VLSI Implementation of Block Error Correction Coding Techniques

Communication Engineering has become the most vital field of Engineering in today’s life. The world is dreaded to think beyond any communication gadgets. Data communication basically involves transfers of data from one place to another or from one point of time to another. Error may be introduced by the channel which makes data unreliable for user. Hence we need different error detection and error correction schemes. In the present work, we perform the comparative study between different FECs like Turbo codes, Reed-Solomon codes and LPDC codes. But among all these we find Reed Solomon to be most efficient for data communication because of low coding complexity and high coding rate. The RS codes are non-binary, linear and cyclic codes used for burst error correction. They are used in numerous applications like CDs, DVDs and deep space communication. We simulate RS Encoder and RS Decoder for double error correcting RS (7, 3) code. Then we implement RS (255,239) code in VHDL. In RS (255,239) code, each data symbol consists of 8 bits which is quite practical as most of the data transfer is done in terms of bytes. The implementation has been done in the most efficient algorithms to optimize the design in terms of space utilization and latency of the code. The behavioral simulation has been carried out for each block and for the whole design also. Finally, the FPGA utilization and clock cycles needed are analyzed and compared with the already developed designs

Document clustering for knowledge synthesis and project portfolio funding decision in R&D organizations

The paper discusses a method of using document clustering for information/knowledge synthesis and decision facilitation in R&D organisations. The emerging methodologies of machine learning, artificial intelligence and data science in conjunction with fuzzy mathematics can be optimally exploited to catalyse development of information bank for research organisations. This knowledge ecosystem can be utilized by the proposed mechanism to accelerate and reinforce interdisciplinary research for R&D organisations and empower them to make efficacious information-driven decisions related to project portfolio selection and proposal funding

Analysis Of Multimodal Data On Social Media Using Deep Learning Techniques

Contextual text mining known as sentiment analysis identifies and extracts subjective information from the source content. It aids in the detection of sentiments that are good, negative, neutral, etc. It helps companies monitor internet debates in order to learn how the public feels about their brands, goods, and services. However, the only metrics generally utilized in social media stream analysis are straightforward sentiment analysis and count-based metrics. This is analogous to simply scratching the surface and leaving out those priceless discoveries that are just waiting to be made. Sentiment analysis is quickly evolving into a crucial tool to track and comprehend the sentiment in all types of data because people express their thoughts and feelings more freely than ever before. This project's sole objective is to use various latest AI techniques to categorize various sentiments present in audio and text forms into categories like humorous, offensive, and sarcastic. Using datasets with audio files and image files, we trained the model, then we tested it using the test data

Evaluation of the pancreatic malignancy with MRI & MDCT modalities

Background:Detection of pancreatic abnormality by routine noninvasive radiological method namely plain radiography and gastrointestinal barium studies is possible but these tests are insensitive and nonspecific. In earlier era more invasive tests like retroperitoneal air insufflations with tomography used never achieved wide spread clinical application and isotope scan proved disappointing owing to their false positive rates. Material & Methods:The present retrospective study was conducted at department of Department of Radiology at MRI Centre, M.B. Govt. Hospital, Udaipur. The study duration was December 2012 to November 2014. The study group of 100 patients, with suspected pancreatic diseases were examined using either MDCT scan or MRI or both as prime diagnostic modality.Results: In the present study, Pancreatic carcinoma is a hypo vascular mass so it does not enhance at all or show a mild enhancement on post contrast study. In this study out of 14 cases, 14 cases showed mild post contrast enhancement and 2 cases showed no enhancement at all. on CECT examination, out of 10 cases of head mass, 8 cases were hypo dense and 2 were Isodense and showed dilated MPD in 9 cases 90.00% which were most common finding followed by dilated CBD in 8 cases (80.00%) and invasion of other organs in 2 cases (20%). On MRI examination, out of 4 cases of head carcinoma, 3 appeared hyperintense & 1 appeared hypointense and showed dilatation of MPD & CBD in all cases and invasion of organ in one case. Out of 14 cases of head carcinoma, 4 (28.57%) cases showed distal metastasis in liver. Out of 7 cases of body-tail mass, six appeared hypo dense and one appeared on NCCT. Distant metastasis and dilated MPD were found in 4 cases and CBD were dilated in any one case of body mass. Conclusion: We concluded from the present study that Dual-phasic contrast-enhanced MDCT in the pancreatic parenchymal and the venous phase is the method of choice for detection and staging of pancreatic cancer, inflammatory lesions and its vascular complications. For detection of small, hyper vascular neuroendocrine tumors, no single imaging method will reveal all tumors. In this respect, MDCT and MRI are complementary methods

Logarithmic Corrections to Twisted Indices from the Quantum Entropy Function

We compute logarithmic corrections to the twisted index $B^g_6$ in four-dimensional $\mathcal{N}=4$ and $\mathcal{N}=8$ string theories using the framework of the Quantum Entropy Function. We find that these vanish, matching perfectly with the large--charge expansion of the corresponding microscopic expressions.Comment: v2 : 22 pages, presentation significantly improved, published in JHE

GREEN BUSINESS- Way to achieve globally sustainable competitive advantage

Green business” refers to sustainable business that meets customers’ needs in ways that solve rather than cause environmental and social problems. Green businesses operate across all business sectors from production of conventional goods/services to developing new breakthrough technologies. This model of socially and environmentally responsible business does not assume sacrificing of corporate profits. On the contrary, sustainable businesses show financial success in long-run, benefit many stakeholders while exploiting none. This paper explores the tremendous impact the green movement is having on marketing and business strategies. It explores the possibilities that green technologies and products have on sustainable competitive advantage in the competitive environment. This paper takes an active approach and proposes to turn business green. Potential benefits of such an action are provided as arguments to support the decision. In this paper, three factors were identified as crucial for achieving a so-called green sustainable competitive advantage: entrepreneurship, commitment to the environment, and corporate social responsibility. As the competitive arena is constantly being shifted by institutions towards ecology through new regulation, utilization of those resources enables firms to better respond to the changes and gain a favorable position in the market. Green business also provides an opportunity to expand through substituting products or by entering new geographical areas