Neural networks for signal detection in non-Gaussian noise

We employ neural networks to detect known signals in additive non-Gaussian noise. Training of the neural network for signal detection and its operation at some specified probability of false alarm are discussed. Performance of neural detectors are presented under several non-Gaussian noise environments and are compared with those of matched filter and locally optimum detectors     

CO2 laser annealing of plasma-deposited high-Tcsuperconducting thick films

Recent advances in the fabrication of high-T _c superconducting thick films demand processing techniques which can eliminate film/substrate interdifiusion that occurs during subsequent post-annealing heat treatment after the film is deposited, thereby limiting the application of the thick films for devices. The present study evaluates laser annealing techniques for plasma-deposited Y-Ba-Cu-O thick films using a high-energy CO₂ laser (10.6m) in a continuous wave mode. The results are compared with those obtained by conventional furnace annealing techniques necessary for post-heat treatment of as-deposited superconducting thick films. The high-T _c superconducting phase is recovered by cationic diffusion during subsequent post-annealing heat treatment. Crystallographic phases and microstructural characterization have been performed using XRD, SEM, and EPMA analytical techniques. The significance of the technology lies in the elimination of film/substrate interdiffusion problems, thereby resulting in high-quality superconducting thick films. The technology will significantly reduce the post-annealing times usually required by conventional furnace annealing techniques.     

Numerical simulation of annular-phased arrays of dipoles forhyperthermia of deep-seated tumors

We have used the finite-difference time domain (FDTD) method to calculate the SAR distributions from an annular-phased array of eight dipole antennas coupled through water "boluses" in anatomically based three-dimensional models of the human body. We evaluated the effect of tapered bolus chambers, frequency (100-120 MHz), dipole length (17-30 cm), and phase and amplitude of power to the various dipoles on the ability to focus energy in the region of deep-seated tumors in the prostate and the liver. Assuming tumor conductivity and permittivity to be similar or slightly higher than surrounding normal tissues, calculations indicate that adjustment of the noted parameters should result in considerable improvement in focusing of SAR distributions in tumor-bearing regions. If such calculations can be shown to correctly predict empirical measurements from complex inhomogeneous (although not necessarily anatomically correct) phantoms, they may be useful for hyperthermia treatment planning based on patient-specific anatomic models.     

Improved corrosion protection of aluminum alloys by electrodeposited silanes

Organofunctional silanes recently have emerged as outstanding, environmentally friendly corrosion protectors for metal substrates, compared with conventional chromate treatments. A simple immersion technique is typically used to coat the metal surface with silane films. However, the thickness and uniformity of the films are uncontrolled in this process. This paper proposes a new deposition technique for the silane films on the metal surface, i.e., by electrodeposition. Hydrolyzed silanes are water-soluble, ionized molecules, so they can be deposited on metals by electrodeposition. Various combinations of silane mixtures were tested at different voltages, pH values, bath concentrations, and exposure times on panels of alloy aluminum and mirror-polished ferro-plate. The surface structure was characterized by scanning electron microscopy (SEM) and ellipsometry. The resistance of the film to corrosion was investigated by direct current (DC) polarization and electrochemical impedance spectroscopy (EIS) techniques. Electrodeposition results in a more organized and uniform film with fewer pores, compared with immersed or dipped films. This paper was presented at the 2nd International Surface Engineering Congress sponsored by ASM International, on September 15–17, 2003, in Indianapolis, Indiana, and appears on pp. 320–26 of the Proceedings.     

The influence of sulfur dioxide on propane oxidation activity over supported platinum and palladium

Earlier studies have shown that sulfur dioxide and metal-support interaction can strongly influence propane oxidation over platinum. In particular, oxidation activity is enhanced when platinum is supported on sulfated -alumina or zirconia compared to -alumina. Therefore, it is of interest to compare the performance of palladium under the same experimental conditions. Four model catalysts were examined: Pt/-alumina, Pt/zirconia, Pd/-alumina and Pd/zirconia. The metal loading was kept at or below 0.05 wt% to emphasize changes in activity attributable to metal-support interaction. Reaction rates were measured with and without sulfur dioxide. Surface sulfation was analyzed by measuring acid strength and evaluating spectra obtained by Fourier-transform infrared spectroscopy. In contrast to platinum, sulfation does not promote propane oxidation on Pd/-alumina, and Pd/zirconia is less active than Pd/-alumina.     

Ageing studies of pineapple leaf fiber–reinforced polyester composites

This experimental study evaluated the water absorption characteristics of pineapple leaf fiber (PALF)–polyester composites of different fiber content. The degree of water absorption was found to increase with fiber loading. The mechanism of diffusion was analyzed and the effect of fiber loading on the sorption kinetics was studied. The diffusion coefficient was calculated and found to increase with fiber content. Studies were also made to correlate water absorption with the cross‐sectional areas of the specimens. The effects of ageing on the tensile properties and dimensional stability of PALF polyester composites were studied under two different ageing conditions. Ageing studies showed a decrease in tensile strength of the composites. The composite specimens subjected to thermal ageing showed only a slight deterioration in strength. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 94: 503–510, 2004     

Equivalence of classicality and separability based on P phase–space representation of symmetric multiqubit states

Classical and quantum world views differ in peculiar ways. Understanding decisive quantum features—for which no classical explanation exist—and their interrelations is of foundational interest. Moreover, recognizing non-classical features carries practical significance in information processing tasks as it offers insights as to why quantum protocols work better than their classical counterparts. We focus here on two celebrated notions of non-classicality viz., negativity of P phase–space representation and entanglement in symmetric multiqubit systems. We prove that they imply each other.     

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.     

Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today’s on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve the issues and provide safe big data transmission, the proposed technique also combines blockchain with a consensus algorithm architecture. Security of the big data is ensured by dividing the BC network into distinct networks, each with a restricted number of allowed entities, data kept in the cloud gate server, and data analysis in the blockchain. The consensus algorithm is crucial for maintaining the speed, performance and security of the blockchain. Then Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Finally, the graph index refinement process is undertaken to improve the query results. Concerning query error, fuzzy logic is used to refine the index of the graph dynamically. The proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems, and the combination of blockchain and subgraph provides a secure communication platform, according to the findings.     

Stochastic Gradient Boosting Model for Twitter Spam Detection

In today’s world of connectivity there is a huge amount of data than we could imagine. The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time. These social networks give the complete independence to the user to post the data either political, commercial or entertainment value. Some data may be sensitive and have a greater impact on the society as a result. The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter. Due to the large user base and its openness there is a huge possibility to spread spam messages in this network. Spam detection is a technique to identify and mark data as a false data value. There are lot of machine learning approaches proposed to detect spam in social networks. The efficiency of any spam detection algorithm is determined by its cost factor and accuracy. Aiming to improve the detection of spam in the social networks this study proposes using statistical based features that are modelled through the supervised boosting approach called Stochastic gradient boosting to evaluate the twitter data sets in the English language. The performance of the proposed model is evaluated using simulation results.