,Alexandre Lombard,Anis Bkakria,Reda Yaich,Abdeljalil Abbas-Turki,Stéphane Galland

Journal

Procedia Computer Science

Published Date

2022/1/1

Simulation of connected driving in hazardous weather conditions: General and extensible multiagent architecture and models

Based on historical records, driving in hazardous weather conditions is one of the most serious causes that lead to fatal accidents on roads in general and in United Arab Emirates (UAE) highways in particular. One solution for improving road safety is to equip the vehicles and infrastructure with connected and smart devices. Before deploying a concrete solution to the field, it must be validated by simulation, and more specifically agent-based simulation. Several key features are expected for the simulation framework, such as the reproduction of different and detailed behaviors for the components of the road infrastructure and for the drivers, simulate specific weather conditions and forecast their impacts on the global system behavior. Additionally, several technological features are related to recent advancements in agent software engineering and simulation. This paper proposes an agent-based model for the …

Authors

Fatma Outay,Stéphane Galland,Nicolas Gaud,Abdeljalil Abbas-Turki

Journal

Engineering applications of artificial intelligence

Published Date

2021/9/1

A novel approach for dynamic traffic assignment based on multi-agent node reservation: Comparative study on two competing roads

This paper aims to contribute to the challenging issue of one microscopic simulation round for dynamic traffic assignment. It relays on the selfish behavior of the vehicle agent that benefits from a more accurate estimation of its travel time. The main novelty is that, rather than considering the average travel times in the network links according to the present vehicles, the vehicle must first know at what time it is able to cross the nodes located at both extremities of the road. This is achieved through a negotiation between the vehicle agent and the node agents to book the crossing time. In order to assess this new paradigm, this paper compares it with well-known approaches in an elementary network. The result invites to extend the approach to more general cases. A discussion of the opportunities and limitations of the approach extension is provided in this paper. One of the notable opportunities, is that the proposed …

Authors

Manal Elimadi,Abdeljalil Abbas-Turki,Abder Koukam

Journal

Procedia Computer Science

Published Date

2021

Multiagent dynamic route assignment: Quick and fair itineraries to connected and autonomous vehicles

In recent years, Connected and Automated Vehicle (CAV) related research has progressed considerably. This paper proposes an approach for finding the best itinerary to CAVs into a road network. If we consider only a single CAV, the problem turns into the shortest path problem according to the real-time traffic information. When it comes to a road network served by several CAVs, such as in Personal Rapid Transit (PRT), the problem of the traffic assignment equilibrium is raised. However, the known algorithms for solving such problems are greedy in terms of computation time and memory. In order to solve the problem, this paper introduces a new distributed approach, using multi-agent systems. We call the approach Mixed Node Reservation (MNR). Each CAV agent computes its itinerary by using an accurate estimation of its travel time through its connectivity to node agents of the network. To this end, two new …

Authors

Manal Elimadi,Abdeljalil Abbas-Turki,Abderrafiaa Koukam

Published Date

2021/10/17

Towards a quantum modeling approach to reactive agents

Quantum computing offers a new approach to the problem modeling and solving. This paper deals with the quantum modeling of reactive agents. It also proposes a quantum algorithm to implement the subsumption architecture, widely used by reactive agents, particularly in robotics. This work shows the contribution of the formalism proposed by quantum mechanics to the modeling and the proof of certain properties of the agent behavior. After, the definition of the reactive agent state modeling, the paper suggests a behavior modeling approach based on two steps for subsumption architecture. The first one models the preset behavior that links each action to the perception states. The second one determines, among several actuated actions, the one that the robot must achieve. The subsumption architecture raises the challenge of modeling hierarchical priority of actions. To this end, a multipartite entanglement is …

Authors

Abder Koukam,Abdeljalil Abbas-Turki,Vincent Hilaire,Yassine Ruichek

Published Date

2021/10/17

Connected and Autonomous Vehicles cooperate with the pedestrian in industrial sites based on trajectory optimization and vehicle signalization system

Connected and autonomous vehicles (CAV) is the development trend in the field of transportation systems. Recent studies show that the resources sharing between pedestrians and CAV is a big challenge. Considering traffic safety and efficiency at that sharing point not only requires a collision avoidance system but also more communicative behaviors of the CAV. More precisely, pedestrian needs to understand the intention of the incoming CAV whether it will cross first or not according to its speed profile. This paper uses the optimal trajectory control to provide CAV with a communicative behavior. A scenario where CAV and pedestrian cooperate together to cross a conflict zone is studied. A communicative CAV behavior is designed through an objective function. Hamiltonian analysis is used to derive the optimal control for the CAV. Based on Oculus virtual reality platform, the proposed approach is tested and the …

Authors

Meng Zhang,Abdeljalil Abbas-Turki,Alexandre Lombard,Abderrafiaa Koukam

Published Date

2020/10/19

Autonomous vehicle with communicative driving for pedestrian crossing: Trajectory optimization

Connected and autonomous vehicles (CAV) is a key technology for this century. One of the main challenges is to define a smart interaction behavior of CAV with the other road users. The challenge is mainly raised at conflicting points where path of CAV intersects with the other users. Recent studies show interaction with humans is a big challenge. It not only requires a collision avoidance system but also more communicative behaviors of the CAV. More precisely, pedestrian needs to understand the intention of the incoming CAV whether it will cross first or not according to its speed profile. One way to overcome this issue is to design optimal trajectory control of the CAV that matches with the pedestrian expectation. However, such a design faces the non-linearity of the space sharing constraint. This paper uses sequence modeling based on Petri Net in order to overcome the problem. It also uses Hamiltonian analysis …

Authors

Meng Zhang,Abdeljalil Abbas-Turki,Alexandre Lombard,Abderrafiaa Koukam,Kang-Hyun Jo

Published Date

2020/9/20

Curvature-based geometric approach for the lateral control of autonomous cars

Several approaches exist for the lateral control of autonomous vehicles. Among them are the geometric approaches. They are shown to be robust to disturbances and able to manage complex tracks. Their main advantage lies on the fact that they are explainable, in the sense that their behavior can be analyzed to provide guarantees about their limitations. However, they do not give the quality of results that can be obtained using other control principles, mostly because of design issues. This paper aims to tackle these issues by proposing a novel geometric approach based on Frenet Serret formulas to reach the level of quality proposed by the other approaches, while still benefiting from the advantages of geometric approaches. A numerical analysis of the proposed control approach show its advantage: Simulation results and tests on a real autonomous car are provided.

Authors

Alexandre Lombard,Jocelyn Buisson,Abdeljalil Abbas-Turki,Stéphane Galland,Abderrafiaa Koukam

Journal

Journal of the Franklin Institute

Published Date

2020/9/1

Abdeljalil ABBAS-TURKI FAQs

What is Abdeljalil ABBAS-TURKI's h-index at Université de Technologie de Belfort-Montbéliard?

The h-index of Abdeljalil ABBAS-TURKI has been 13 since 2020 and 16 in total.

What are Abdeljalil ABBAS-TURKI's top articles?

The articles with the titles of

Review of Traffic Assignment and Future Challenges

Hybrid Deep Reinforcement Learning Model for Safe and Efficient Autonomous Vehicles at Pedestrian Crossings

Distributed artificial intelligence for traffic assignment in smart cities

A Systematic Approach for Automotive Privacy Management

Deep reinforcement learning approach for V2X managed intersections of connected vehicles

2023 Index IEEE Transactions on Intellitent Transportation Systems Vol. 24

Agent-based model and service-oriented architecture for shifting from consumer to prosumer e-mobility behaviors in flex community

Addressing hazardous weather conditions on Middle East highways with smart infrastructure and connected vehicles using agent-based simulation

...

are the top articles of Abdeljalil ABBAS-TURKI at Université de Technologie de Belfort-Montbéliard.

What are Abdeljalil ABBAS-TURKI's research interests?

The research interests of Abdeljalil ABBAS-TURKI are: DEDS, Urban Mobility, Cooperative Driving, Combinatorial Optimization, Train Timetabling

What is Abdeljalil ABBAS-TURKI's total number of citations?

Abdeljalil ABBAS-TURKI has 1,205 citations in total.

What are the co-authors of Abdeljalil ABBAS-TURKI?

The co-authors of Abdeljalil ABBAS-TURKI are A. Koukam, Florent Perronnet.

    Co-Authors

    H-index: 33
    A. Koukam

    A. Koukam

    Université de Technologie de Belfort-Montbéliard

    H-index: 9
    Florent Perronnet

    Florent Perronnet

    Université de Technologie de Belfort-Montbéliard

    academic-engine

    Useful Links