Vignan University

About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Control theory & CMOS. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.

Low-Bandgap DPP-Based Quinoxaline with Extended EQE and Low Energy Loss for Efficient Polymer Solar Cells

Abstract: In this study, we have designed and synthesized a low-bandgap polymer, DPP-Qx, using quinoxaline (Qx), a non-alkylated weak acceptor, with diketopyrrolopyrrole (DPP) as an electron donor The incorporation of Qx into DPP leads to the highest occupied molecular orbital level at −51 eV with a narrow optical bandgap (125 eV) The synthesized polymer is utilized in photovoltaics, and the photovoltaic properties are studied This DPP-Qx with fullerene acceptors in polymer solar cells (PSCs) exhibits efficiency of 326% Moreover, a minimum energy loss of 057 eV along with high extended external quantum efficiency is achieved with the DPP-Qx electron donor The thermal, electrical and optical properties, energy levels and hole mobility of the synthesized polymer are studied and discussed

Adaptive traffic for VoIP to improve the QoS

Abstract: Voice over Internet Protocol is an important service with strict quality of requirements in Wireless Local Area Network (WLANs). The IEEE 802.11e Standard has been introduced recently for providing Quality of Service (QoS) capabilities in the emerging wireless local area networks. The IEEE802.11e is an approved amendment to the IEEE802.11 standard that defines a set of Quality of Service enhancements for Wireless LAN applications through modifications to the medium access control layer. This 802.11e introduces a contention window based that is Enhanced Distribution Channel Access (EDCA) technique that provides a prioritized traffic to guarantee minimum bandwidth needed for time critical applications. However this EDCA technique resets statistically the contention window of the mobile station after each successful transmission. This static behavior does not adapt to the network state hence reduces the network usage and results in bad performance and poor link utilization whenever the demand for link utilization increases. For that purpose a new adaptive differentiation technique has been proposed for IEEE 802.11e wireless local area networks that take into account the network state before resetting the contention window.

Document Image Binarization in JPEG Compressed Domain Using Dual Discriminator Generative Adversarial Networks

Abstract: Image binarization techniques are being popularly used in enhancement of noisy and/or degraded images catering different Document Image Analysis (DIA) applications like word spotting, document retrieval, and OCR. Most of the existing techniques focus on feeding pixel images into the convolution neural networks to accomplish document binarization, which may not produce effective results when working with compressed images that need to be processed without full decompression. Therefore in this research paper, the idea of document image binarization directly using JPEG compressed stream of document images is proposed by employing Dual Discriminator Generative Adversarial Networks (DD-GANs). Here the two discriminator networks—Global and Local work on different image ratios and use focal loss as generator loss. The proposed model has been thoroughly tested with different versions of DIBCO dataset having challenges like holes, erased or smudged ink, dust, and misplaced fibers. The model proved to be highly robust, efficient both in terms of time and space complexities, and also resulted in state-of-the-art performance in JPEG compressed domain.

T2CI-GAN: Text to Compressed Image Generation Using Generative Adversarial Network

Abstract: The problem of generating textual descriptions for the visual data has gained research attention in the recent years. In contrast to that the problem of generating visual data from textual descriptions is still very challenging, because it requires the combination of both Natural Language Processing (NLP) and Computer Vision techniques. The existing methods utilize the Generative Adversarial Networks (GANs) and generate the uncompressed images from textual description. However, in practice, most of the visual data are processed and transmitted in the compressed representation. Hence, the proposed work attempts to generate the visual data directly in the compressed representation form using Deep Convolutional GANs (DCGANs) to achieve the storage and computational efficiency. We propose GAN models for compressed image generation from text. The first model is directly trained with JPEG compressed DCT images (compressed domain) to generate the compressed images from text descriptions. The second model is trained with RGB images (pixel domain) to generate JPEG compressed DCT representation from text descriptions. The proposed models are tested on an open source benchmark dataset Oxford-102 Flower images using both RGB and JPEG compressed versions, and accomplished the state-of-the-art performance in the JPEG compressed domain. The code will be publicly released at GitHub after acceptance of paper.