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

Since the first time the term "Virtual Reality" (VR) has been used back in the 60s, VR has evolved in different manners becoming more and more similar to the real world. Two different kinds of VR can be identified: non-immersive and immersive. The former is a computer-based environment that can simulate places in the real or imagined worlds; the latter takes the idea even further by giving the perception of being physically present in the non-physical world. While non-immersive VR can be based on a standard computer, immersive VR is still evolving as the needed devices are becoming more user friendly and economically accessible. In the past, there was a major difficulty about using equipment such as a helmet with goggles, while now new devices are being developed to make usability better for the user. VR, which is based on three basic principles: Immersion, Interaction, and User involvement with the environment and narrative, offers a very high potential in education by making learning more motivating and engaging. Up to now, the use of immersive-VR in educational games has been limited due to high prices of the devices and their limited usability. Now new tools like the commercial "Oculus Rift", make it possible to access immersive-VR in lots of educational situations. This paper reports a survey on the scientific literature on the advantages and potentials in the use of Immersive Virtual Reality in Education in the last two years (2013-14). It shows how VR in general, and immersive VR in particular, has been used mostly for adult training in special situations or for university students. It then focuses on the possible advantages and drawbacks of its use in education with reference to different classes of users like children and some kinds of cognitive disabilities (with particular reference to the Down syndrome). It concludes outlining strategies that could be carried out to verify these ideas.

A literature review on immersive virtual reality in education: state of the art and perspectives

Abundance and availability of video capture devices, such as mobile phones and surveillance cameras, have instigated research in video face recognition, which is highly pertinent in law enforcement applications. While the current approaches have reported high accuracies at equal error rates, performance at lower false accept rates requires significant improvement. In this paper, we propose a novel face verification algorithm, which starts with selecting feature-rich frames from a video sequence using discrete wavelet transform and entropy computation. Frame selection is followed by representation learning-based feature extraction, where three contributions are presented: 1) deep learning architecture, which is a combination of stacked denoising sparse autoencoder (SDAE) and deep Boltzmann machine (DBM); 2) formulation for joint representation in an autoencoder; and 3) updating the loss function of DBM by including sparse and low rank regularization. Finally, a multilayer neural network is used as the classifier to obtain the verification decision. The results are demonstrated on two publicly available databases, YouTube Faces and Point and Shoot Challenge. Experimental analysis suggests that: 1) the proposed feature-richness-based frame selection offers noticeable and consistent performance improvement compared with frontal only frames, random frames, or frame selection using perceptual no-reference image quality measures and 2) joint feature learning in SDAE and sparse and low rank regularization in DBM helps in improving face verification performance. On the benchmark Point and Shoot Challenge database, the algorithm yields the verification accuracy of over 97% at 1% false accept rate whereas, on the YouTube Faces database, over 95% verification accuracy is observed at equal error rate.

Face Verification via Learned Representation on Feature-Rich Video Frames

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Virtual reality and gamification in marketing higher education: a review and research agenda

Skill training in the medical device manufacturing industry is essential to optimize and expedite the efficiency level of new workers. This process, however, gives rise to many underlying issues such as contamination and safety risks, long training period, high skill and experience requirements of operators, and greater training costs. In this paper, we proposed and evaluated a novel virtual reality (VR) training system for the assembly of hybrid medical devices. The proposed system, which is an integration of Artificial Intelligence (AI), VR and gaming concepts, is self-adaptive and autonomous. This enables the training to take place in a virtual workcell environment without the supervision of a physical trainer. In this system, a sequential framework is proposed and utilized to enhance the training through its various “game” levels of familiarity-building processes. A type of hybrid medical device: carbon nanotubes-polydimethylsiloxane (CNT-PDMS) based artificial trachea prosthesis is used as a case study in this paper to demonstrate the effectiveness of the proposed system. Evaluation results with quantitative and qualitative comparisons demonstrated that our proposed training method has significant advantages over common VR training and conventional training methods. The proposed system has addressed the underlying training issues for hybrid medical device assembly by providing trainees with effective, efficient, risk-free and low cost training.

Virtual reality training for assembly of hybrid medical devices

The Cave Automatic Virtual Environment (CAVE) is a fully immersive Virtual Reality (VR) system. CAVE systems have been widely used in many applications, such as architectural and industrial design, medical training and surgery plan, museums and education. However, one limitation for most of the current CAVE systems is that they are separated from the real world. The user in the CAVE is not able to sense the real world around him or her. In this paper, we propose a vConnect architecture, which aims to establish real-time bidirectional information exchange between the virtual world and the real world by utilizing the advanced technologies in cloud computing, mobile communications, wireless sensor networks, and computer vision. Specifically, we address three technical challenges in the proposed vConnect architecture. First, we propose an optimal allocation scheme for the wireless sensor networks to ensure that the data streams captured by the sensors can be delivered to the cloud servers in a reliable and prompt way. Second, we optimize the allocation of the cloud resources to ensure that the data streams sent from the clients can be processed promptly by the cloud servers at a minimal resource cost. Third, we propose to use marker-based finger interactions such that the user in the CAVE can manipulate the information in a natural and intuitive way. Fourth, we implemented a vHealth prototype, a CAVE-based real-time health monitoring system, to validate the proposed vConnect architecture. We demonstrated in the vHealth prototype that the user in the CAVE can visualize and manipulate the real-time physiological data of the patient who is being monitored, and interact with the patient.

vConnect: perceive and interact with real world from CAVE

Sparse representation-based face recognition has gained considerable attention recently due to its robustness against illumination and occlusion. Recognizing faces from videos has become a topic of importance to alleviate the limit of information content in still images. However, the sparse recognition framework is not applicable to video-based face recognition due to its sensitivity towards pose and alignment changes. In this paper, we propose a video-based face recognition method which improves upon the sparse representation framework. Our key contribution is an intelligent and adaptive sparse dictionary that updates the current probe image into the training matrix based on continuously monitoring the probe video through a novel confidence criterion and a Bayesian inference scheme. Due to this novel approach, our method is robust to pose and alignment and hence can be used to recognize faces from unconstrained videos successfully. Moreover, in a moving scene, camera angle, illumination and other imaging conditions may change quickly leading to performance loss in accuracy. In such situations, it is impractical to re-enroll the individual and re-train the classifiers on a continuous basis. Our novel approach addresses these practical issues. Experimental results on the well known YouTube Face database demonstrates the effectiveness of our method.

On video based face recognition through adaptive sparse dictionary

Face recognition in videos has been an active topic in the field of object recognition and computer vision. In this paper we propose an automatic face recognition algorithm from video sequences using a template based cross correlation (TBCC) method. It utilizes random selection of frames to form the training template for the discriminant feature representation of a face. The proposed method was tested by randomly selecting a subject from the RML and Cohn-Kanade (CK) databases with non-overlapping sequences between test and training sequences. Form the experiments on several datasets, the system performance achieved an average recognition rate of 88%.

Automatic face recognition from video sequences using a template based cross correlation method

The Cave Automatic Virtual Environment (CAVE) is a fully immersive Virtual Reality (VR) system. CAVE systems have been widely used in many applications, such as architectural and industrial design, medical training and surgical planning, museums and education. However, one limitation for most of the current CAVE systems is that they are separated from the real world. The user in the CAVE is not able to sense the real world around him or her. In this paper, we propose a vConnect architecture, which aims to establish real-time bidirectional information exchange between the virtual world and the real world. Furthermore, we propose finger interactions which enable the user in the CAVE to manipulate the information in a natural and intuitive way. We implemented a vHealth prototype, a CAVE-based real-time health monitoring system, through which we demonstrated that the user in the CAVE can visualize and manipulate the real-time physiological data of the patient who is being monitored, and interact with the patient.

vConnect: Connect the real world to the virtual world

In the method, a face finding operation is periodically conducted on images in an image stream defining a video. In respect of the last image in the stream preceding the image in which one or more faces was found, a tracker, based upon wavelet decomposition, is used to find a face for each found in the last image for which no counterpart was found in the image.

Edward Rosales

Papers

vConnect: perceive and interact with real world from CAVE

On video based face recognition through adaptive sparse dictionary

Automatic face recognition from video sequences using a template based cross correlation method

vConnect: Connect the real world to the virtual world

Method for driver face detection in videos