The emergence of Cooperative Intelligent Transportation Systems (C-ITS) has revolutionized urban mobility by enabling seamless collaboration among vehicles, infrastructure, and individuals to improve traffic management, safety, and efficiency. Deep Reinforcement Learning (DRL) has become a key technology in this ecosystem, empowering autonomous agents to make real-time decisions that optimize traffic flow, reduce congestion, and enhance road safety. Although many surveys on Intelligent Transportation Systems (ITS) either overlook cooperative aspects or primarily emphasize security, this paper bridges the gap by examining the diverse applications of DRL in C-ITS. It examines critical areas such as traffic signal control, AV coordination, route planning, and human-vehicle interaction. The study also traces the evolution of DRL algorithms, their adaptation to transportation challenges, and their integration with cutting-edge projects and standards. Additionally, the paper provides a comprehensive analysis of current research trends, identifying achievements, unresolved challenges, and future directions in the field. By synthesizing existing literature and highlighting the synergy between DRL and C-ITS, this survey serves as a valuable resource for researchers, policymakers, and industry professionals striving to develop intelligent, cooperative, and sustainable transportation systems. The insights offered aim to guide advancements in this rapidly growing domain, fostering innovation and practical implementation.

Efficient utilization of Big data in smart cities is crucial for smooth operation of urban environments. Machine learning-enabled big data analytics is essential for optimizing city operations, improving resource management, and enhancing the quality of urban life. By leveraging machine learning (ML) algorithms to process and analyze the vast amounts of data generated in smart cities, authorities can gain insights and make real-time data-driven decisions. This article provides a comprehensive survey of how ML techniques are applied to analyze the large volumes of data generated by smart cities to improve urban living. Various ML algorithms, such as supervised, unsupervised, and reinforcement learning (RL) are discussed by highlighting their roles in numerous applications. Moreover, their distinguishing features are examined, enabling the selection of the most suitable algorithms for various applications in smart cities. Finally, the survey explores various challenges and suggests numerous research directions.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder causing cognitive decline and memory loss. With its significant impact on individuals’ lives, AD is the most prevalent form of dementia, contributing to 60-80% of all dementia cases. At the same time, symptoms may not surface until years later, making early detection vital for effective intervention. Thus, this work presents an approach to early AD detection by integrating Genome-Wide Association Studies (GWAS) with deep learning models and Explainable Artificial Intelligence (XAI). First, different classical machine learning models are developed for AD, and a Convolutional Neural Network (CNN) model is trained using the AD GWAS dataset obtained from the AD neuroimaging initiative. We then employ transfer learning to train our CNN model as a base model over the ADNI dataset. In addition, XAI methods are used to interpret the transfer learning model decision. Acknowledging the well-known limitation that classical machine learning is not inherently a generic model. The results from this study will help determine the most critical genetic markers associated with AD and provide transparency in understanding the deep learning model decisions.

The growth of cloud-based services is mainly supported by the core networking infrastructures of large-scale data centers, while the scalability of these services is influenced by the performance and dependability characteristics of data centers. Hence, the data center network must be agile and reconfigurable in order to respond quickly to the ever-changing application demands and service requirements. The network must also be able to interconnect the big number of nodes, and provide an efficient and fault-tolerant routing service to upper-layer applications. In response to these challenges, the research community began exploring novel interconnect topologies, namely: Flecube, DCell, Ficonn, HyperFlaNet and BCube. However, these topologies either scale too fast (grows exponentially in size), or too slow, and therefore suffer from performance bottlenecks. In this paper, we propose a novel data center topology called LaScaDa (Layered Scalable Data Center) as a new solution for building scalable and cost-effective data center networking infrastructures. The proposed topology organizes nodes in clusters of similar structure, then interconnect these clusters in a well-crafted pattern and system of coordinates for nodes to reduce the number of redundant connections between clusters, while maximizing connectivity. LaScaDa forwards packets between nodes using a new hierarchical row-based routing algorithm. The algorithm constructs the route to the source based on the modular difference between the source and destination coordinates. Furthermore, the proposed topology interconnects a large number of nodes using a small node degree. This strategy increases the number of directly connected clusters and avoids redundant connections. As a result, we get a good quality of nodes in terms of average path length (APL), bisection bandwidth, and aggregated bottleneck throughput. Experimental results show that LaScaDa has better performance than DCell, BCube, and HyperBcube in terms of scalability, while providing a good quality of service.

Data Center Networks (DCNs) are attracting immense interest from the industry, research and academia to keep pace with the increase of Internet services demands. One of the major concerns that draws the attention of researchers is the exponential growth of the energy consumption and carbon emission of the DCNs. Studies conducted to identify the causes of the increasing energy consumption have proved that the growing size of computing demand, the over-provisioning of the networking resources, the under-utilization of the infrastructure, the fault-tolerance, the high bandwidth exigence and the inefficient hardware and cooling structure are leading to considerable energy waste. Therefore, in recent years, new data center (DC) architectures are proposed where new hardware types and new technologies are implemented for the sake of energy efficiency. Other efforts are focusing on designing algorithms and strategies to enhance the utilization of the network resources. Replacing brown power by renewable energy was also one of the attractive ideas to minimize the energy costs. In this survey paper, we will present energy-related problems in data centers and review the state of the art of the research literature on energy efficient architectures, techniques, technologies, resource management, and thermal control and monitoring. Additionally, we present the challenges facing each approach and the strategies to build a green DC. This paper serves as a specification document that shows step by step how to minimize the energy consumption of different components of the system.

Digital technologies such as 3D imaging, data analytics and computer vision opened the door to a large set of applications in cultural heritage. Digital acquisition of a cultural assets takes nowadays a couple of seconds thanks to the achievements in 2D and 3D acquisition technologies. However, enriching these cultural assets with labels and relevant metadata is still not fully automatized especially due to their nature and specificities. With the recent publication of several cultural heritage datasets, many researchers are tackling the challenge of effectively classifying and annotating digital heritage. The challenges that are often addressed are related to visual recognition and image classification. In this paper, we present a novel approach of hierarchical classification for cultural heritage assets. The metadata structural differences that exist between cultural assets motivated us to design a classification framework that can efficiently perform the classification of multiple types of assets. Our approach relies on several deep learning classifiers, each of them is assigned the task of classifying a certain type of assets. The classification framework starts the labeling process by first determining the asset type. The asset is then assigned to a specific classifier in order to be annotated with data fields related to its type. As a preliminary step, we successfully designed a general cultural type classifier and a specific type classifier for paintings. Our approach is currently achieving interesting results and is set to be improved by the integration of more asset types.

Many proposed data center architectures are constructed with a huge number of network devices in order to support the increasing cloud based services. These devices are used to achieve the highest performance in case of full utilization of the network. However, the peak capacity of the network is rarely reached. As a result, many devices are set into idle state which increases the network energy consumption and lead to a non-proportionality between the consumed energy and the network load. In this paper, we present a new approach that reduces the data center energy consumption with a reduced trade off on network performance. By exploiting the correlation in time of internode communication and some topological features, the proposed approach uses the outdated traffic matrix to control the set of active communication links and ports in the network (switches ports and nodes ports). The ports activation management process is done using a proposed algorithm that guarantees network connectivity. Extensive simulations have been conducted to validate the performance of the proposed scheme in terms of average path length and energy consumption.

The study of biological systems is growing rapidly, and can be considered as an intrinsic task in biological research, and a prerequisite for diagnosing diseases and drug development. The integration of biological studies with computer technologies led to noticeable developments in biology with the appearance of many powerful modeling and simulation techniques and tools. The help of computers in biology resulted in deeper knowledge about complex biological systems and biopathways behaviors. Among modeling tools, the Petri Net formalism plays an important role. Petri Net is a powerful computerized and graphical modeling technique originally developed by Carl Adam Petri in 1960 to model discrete event systems. With its various extensions, Petri Nets find applications in many other fields including Biology. The extension known under the name Hybrid Functional Petri Net (HFPN) was developed specifically to model biological systems. Traditionally, biological processes are captured as systems of ordinary differential equations (ODEs). However, HFPNs offer a much more elegant and versatile approach to represent these processes more accurately. In fact, HFPNs allow to capture phenomena which are impossible to capture with ODES, while being more intuitive and easy to understand and model with. In this work we propose an approach to translate a system of ODEs representing a biological process into a HFPN. The resulting HFPN, not only preserves the semantics of the original model, but is also more humanly readable thanks to the use of a novel technique to connect its components in a smart way. To validate our approach, we implemented it as an extension to the tool Real Time Studio (an integrated environment for modeling, simulation and automatic verification of real-time systems), and compared our simulation results with those obtained by simulating systems of ODEs using MATLAB.

In this paper, the problems of relay selection and distributed beamforming are investigated for bi-directional dual-hop amplify-and-forward frequency-division duplex cooperative wireless networks. When using individual per-relay maximum transmission power constraint, it has been proven that the relay selection and beamforming optimization problem becomes NP hard and requires exhaustive search to find the optimal solution. Therefore, we propose a computationally affordable suboptimal multiple relay selection and beamforming optimization scheme based on the ℓ1norm squared relaxation. The proposed scheme performs the selection for the two transmission directions, simultaneously, while aiming at maximizing the aggregated SNR of the two communicating nodes. Furthermore, by exploiting the previous solutions to accelerate the algorithm's convergence, our proposed algorithm converges to a suboptimal solution compared to the exhaustive search technique with much less complexity.

Data centers have an important role in supporting cloud computing services (i.e. checking social media, sending emails, video conferencing,..). Hence, data centers topologies design became more important and must be able to respond to ever changing service requirements and application demands. An ultimate challenge in this research is the design of data center network that interconnects the massive number of servers, and provides efficient and fault-tolerant routing algorithm. Several topologies such as DCell, FlatNet and ScalNet have been proposed. However, these topologies generally seek to improve the scalability without taking into consideration the energy usage neither the network nfrastructure cost which is critical parameters in data centers. Motivated by these challenges, we propose a new network topology for data center, called AdyNet. It is an adaptive, dynamic, cost effective and highly performing topology. While reducing largely the infrastructure cost and the energy consumption, AdyNet outperforms FlatNet and ScalNet in terms of Average Path Length.

Manufacturing robotics is moving towards human-robot collaboration with light duty robots being used side by side with workers. Similarly, exoskeletons that are both passive (spring and counterbalance forces) and active (motor forces) are worn by humans and used to move body parts. Exoskeletons are also called ‘wearable robots’ when they are actively controlled using a computer and integrated sensing. Safety standards now allow, through risk assessment, both manufacturing and wearable robots to be used. However, performance standards for both systems are still lacking. Ongoing research to develop standard test methods to assess the performance of manufacturing robots and emergency response robots can inspire similar test methods for exoskeletons. This paper describes recent research on performance standards for manufacturing robots as well as search and rescue robots. It also discusses how the performance of wearable robots could benefit from using the same test methods.

Smart devices applications can assist children in improving their learning capabilities and comprehension skills. However most applications are built without taking into consideration the effective needs and background of Arab children and youth. They are somehow incompatible with their local environment. We propose in this paper an Arabic-based mobile educational system that displays illustrations automatically to characterize Arabic stories' contents. In order to generate these illustrations, different phases are carried out which include processing of Arabic texts, extraction of word-to-word relationships, building and accessing an educational ontology and usage of Internet search engines. The aim of our system is to improve the Arab children educational skills to grasp Arabic vocabulary and grammar using multimedia with a portable smart device which includes observation, comprehension, realization, and deduction. Children will then be able to continue learning outside the limited time of their schools and from any location with enabled Wi-Fi connectivity. Preliminary results show that the system enhances the learners' comprehension, deduction and realization.

Underlay cognitive networks should follow strict interference thresholds to operate in parallel with primary networks. This constraint limits their transmission power and eventually the coverage area. Therefore, in this paper, we first design a new approach for asynchronous narrow-band interference (NBI) estimation and mitigation in orthogonal frequency-division multiplexing cognitive radio networks that does not require prior knowledge of the NBI characteristics. Our proposed approach allows the primary user to exploit the sparsity of the secondary users' interference signal to recover it and cancel it based on sparse signal recovery theory. We also propose two subcarrier selection schemes that allow the primary user to further reduce the effect of the secondary users' interference based on sparse signal recovery algorithms. We show that although the primary and secondary transmissions are performed at the same time, the performance of our proposed techniques approach the interference- free limit over practical ranges of NBI power levels.

In this paper, the performance of an orthogonal frequency division multiplexing overlay cognitive radio network with subcarrier selection schemes is investigated. We propose three subcarrier selection techniques that reduce the level of interference at the primary base station based on collected channel state information from the different network nodes. Approximated outage probability expressions are also derived and verified by simulations for the different studied techniques. In addition, we propose and investigate a new approach for asynchronous narrowband interference (NBI) estimation and mitigation in cognitive radio networks. The proposed approach does not require prior knowledge of the NBI characteristics and allows the primary user to exploit the sparsity of the secondary users interference to recover it based on sparse signal recovery theory and approach the interference-free limit over practical ranges of NBI power levels.

This paper presents PTNet, a new data center topology that is specifically designed to offer a high and parameterized scalability with just one layer architecture. Furthermore, despite its high scalability, PTNet grants a reduced latency and a high performance in terms of capacity and fault tolerance. Consequently, compared to widely known data center networks, our new topology shows better capacity, robustness, cost-effectiveness and less power consumption. Conducted experiments and theoretical analyses illustrate the performance of the novel system.

Todays data centers may contain tens of thousands of computers with progressively more specialized and expensive equipments. Thus, the research community has proposed various interconnect topologies e.g FatTree, Dcell and Bcube. However, these architectures are too complex and very expensive to construct and they suffer from high average path length (APL) and latency. Motivated by these challenges, we propose a new data center architecture called VacoNet that combines the advantages of existing architectures while avoiding their limitations. VacoNet is a reliable, high-performance, and cost effective data center topology that can improve the network performance in terms of average path length and network latency. In addition, VacoNet can reach even 50% in infrastructure cost reduction and the power consumption will be decreased with more than 50000 watt compared to all the previous architectures. Both theoretical analysis and simulation experiments are conducted to evaluate the overall performance of the proposed architecture.

In this paper, three novel techniques are proposed and investigated for multiple relay selection in dual hop OFDM networks. These techniques are based on the exploitation of sparse signal recovery theory and on carefully-designed groupings of the subcarriers depending on the channel quality. In particular, the proposed techniques use the Orthogonal Matching Pursuit algorithm which enables them to outperform existing techniques in terms of both outage probability and computation complexity. Furthermore, a detailed performance-complexity tradeoff investigation is presented for the different studied techniques and verified by Monte Carlo simulations.

Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique, called neural networks (NNs), to extract meaningful information from large data sets, so called big data. However, the application of NN to manufacturing problems remains limited because it involves the specialized skills of a data scientist. This paper introduces an approach to automate the application of analytical models to manufacturing problems. We present an NN meta-model (MM), which defines a set of concepts, rules, and constraints to represent NNs. An NN model can be automatically generated and manipulated based on the specifications of the NN MM. In addition, we present an algorithm to generate a predictive model from an NN and available data. The predictive model is represented in either Predictive Model Markup Language (PMML) or Portable Format for Analytics (PFA). Then we illustrate the approach in the context of a specific manufacturing system. Finally, we identify future steps planned towards later implementation of the proposed approach.

Multispectral images (MI) have shown promising capabilities to solve problems resulting from high illumination variation in face recognition. However, the use of MI, with the huge number of captured spectral bands for each subject, is impractical unless a system for best spectral bands selection (BSBS) is used. In this work, first we give an up to date overview of the existing BSBS techniques proposed for face recognition. We aim to highlight the imporatnce of this component of MI based systems. The reviewed techniques are then experimented using the multispectral face database IRIS - M3to compare their performances. To the best of our knowledge this is the first study that reviews and compares existing techniques for BSBS. The Obtained results emphasized the importance of setting up techniques for BSBS with MI based systems as well as the need for new techniques that investigate better the increasing speed of image processing tools.

In this paper, we propose to combine the shape context (SC) descriptor with quantum genetic algorithms (QGA) to define a new shape matching and retrieval method. The SC matching method is based on finding the best correspondence between two point sets. The proposed method uses the QGA to find the best configuration of sample points in order to achieve the best possible matching between the two shapes. This combination of SC and QGA leads to a better retrieval results based on our tests. The SC is a very powerful discriminative descriptor which is translation and scale invariant, but weak against rotation and flipping. In our proposed quantum shape context algorithm (QSC), we use the QGA to estimate the best orientation of the target shape to ensure the best matching for rotated and flipped shapes. The experimental results showed that our proposed QSC matching method is much powerful than the classic SC method for the retrieval of shapes with orientation changes.

Semantic means adding meaning and understanding. Applying semantic web concepts to web services produces semantic web services. These combined type of services transfer web services from such static functionalities to more intelligent components that can be processed and interpreted automatically on machines without human intervention. Applying semantic web to web data produces Linked Data. This data allows the reuse, share and linking between different sources of data easily and in a standard manner. In this paper, we discuss how semantic web concepts are applied on both web services and web data.

In this paper, we propose a new cluster validity index (CVI) based on geometrical shape. Classic CVIs are based on a combination of separation and compactness measures and may include a measure of overlap between clusters. The proposed CVI combines measures of compactness and over-lap using n-sphere shape. We conducted experiments on several real data sets from the UCI repository and compared the performances of the proposed CVI with widely used CVIs. Results demonstrate that the proposed CVI performs better than the others even when used with complicated data sets.

From the perspective of resource allocation and routing, this paper aims to save as much energy as possible in data center networks. We present a general framework, based on the blocking island paradigm, to try to maximize the network power conservation and minimize sacrifices of network performance and reliability. The bandwidth allocation mechanism together with power-aware routing algorithm achieve a bandwidth guaranteed tighter network. Besides, our fast efficient heuristics for allocating bandwidth enable the system to scale to large sized data centers. The evaluation result shows that up to more than 50% power savings are feasible while guaranteeing network performance and reliability.

Recently, surgical simulation is a widely used method to train surgeons on specific surgeries due to the fact that it helps reducing surgical errors. Available surgical simulations lack realism since they only incorporate one or two senses which are vision and hap tic. This paper proposes a novel multimode interactive surgical simulator that incorporates hap tic, olfactory, as well as traditional vision feedback. A scent diffuser was created and developed to interact with the simulation, in order to produce odors when errors took place. Phantom hap tic device was used to provide the sense of touch to the user. Our system has been tested and evaluated and the results show that incorporating more senses to the simulation enhances the performance of the user. This is due to the fact that using olfaction sensation increases the remembrance of the trainee.

In this paper, we describe a novel approach for representing state information for the purpose of intention recognition in cooperative human-robot environments. States are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. This approach is applied to a manufacturing kitting operation, where humans and robots are working together to develop kits. Based upon a set of predefined high-level states relationships that must be true for future actions to occur, a robot can use the detailed state information presented in this paper to infer the probability of subsequent actions occurring. This would enable the robot to better help the human with the operation or, at a minimum, better stay out of his or her way.

Supershape model is a recent primitive that represents numerous 3D shapes with several symmetry axes. The main interest of this model is its capability to reconstruct more complex shape than superquadric model with only one implicit equation. In this paper we propose a genetic algorithms to reconstruct a point cloud using those primitives. We used the pseudo-Euclidean distance to introduce a threshold to handle real data imperfection and speed up the process. Simulations using our proposed fitness functions and a fitness function based on inside-outside function show that our fitness function based on the pseudo-Euclidean distance performs better.

This paper presents a new solver for systems of nonlinear equations. Such systems occur in Geometric Constraint Solving, e.g., when dimensioning parts in CAD-CAM, or when computing the topology of sets defined by nonlinear inequalities. The paper does not consider the problem of decomposing the system and assembling solutions of subsystems. It focuses on the numerical resolution of well-constrained systems. Instead of computing an exponential number of coefficients in the tensorial Bernstein basis, we resort to linear programming for computing range bounds of system equations or domain reductions of system variables. Linear programming is performed on a so called Bernstein polytope: though, it has an exponential number of vertices (each vertex corresponds to a Bernstein polynomial in the tensorial Bernstein basis), its number of hyperplanes is polynomial: O(n2) for a system in n unknowns and equations, and total degree at most two. An advantage of our solver is that it can be extended to non-algebraic equations. In this paper, we present the Bernstein and LP polytope construction, and how to cope with floating point inaccuracy so that a standard LP code can be used. The solver has been implemented with a primal-dual simplex LP code, and some implementation variants have been analyzed. Furthermore, we show geometric-constraint-solving applications, as well as numerical intersection and distance computation examples.

Classically, geometric constraint solvers use graph-based methods to analyze systems of geometric constraints. These methods have intrinsic limitations, which the witness method overcomes. This paper details the computation of a basis of the vector space of the free infinitesimal motions of a typical witness, and explains how to use this basis to interrogate the witness for detecting all dependencies between constraints: structural dependencies already detectable by graph-based methods, and also non-structural dependencies, due to known or unknown geometric theorems, which are undetectable with graph-based methods. The paper also discusses how to decide about the rigidity of a witness.

In this paper, we apply genetic algorithms to reconstruct Gielis surfaces from 3D data sets. The Levenberg-Marquardt method has been used as a standard for superquadrics recovery and has recently been extended to Gielis surfaces. Unfortunately, the non homogeneity of the Gielis surface parameters requires additional heuristic to determine discrete parameters such as the number of symmetries. Genetic algorithms overcome this issue and provide a more general framework for Gielis surface reconstruction.

In this paper, we apply supershapes and R-functions to surface recovery from 3D data sets. Individual supershapes are separately recovered from a segmented mesh. R-functions are used to perform Boolean operations between the reconstructed parts to obtain a single implicit equation of the reconstructed object that is used to define a global error reconstruction function. We present surface recovery results ranging from single synthetic data to real complex objects involving the composition of several supershapes and holes.

This paper presents some important issues and potential research tracks for Geometric Constraint Solving: the use of the simplicial Bernstein base to reduce the wrapping effect in interval methods, the computation of the dimension of the solution set with methods used to measure the dimension of fractals, the pitfalls of graph based decomposition methods, the alternative provided by linear algebra, the witness configuration method, the use of randomized provers to detect dependences between constraints, the study of incidence constraints, the search for intrinsic (coordinate-free) formulations and the need for formal specifications.

We present a new multiresolution analysis framework for irregular meshes with attributes based on the lifting scheme. We introduce a surface prediction operator to compute the detail coefficients for the geometry and the attributes of the model. Attribute analysis gives appearance information to complete the geometrical analysis of the model. A set of experimental results are given to show the efficiency of our framework. We present two applications to adaptive visualization and denoising.

Geometric constraint solving is a key issue in CAD/CAM. Since Owen's seminal paper, solvers typically use graph based decomposition methods. However, these methods become difficult to implement in 3D and are misled by geometric theorems. We extend the Numerical Probabilistic Method (NPM), well known in rigidity theory, to more general kinds of constraints and show that NPM can also decompose a system into rigid subsystems. Classical NPM studies the structure of the Jacobian at a random (or generic) configuration. The variant we are proposing does not consider a random configuration, but a configuration similar to the unknown one. Similar means the configuration fulfills the same set of incidence constraints, such as collinearities and coplanarities. Jurzak's prover is used to find a similar configuration.

We present a new and efficient algorithm to accurately polygonize an implicit surface generated by multiple Boolean operations with globally deformed primitives. Our algorithm is special in the sense that it can be applied to objects with both an implicit and a parametric representation, such as superquadrics, supershapes, and Dupin cyclides. The input is a Constructive Solid Geometry tree (CSG tree) that contains the Boolean operations, the parameters of the primitives, and the global deformations. At each node of the CSG tree, the implicit formulations of the subtrees are used to quickly determine the parts to be transmitted to the parent node, while the primitives' parametric definition are used to refine an intermediary mesh around the intersection curves. The output is both an implicit equation and a mesh representing its solution. For the resulting object, an implicit equation with guaranteed differential properties is obtained by simple combinations of the primitives' implicit equations using R-functions. Depending on the chosen R-function, this equation is continuous and can be differentiable everywhere. The primitives' parametric representations are used to directly polygonize the resulting surface by generating vertices that belong exactly to the zero-set of the resulting implicit equation. The proposed approach has many potential applications, ranging from mechanical engineering to shape recognition and data compression. Examples of complex objects are presented and commented on to show the potential of our approach for shape modeling.

Abstract We introduce a novel method to define Dupin cyclide blends between quadric primitives. Dupin cyclides are non‐spherical algebraic surfaces discovered by French mathematician Pierre‐Charles Dupin at the beginning of the 19th century. As a Dupin cyclide can be fully characterized by its principal circles, we have focussed our study on how to determine principal circles tangent to both quadrics being blended. This ensures that the Dupin cyclide we are constructing constitutes a G 1 blend. We use the Rational Quadratic Bézier Curve (RQBC) representation of circular arcs to model the principal circles, so the construction of each circle is reduced to the determination of the three control points of the RQBC representing the circle. In this work, we regard the blending of two quadric primitives A and B as two complementary blending operations: primitive A‐cylinder and cylinder‐primitive B; two Dupin cyclides and a cylinder are then defined for each blending operation. In general the cylinder is not useful and may be reduced to a simple circle. A complete shape design example is presented to illustrate the modeling of Eurographics'04 Hugo using a limited number of quadrics combined using Dupin cyclide blends. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling

This paper describes an efficient method to compare two triangular meshes. Meshes considered here contain geometric features as well as other surface attributes such as material colors, texture, temperature, radiation, etc. Two deviation measurements are presented to assess the differences between two meshes. The first measurement, called geometric deviation, returns geometric differences. The second measurement, called attribute deviation, returns attribute differences regardless of the attribute type. We present an application of this method to the mesh simplification algorithm (MSA) quality assessment according to the appearance attributes. This assessment allows the appreciation of local quality and the computation of global quality statistics of a simplified mesh.