Optimization of energy-constrained wireless powered communication networks with heterogeneous nodes

In this paper, we generalize conventional time division multiple access (TDMA) wireless networks to a new type of wireless networks coined generalized wireless powered communication networks (g-WPCNs). Our prime objective is to optimize the design of g-WPCNs where nodes are equipped with radio frequency (RF) energy harvesting circuitries along with constant energy supplies. This constitutes an important step towards a generalized optimization framework for more realistic systems, beyond prior studies where nodes are solely powered by the inherently limited RF energy harvesting. Towards this objective, we formulate two optimization problems with different objective functions, namely, maximizing the sum throughput and maximizing the minimum throughput (maxmin) to address fairness. First, we study the sum throughput maximization problem, investigate its complexity and solve it efficiently using an algorithm based on alternating optimization approach. Afterwards, we shift our attention to the maxmin optimization problem to improve the fairness limitations associated with the sum throughput maximization problem. The proposed problem is generalized, compared to prior work, as it seemlessly lends itself to prior formulations in the literature as special cases representing extreme scenarios, namely, conventional TDMA wireless networks (no RF energy harvesting) and standard WPCNs, with only RF energy harvesting nodes. In addition, the generalized formulation encompasses a scenario of practical interest we introduce, namely, WPCNs with two types of nodes (with and without RF energy harvesting capability) where legacy nodes without RF energy harvesting can be utilized to enhance the system sum throughput, even beyond WPCNs with all RF energy harvesting nodes studied earlier in the literature. We establish the convexity of all formulated problems which opens room for efficient solution using standard techniques. Our numerical results show that conventional TDMA wireless networks and WPCNs with only RF energy harvesting nodes are considered as lower bounds on the performance of the generalized problem setting in terms of the maximum sum throughput and maxmin throughput. Moreover, the results reveal valuable insights and throughput-fairness trade-offs unique to our new problem setting.

     

Satellite image fusion based on modified central force optimization

Multimedia Tools and Applications - Nowadays, optimization has become a brand methodology for different applications. One of the most promising fields for application of optimization is the image...     

On the embedding limits of the discrete cosine transform

This paper investigates the embedding capacity limits of high-capacity data hiding in color images based on a locally Adaptive-Region Discrete Cosine Transform (AR-DCT) frequency domain data hiding scheme, and explores the relationship between hiding capacity and image quality. It also compares the embedding capacities of various steganography schemes which have been recently published in the literature. Experimental results confirm that our proposed scheme successfully enhances hiding capacity while maintaining acceptable image quality and concludes that the capacity for our DCT hiding scheme can achieve extremely high bit rates of 20 bits-per-pixel, which is much higher than other DCT-based approaches, as well as other spatial and frequency domain schemes.     

High-capacity steganography: a global-adaptive-region discrete cosine transform approach

An increasing number of spatial and frequency domain data hiding techniques have been proposed to address the relatively low embedding capacities of image-based steganography. These techniques have brought promise of higher embedding capacities, albeit at the expense of lower perceptibility. This work proposes a new discrete cosine transform (DCT) approach for color image steganography and implements a global-adaptive-region (GAR) embedding scheme that allows for extremely high embedding capacities while maintaining enhanced perceptibility. The idea is to adapt the variable region size, used to hide the data, in each DCT block of the cover image to the amount of correlation of the image values in the corresponding block. We will demonstrate how this new technique achieves enhanced hiding capacities and perceptibility compared to other spatial, Fourier, and adaptive-region DCT based steganography schemes.     

Nanoparticle embedded chitosan film for agglomeration free TEM images

Transmission electron microscopy (TEM) is a very useful and commonly used microscopy technique, used especially for the characterization of nanoparticles. However, the identification of the magnetic nanoparticle could be thought problematic in TEM analysis, due to the fact that the magnetic nanoparticles are usually form aggregates on the TEM grid to form bigger particles generating higher stability. This prevents to see exact shape and size of each nanoparticle. In order to overcome this problem, a simple process for the formation of well‐dispersed nanoparticles was conducted, by covering chitosan film on the unmodified copper grid, it was said to result in aggregation‐free TEM images. It is also important to fix the magnetic nanoparticles on the TEM grids, due to possible contamination of TEM filament which is operated under high vacuum conditions. The chitosan film matrix also helps to protect the TEM filament from contact with magnetic nanoparticles during the imaging process. The proposed procedure offers a quick method to fix the nanoparticles in a conventional copper TEM grid and chitosan matrix prevents agglomeration of nanoparticles, and thus getting TEM images showing well‐dispersed individual nanoparticles.     

Reorganization and improvement of bulk polymers by processing with their cyclodextrin inclusion compounds

The formation of polymer-cyclodextrin inclusion compounds of polycarbonate (PC), poly(methylmethacrylate) (PMMA) and poly(vinylacetate) (PVAc) guests with host γ-cyclodextrin (γ-CD) have been successfully achieved. Coalesced bulk polymer samples were obtained by removal of γ-CD from their inclusion compounds (ICs). The chemical and crystalline structures of ICs and coalesced PC, PMMA and PVAc were studied by Fourier transform infrared spectroscopy (FTIR) and wide-angle X-ray diffraction (WAXD). The thermal transitions, thermal stability, and degradation mechanisms of the samples were investigated by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and direct insertion probe pyrolysis mass spectrometry (DIP-MS). FTIR findings indicated that the chain conformations of the bulk polymers were altered when they were included inside the CD channels and extended chain conformations were retained when coalesced from their ICs. Significant improvements were observed in the thermal transitions observed for the coalesced polymers, with glass transitions shifted to higher temperatures. The TGA results reveal that the thermal stabilities of coalesced polymers increased slightly compared to the corresponding as-received polymers. The DIP-MS observations indicated that the thermal stability and degradation products of the polymers are affected once the polymers chains are included inside the γ-CD-IC cavities.     

Electrosynthesis of 4,4′-dinitroazobenzene on PbO2 electrodes

Parameters which affect the electrosynthesis of 4,4-dinitroazobenzene from p-nitroaniline on platinum and PbO₂ electrodes were investigated and optimum conditions were determined. Maximum conversion efficiency for electrosynthesis was 95% with a pure -PbO₂ electrode. It was found that the electrocatalytic activity of a PbO₂ electrode depends upon its / ratio and its degree of crystallinity. The effects of the added base and water on the conversion efficiency were also elucidated.     

Scaling XML query processing: distribution, localization and pruning

Distributing data collections by fragmenting them is an effective way of improving the scalability of a database system. While the distribution of relational data is well understood, the unique characteristics of the XML data and query model present challenges that require different distribution techniques. In this paper, we show how XML data can be fragmented horizontally and vertically. Based on this, we propose solutions to two of the problems encountered in distributed query processing and optimization on XML data, namely localization and pruning. Localization takes a fragmentation-unaware query plan and converts it to a distributed query plan that can be executed at the sites that hold XML data fragments in a distributed system. We then show how the resulting distributed query plan can be pruned so that only those sites are accessed that can contribute to the query result. We demonstrate that our techniques can be integrated into a real-life XML database system and that they significantly improve the performance of distributed query execution.     

Spatial approaches to broadband jamming in heterogeneous mobile networks: a game-theoretic approach

In this paper, we address the problem of a mobile intruder jamming the communication network in a vehicular formation. In order to understand the spatial aspect of the jamming problem, we consider a jamming model that takes into account the relative distance of the jammer from the vehicles. We formulate the problem as a zero-sum pursuit-evasion game between a jammer and a team of players with players possessing heterogeneous dynamics. We use Isaacs’ approach to arrive at the equations governing the optimal strategies of the team of players. Finally, we obtain the optimal trajectories in the neighborhood of termination by numerically simulating the strategies for some variants of the problem.     

Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets

A correct diagnosis of tuberculosis disease can be only stated by applying a medical test to patient’s phlegm. The result of this test is obtained after a time period of about 45 days. The purpose of this study is to develop a data mining solution that makes diagnosis of tuberculosis as accurate as possible and helps deciding whether it is reasonable to start tuberculosis treatment on suspected patients without waiting for the exact medical test results. We proposed the use of Sugeno-type “adaptive-network-based fuzzy inference system” (ANFIS) to predict the existence of mycobacterium tuberculosis. Data set collected from 503 different patient records which are obtained from a private health clinic (consent of physicians and patients). Patient record has 30 different attributes which covers demographical and medical test data. ANFIS model was generated by using 250 records. Also, rough set method was implemented by using the same data set. The ANFIS model classifies the instances with correctness of 97 %, whereas rough set algorithm does the same classification with correctness of 92 %. This study has a contribution on forecasting patients before the medical tests.