Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing

The Cooperative Spectrum sensing model is gaining importance among the cognitive radio network sharing groups. While the crowd-sensing model (technically the cooperative spectrum sensing) model has positive developments, one of the critical challenges plaguing the model is the false or manipulated crowd sensor data, which results in implications for the secondary user’s network. Considering the efficacy of the spectrum sensing by crowd-sensing model, it is vital to address the issues of falsifications and manipulations, by focusing on the conditions of more accurate determination models. Concerning this, a method of avoiding falsified crowd sensors from the process of crowd sensors centric cooperative spectrum sensing has portrayed in this article. The proposal is a protocol that selects affirmed crowd sensor under diversified factors of the decision credibility about spectrum availability. An experimental study is a simulation approach that evincing the competency of the proposal compared to the other contemporary models available in recent literature

A New Approach for SAR Image Denoising

In synthetic aperture radar (SAR)  imaging, the transmitted pulses from space born antenna interacts with ground objects and returned energy or back scattered energy will be collected  to get backscattered image. In this process, a speckle noise will be added because of the coherent imaging system and  makes the study of SAR images very difficult. For better SAR image processing, the speckle has to be removed in the initial stages of processing  and maintain all texture features efficiently. The BM3D method is generally considered as state of art method in denoising of SAR images. In this paper, it is proposed a technique to despeckle the speckle noise to the maximum extent while maintaining the edge characteristics

RTL Implementation of image compression techniques in WSN

The Wireless sensor networks have limitations regarding data redundancy, power and require high bandwidth when used for multimedia data. Image compression methods overcome these problems. Non-negative Matrix Factorization (NMF) method is useful in approximating high dimensional data where the data has non-negative components. Another method of the NMF called (PNMF) Projective Nonnegative Matrix Factorization is used for learning spatially localized visual patterns. Simulation results show the comparison between SVD, NMF, PNMF compression schemes. Compressed images are transmitted from base station to cluster head node and received from ordinary nodes. The station takes on the image restoration. Image quality, compression ratio, signal to noise ratio and energy consumption are the essential metrics measured for compression performance. In this paper, the compression methods are designed using Matlab.The parameters like PSNR, the total node energy consumption are calculated. RTL schematic of NMF SVD, PNMF methods is generated by using Verilog HDL

Agricultural Management through Wireless Sensors and Internet of Things

Agriculture plays a significant role in most countries and there is an enoromous need for this industry to become “Smart”. The Industry is now moving towards agricultural modernization by using modern smart technologies to find solutions for effective utilization of scarce resources there by meeting the ever increasing consumtion needs of global population. With the advent of Internet of Things and Digital transformation of rural areas, these technologies can be leveraged to remotely monitor soil moisture, crop growth and take preventive measures to detect crop damages and threats. Utilize artificial intelligence based analytics to quickly analyze operational data combined with 3rd party information, such as weather services, expert advises etc., to provide new insights and improved decision making there by enabling farmers to perform “Smart Agriculture”. Remote management of agricultural activities and their automation using new technologies is the area of focus for this research activity. A solar powered remote management and automation system for agricultural activities through wireless sensors and Internet of Things comprising, a hardware platform based on Raspberry Pi Micro controller configured to connect with a user device and accessed through the internet network. The data collection unit comprises a set of wireless sensors for sensing agricultural activities and collecting data related to agricultural parameters; the base station unit comprising: a data logger; a server; and a software application for processing, collecting, and sending the data to the user device. The user device ex: mobile, tablet etc. can be connected to an internet network, whereby an application platform (mobile-app) installed in the user device facilitates in displaying a list of wireless sensor collected data using Internet of Things and a set of power buttons. This paper is a study and proposal paper which discusses the factors and studies that lead towards this patent pending invention, AGRIPI

THE ROBUST DESIGN OF LINEAR PHASE FIR FILTER USING MIX-MUTATION EVOLUTIONARY PROGRAMMING

ABSTRACT In the design of frequency-selective filters, the desired filter characteristics are specified in the frequency domain in terms of desired magnitude and phase response of the filter. In this paper we present a design approach by determining the closely approximated coefficients using powerful Evolutionary Programming to find the solution for the optimization problem in selecting the coefficients. In this paper the design of Causal FIR filter with desired frequency response and phase response is presented. In practice, FIR filters are employed in filtering problems where there is a requirement for linear phase characteristics within the passband of the filter. The Evolutionary Programming is the best search procedure and most powerful than Linear Programming in providing the optimal solution that is desired to minimize the ripple content in both passband and stopband. We presented here how the values of δ 1 and δ 2 are minimized with best optimized approach using Evolutionary Computation. The optimized filter bank structure is implemented in our research work for effective compression of images

Review of journal of cardiovascular magnetic resonance 2010

There were 75 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2010, which is a 34% increase in the number of articles since 2009. The quality of the submissions continues to increase, and the editors were delighted with the recent announcement of the JCMR Impact Factor of 4.33 which showed a 90% increase since last year. Our acceptance rate is approximately 30%, but has been falling as the number of articles being submitted has been increasing. In accordance with Open-Access publishing, the JCMR articles go on-line as they are accepted with no collating of the articles into sections or special thematic issues. Last year for the first time, the Editors summarized the papers for the readership into broad areas of interest or theme, which we felt would be useful to practitioners of cardiovascular magnetic resonance (CMR) so that you could review areas of interest from the previous year in a single article in relation to each other and other recent JCMR articles [1]. This experiment proved very popular with a very high rate of downloading, and therefore we intend to continue this review annually. The papers are presented in themes and comparison is drawn with previously published JCMR papers to identify the continuity of thought and publication in the journal. We hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your quality manuscripts to JCMR for publication

The Physics of the B Factories

This work is on the Physics of the B Factories. Part A of this book contains a brief description of the SLAC and KEK B Factories as well as their detectors, BaBar and Belle, and data taking related issues. Part B discusses tools and methods used by the experiments in order to obtain results. The results themselves can be found in Part C

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe