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 32-nm Subthreshold 7T SRAM Bit Cell With Read Assist

Abstract: The implementation of the six-transistor (6T) static random access memory cell in deep submicrometer region has become difficult due to the compromise between area, power, and performance, with local and global variations only exacerbating the problem further. To impede the read–write conflict of the 6T cell, the seven-transistor (7T) cell with a noise-margin-free read operation has previously been proposed. But it severely lags in terms of its write ability at lower voltages due to its single-ended write operation. Its single-ended read operation also degrades severely in performance when operating in subthreshold (ST) region. To combat these problems, we propose a 7T cell which operates in the ST region down to 0.4 V with improved dynamic write ability. The novel topology also helps reduce power consumption by achieving a lower data retention voltage point. A read assist has been proposed to greatly enhance the performance of the single-ended read operation in ST region. Large improvements in various performance metrics of the proposed cell have been attained while simultaneously achieving a low area of $0.254~\mu \text{m}^{2}$ per bit cell on the 32-nm technology node.

Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis

Abstract: Cancer is a fatal disease caused due to the undesirable spread of cells. Breast carcinoma is the most invasive tumors and is the main reason for cancer deaths in females. Therefore, early diagnosis and prognosis have become necessary to increase survivability and reduce death rates in the long run. New artificial intelligence technologies are assisting radiologists in medical image scrutiny, thereby improving cancer patients’ status. This survey enrolls peer-reviewed, newly developed computer-aided diagnosis (CAD) systems implementing machine learning (ML) and deep learning (DL) techniques for diagnosing breast carcinoma, compares them with previously established methods, and provides technical details with the pros and cons for each model. We also discuss some open issues, research gaps, and future research directions for the advanced CAD models in medical image analysis. Over the past decade, machine learning and deep learning have emerged as a subfield of artificial intelligence (AI), whose healthcare industry applications have provided excellent results with reduced cost and improved efficiency. This survey analyzes different classifiers of machine learning and deep learning approaches for breast cancer diagnosis. Results from previous studies proved that deep learning outperforms conventional machine learning for diagnosing breast carcinoma when the dataset is broad. Research gaps from the recent studies depict that practical and scientific research is an urgent necessity for improving healthcare in the long run.

Impact of device parameter variation on RF performance of gate electrode workfunction engineered (GEWE)-silicon nanowire (SiNW) MOSFET

Abstract: In this paper, we explore the quantitative investigation of the high-frequency performance of gate electrode workfunction engineered (GEWE) silicon nanowire (SiNW) MOSFET and compared with silicon nanowire MOSFET(SiNW MOSFET) using device simulators: ATLAS and DEVEDIT 3D. Simulation results demonstrate the improved RF performance exhibited by GEWE-SiNW MOSFET over SiNW MOSFET in terms of transconductance $$(\hbox {g}_{\mathrm{m}})$$(gm), cut-off frequency $$(f_{\mathrm{T}})$$(fT), maximum oscillator frequency $$(f_{\mathrm{MAX}})$$(fMAX), power gains (Gma, G$${_\mathrm{MT}}$$MT) parasitic capacitances, stern's stability factor and intrinsic delay. Further, using three-dimensional device simulations, we have also examined the efficacy of parameter variations in terms of oxide thickness, radius of silicon nanowire, channel length and gate metal workfunction engineering on RF/microwave figure of merits of GEWE-SiNW MOSFET. Simulation result reveals significant enhancement in $$f_{\mathrm{T}}$$fT and $$f_{\mathrm{MAX}}$$fMAX; and a reduction in switching time in GEWE-SiNW MOSFET due to alleviated short channel effects, improved drain current and smaller parasitic capacitance, thus providing detailed knowledge about the device's RF performance at such aggressively scaled dimensions.

Movie recommender system with metaheuristic artificial bee

Abstract: Recommender systems are information retrieval tool that allocates accurate recommendations to the specific users. Collaborative movie recommender systems support users in accessing their popular movies by suggesting similar users or movies from their past common ratings. In this research work, a hybrid recommender system has been proposed which utilized k-means clustering algorithm with bio-inspired artificial bee colony (ABC) optimization technique and applied to the Movielens dataset. Our proposed system has been described systematic manner, and the subsequent results have been demonstrated. The proposed system (ABC-KM) is also compared with existing approaches, and the consequences have been examined. Estimation procedures such as precision, mean absolute error, recall, and accuracy for the movie recommender system delivered improved results for ABC-KM collaborative movie recommender system. The experiment outcomes on Movielens dataset established that the projected system provides immense achievement regarding scalability, performance and delivers accurate personalized movie recommendations by reducing cold start problem. As far as our best research knowledge, our proposed recommender system is novel and delivers effective fallouts when compared with already existing systems.