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

We propose a protocol verification method capable of evaluating a telematics system using the ns simulation & emulation facilities. With simulation, we experiment delay and throughput performance of TCP and UDP transport layer protocols under wireless channels. In a repeatable, detailed and realistic emulator, we investigate performance of real telematics system. We make our emulation system more realistic by using the data and parameters of commercial cellular network provider. Our simulation results show that TCP and UDP protocols need some modifications to improve performance over wireless channel with high-mobility mobile nodes. We verified by emulation that our telematics system operates well using a proper retransmission scheme in an application layer.

Performance evaluation of a telematics system using the ns (network simulator)

Creation of the importance scanning worm using information collected by Botnets

PURPOSE: A high-speed string pattern matching method using layered shift tables is provided to shift a search location by a maximum movable length in a text, thereby improving pattern detecting speed. CONSTITUTION: The length of the shortest pattern is saved as an LSP(Length of Shortest Pattern). Values corresponding to each index are initialized as LSPs. A hash table and a prefix table are generated. One pattern among N patterns is selected. Values of B shift tables are updated. The value of the hash table is saved as a pointer about the selected pattern.

High-speed string pattern matching method using layered shift tables

In this paper we present an OTDM multihop prototype network developed in the Lightwave Communication Laboratory at Princeton University. Employing a new self-routing scheme with special address coding suitable for optical packet switching, we demonstrate 100 Gbit/s optical packet switching in an 8-node transparent shufflenetwork which offers extremely high bandwidth and low latency. Our design has also made the network highly scalable, the results from the performance studies of this network can be extended to many other multihop network topologies of larger sizes.

TDM 100Gbit/s packet switching in an optical shufflenet

In this paper, we investigate the performance characteristics of parallel switching architectures constructed by a stack of multistage switching networks. First, we propose a general analysis model which describes two directional traffic flows with a traffic-distribution characteristic. By applying this model to an MBCP (multiple banyan connected in parallel) switch, we show that the traffic-distribution characteristic plays an important role for the accuracy of a performance model. Next, to verify the accuracy of our approach to model the parallel switching architecture with a traffic-distribution capability, we select and analyze the WGSN (weaved generalized shuffle network) which is based on a multihop switching architecture. From the comparison with simulation results, we show that the developed model accurately predicts the performance of a WGSN under the assumption that the traffic-distribution characteristic is uniform.

On the Traffic-Distribution Characteristics of Parallel Switching Architectures

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