AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Millimeter and sub-millimeter wave radiometers for atmospheric remote sensing from CubeSat platforms

2018 Fall.Includes bibliographical references.To view the abstract, please see the full text of the document

Journal of Telecommunications and Information Technology, 2009, nr 4

kwartalni

Sborník příspěvků PAD 2021

Sborník příspěvků česko-slovenského semináře pro studenty doktorského studia. Dostupné z: https://pad2021.fm.tul.cz/sbornik_files/PAD2021esbor.pd

Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust

Exploiting the location information for adaptive beamforming in transport systems

As mobile communication systems evolve, the demand for enhanced network efficiency and pinpoint accuracy in user localization grows, particularly in the context of dynamic environments such as transport systems. This thesis is motivated by the critical challenge of adapting beamforming techniques to the rapidly changing positions of users, a task analogous to hitting a moving target with precision. The aim is to significantly improve cellular network performance by leveraging advanced beamforming and machine learning (ML) for precise user localization. A novel dataset, crucial to this endeavor, has been developed from simulations in open spaces and a digital twin of the University of Glasgow campus, incorporating vital parameters such as direction of arrival (DoA), time of arrival (ToA), and received signal strength indicators (RSSI). Our investigation commences with the deployment of Maximum Ratio Transmission (MRT) and Zero Forcing (ZF) beamforming techniques to evaluate their effectiveness in enhancing network efficiency through both real and simulated user locations. The application of an adaptive MRT algorithm in our beamforming strategy resulted in a remarkable 53% increase in Signal-to-Noise Ratio (SNR), showcasing the potential of contextual beamforming (Cont-BF) using location information. Furthermore, to refine localization accuracy, deep neural networks were employed, achieving a localization error of less than 1 meter surpassing conventional methods in accuracy. This research also introduces technique for user-assisted beam alignment in high-speed scenarios, addressing the challenges in dynamic transport systems. Venturing beyond traditional approaches, it explores the integration of user locations into beamforming decisions via a P4 switch, crafting a dynamic system responsive to user mobility. This is complemented by extensive data collection from 5G base stations (BS) using a TSMA 6 scanner, which enriches our analysis with detailed parameters for precision localization. Moreover, the study evaluates various MIMO beamforming techniques in 5G networks, demonstrating an average throughput increase from 9 Mbps to 14 Mbps, thereby underscoring the effectiveness of our proposed solutions. The potential of low-cost Software Defined Radios (SDR) forDoA estimation and the design of a beam steering setup was also assessed, aiming to evaluate their utility in highfrequency beamforming. Despite uncovering limitations in sub-6GHz environments, this exploration led to the successful development of a DoA estimation setup using USRPs and antennas, alongside a beam steering system crafted through the design of phase shifters and antennas. By integrating precise location information into adaptive beamforming techniques, especially within the dynamic context of transport systems, this thesis underscores the imperative role of such integration in significantly enhancing communication efficiency. Our findings, which include significant improvements in signal-to-interference-to-noise ratio (SINR) (up to 50%) and received power (up to 40%) through advanced beamforming methods, are pivotal for advancing high-demand applications, including smart vehicles and immersive virtual reality. This marks a crucial advancement towards the realization of next-generation cellular networks, paving the way for more efficient and reliable performance in an evolving technological landscape

Supervisory Control System Architecture for Advanced Small Modular Reactors

This technical report was generated as a product of the Supervisory Control for Multi-Modular SMR Plants project within the Instrumentation, Control and Human-Machine Interface technology area under the Advanced Small Modular Reactor (SMR) Research and Development Program of the U.S. Department of Energy. The report documents the definition of strategies, functional elements, and the structural architecture of a supervisory control system for multi-modular advanced SMR (AdvSMR) plants. This research activity advances the state-of-the art by incorporating decision making into the supervisory control system architectural layers through the introduction of a tiered-plant system approach. The report provides a brief history of hierarchical functional architectures and the current state-of-the-art, describes a reference AdvSMR to show the dependencies between systems, presents a hierarchical structure for supervisory control, indicates the importance of understanding trip setpoints, applies a new theoretic approach for comparing architectures, identifies cyber security controls that should be addressed early in system design, and describes ongoing work to develop system requirements and hardware/software configurations

Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS

Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts