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

Batteries and supercapacitors (SC) complement one another; a battery has a relatively high energy density but a low power density, whereas an SC has a relatively high power density but a low energy density. In order to offset their opposing limitations, an active battery/SC hybrid energy storage system (HESS) using a dc/dc converter has been proposed. The major problem concerning an active HESS is in how to control the current flow in order to achieve two objectives: the minimization of the magnitude/fluctuation of the current flowing in and out of the battery and the energy loss seen by the SCs. This problem has not been analytically investigated for an optimal solution regarding these two goals. In this paper, we present an optimal energy management scheme for active HESS. In order to obtain the optimal solution, we formulate the problem as an optimization problem concerning these two objectives. Observing that the feasibility and optimality of the solution critically depends on the boundary parameters of the problem, we present an algorithm that effectively adjusts the parameter values. The proposed algorithm is based on the multiplicative-increase- additive-decrease principle, which guarantees a feasible optimal solution. Through MATLAB simulations, we demonstrate that the proposed scheme can optimally minimize the magnitude/fluctuation of the battery current and the SC energy loss.

Energy Management Optimization in a Battery/Supercapacitor Hybrid Energy Storage System

Batteries mounted on electric vehicles (EVs) are often damaged by high peak power and rapid charging/discharging cycles, which are originated from repetitive acceleration/deceleration of vehicles particularly in urban situations. To reduce battery damage, the battery/supercapacitor (SC) hybrid energy storage system (HESS) has been considered as a solution because the SC can act as a buffer against large magnitudes and rapid fluctuations in power. While the traditional purpose of employing the HESS in EVs is to minimize the magnitude/variation of battery power or power loss, the previous approaches proposed for controlling the HESS have some drawbacks; they neither consider these objectives simultaneously nor reflect real-time load dynamics for computing the SC reference voltage. In this paper, we present a power control framework consisting of two stages: one for computing the SC reference voltage and another for optimizing the power flowing through the HESS. In the presented framework, we propose a methodology for calculating the SC reference voltage considering the real-time load dynamics without given future operation profiles. In addition, we formulate the HESS power control problem as a convex optimization problem that minimizes the magnitude/fluctuation of battery power and power loss at the same time. The optimization problem is formulated so that it can be repeatedly solved by general solvers in polynomial time. Simulation results carried out on MATLAB show that the magnitude/variation of battery power and power loss can be concurrently reduced in real time by the proposed framework.

Real-Time Optimization for Power Management Systems of a Battery/Supercapacitor Hybrid Energy Storage System in Electric Vehicles

Over the past few decades, the need has arisen for multi-lane detection algorithms for use in vehicle safety-related applications. In this paper we propose a new multi-lane detection algorithm that works well in urban situations. This algorithm detects four lane marks, including driving lane marks and adjacent lane marks. Conventional research assumes that lanes are parallel. In contrast, our approach does not require this assumption, thus enabling the algorithm to manage various non-parallel lane situations, such as are found at intersections, in splitting lanes, and in merging lanes. To detect multi-lane marks successfully in the absence of parallelism, we adopt Conditional Random Fields (CRFs), which are strong models for solving multiple association tasks. We show that CRFs are very effective tools for multi-lane detection because they find an optimal association of multiple lane marks in complex and challenging urban road situations. Through simulations, and by using video sequences with 752-480 resolution and Caltech Lane Datasets with runtime rates of 30 fps, we verify that our algorithm successfully detects non-parallel lanes as well as parallel lanes appearing in urban streets.

Multi-lane detection in urban driving environments using conditional random fields

The development of intelligent vehicle systems has resulted in an increased need for a high-precision road map. However, conventional road maps that are used for vehicle navigation systems or geographical information systems (GISs) are insufficient to satisfy new requirements of intelligent vehicle systems such as autonomous driving. There are three primary road-map requirements for intelligent vehicle systems: centimeter-level accuracy, storage efficiency, and usability. However, no existing researches have met these three requirements simultaneously. In this paper, we propose a precise and efficient lane-level road-map generation system that conforms to the requirements all together. The proposed map-building process consists of three steps: 1) data acquisition, 2) data processing, and 3) road modeling. The road data acquisition and processing system captures accurate 3-D road geometry data by acquiring data with a mobile 3-D laser scanner. The road geometry data are then refined to extract meta information, and in the road modeling system, the refined data are represented as sets of piecewise polynomials to ensure storage efficiency and usability of the map. The proposed mapping system has been extensively tested and evaluated on a real urban road and highway. The experimental results show that the proposed mapping system outperforms conventional systems in terms of the road-map requirements.

Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems

Most research efforts to date on optical networks have concentrated on wavelength-division multiplexing (WDM) techniques where the information from different channels is routed via separate optical wavelengths. The data corresponding to a particular channel is selected at the destination node by a frequency filter. Optical time-division multiplexing (OTDM) has been considered as an alternative to WDM for future networks operating in excess of 10 Gb/s. Systems based on TDM techniques rely upon a synchronized clock frequency and timing to separate the multiplexed channels. Advances in device technologies have opened new opportunities for implementing OTDM in very high-speed long-haul transmission as well as networking. The multiterahertz bandwidth made available with the advent of optical fibers has spurred investigation and development of transparent all-optical networks that may overcome the bandwidth bottlenecks caused by electro-optic conversion. This paper presents an overview of current OTDM networks and their supporting technologies. A novel network architecture is introduced, aimed at offering both ultra-high speed (up to 100 Gb/s) and maximum parallelism for future terabit data communications. Our network architecture is based on several key state-of-the-art optical technologies that we have demonstrated.

Seung-Woo Seo

Papers

Energy Management Optimization in a Battery/Supercapacitor Hybrid Energy Storage System

Real-Time Optimization for Power Management Systems of a Battery/Supercapacitor Hybrid Energy Storage System in Electric Vehicles

Multi-lane detection in urban driving environments using conditional random fields

Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems

Transparent optical networks with time-division multiplexing