Springer Science and Business Media LLC

III-nitride wide bandgap semiconductors such as alloys of (Al, Ga, In) N play a vital role in high frequency, power electronics, and opto-nanoelectronics applications. Apart from this recently, wide bandgap semiconductor, β-Ga2O3 has attracted the attention of worldwide researchers as an alternative of GaN substrates due to its low-cost processing and wide availability of large scale wafers. Due to its suitable material properties, β-Ga2O3 can also be used for high frequency and high power electronics applications. In this paper the quantum transport in AlN/β-Ga2O3 based high electron mobility transistor (HEMT) along with the compact model development of GaN-based metal oxide semiconductor (MOS)-HEMT is presented for high frequency and high power electronics. Finally, the modeling and simulations of InGaN based nanowire is developed for solar photovoltaics and opto-nanoelectronics applications.

Protection of biometric data is gaining interest and hence digital watermarking techniques are one of the best ways to protect biometric data from accidental or intentional attacks. We have proposed fingerprint watermarking scheme over natural image based on visual cryptography for identification and authentication. The scheme does not embed the fingerprint directly onto the natural image instead using the concept of visual cryptography fingerprint is divided into parts called as shares. The verification share is generated at the time of fingerprint embedding process and while performing identification and authentication master share is generated. Fingerprint is extracted using these two shares. Correlation value between original fingerprint and extracted fingerprint is the deciding factor for identification and authentication. The experimental results show that the scheme is robust against various watermarking attacks.

This paper considers the problem of designing trajectories for scanning k-space in MRI. Various cartesian and non-cartesian trajectories such as rectangular, spiral, radial, etc. have been proposed in the literature. Non-cartesian trajectories are observed to perform optimally in terms of scan time and reduced artifacts in the constructed image. Novel k-space sampling trajectories that use sinusoidal smooth functions and four RF excitations (four-shots) have been proposed here. Simulations on Shepp–Logan phantom image show that the proposed trajectory provides slightly better peak signal-to-noise ratio compared to the commonly used spiral trajectory .

Ignite began as an innovative learning program started by Tata Consultancy Services (TCS) in 2007, under the close mentorship of the then CEO Mr. S Ramadorai, to include science graduates into the mainstream Information Technology (IT) roles. These roles were otherwise traditionally exclusively earmarked for engineering graduates. Designing a suitable program for this cohort was a challenge since the cohort was traditionally under-represented in IT companies and came with a minimal formal intellectual apparatus in software engineering. This includes women, first-generation graduates and students from low-income brackets who did not get an opportunity to pursue an engineering education. Over the years, we have been able to identify talent from this cohort through a democratic hiring process. We have designed a world class, training program for a thousand graduates who get selected and trained each year for IT roles. The program is high-tech and high touch; designed to support and catalyse several opportunities and modes of learning. This includes hands-on tools for practice, gamification via competitive programming, peer-to-peer learning, ambient learning from the design of the space and an incubator model of learning, to name a few. We have continuously and deliberately sought to refine our processes, curricula and pedagogy for this cohort through experimentation. Along the way, we have designed a learning organization (Senge in 126–138, 1990) [1] that continuously evolves with time. In this paper, we share a replicable system of identifying and training this under-represented and under-served talent; and set them on a path of becoming world class professionals. We share some of our most effective pedagogical practices along with qualitative outcomes that were learnt over a 15-year long period and with over 12,400 + science graduates who have passed through our program.

The potential of data, data sharing, and related computational tools in generating societal value or public good has now been widely recognised. Globally, policymakers are moving not only to secure personal data protection and mature digital rights for data subjects, but also to build pathways to harness broader public benefit from data. In order to create such value and unlock existing, siloed datasets—data sharing is a necessity. Within this, non-personal data has found particular focus. India’s expert committee report recommends a mandatory sharing framework to the governance of non-personal data sharing. This paper analyses the mandatory approach through a review of evolving global approaches to data sharing (mandatory, voluntary or otherwise), and suggests pathways that may be best suited to the Indian data economy. While most data regimes are nascent and dynamic, broader policy directions create strong indicators for varied approaches, and likely outcomes based on current practice. Based on this analysis, it is found that a staggered, ecosystem-oriented approach is the most feasible and likely to yield outcomes that the framework has identified as key purpose. This approach enables collaboration across stakeholders, places focus on building technical and infrastructural capacity, incentivising data sharing, and uses pilots as a means to build iterative and durable policy. Therefore, it is important for India to invest in fundamental building blocks, underlying jurisprudence and consultative policymaking in order to mould a robust data sharing ecosystem.

Faulty measurements of sensors in critical applications like remote patient monitoring systems may sometimes lead to misdiagnose or unnecessary interventions from healthcare professionals. The main motivation of this paper is to mitigate false alarms thereby improving the accuracy of the system. In this paper, we propose an approach for detecting anomalous sensor measurements. This paper utilizes dynamic sliding window, Weighted Moving Average for prediction purposes and accumulator rule for improving the accuracy in identifying true medical conditions. Finally we validate the performance of the proposed approach using a publicly available dataset and has been compared with existing approaches using statistical metrics. We achieved 37.40% reduction in False Positive Rate when compared with existing approaches.