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

Epidemic malicious codes including Internet worms and botnets have continuously evolved to be more intelligent and complicated. In particular, the recent distributed denial-of-service (DDoS) attack that occurred in United States and South Korea in July, 2009 gives an opportunity to reconsider the epidemic malicious code. Since automatic patching systems and intelligent intrusion detection and prevention systems mitigate rapid infection, fast infections such as Slammer-like worms cannot successfully spread. As of the 2009 July DDoS attack, malicious codes prefer hiding their malicious activities and trying to infect others silently until D-day. Since slow infection is difficult to detect by the current IDS or IPS, this infection strategy is likely to become prevalent. In a slow infection, the incubation period is a key factor in determining the extent to which an epidemic malicious code spreads. This study provides an analysis framework to understand the impact of incubation period in the spread of epidemic malicious code. Intuitively, a longer latent period increases the number of infected hosts, but the detection probability also increases. This trade-off suggests an optimal incubation period determination problem to maximize the number of infected hosts. Solving this problem is essential to predicting the explicit or implicit intention of attackers and to counteract against the attack in a strategic manner. Through analysis and simulations, we provide data and insight regarding epidemic malicious code that exploits incubation period.

Threat Analysis of Incubation Period in Malware Epidemics

For the purpose of compromising hosts, attackers including infected hosts initially perform a portscan using IP addresses in order to find vulnerable hosts. Considerable research related to portscan detection has been done and many algorithms have been proposed and implemented in the network intrusion detection system (NIDS). In order to distinguish portscanners from remote hosts, most portscan detection algorithms use a fixed threshold that is manually managed by the network manager. Because the threshold is a constant, even though the network environment or the characteristics of traffic can change, many false positives and false negatives are generated by NIDS. This reduces the efficiency of NIDS and imposes a high processing burden on a network management system (NMS). In this paper, in order to address this problem, we propose an automatic portscan detection system using an fast increase slow decrease (FISD) scheme, that will automatically and adaptively set the threshold based on statistical data for traffic during prior time periods. In particular, we focus on reducing false positives rather than false negatives, while the threshold is adaptively set within a range between minimum and maximum values. We also propose a new portscan detection algorithm, rate of increase in the number of failed connection request (RINF), which is much more suitable for our system and shows better performance than other existing algorithms. In terms of the implementation, we compare our scheme with other two simple threshold estimation methods for an adaptive threshold setting scheme. Also, we compare our detection algorithm with other three existing approaches for portscan detection using a real traffic trace. In summary, we show that FISD results in less false positives than other schemes and RINF can fast and accurately detect portscanners. We also show that the proposed system, including our scheme and algorithm, provides good performance in terms of the rate of false positives.

An automatic portscan detection system with adaptive threshold setting

Curb detection is an essential function of autonomous vehicles in urban areas. However, curbs are difficult to detect in complex urban environments in which many dynamic objects exist. Additionally, curbs appear in a variety of shapes and sizes. Previous studies have been based on the traditional pipeline, which consists of the extraction and aggregation of hand-crafted features that are then fed to classifiers. However, this sequential process is inefficient and designing the hand-crafted features is a complex process. Recently, this kind of process has been replaced by Deep Neural Networks (DNN), in which classifiers and features are learned from large-scale data. Very few works have exploited DNN for the curb detection problem. Most works use multi-modal sensor-based methods that combine images and accumulated 3D point clouds from LIDAR. However, these approaches require synchronization and calibration between sensors. In addition, they do not quantify the uncertainty of their predictions for autonomous system safety. In this paper, we present a two-stage DNN-based curb detection method that includes uncertainty quantification. An autoencoder-based network predicts the curbs, and then conditional neural processes rectify the predictions with uncertainty estimations. The experimental results show that our approach achieves high accuracy and recall in complex areas. We also constructed a large-scale dataset to create benchmarks consisting of approximately 5,224 scans with bird’s-eye view labels collected from urban areas. To the best of our knowledge, there are no public datasets for DNN-based curb detectors. The benchmarks and datasets are publicly available at https://github.com/YounghwaJung/curb_detection_DNN.

Uncertainty-Aware Fast Curb Detection Using Convolutional Networks in Point Clouds

We consider multilength variable-weight time-and-frequency-hopping (TFH) codes for optical code-division multiple-access (CDMA) networks. To design a TFH code that can support differentiated requirements on transmission rates (TRs) and bit error rates (BERs), we propose a general construction method for two-dimensional optical CDMA code sequences with arbitrary code lengths and variable code weights. The cross-correlation peak and the autocorrelation sidelobe of the proposed code are made to be at most '1' and '0', respectively, for any pair of different-length and different-weight code sequences. We show that the ratio between signal-to-interference ratios (SIRs) of the codes with different weight and length can effectively be approximated by the ratio of the code weights, regardless of the code lengths for the proposed codes. Therefore, independent adjustment of code length and weight can differentiate TR and BER for multiple service classes, and it simplifies code design for the service differentiation. We also demonstrate BER performances under various conditions and corroborate that the proposed codes accomplish accurate and fine-tuned differentiation of TR and BER compared to the fixed-weight or fixed-length codes.

Quality-of-service differentiation by multilength variable-weight time-and-frequency-hopping optical orthogonal codes in optical code-division multiple-access networks

Two asymmetric mechanisms are often employed in large-scale systems to achieve scalable and efficient authenticated broadcast. However, cryptographic asymmetry based on public-key schemes is computationally expensive, while time asymmetry based on delayed-key release requires time synchronization cross the entire network and temporal buffering of messages at receivers. Neither approach is suitable for large-scale sensor networks composed of computation and storage constrained low-end sensor nodes. In this paper, we propose novel flooding authentication mechanism based on our "information asymmetry" model. Our design is built on top of symmetric cryptography for computation efficiency, and leverages the asymmetric key distribution between the sink and sensor nodes. Through intensive analysis we demonstrate optimized tradeoff between the resilience to compromised sensor nodes and the scalability to system size through space-efficient bloom filters as the authenticator. With a novel "false negative" tuning knob introduced in the construction of bloom filter, we show that the scalability of the authentication primitive can be greatly improved at the cost of small controlled degradation of security, therefore rendering a practical authenticated flooding for large-scale sensor networks

Seung-Woo Seo

Papers

Threat Analysis of Incubation Period in Malware Epidemics

An automatic portscan detection system with adaptive threshold setting

Uncertainty-Aware Fast Curb Detection Using Convolutional Networks in Point Clouds

Quality-of-service differentiation by multilength variable-weight time-and-frequency-hopping optical orthogonal codes in optical code-division multiple-access networks

Authenticated flooding in large-scale sensor networks