Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

Self-driving cars: A survey

Fast depleting fossil fuels and the growing awareness for environmental protection have led us to the energy crisis. Hence, efforts are being made by researchers to investigate new ways to extract energy from renewable sources. ‘Microgrids’ with Distributed Generators (DG) are being implemented with renewable energy systems. Optimization methods justify the cost of investment of a microgrid by enabling economic and reliable utilization of the resources. This paper strives to bring to light the concept of Hybrid Renewable Energy Systems (HRES) and state of art application of optimization tools and techniques to microgrids, integrating renewable energies. With an extensive literature survey on HRES, a framework of diverse objectives has been outlined for which optimization approaches were applied to empower the microgrid. A review of modelling and applications of renewable energy generation and storage sources is also presented.

Optimization in microgrids with hybrid energy systems – A review

Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted features and heuristics, usually followed by post-processing techniques, that are computationally expensive and prone to scalability due to road scene variations. More recent approaches leverage deep learning models, trained for pixel-wise lane segmentation, even when no markings are present in the image due to their big receptive field. Despite their advantages, these methods are limited to detecting a pre-defined, fixed number of lanes, e.g. ego-lanes, and can not cope with lane changes. In this paper, we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end. To parametrize the segmented lane instances before fitting the lane, we further propose to apply a learned perspective transformation, conditioned on the image, in contrast to a fixed ”bird’s-eye view” transformation. By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, predefined transformation. In summary, we propose a fast lane detection algorithm, running at 50 fps, which can handle a variable number of lanes and cope with lane changes. We verify our method on the tuSimple dataset and achieve competitive results.

Towards End-to-End Lane Detection: an Instance Segmentation Approach

With the rapid proliferation of new technologies and services in the wireless domain, spectrum scarcity has become a major concern. The allocation of the Industrial, Medical and Scientific (ISM) band has enabled the explosion of new technologies (e.g. Wi-Fi) due to its licence-exempt characteristic. The widespread adoption of Wi-Fi technology, combined with the rapid penetration of smart phones running popular user services (e.g. social online networks) has overcrowded substantially the ISM band. On the other hand, according to a number of recent reports, several parts of the static allocated licensed bands are under-utilized. This has brought up the idea of the opportunistic use of these bands through the, so-called, cognitive radios and cognitive radio networks. Cognitive radios have enabled the opportunity to transmit in several licensed bands without causing harmful interference to licensed users. Along with the realization of cognitive radios, new security threats have been raised. Adversaries can exploit several vulnerabilities of this new technology and cause severe performance degradation. Security threats are mainly related to two fundamental characteristics of cognitive radios: cognitive capability, and reconfigurability. Threats related to the cognitive capability include attacks launched by adversaries that mimic primary transmitters, and transmission of false observations related to spectrum sensing. Reconfiguration can be exploited by attackers through the use of malicious code installed in cognitive radios. Furthermore, as cognitive radio networks are wireless in nature, they face all classic threats present in the conventional wireless networks. The scope of this work is to give an overview of the security threats and challenges that cognitive radios and cognitive radio networks face, along with the current state-of-the-art to detect the corresponding attacks. In addition, future challenges are addressed.

A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks

The arc fault circuit interrupter (AFCI) is expected to be one of the most essential components in smart grid systems for providing physical security and safety against electrical fire hazards caused by arc faults. As AFCIs are widely deployed as a part of mandatory installation requirements, a hierarchy consisting of multilevel AFCIs has been established, where a portable AFCI is connected serially to an outlet box AFCI. However, this multilevel AFCI structure causes serious problems when the AFCI detects arc faults in the surveillance area of its descendant AFCIs. This can cause de-energizing of the entire area covered by the upper-level AFCI, which may lead to blackouts over large areas. This paper proposes an integrated security framework comprised of physical and logical security measures as a solution for this problem. Firstly, the problem is tackled through communication between the hierarchy levels. Since the proposed system deals with physical security and safety, the communication must guarantee reliable message delivery within the specified deadlines. A controller area network (CAN) is chosen as the communication technology because it provides deterministic message delivery that meets the system requirements. Moreover, CAN has the advantages of verified performance and cost competitiveness through accelerated industrial adoption. Along with the physical security framework, a logical security framework is also proposed with group key management that prevents unauthorized access. Finally, this paper reports an integrated methodology for optimizing the design parameters satisfying the bandwidth and security demands of physical and logical measures because both demands competitively share a common communication resource.

A Physical and Logical Security Framework for Multilevel AFCI Systems in Smart Grid

An arc fault circuit interrupter (AFCI) is a device that provides protection from the effects of arc faults by de-energizing a circuit within a specified time after an arc fault is detected. In the United States, AFCI installation in bedroom receptacle outlets has been mandated since 2002. As AFCIs have been widely adapted to real situations, several kinds of AFCIs have been developed including branch/feeder, cord, outlet box, and portable designs. Using various types of AFCIs creates a hierarchy consisting of multi-level AFCIs such as a portable AFCI serially connected to an outlet box AFCI. This multi-level AFCI structure causes a serious problem by enabling an upper level AFCI to detect an arc fault occurring in the surveillance area of lower level AFCIs. This problem results in de-energizing the whole area covered by the upper level AFCI. In this paper, we propose a solution to the multi-level AFCI problem that occurs when several types of AFCIs are used in the same hierarchy. To provide a practical solution, we first present the theoretical background of a multi-level AFCI and suggest a solution that integrates collaborative communication and computing power into a conventional AFCI device. Finally, we introduce a prototype implementation of our approach. Our proposed approach can protect people and facilities against electrical fire hazards, which is one of major challenges that the smart grid aims to overcome using information technology.

Multi-Level Arc Fault Circuit Interrupter with Collaborative Communications for Smart Grid

In this paper, we propose a novel architecture for optical transport networks and its operation scheme guaranteeing the QoS requirements based on real-time traffic measurement. The key concept of the proposed architecture, which we call hybrid optical transport network (HOTNET), is to adopt both optical circuit switching and optical message switching in an optical network. To implement two different switching technologies in a single network, we modify the optical burst switching scheme and merge it into a TDM wavelength routed network. Then, we propose a control framework and an architecture of a switching node for this hybrid switching paradigm. We also discuss a real-time bandwidth provisioning scheme which utilizes the advantages of two respective switching schemes for traffic engineering. Finally, we evaluate the performance of the proposed scheme via computer simulation and the results show that it can guarantee the traffic QoS requirements while maintaining high channel utilization.

Hybrid Optical Transport Network (HOTNET): An Optical Network with Hybrid Switching Technologies for Integrated Services

In this paper, we propose a new algorithm which can be used to measure the distances to objects on a stereo camera platform. Our algorithm exploits the key information obtained from a frequency domain analysis of captured images. Experimental results show that our approach provides more accurate distance measurements in real-time when compared with a conventional algorithm.

Object distance estimation based on frequency domain analysis using a stereo camera

We present an accurate and robust image-to-point cloud registration method that is viable in urban and off-road environments. Existing image-to-point cloud registration methods have focused on vehicle platforms along paved roads. Therefore, image-to-point cloud registration on UGV platforms for off-road driving remains an open question. Our objective is to find a versatile solution for image-to-point cloud registration. We present a method that stably estimates a precise transformation between an image and a point cloud using a two-phase method that aligns the two input data in the virtual reference coordinate system (virtual-alignment) and then compares and matches the data to complete the registration (compare-and-match). Our main contribution is the introduction of divide-and-conquer strategies to image-to-point cloud registration. The virtual-alignment phase effectively reduces relative pose differences without cross-modality comparison. The compare-and-match phase divides the process of matching the image and point cloud into the rotation and translation steps. By breaking down the registration problem, it is possible to develop algorithms that can robustly operate in various environments. We performed extensive experiments on four datasets (Rellis-3D, KITTI odometry, nuScenes, and KITTI raw). Experiments cover a variety of situations in which image-to-point cloud registration is applied, from image-based localization in off-road environments to camera-LiDAR extrinsic calibration in urban environments. The experiments demonstrate that the proposed method outperforms the existing methods in accuracy and robustness.

Seung-Woo Seo

Papers

A Physical and Logical Security Framework for Multilevel AFCI Systems in Smart Grid

Multi-Level Arc Fault Circuit Interrupter with Collaborative Communications for Smart Grid

Hybrid Optical Transport Network (HOTNET): An Optical Network with Hybrid Switching Technologies for Integrated Services

Object distance estimation based on frequency domain analysis using a stereo camera

EFGHNet: A Versatile Image-to-Point Cloud Registration Network for Extreme Outdoor Environment