Effect of heat treatment on the structure and stability of multiwalled carbon nanotubes produced by catalytic chemical vapor deposition technique

The multiwalled carbon nanotubes (MWCNTs) produced by catalytic chemical vapor deposition (CCVD) route were heat treated to 2500 °C to improve the structure, morphology and purity level. The process has lead to substantial reduction in the catalytic impurity along with an improved thermal stability and degree of graphitization of these tubes that can possibly lead to its better utilization in various applications. The structural changes following heat treatment have been correlated using various characterization techniques such as Raman spectroscopy, X-ray diffraction, scanning electron microscopy, high resolution transmission electron microscopy, thermo gravimetric analysis and electron paramagnetic resonance spectroscopy. The electrical and mechanical properties of the polymer composites prepared with heat treated MWCNT show improved properties over the one prepared by as produced MWCNT.     

Network Planning for Deep Fading Area at 1.8 GHz LTE Network

Wireless Personal Communications - In the current scenario path loss, coverage and capacity by operating network under fading condition is research challenge. In this paper, signal strength of long...     

Family studies and human leukocyte antigen class II typing in Indian probands with seizures in association with single small enhancing computed tomography lesions

PURPOSE: To define the clinical features of the syndrome of seizures associated with single, small, enhancing computed tomography (CT) lesions (SSELs) in 235 Indian probands and seizure types among their family members. Human leukocyte antigen (HLA) class II genomic typing in randomly selected 41 probands was done to identify the role of hereditary factors in this syndrome. METHODS: The seizure types among 235 probands, their clinical outcome, and seizures in their family members were studied. Family data were collected on relatives of 212 additional probands with neurologic diseases other than epilepsy. HLA class II antigens were studied by using polymerase chain reaction (PCR) amplified DNA and sequence-specific oligonucleotide probe (PCR-SSOP) hybridization. RESULTS: The seizures in 86% were partial with or without generalization; 77% had fewer than five seizures before the first CT scan. Evanescent focal neurologic deficits after seizures were noted in 40%. Most patients (97%) were treated with a single antiepileptic drug (AED). Significant resolution of the CT scan lesion was noted within 6 months in 125 (53%) of 235 cases. Two thirds of patients had no seizures while taking a single AED, and an additional 18% had no seizures even after their AEDs were discontinued. Epilepsy among relatives of Indian probands having seizures in association with SSELs was more common as compared with relatives of probands with other neurologic diseases. A family history of seizures was noted in 21% probands, the ratio of affected first- to second-degree relatives was 4.3:1, and 60% of affected sibs had syndromic concordance with probands. There was a positive association of HLA-DRB1*13 (Pc = 0.036) with this syndrome. CONCLUSIONS: The syndrome of seizures in association with SSELs seems to be a benign localization-related epileptic syndrome. Our results of HLA studies point to an inherited susceptibility to an infective agent, which in most cases is of cysticercal etiology.     

Fluoride in drinking water and its removal

Excessive fluoride concentrations have been reported in groundwaters of more than 20 developed and developing countries including India where 19 states are facing acute fluorosis problems. Various technologies are being used to remove fluoride from water but still the problem has not been rooted out. In this paper, a broad overview of the available technologies for fluoride removal and advantages and limitations of each one have been presented based on literature survey and the experiments conducted in the laboratory with several processes. It has been concluded that the selection of treatment process should be site specific as per local needs and prevailing conditions as each technology has some limitations and no one process can serve the purpose in diverse conditions.     

Bulk Synchronous Parallel Algorithms for the External Memory Model

Abstract. Blockwise access to data is a central theme in the design of efficient external memory (EM) algorithms. A second important issue, when more than one disk is present, is fully parallel disk I/ O. In this paper we present a simple, deterministic simulation technique which transforms certain Bulk Synchronous Parallel (BSP) algorithms into efficient parallel EM algorithms. It optimizes blockwise data access and parallel disk I/ O and, at the same time, utilizes multiple processors connected via a communication network or shared memory. We obtain new improved parallel EM algorithms for a large number of problems including sorting, permutation, matrix transpose, several geometric and GIS problems including three-dimensional convex hulls (two-dimensional Voronoi diagrams), and various graph problems. We show that certain parallel algorithms known for the BSP model can be used to obtain EM algorithms that meet well known I /O complexity lower bounds for various problems, including sorting.     

An Architecture-Independent Graphical Tool for Automatic Contention-Free Process-to-Processor Mapping

Mapping of parallel programs onto parallel computers for efficient execution is a fundamental problem of great significance in parallel processing. This paper presents an architecture-independent software tool for contention-free mapping of arbitrary parallel programs onto parallel computers with arbitrary configurations. This mapping tool is based on an efficient heuristic algorithm that runs in time O(n ³+m ⁴) in the worst case for mapping n tasks onto m processors, where m n in most practical cases. It is fully implemented and incorporated into a graph editing system to produce a graphical mapping tool which enables its user to monitor and control the mapping process. The user can assist the mapping process or employ the algorithm to map automatically. Our mapping tool has been tested and its performance evaluated extensively. Experimental results show that our tool combines user intuition and mapping heuristics effectively to make it a powerful mapping tool which is practical to use. Our mapping tool can be easily extended for use in the more general case when the link contention-degree is bounded to a fixed system-specified value without increasing its complexity.     

A Comprehensive Neural Network Model for Colorization and Captioning of Images Along with IoT Deployment

The convergence of artificial neural networks and the internet of things (IoT) has gained popularity in the field of computer science research. In this work, an efficient neural network model for the image colorization problem is proposed along with deploying these models to the remote system using IoT deployment tools. Further, this work proposed two convolution neural network models namely the Alpha model and Beta model towards solving the image colorization of the grayscale format. An efficient combination of models is proposed and analyzed such that the loss rate is minimized as?~?0.005. Next, an efficient model for solving image captioning is proposed based on the bi-directional long short term memory model. Finally, the work discusses the merits and demerits of deploying the neural network model using the AWS Greengrass and Docker IoT environment on remote systems.

     

Data Congestion Control Using Offloading in IoT Network

Internet of Things (IoT) is being used by a large number of applications and transmitting huge amounts of data. IPv6 routing protocol for low power and lossy networks (RPL) is being standardized for routing in IoT networks. However, it is difficult to handle such huge transmission as it is initially designed for Low power and lossy networks. In this paper, we present the mechanism for the reduction of overhead from the congested parent node by offloading its partial load. For offloading the packet, a suitable neighbor is selected based on its status of energy, buffer, link quality, number of child nodes, and distance. This approach focuses on the enhancement of RPL by including the mechanism for congestion control. The approach reduces the delay and packet loss rate while avoiding congestion in a suitable manner. The proposed approach is beneficial in terms of throughput and packet receiving ratio as compared to the comparative approaches.

     

A low-power high-performance SiGe BiCMOS 802.11a/b/g transceiver IC for cellular and bluetooth Co-existence applications

This paper describes a high-performance WLAN 802.11a/b/g radio transceiver, optimized for low-power in mobile applications, and for co-existence with cellular and Bluetooth systems in the same terminal. The direct-conversion transceiver architecture is optimized in each mode for low-power operation without compromising the challenging RF performance targets. A key transceiver requirement is a sensitivity of -77 dBm (at the LNA input) in 54 Mb/s OFDM mode while in the presence of a GSM1900 transmitter interferer. The receiver chain achieves an overall noise figure of 2.8/3.2 dB, consuming 168/185 mW at 2.8 V for the 2.4/5GHz bands, respectively. Signal loopback and transmit power detection techniques are used in conjunction with the baseband modem processor to calibrate the transmitter LO leakage and the transceiver I/Q imbalances. Fabricated in a 70 GHz f/sub T/ 0.25-/spl mu/m SiGe BiCMOS technology for system-in-package (SiP) use, the dual-band, tri-mode transceiver occupies only 4.6 mm/sup 2/.     

A joint parametric prediction model for wireless internet traffic using Hidden Markov Model

Addressing performance related issues of networks and ensuring better Quality of Service (QoS) for end-users calls for simple, tractable and realistic traffic models. The work reported here focuses on modelling the Wireless Internet traffic using realistic traffic traces collected over wireless networks and forecasting the end-to-end QoS parameters for the networks. A measurement framework is set-up to collect the QoS parameters and a traffic model is designed based on Hidden Markov Model considering joint distribution of End to End Delay (E2ED or d), Inter-Packet Delay Variation (IPDV) and Packet Size. States are mapped to the four traffic classes namely conversational, streaming, interactive, and background. The model is validated by forecasting QoS parameters and the results are shown to be within the tolerance limit.