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.

pdf/self-driving-cars-a-survey-1n8kw2emj9.pdf

Self-driving cars: A survey

We address semantic segmentation of road-objects from 3D LiDAR point clouds. In particular, we wish to detect and categorize instances of interest, such as cars, pedestrians and cyclists. We formulate this problem as a point-wise classification problem, and propose an end-to-end pipeline called SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a transformed LiDAR point cloud as input and directly outputs a point-wise label map, which is then refined by a conditional random field (CRF) implemented as a recurrent layer. Instance-level labels are then obtained by conventional clustering algorithms. Our CNN model is trained on LiDAR point clouds from the KITTI [1] dataset, and our point-wise segmentation labels are derived from 3D bounding boxes from KITTI. To obtain extra training data, we built a LiDAR simulator into Grand Theft Auto $\boldsymbol{V}$ (GTA-V), a popular video game, to synthesize large amounts of realistic training data. Our experiments show that SqueezeSeg achieves high accuracy with astonishingly fast and stable runtime ($8.7\pm 0.5$ ms per frame), highly desirable for autonomous driving. Furthermore, additionally training on synthesized data boosts validation accuracy on real-world data. Our source code is open-source released111https://github.com/BichenWuUCB/SqueezeSeg. The paper is accompanied by a video222https://youtu.be/Xyn5Zd31m6s containing a high level introduction and demonstrations of this work.

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

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

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

A group key management is essential method guaranteeing of data confidentiality and integrity in secure multicast communication. For secure multicast, group key management schemes have focused on communication cost, i.e., the number of update messages, to efficiently use bandwidth. To reduce the communication cost, many group key management schemes adopt a key tree structure. But, to adopt a key tree structure, a more powerful capability is needed, as a tradeoff, to store various kinds of keys and to execute several cryptography algorithms, i.e., storage cost and computation cost, respectively. But, it is important to simultaneously reduce not only the overhead of communication cost but also the overheads of storage and computation costs in mobile devices which have limited storage space and computation capability. In this paper, we propose a key tree structure, and a key tree management protocol for the efficiency of storage and computation costs. The proposed protocol adaptively controls the key tree structure not only to reduce the increase of the communication cost and but also to minimize the storage and the computation costs by using the limited resource information from each group members. We analyze the proposed protocol with three kinds of cost metrics and conduct simulations. The results show that the proposed protocol can save storage and computation costs. As a tradeoff, the proposed protocol requires a very small increment of communication cost if the ratio of leaving group members and the number of group members are moderately large in a batch rekeying interval.

New key tree management protocol for the efficiency of storage and computation time in secure multicast communication

Highly accurate and robust real-time localization is an essential technique for various autonomous driving applications. Numerous localization methods have been proposed that combine various types of sensors, including an environmental sensor, IMU and GPS. However, the usage of a single environmental sensor is rather fragile. Although the use of multi-environment sensors is a better alternative, fusion methods from previous studies have not adequately compensated for shortcomings in dissimilar sensors or have not considered errors in the pre-built map. In this paper, we propose a decentralized localization framework using heterogeneous map-matching sources. Decentralized localization performs two independent map-matchings and integrates them with a stochastic situational analysis model. By applying a stochastic model, the reliability of the two map matchings is collected and system stability is verified. A number of experiments with autonomous vehicles within the actual driving environment have shown that combining multiple map-matching sources ensures more robust results than the use of a single environmental sensor.

Decentralized Localization Framework using Heterogeneous Map-matchings

In this paper, we present a generalized version of the routing algorithm[1] for a class of 2log_2 N-stage networks which are made by concatenating two log_2 Nstage blocking networks. We show that the generalized algorithm can also cover a class of(2log_2 N - 1)-stage networks. It is shown that the inside-out algorithm is a more general algorithm which covers a large class of inherently symmetric rearrangeable networks, including the Benes and its equivalent networks. Moreover, it is shown that the time complexity of the algorithm is in O(N), which is superior to that of the looping algorithm. The algorithm is discussed using a graph representation of the network and its connectivity properties are shown by a graph describing rule. To show that the algorithm covers a class of 2log_2 N-stage networks, we introduce the concept of a base-network. These base-networks satisfy some common connectivity properties, and we show that any concatenation of two base-networks can be routed by our new algorithm.

A General Inside-Out Routing Algorithm for a Class of Rearrangeable Networks

A sensor network operating in open environments requires a network-wide group key for confidentiality of exchanged messages between sensor nodes. When a node behaves abnormally due to its malfunction or a compromise attack by adversaries, the central sink node should update the group key of other nodes. The major concern of this group key update procedure will be the multi-hop communication overheads of the rekeying messages due to the energy constraints of sensor nodes. Many researchers have tried to reduce the number of rekeying messages by using the logical key tree. In this chapter, we propose an energy-efficient group key management scheme called Topological Key Hierarchy (TKH). TKH generates a key tree by using the underlying sensor network topology with consideration of subtree-based key tree separation and wireless multicast advantage. Based on our detailed analysis and simulation study, we compare the total rekeying costs of our scheme with the previous logical key tree schemes and demonstrate its energy efficiency.

/pdf/group-key-managements-in-wireless-sensor-networks-5g9bq2wsu7.pdf

Group Key Managements in Wireless Sensor Networks

In this paper, we propose novel motion field estimation method using U-disparity map and forward-backward error removal in vehicles environment. Generally, in an image obtained from a camera attached in a vehicle, a motion vector occurs according to the movement of the vehicle. but this motion vector is less accurate by effect of surrounding environment. In particular, it is difficult to extract an accurate motion vector because of adjacent pixels which are similar each other on the road surface. Therefore, proposed method removes road surface by using U-disparity map and performs optical flow about remaining portion. forward-backward error removal method is used to improve the accuracy of the motion vector. Finally, we predict motion of the vehicle by applying RANSAC(RANdom SAmple Consensus) from acquired motion vector and then generate motion field. Through experimental results, we show that the proposed algorithm performs better than old schemes.

Motion Field Estimation Using U-disparity Map and Forward-Backward Error Removal in Vehicle Environment

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