3. Book Chapters
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Item Techniques to improve gain-bandwidth 5g ics(2021) Vignesh R.; Kumar R.; Song H.; Kumar S.This chapter introduces a basics of designs and techniques to improve gain-bandwidth for 5G ICs. The major focus would be on the various network topologies that yield to provide easy implementation of on-chip components for 5G-ICs. Section 1 discusses the basics of RLC tank networks, which includes RC parallel network, RLC network and series to parallel resonant network. The parameters such as quality factor, noise of filter networks are shortly refresh while foundation of resonant circuits would set-up for 5G transceiver ICs. Section 2 introduces coupled resonator networks can be used as microwave components to achieve a better gain-bandwidth trade-off. Finally, Sect. 3 will provide transformer resonators and circuit to reduce bulky components and enhance gain-bandwidth of ICs. © Springer Nature Singapore Pte Ltd. 2021.Item Mm-wave cmos power amplifiers for 5g(2021) Gorre P.; Kumar R.; Song H.; Kumar S.The chapter discusses the basic elements in the design of mm-wave CMOS Power Amplifier (PA) for phased arrays integration, focusing the next-generation 5G mobile communication. Power Amplifier design metrics, along with implementation of beam-forming phased arrays to merge power over-the-air are discussed in brief. The explanation begins with CMOS unique advantages, real-time handset challenges, system-level constraints, and design challenges are conceptually demonstrated with the help of a basic single-stage transistor Power Amplifier. © Springer Nature Singapore Pte Ltd. 2021.Item Distributed Cloud Deep Learning Architecture for Complex Image Analysis and Run-time Prediction Tool(2021) Kumar S.; Thomas E.; Horo A.; Annappa B.Hyperspectral imaging is a rare research tool and has been transformed into a commodity product found in a wide field. Currently, standard data processing methods that specialize in special hyperspectral accumulation structures are required. Also, with the advent of data collection and development in the field of sensory devices, it has rendered previous processing tools in vain. To manage this huge increase in the amount of data, a consistent cloud distribution method is required. Hyperspectral images (HSIs) have several spectral band channels that make the study very difficult. In this paper, an in-depth reading method of the novel with a modified autoencoder is proposed as a cloud-based use of HSI analysis, which provides a measure of lesser error rates and high accuracy of classification models. In line with this, a list of four tools has been proposed to calculate the actual number of workers, cores, and iterations required to achieve the desired accuracy for a specified amount of run-time. This will help cloud managers get a basic idea of computational needs and help them allocate resources more efficiently. The entire architecture was simulated on Spark servers and was verified experimentally by checking that the proposed architecture performs the function of efficient management and analysis of large HSI. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.