Energy Harvesting for Cooperative Cognitive Radio Networks

Wireless Personal Communications - In this paper, we analyze the performance of cooperative cognitive radio networks where the secondary nodes harvest energy from radio frequency signals. Our...     

Time series joins,motifs, discords and shapelets: a unifying view that exploits the matrix profile

The last decade has seen a flurry of research on all-pairs-similarity-search (or similarity joins) for text, DNA and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. However, there has been surprisingly little progress made on similarity joins for time series subsequences. The lack of progress probably stems from the daunting nature of the problem. For even modest sized datasets the obvious nested-loop algorithm can take months, and the typical speed-up techniques in this domain (i.e., indexing, lower-bounding, triangular-inequality pruning and early abandoning) at best produce only one or two orders of magnitude speedup. In this work we introduce a novel scalable algorithm for time series subsequence all-pairs-similarity-search. For exceptionally large datasets, the algorithm can be trivially cast as an anytime algorithm and produce high-quality approximate solutions in reasonable time and/or be accelerated by a trivial porting to a GPU framework. The exact similarity join algorithm computes the answer to the time series motif and time series discord problem as a side-effect, and our algorithm incidentally provides the fastest known algorithm for both these extensively-studied problems. We demonstrate the utility of our ideas for many time series data mining problems, including motif discovery, novelty discovery, shapelet discovery, semantic segmentation, density estimation, and contrast set mining. Moreover, we demonstrate the utility of our ideas on domains as diverse as seismology, music processing, bioinformatics, human activity monitoring, electrical power-demand monitoring and medicine.     

Simulating soil carbon sequestration from long term fertilizer and manure additions under continuous wheat using the DailyDayCent model

The widely used ecosystem model DailyDayCent (DDC) was used to predict soil organic carbon (SOC) sequestration and yield under different fertilization treatments on a wheat (Triticum aestivum) monoculture on Broadbalk field, Rothamsted, UK. Over a period of about 170 years, the management practices on the test site, which is separated into plots with different treatments of fertilizer and manure application, are well documented. Four treatments consisting of control (no fertilizer), mineral nitrogen (N) fertilizer (MN), farm yard manure (FYM) and a combination of both (FYMN) have been selected for the study. DDC simulated the greatest increases in SOC (three times higher compared to simulation of the control plot) in the FYM plot. Overall a good agreement between modelled and measured yield was obtained (except in control plots). As observed in the experimental plots, the highest yield predicted by the model is with FYMN plot (more than three times higher relative to the control plot). A sensitivity test for the MN and FYM plots demonstrated that predicted SOC was relatively insensitive (1%) to bulk density, pH and field capacity. Higher sensitivity (4–7%) of predicted SOC changes were observed by changes in external C input. An increase of temperature by 1 °C or precipitation by 10% decreased predicted SOC by 2–4% and vice versa, and simulated a 1–4% variability in yield. DDC is a useful tool for simulating SOC in the long term plots, with a good fit to data, and is useful for examining alternate hypotheses of yield and SOC responses.     

Optimal business process deployment cost in cloud resources

Cloud computing is the fastest emerging technology that proposes several resources under various pricing strategies that are specified based on temporal constraints. The main aim of cloud computing is to enhance the performance level and minimize operating costs. Thus, organizations looking towards optimizing their spending on IT infrastructure find such pricing strategies very attractive, especially, to deploy their business process models. However, discovering the optimal deployment cost of a business process in cloud resources proposed under various pricing strategies becomes a highly challenging problem. So, the objective of the present paper is to present an approach that assists business process designers in finding an optimal assignment or scheduling based on the variety of pricing strategies. We use linear programming models with an objective function under a set of constraints. Besides, we propose an extension of the famous cloud simulator provided in the market, CloudSim, to simulate the cloud resources consumed to deploy a business process model. The experimental results show the feasibility, effectiveness, and performance of our approach.

     

Optimization using central composite design (CCD) for the biosorption of Cr(VI) ions by cross linked chitosan carbonized rice husk (CCACR)

The presence of heavy metals in aqueous streams arising from the discharge of industrial effluents into water bodies is one of the most important environmental issues because of their toxic nature and its removal is highly essential. This paper deals with the adsorption studies for the removal of hexavalent chromium ions from aqueous solutions using Schiff based chitosan activated carbonized rice husk composites as adsorbent. The activation and surface properties of the adsorbent were characterized by Scanning Electron Microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and Brauner Emmet and Teller (BET) analyzer. Central Composite Design (CCD) was used to optimize the process variables such as initial metal ion concentration, adsorbent dosage and pH of the solution on the performance of percentage removal and adsorption capacity. The experimental data were validated with different isotherms and kinetic models to evaluate the solute interaction behavior and nature of adsorption.     

Preparation of single-site tin(IV) compounds and their use in the polymerization of ε-caprolactone

Butyltin(IV) carboxylate compounds were obtained by reactions of butyltrichlorotin(IV) with potassium pivalate, perfluoroheptanoate, methacrylate, 2,6-pyridinedicarboxylate, and phthalate. The synthesized complexes were fully characterized by nuclear magnetic resonance (¹H-, ¹³C-NMR), Fourier transform infrared (FTIR), mass spectroscopies (MS) and elemental analysis. These tin complexes were used as catalysts for the ring opening polymerization of ε-caplolactone and the conversion of monomers to polymers was completed in just 1 h. The structures of polymers were characterized by a combination of spectroscopic techniques (NMR, FTIR, MS), differential scanning calorimeter (DSC) and gel permeation chromatography. In this study, the ε-caplolactone polymers with different average molecular weights between 5000 and 40,000 Da having a regular structure were obtained.     

Hierarchical Tree Structure Based Clustering Schemes for Secure Group Communication

In recent years, many network applications are developed based on group communications (GC), in which the security has to be provided in terms of confidentiality, authenticity and integrity of messages delivered between the group members. A Hierarchical tree structure has to be constructed in such a way that it can handle large dynamic groups with the effective key formation and key distribution. In this paper, an Optimal Cluster Hierarchical Tree (OCHT) structure is presented for effective group communication. The proposed OCHT structure provides a novel solution for multicast key management with decentralized architecture to ensure scalability, reliability and cost effectiveness. Simulation results reveal that proposed OCHT based decentralized architectures provide better performance when compared with existing Logical Hierarchical Tree (LKH). The parameters used for simulation are Memory Overhead, Throughput, Packet Delivery Ratio, End-to-End Latency and Energy consumption.     

Layer thickness impact on the capacity of poly(urethaneimide) for sorbing toxic gases

The sensitivity of a thick layer (500 nm) of poly(urethaneimide) to toxic gases such as pyridine and hexamethyleneimine was studied by the quartz crystal microbalance method. The resonant frequency was registered by a spectrum analyzer. The successful overcoming of the unfavorable impact of the increased viscous damping, typical for the standard oscillator measurement method, allowed complete understanding of the behavior of a thick polymer layer toward highly pollutant gases. Quantitative information for polymer adsorption capacity and gas diffusivity was extracted. The comparison with our recent results for thinner layers revealed strong dependence on layer thickness for pyridine whereas the corresponding effect with hexamethyleneimine was found much less significant. Intermolecular interactions and the impact of the gas physical adsorption on the polymer matrix properties were shown as the driving parameters for the sensors response. The thickness dependence provides a tool for tuning the sensitivity of a potentially efficient poly(urethaneimide) sensor for pyridine. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44214.