Algorithmic Aspects

The paper presents hardware implementation aspects of new efficient image compression algorithm designed for wireless capsule endoscopy with Bayer color filter array (CFA). Since power limitation, small size conditions and specific image data format (CFA) exclude application of traditional image compression techniques dedicated ones are necessary. Discussed algorithm is based on integer version of discrete cosine transform (DCT). Therefore it has low complexity and power consumption. It is demonstrated that the perfor-mance of proposed algorithm is comparable to the performance of JPEG2000 --- very complex, sophisticated wavelet based coder. In the paper a VLSI coder architecture is proposed and power requirements are discussed.

Customer churn, a critical metric in the telecommunications industry, reflects the propensity of customers to discontinue service usage, thereby affecting the revenue and growth of a company. Customer churn is greatly affected by individuals and products, and there is no obvious rule, which greatly increases the difficulty of customer loss prediction. With the development of machine learning, the prediction in telecom churn using machine learning has attracted wide attention from researchers. However, there are great differences between different algorithms, which increases the difficulty of application. Therefore, this paper studies the issue of customer churn prediction from the aspect of algorithm. This paper introduces the common data sets and evaluation indexes of customer churn prediction, and then introduces logistic regression, decision tree, K-nearest neighbor, random forest, Gaussian naive Bayes, XGBoost, gradient enhancement and neural network algorithms. Then, through the experimental analysis, the differences between these models are compared. Finally, the paper summarizes the full text and discusses the future research hotspots.

In order to encourage energy conservation and emission reduction, electric vehicles (EV) have gradually become one of the most important emerging strategic industries in many countries. With the gradual maturity and application of battery technology, policies support of new energy industry and the marketization of EV in many countries, the location problem of charging stations has become one part of the urban development strategies, but the bottleneck that hinders the development of new EV is the factors of vehicle life and the battery life. The focus of problem solving is the reasonable location and deployment of charging stations. Therefore, the charging station location problem has become a research hotspot in recent years. The EV charging station location is essentially an application scenario of facility location problem. As a long-standing problem, many researchers have proposed many classic models and algorithms, this paper reviews the research progress and existing problems related to charging station location in recent years from the aspects of algorithm and model, and finally gives some future research directions.

Considerable attention has been devoted to the problem of deriving reduced order models for complex systems [7]. In this study we comment some aspects of model reduction problems based on considerations of empirical investigations carried out by using a useful tool for such aims: UMLLSR [2], This is a Unified Interactive Modular Library of subprograms devoted to time-invariant linear system reduction. The development of a subprogram package is a very useful tool in view of the particular importance of Computer-Aided-Design in modular unified synthesis approach to control system design, but it also offers powerful support to investigate some theoretical aspects of the various approximation algorithms. UMLLSR consists of a set of modules implementing the various approximation techniques via a common data-base that allows the user to evaluate the performances of the different reduced models obtained. But the data-base also allows the exchanging of information among the various modules with the following aims: 1) to set further optimization in the synthesis of reduced models; 2) to study possible correlations among the obtained results using different approximation procedures in order to evaluate the efficiency of one algorithm respect to another, and possibly to predict the performance of a particular algorithm based on previous results; 3) to investigate numerical aspects of the algorithms in order to analyze their numerical stability (a) and to improve convergence of some iterative schemes (b).

In recent years, there is a strong demand for automated guided vehicles in many fields such as manufacturing or studying and a variety of omnidirectional vehicle systems has been developed for these purposes, which includes the Mecanum-wheel vehicles. In the meantime, identifying the environment by laser scanner has increasingly become an efficient solution for robotic recognition. In order to implement an intelligent vehicle for industrial environment, a lane keeping method for Mecanum wheeled Omni-directional vehicles is proposed in this paper. We first design a LQR controller for the omnidirectional vehicle motion control. After that, a laser scanner is used to sense the lane for the vehicle. In the aspect of algorithm, a set of waypoints is used to determine the heading direction of the vehicle when cornering and a PID controller is designed to keep the vehicle in the detected lane when it is passing a relatively straight lane. The simulation experiment shows that the omnidirectional platform tracking system has a good response by using PID control. Finally, experimental result shows that the real vehicle can track the desired trajectory rapidly and smoothly, and the vehicle meanwhile is constrained in the lane in the whole process.

Cone-beam computed tomography (CBCT) technique is largely used in medical diagnostic imaging and nondestructive materials testing, especially in cases which require fast times and high accuracy level. In this paper, the pros and cons of Feldkamp-Davis-Kress (FDK) and simultaneous iterative reconstruction technique (SIRT) algorithms used in CBCT technique is studied. The method of simulating CBCT systems is also used to provide richer projection data, which helps the research to evaluate many aspects of algorithms.

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect bias and is discussed in this article from the cognitive psychology perspective. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Background:</i> ML algorithms are common components in electrical engineering, computer science, and data science curricula. Learning ML is challenging not only due to its complex mathematical and algorithmic aspects, but also due to: 1) the complexity of using these algorithms in the context of real-life situations correctly and 2) the comprehension of related social and ethical issues. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Research Question:</i> To what extent do students fail to correctly interpret the performance of ML algorithms due to the domain neglect bias and how can this bias be explained? <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Methodology:</i> A questionnaire containing two questions was designed, in which the participants were asked to compare the performance of ML algorithms in the contexts of different application domains. The questions were designed to trigger the domain neglect bias which in turn, allowed quantification of its prevalence. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Findings:</i> One third of the students failed to recognize the outperforming ML algorithm due to the domain neglect bias. Educators should note this bias and develop new pedagogical methods for teaching ML performance in order to mitigate the bias.

Nonlinear diffusion methods have proved to be powerful methods in the processing of 2D and 3D images. They allow a denoising and smoothing of image intensities while retaining and enhancing edges. On the other hand, compression is an important topic in image processing as well. Here a method is presented which combines the two aspects in an efficient way. It is based on a semi-implicit Finite Element implementation of nonlinear diffusion. Error indicators guide a successive coarsening process. This leads to locally coarse grids in areas of resulting smooth image intensity, while enhanced edges are still resolved on fine grid levels. Special emphasis has been put on algorithmical aspects such as storage requirements and efficiency. Furthermore, a new nonlinear anisotropic diffusion method for vector field visualization is presented.

Simplicial Decompositions of Graphs: a Survey of Applications

ABSTRACTRecently much attention has been given to the possibility of shooting in an overlapping fashion, the so‐called blended or simultaneous acquisition. In conventional acquisition the time intervals between successive sources are large enough to avoid interference in time. In blending, a temporal overlap between source responses is allowed. This additional degree of freedom in survey design has the potential to significantly reduce seismic acquisition costs while maintaining or improving the data quality. Deblending is the procedure of retrieving data as if they were acquired in the conventional, unblended way. This is an essential step in the case where standard processing flows are applied. Several methods have been proposed to perform data separation with the majority of them falling into two categories. The first category consists in methods that filter out the blending noise by arranging seismic data in some domain, whereas inversion techniques fall into the second category. We recently introduced an iterative estimation and subtraction algorithm that integrates elements of both categories. This method exploits the fact that the character of the blending noise differs in different domains, e.g., it is coherent in the common source domain but incoherent in the common receiver, common‐offset or common midpoint domains. Up to now, our method relies on the interaction with a human operator. The automation of the thresholding process is addressed in this paper, leading to a hands‐off algorithm for the separation of blended data, optimized for both efficiency and effectiveness. We found that one of the major limiting factors is the edge artefacts generated by the coherence‐pass filter. The effectiveness of the coherence‐pass filter has a considerable influence on the convergence of our deblending algorithm. This is shown by testing different coherence‐pass filters on marine data as well as introducing errors in the coherence‐pass process. We show that these data estimation errors can be handled properly and the best result is obtained using a τ‐p filter as a coherence‐pass filter. Furthermore, very promising results are obtained on numerically‐blended land data.

ADAPTIVE STRUCTURAL ANALYSIS FOR FDI DESIGN IN EVOLVING SYSTEMS

Logit dynamics is a form of randomized game dynamics in which players have a bias toward strategic deviations that give a higher improvement in cost. It is used extensively in practice. In congestion (or potential) games, the dynamics converge to the so-called Gibbs distribution over the set of all strategy profiles when interpreted as a Markov chain. In general, logit dynamics can converge slowly to the Gibbs distribution, but beyond that, not much is known about its algorithmic aspects, nor that of the Gibbs distribution. In this work, we are interested in the following two questions for congestion games: (i) Is there an efficient algorithm for sampling from the Gibbs distribution? (ii) If yes, does there also exist natural randomized dynamics that converge quickly to the Gibbs distribution? We first study these questions in extension parallel congestion games, a well-studied special case of symmetric network congestion games. As our main result, we show that there is a simple variation on the logit dynamics that converges quickly to the Gibbs distribution in such games. We also address the first question for the class of so-called capacitated uniform congestion games and the second question for max cut games played on a complete graph. To prove our results, we rely on the recent breakthrough work of Anari et al. (2019) regarding the approximate sampling of a base of a matroid according to a strongly log-concave probability distribution.

The least median of squares (LMS) regression method is highly robust to outliers in the data. It can be computed by means of PROGRESS (from Program for RObust reGRESSion). After ten years we have developed a new version of PROGRESS, which also computes the least trimmed squares (LTS) method. We will discuss the various new features of PROGRESS, with emphasis on the algorithmic aspects.

Algorithmic Aspects in Information and Management

In this thesis we contribute with new theoretical results and algorithms to the research area related to cycles and paths in (directed) graphs. In Chapter 2, we improve a classical result of Woodall for Hamiltonicity in digraphs, involving the sum of the in-degree and out-degree of two vertices for which there is no arc from one to another, by characterizing the exceptional digraphs for the weakened condition. In Chapter 3, we introduce and study the concept of path extendability in digraphs. Several degree and extremal conditions for path extendability are presented, and path extendability of regular tournaments is settled. Some open problems are raised. In Chapter 4, we study cycle extendability in bipartite tournaments. We prove that in bipartite tournaments, Hamiltonicity, pancyclicity and cycle extendability are equivalent properties, with only one exceptional class that can be clearly described. We propose problems for further consideration involving the equivalence of these properties in the more general class of multipartite tournaments. In Chapter 5, we calculate the number of 2-paths between every vertex pair, a concept that one often encounters in conditions for cycle and path properties, in random oriented graphs and random tournaments. Based on our enumeration, we obtain several results related to path extendability in tournaments. In Chapter 6, we study computational problems in the context of cycle partitions and clique partitions of edge-colored graphs, which can be viewed as a generalization of digraphs. The results of this chapter deal with computational complexity and algorithmic aspects. Some new methods and tools are developed. In Chapter 5, the number of 2-paths is used in conditions or as a tool for establishing properties related to cycles and paths in tournaments. In Chapter 4, we define the concept of the in-out graph of a digraph, which is used to construct and analyze the structure of cycle factors of two intersecting cycles. To establish a degree condition for path extendability in Chapter 3, we introduce a contraction operation to relate path-extendability and cycle-extendability, so that we can use a former result of Hendry to simplify one of our proofs.