Electron cyclotron resonance frequency system on tokamak Aditya

A 28 GHz ECRH system has been successfully commissioned on the tokamak Aditya to carry out breakdown, start up and heating experiments. The microwave source gyrotron VGA8000A19 capable of delivering 200 kW CW is commissioned and tested with a water dummy load for pulsed operation. The output mode of the gyrotron (TE02) is externally converted to the HE11 mode with the help of a mode converter and Matching Optics Unit of the transmission line. The transmission line consists of a mode converter, MOU, DC breaks, mitre bend, polarizer unit and different sizes of corrugated waveguides. The total transmission loss of the transmission line including 10 m long waveguides is measured to be less than 1.1 dB. The burn patterns at different locations of the transmission line confirm the mode purity to be better than 93% in the TE02 mode. The transmission line terminates at a launcher box through a barrier window. The ECRH launcher consists of two mirrors to focus the microwave beam at the plasma center. The first mirror is convex while the second mirror is a concave focusing mirror. The mirrors are designed based on quasi optical analysis of the launcher system. The focal length of second mirror is 392.9 mm, which focuses the microwave beam to 35 mm (beam waist radius) at the plasma center. Beam steering in the plasma volume is restricted to ±2°. The gyrotron is tested up to ∼80 kW output power. A hard-wired interlock for various fault conditions, operates a rail-gap crowbar in less than 10 μS to protect the gyrotron. The gyrotron output is coupled to the tokamak Aditya (O-mode, perpendicular launch from low field side) and successful breakdown of the neutral gas is observed at different tokamak parameters. The paper describes the technical aspects of commissioning of the ECRH system and breakdown results on Aditya.© IEE

The role of deep learning in improving healthcare

\u3cp\u3eHealthcare is transforming through adoption of information technologies (IT) and digitalization. Machine learning (ML) and artificial intelligence (AI) are two of the IT technologies that are leading this transformation. In this chapter we focus on Deep Learning (DL), a subfield of ML that relies on deep artificial neural networks to deliver breakthroughs in long-standing AI problems. DL is about working with high-dimensional data (e.g., images, speech recording, natural language) and learning efficient representations that allow for building successful models. We present a structured overview of DL methods applied to healthcare problems based on their suitability of the different technologies to the available modalities of healthcare data. This data-centric perspective reflects the data-driven nature of DL methods and allows side-by-side comparison with different domains in healthcare. Challenges, in broad adoption of DL, are commonly related to some of its main drawbacks, particularly lack of interpretability and transparency. We discuss the drawbacks and limitations of DL technology that specifically come to light in the domain of healthcare. We also address the need for a considerable amount of data and annotations to successfully build these models that can be a particularly expensive and time-consuming effort. Overall, the chapter offers insights into existing applications of DL to healthcare on their suitability for specific types of data and their limitations.\u3c/p\u3