It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

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

The Art and Science of Negotiation

Bottom-up segmentation based only on low-level cues is a notoriously difficult problem. This difficulty has lead to recent top-down segmentation algorithms that are based on class-specific image information. Despite the success of top-down algorithms, they often give coarse segmentations that can be significantly refined using low-level cues. This raises the question of how to combine both top-down and bottom-up cues in a principled manner. In this paper we approach this problem using supervised learning. Given a training set of ground truth segmentations we train a fragment-based segmentation algorithm which takes into account both bottom-up and top-down cues simultaneously, in contrast to most existing algorithms which train top-down and bottom-up modules separately. We formulate the problem in the framework of Conditional Random Fields (CRF) and derive a novel feature induction algorithm for CRF, which allows us to efficiently search over thousands of candidate fragments. Whereas pure top-down algorithms often require hundreds of fragments, our simultaneous learning procedure yields algorithms with a handful of fragments that are combined with low-level cues to efficiently compute high quality segmentations.

Learning to Combine Bottom-Up and Top-Down Segmentation

Incremental feature selection based on rough set in dynamic incomplete data

This paper presents the concept of temporal association rules in order to solve the problem of handling time series by including time expressions into association rules. Actually, temporal databases are continually appended or updated so that the discovered rules need to be updated. Re-running the temporal mining algorithm every time is ineffective since it neglects the previously discovered rules, and repeats the work done previously. Furthermore, existing incremental mining techniques cannot deal with temporal association rules. In this paper, an incremental algorithm to maintain the temporal association rules in a transaction database is proposed. The algorithm benefits from the results of earlier mining to derive the final mining output. The experimental results on both the synthetic and the real dataset illustrate a significant improvement over the conventional approach of mining the entire updated database.

An efficient algorithm for incremental mining of temporal association rules

Segmentation of retinal blood vessels is the first step for several computer aided-diagnosis systems (CAD), not only for ocular disease diagnosis such as diabetic retinopathy (DR) but also of non-ocular disease, such as hypertension, stroke and cardiovascular diseases. In this paper, a supervised learning-based method, using a multi-layer perceptron neural network and carefully selected vector of features, is proposed. In particular, for each pixel of a retinal fundus image, we construct a 24-D feature vector, encoding information on the local intensity, morphology transformation, principal moments of phase congruency, Hessian, and difference of Gaussian values. A post-processing technique depending on mathematical morphological operators is used to optimise the segmentation. Moreover, the selected feature vector succeeded in outfitting the symmetric features that provided the final blood vessel probability as a binary map image. The proposed method is tested on three known datasets: Digital Retinal Image for Extraction (DRIVE), Structure Analysis of the Retina (STARE), and CHASED_DB1 datasets. The experimental results, both visual and quantitative, testify to the robustness of the proposed method. This proposed method achieved 0.9607, 0.7542, and 0.9843 in DRIVE, 0.9632, 0.7806, and 0.9825 on STARE, 0.9577, 0.7585 and 0.9846 in CHASE_DB1, with respectable accuracy, sensitivity, and specificity performance metrics. Furthermore, they testify that the method is superior to seven similar state-of-the-art methods.

Retinal Blood Vessel Segmentation Using Hybrid Features and Multi-Layer Perceptron Neural Networks

In this paper, we focus on an interesting E-Health monitoring system that efficiently utilizes the concept of the Internet of Things (IoT). Wireless body area network (WBAN) composed of a set of target-oriented sensors placed around the human body and transmit its collected data to a coordinator that carries it to some cloud system. WBAN standardized with the newly emerged IEEE802.15.6 with its specifications for Physical (PHY) and medium access control (MAC) layers only. Regrettably, the Network layer is not addressed by the standard where it plays an important role in the overall performance. In this paper, we design the temperature heterogeneity energy (THE) aware routing protocol for WBAN as a complement for the standard. “THE” aims to control the temperature raising caused by the on-body sensor and affects the skin comfortableness. In the meantime, “THE” maintains the network in high-performance conditions in terms of long node lifetime and high packet throughput. To fulfill these desired tradeoffs, the sensed data is classified into three data levels with variable transmission priority to each level, namely, emergency (abnormal) data priority 7 (highest priority), critical data priority 6, and normal data assigned priority 5. “THE” protocol is based on a utility function that chooses the WBAN's parent node (PN) that has the largest amount of remaining energy, the highest data rate, the minimum distance to the coordinator, and the minimum sensor's temperature. Hopping the data through the parent node (two-hops) is applicable for the data with normal priority while high priority data (critical and emergency) is transmitted to the coordinator in one-hop only. The proposed “THE” protocol's performance validation performed via Monte Carlo simulation analysis which proves that “THE” protocol achieved better performance against conventional protocols (SIMPLE and iM-SIMPLE) in terms of network lifetime, number of dead nodes, total remaining energy, and throughput.

/pdf/the-temperature-heterogeneity-energy-aware-routing-protocol-zry9lrfi0j.pdf

THE (Temperature Heterogeneity Energy) Aware Routing Protocol for IoT Health Application

A queueing-theoretic analysis of an ATM multiplexer handling two-class multimedia traffic is described. Specifically, it assigns class-1 cells, constituting real-time traffic, high service priority, and class-2 cells, constituting non-real time traffic, low service priority. The priority discipline used is non-pre-emptive. For the purpose of the analysis, the multiplexer is modelled as a priority, discrete time, single-arrival, single-server queueing system with an infinite buffer and geometric service time. The model dynamics are extracted by a rather complex difference equation whose solution is sought using a generating function technique. This difference equation approach is a major contribution of the paper. Unlike the prevalent stochastic equation approach, it makes the physical details of the system present and visible during the analysis. Results are obtained for the multiplexer occupancy and cell waiting time. These results are verified analytically by producing from them some previously published results as special cases. They are also verified by applying them to numerical examples and obtaining intuitively acceptable values.

Queueing analysis of an ATM multimedia multiplexer with non-pre-emptive priority

Great efforts are now underway to control the coronavirus 2019 disease (COVID-19). Millions of people are medically examined, and their data keep piling up awaiting classification. The data are typically both incomplete and heterogeneous which hampers classical classification algorithms. Some researchers have recently modified the popular KNN algorithm as a solution, where they handle incompleteness by imputation and heterogeneity by converting categorical data into numbers. In this article, we introduce a novel KNN variant (KNNV) algorithm that provides better results as demonstrated by thorough experimental work. We employ rough set theoretic techniques to handle both incompleteness and heterogeneity, as well as to find an ideal value for K. The KNNV algorithm takes an incomplete, heterogeneous dataset, containing medical records of people, and identifies those cases with COVID-19. We use in the process two popular distance metrics, Euclidean and Mahalanobis, in an effort to widen the operational scope. The KNNV algorithm is implemented and tested on a real dataset from the Italian Society of Medical and Interventional Radiology. The experimental results show that it can efficiently and accurately classify COVID-19 cases. It is also compared to three KNN derivatives. The comparison results show that it greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score. The algorithm given in this article can be easily applied to classify other diseases. Moreover, its methodology can be further extended to do general classification tasks outside the medical field.

/pdf/accurate-classification-of-covid-19-based-on-incomplete-mj5tix7mxb.pdf

Hamed Nassar

Papers

An efficient algorithm for incremental mining of temporal association rules

Retinal Blood Vessel Segmentation Using Hybrid Features and Multi-Layer Perceptron Neural Networks

THE (Temperature Heterogeneity Energy) Aware Routing Protocol for IoT Health Application

Queueing analysis of an ATM multimedia multiplexer with non-pre-emptive priority

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm