Summary The primary objective of our research work is to enhance the prediction of the quality of a component‐based software system and to develop an artificial neural network (ANN) model for the system reliability optimization problem. In this paper, we introduced the ANN‐supported Teaching‐Learning Optimization by transforming constraints to objective functions. Artificial neural network techniques are found to be powerful in the modeling software package quality metrics compared with the ancient statistical techniques. Therefore, by using the neural network, the quality characteristics of software components of the proposed work are predicted. A nonlinear differentiable transfer function of ANN used in the proposed approach is hyperbolic tangent sigmoid. A new efficient optimization methodology referred to as the Teaching‐Learning–based Optimization is proposed in this paper to optimize reliability and different cost functions. The weight values of the network are then adjusted consistent with a proposed optimization rule, therefore minimizing the network error. The proposed work is implemented in MATLAB by using the Neural Network Toolbox. The proposed work provides improved performance in terms of sensitivity, precision, specificity, negative predictive value, fall‐out or false positive rate, false discovery rate, accuracy, Matthews correlation coefficient, and rate of convergence.

The larger core diameter of multimode fibers (MMFs) allows several propagating modes. The differential group delays of these modes cause modal dispersion, thus limiting the data rate. Recent results, however, have shown the existence of a set of principal modes (PMs) that allow dispersion free communication, to the first order. Moreover, the orthogonal nature of these PMs allows for convenient multiplexing in MMFs with low mode-dependent losses. To effectively utilize these PMs at the transmitter, it is essential to estimate them and feed them back to the transmitter. In this paper, we propose two quantization schemes that enable efficient encapsulation of the PMs for feedback. For shorter length MMF links, we generate a vector quantization codebook based on the Linde-Buzo-Gray algorithm, while for long links with strong mode coupling, we show analytically that the PMs are Haar distributed, and use a Grassmannian line packing based quantization codebook. Simulations reveal that the quantization effectively limits dispersion and the performance of the quantized PMs is within 2 dB of the ideal principal modes with only 6 bits used for quantization. In addition, the quantized PMs allow for efficient multiplexing with just 3% cross-talk.

The larger core diameter of multimode fibers (MMFs) allows several propagating modes, but differential group delays and modal cross talk cause modal dispersion, thus limiting the data rate. Recent results have shown the existence of a set of principal modes (PMs) that allow dispersion free communication to the first order. Moreover, the linear independence of the PMs also permits multiplexing in MMFs. To effectively utilize these PMs at the transmitter, it is essential to estimate them and feed them back to the transmitter. In this paper, we propose quantization schemes that enable efficient encapsulation of the PMs for feedback for MMFs with a small number of modes. For shorter links, we use vector quantization codebooks based on the Linde-Buzo-Gray (LBG) algorithm, while for long links with low mode dependent losses (MDL), we show analytically that the PMs are Haar distributed, and use Grassmannian line packing based codebooks. Simulations reveal that the quantization limits dispersion and the performance of the quantized PMs is within 2 dB of the ideal principal modes with only 6 bits quantization, both with and without MDL. In addition, the quantized PMs permit efficient multiplexing with low crosstalk in both the lossless and MDL cases.

This paper presents a minimal cutsets-based unified framework to evaluate some commonly used reliability measures: g-terminal, 2-terminal, and k-terminal reliability. In doing so, the paper proposes a single algorithm, which generates the network node sets. From these node sets, depending on the reliability measure(s), only those node sets are kept, which contains at least one node from the specified node set. These selected node sets form link-minimal cutsets. These minimal cutsets are used as an input to multi-variable inversion-based sum-of-disjoint product approach to obtain the unreliability expression thereafter. By solving several network examples of varied complexities, the comparison with the existing approach is provided.

Wireless mesh networks (WMNs) are formed by self-organized wireless nodes that use multi-hop wireless relaying. These networks are useable in a variety of situations ranging from fixed residential broadband networking based on rooftop wireless mesh nodes to emergency response networks for handling large scale disasters. Quick deployability, minimal configuration, broadband communication, and easiness of reconfigurability are the major characteristics that make WMNs a suitable choice for emergency applications. There exist several open research issues in using such WMNs for emergency response applications. One example is the deployment strategy which must be in accordance with the principal application and its topological requirements. We, in this paper, present a hybrid distributed wireless networking architecture, extreme networking system (ENS), and present large set of performance observations collected from a real distributed hybrid wireless mesh network used for supporting a medical emergency response application. We present the traffic behavior observed in our network when a client server medical emergency response application is employed. The performance observations on real-traffic scenarios for emergency response application underlines the need for focusing further research on topology control, reliability, service availability, and distributed management.