Accurate analysis of carbon nanotube interconnects using transmission line model

A new technique for analysing the time-domain response of carbon nanotube (CNT) interconnects, based on transmission line modelling, that takes the effects of both contact and fundamental (quantum) resistances into account is introduced. A new sixth-order linear parametric expression for the transfer function of these lines has been presented for the first time. For verification purposes, the step response of a driver-CNT bundle-load configuration for a 32 nm technology node, using the new technique and HSPICE simulation have been compared, with which the obtained results show an excellent match. Also the effect of contact resistance on the step response, especially the propagation delay, has been studied. The obtained results show that for the length of a CNT bundle equal to 50 mum with the diameter of each individual CNT 1 nm, the propagation delay changes from 0.138 to 5.58 ns for the contact resistance values from 1 to 50 k Omega, i.e. a variation range of 39.43 times the minimum value. The related delay variations for the length values 200 mum, 500 mum and 1000 mum, are 31.37, 22.61 and 15.42 times the minimum value, respectively.     

Model Misspecification of Generalized Gamma Distribution for Accelerated Lifetime-Censored Data

Abstract

The performance of reliability inference strongly depends on the modeling of the product’s lifetime distribution. Many products have complex lifetime distributions whose optimal settings are not easily found. Practitioners prefer to use simpler lifetime distribution to facilitate the data modeling process while knowing the true distribution. Therefore, the effects of model mis-specification on the product’s lifetime prediction is an interesting research area. This article presents some results on the behavior of the relative bias (RB) and relative variability (RV) of pth quantile of the accelerated lifetime (ALT) experiment when the generalized Gamma (GG₃) distribution is incorrectly specified as Lognormal or Weibull distribution. Both complete and censored ALT models are analyzed. At first, the analytical expressions for the expected log-likelihood function of the misspecified model with respect to the true model is derived. Consequently, the best parameter for the incorrect model is obtained directly via a numerical optimization to achieve a higher accuracy model than the wrong one for the end-goal task. The results demonstrate that the tail quantiles are significantly overestimated (underestimated) when data are wrongly fitted by Lognormal (Weibull) distribution. Moreover, the variability of the tail quantiles is significantly enlarged when the model is incorrectly specified as Lognormal or Weibull distribution. Precisely, the effect on the tail quantiles is more significant when the sample size and censoring ratio are not large enough. Supplementary materials for this article are available online.     

Enhanced fair earliest due date first scheduling strategy for multimedia applications in LTE downlink framework

Guaranteeing a certain delay threshold for delay‐sensitive applications in long term evolution (LTE) cellular communication system is a very challenging mission. By implementing an optimal scheduling strategy, this mission will be achieved. In this article, a novel scheduler is introduced in order to meet a predefined level of service quality by guaranteeing a specific delay threshold for delay‐sensitive applications in LTE cellular systems. The proposed scheduler assigns the available resource blocks (RBs) to active user equipments (UEs) tacking into consideration several attributes. The expiration date of each packet, the channel quality, the average data rate previously achieved by each UE, and the number of dropped packets for each UE compared with the average number of packets totally dropped are all considered in the proposed scheduler working mechanism. Consequently, the proposed scheduling strategy reduces the number of packets dropped for multimedia applications, and at the same time maximizes the overall throughput of the network. Simulation results are provided to study and evaluate the performance of the proposed scheduling strategy. A comparative study is presented between the proposed strategy and the most recent scheduling techniques. The obtained results prove that the proposed scheduling strategy has considerably acceptable and appreciated results compared with the results of the state‐of‐the‐art scheduling techniques.     

Robust stability analysis of fractional‐order interval systems with multiple time delays

This paper investigates the robust stability analysis of fractional‐order interval systems with multiple time delays, including retarded and neutral systems. A bound on the poles of fractional‐order interval systems of retarded and neutral type is obtained. Then, the concept of the value set and zero exclusion principle is extended to these systems, and a necessary and sufficient condition is produced for checking the robust stability of them. The value set of the characteristic equation of the systems is obtained analytically and, based on it, an auxiliary function is introduced to check the zero exclusion principle. Finally, two numerical examples are given to illustrate the effectiveness of the results presented.     

Gauss Gradient and SURF Features for Landmine Detection from GPR Images

Recently, ground-penetrating radar (GPR) has been extended as a well-known area to investigate the subsurface objects. However, its output has a low resolution, and it needs more processing for more interpretation. This paper presents two algorithms for landmine detection from GPR images. The first algorithm depends on a multi-scale technique. A Gaussian kernel with a particular scale is convolved with the image, and after that, two gradients are estimated; horizontal and vertical gradients. Then, histogram and cumulative histogram are estimated for the overall gradient image. The bin values on the cumulative histogram are used for discrimination between images with and without landmines. Moreover, a neural classifier is used to classify images with cumulative histograms as feature vectors. The second algorithm is based on scale-space analysis with the number of speeded-up robust feature (SURF) points as the key parameter for classification. In addition, this paper presents a framework for size reduction of GPR images based on decimation for efficient storage. The further classification steps can be performed on images after interpolation. The sensitivity of classification accuracy to the interpolation process is studied in detail.     

Design of stochastic assembly lines considering line balancing and part feeding with supermarkets

This article aims to address the assembly line balancing problem (ALBP) and supermarket location problem (SLP) as two long-term interrelated decision problems considering the stochastic nature of the task times and demands. These problems arise in real-world assembly lines during the strategic decision-making phase of configuring new assembly lines from both line balancing and part-feeding (PF) aspects. A hierarchical mathematical programming model is developed, in which the first level resolves the stochastic ALBP by minimizing the workstation numbers and the second level deals with the stochastic SLP while optimizing the PF shipment, inventory and installation costs. The results of case data from an automotive parts manufacturer and a set of standard test problems verified that the proposed model can optimize the configuration of assembly lines considering both ALBP and SLP performance measures. This study also validates the effect of the stochastic ALBP on the resulting SLP solutions.     

Enhancement of Infrared Images Using Super Resolution Techniques Based on Big Data Processing

This paper presents a super-resolution (SR) technique for enhancement of infrared (IR) images. The suggested technique relies on the image acquisition model, which benefits from the sparse representations of low-resolution (LR) and high-resolution (HR) patches of the IR images. It uses bicubic interpolation and minimum mean square error (MMSE) estimation in the prediction of the HR image with a scheme that can be interpreted as a feed-forward neural network. The suggested algorithm to overcome the problem of having only LR images due to hardware limitations is represented with a big data processing model. The performance of the suggested technique is compared with that of the standard regularized image interpolation technique as well as an adaptive block-by-block least-squares (LS) interpolation technique from the peak signal-to-noise ratio (PSNR) perspective. Numerical results reveal the superiority of the proposed SR technique.

     

Encoder-independent decoder-dependent depth-assisted error concealment algorithm for wireless 3D video communication

Three-Dimensional Multi-View Video (3D MVV) contains diverse video streams taken by different cameras around an object. Thence, it is an imperative assignment to fulfill efficient compression to attain future resource bonds whilst preserving a decisive reception MVV quality. The extensive 3D MVV encoding and transmission over mobile or Internet are vulnerable to packet losses on account of the existence of severe channel faults and restricted bandwidth. In this work, we propose a new Encoder-Independent Decoder-Dependent Depth-Assisted Error Concealment (EIDD-DAEC) algorithm. It invests the depth correlations between the temporally, spatially, and inter-view adjoining Macro-Blocks (MBs) to conceal the erroneous streams. At the encoder, the existing inter-view, temporal, and spatial matching are exploited to efficiently compress the 3D MVV streams and to estimate the Disparity Vectors (DVs) and Motion Vectors (MVs). At the decoder, the gathered MVs and DVs from the received coded streams are used to calculate additional depth-assisted MVs and DVs, which are afterwards combined with the collected candidate texture color MVs and DVs groups for concealing the lost MBs of inter- and intra-encoded frames. Finally, the optimum DVs and MVs concealment candidates are selected by the Directional Interpolation Error Concealment Algorithm (DIECA) and Decoder Motion Vector Estimation Algorithm (DMVEA), respectively. Experimental results on several standardized 3D MVV sequences verified the efficacy of the proposed EIDD-DAEC algorithm by achieving ameliorated efficacious objective and subjective results without generating and transporting depth maps at the encoder. The proposed work achieves high 3D MVV quality performance with an improved average Peak Signal-to-Noise Ratio (PSNR) gain by up to 0.95 ~ 2.70 dBs compared to the state-of-the-art error concealment algorithms, which do not employ depth-assisted correlations at different Quantization Parameters (QPs) and Packet Loss Rates (PLRs) of 40%.     

Impulsive second‐order sliding mode control in reduced information environment

The perturbed system with input‐output dynamics of arbitrary and well‐defined relative degree is considered in a reduced information environment. A novel impulsive second‐order sliding mode control in the reduced information environment is proposed. The almost instantaneous convergence to the origin is achieved via impulsive control acting in a concert with second‐order sliding mode control, specifically supertwisting and twisting algorithms. The impulsive actions are implemented in a piecewise constant format. Numerical examples illustrate the efficiency of the proposed control algorithms.