Delhi Technological University

About: Delhi Technological University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 4427 authors who have published 6761 publications receiving 71035 citations. The organization is also known as: Delhi College of Engineering & DTU.

A Novel Optimal Fuzzy System for Color Image Enhancement Using Bacterial Foraging

Abstract: A new approach is presented for the enhancement of color images using the fuzzy logic technique. An objective measure called exposure has been defined to provide an estimate of the underexposed and overexposed regions in the image. This measure serves as the dividing line between the underexposed and overexposed regions of the image. The hue, saturation, and intensity (HSV) color space is employed for the process of enhancement, where the hue component is preserved to keep the original color composition intact. A parametric sigmoid function is used for the enhancement of the luminance component of the underexposed image. A power-law operator is used to improve the overexposed region of the image, and the saturation component of HSV is changed through another power-law operator to recover the lost information in the overexposed region. Objective measures like fuzzy contrast and contrast and visual factors are defined to make the operators adaptive to the image characteristics. Entropy and the visual factors are involved in the objective function, which is optimized using the bacterial foraging algorithm to learn the parameters. Gaussian and triangular membership functions (MFs) are chosen for the underexposed and overexposed regions of the image, respectively. Separate MFs and operators for the two regions make the approach universal to all types of contrast degradations. This approach is applicable to a degraded image of mixed type. On comparison, this approach is found to be better than the genetic algorithm (GA)-based and entropy-based approaches.

Interfacial Charge Analysis of Heterogeneous Gate Dielectric-Gate All Around-Tunnel FET for Improved Device Reliability

Abstract: In this paper, we have investigated device reliability by studying the impact of interface traps, both donor (positive interface charges) and acceptor (negative interface charges), present at the Si/SiO2 interface, on analog/RF performance and linearity distortion analysis of heterogeneous-gate-dielectric gate-all-around tunnel FET (HD-GAA-TFET), which is used to enhance the tunneling current of TFET. Various figures of merit such as cutoff frequency $f_{{T}}$ , maximum oscillation frequency $f_{\max}$ , transconductance frequency product, higher order transconductance coefficients $({g}_{{m}1}, {g}_{{m}3})$ , VIP2, VIP3, IIP3, IMD3, zero crossover point, and 1-dB compression point have been investigated, and the results obtained are simultaneously compared with a gate-all-around TFET (GAA-TFET). Simulation results indicate that HD-GAA-TFET is more immune toward the interface trap charges present at the Si/SiO2 interface than the GAA TFET and hence can act as a better candidate for low power switching applications. All simulations have been done on an ATLAS device simulator.

DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch

Abstract: Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference. Towards this goal, we developed a differentiable PatchMatch module that allows us to discard most disparities without requiring full cost volume evaluation. We then exploit this representation to learn which range to prune for each pixel. By progressively reducing the search space and effectively propagating such information, we are able to efficiently compute the cost volume for high likelihood hypotheses and achieve savings in both memory and computation.Finally, an image guided refinement module is exploited to further improve the performance. Since all our components are differentiable, the full network can be trained end-to-end. Our experiments show that our method achieves competitive results on KITTI and SceneFlow datasets while running in real-time at 62ms.

Recent advances in carbon based nanosystems for cancer theranostics.

Abstract: One of the major challenges in our contemporary society is to facilitate healthy life for all human beings. In this context, cancer has become one of the most deadly diseases around the world, and despite many advances in theranostics techniques the treatment of cancer still remains an important problem. With recent advances made in the field of nano-biotechnology, carbon-based nanostructured materials have drawn special attention because of their unique physicochemical properties, giving rise to great potential for the diagnosis and therapy of cancer. This review deals with four different types of carbon allotrope including carbon nanotubes, graphene, fullerenes and nanodiamonds and summarizes the results of recent studies that are likely to have implications in cancer theranostics. We discuss the applications of these carbon allotropes for cancer imaging and drug delivery, hyperthermia, photodynamic therapy and acoustic wave assisted theranostics. We focus on the results of different studies conducted on functionalized/conjugated carbon nanotubes, graphene, fullerenes and nanodiamond based nanostructured materials reported in the literature in the current decade. The emphasis has been placed on the synthesis strategies, structural design, properties and possible mechanisms that are perhaps responsible for their improved theranostic characteristics. Finally, we discuss the critical issues that may accelerate the development of carbon-based nanostructured materials for application in cancer theranostics.

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Abstract: Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.