Measurement techniques for the characterization of radio frequency gallium nitride devices and power amplifiers

The rapid growth of mobile telecommunications has fueled the development of the fifth generation (5G) of standards, aiming to achieve high data rates and low latency. These capabilities make use of new regions of spectrum, wider bandwidths and spectrally efficient modulations. The deployment of 5G relies on the development of radio-frequency (RF) technology with increased performance. The broadband operation at high-power and high-frequency conditions is particularly challenging for power amplifiers (PA) in transmission stages, which seek to concurrently maximize linearity and energy efficiency. The properties of Gallium Nitride (GaN) allow the realization of active devices with favorable characteristics in these applications. However, GaN high-electron mobility transistors (HEMTs) suffer from spurious effects such as trapping due to physical defects introduced during the HEMT growth process. Traps dynamically capture and release mobile charges depending on the applied voltages and temperature, negatively affecting the RF PA performance. This work focuses on the development of novel measurement techniques and setups to investigate trapping behavior of GaN HEMTs and PAs. At low-frequency (LF), charge dynamics is analyzed using pulsed current transient characterizations, identifying relevant time constants in state-of-the-art GaN technologies for 5G. Instead, at high-frequency, tailored methods and setups are used in order to measure trapping effects during the operation of HEMTs and PAs in RF modulated conditions. These RF characterizations emulate application-like regimes, possibly involving the control of the device’s output load termination. Therefore, an innovative wideband active load pull (WALP) setup is developed, using the acquisition capabilities of standard vector-network-analyzers. Moreover, the implications of performing error-vector-magnitude characterizations under wideband load pull conditions are studied. Finally, an efficient implementation of a modified-Volterra model for RF PAs is presented, making use of a custom vector-fitting algorithm to simplify the nonlinear memory operators and enable their realization in simulation environments

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts

The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of Extended Abstracts of the ICMASC’2022 collects the extended abstracts of the talks presented at the International Conference on Mathematical Analysis and Applications in Science and Engineering – ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days). Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering, industry, and technology, to discuss the development of new mathematical models, theories, and applications that contribute to the advancement of scientific knowledge and practice. Authors proposed research in topics including partial and ordinary differential equations, integer and fractional order equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization, control, probability, computational mathematics, amongst others. The conference was designed to maximize the involvement of all participants and will present the state-of- the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio

Bio-Inspired Compressive Sensing based on Auditory Neural Circuits for Real-time Monitoring and Control of Civil Structures using Resource Constrained Sensor Networks.

Recent natural hazard disasters including Hurricane Sandy (2012) and the Tohoku Earthquake (2011) have called public attention to the vulnerability of civil infrastructure systems. To enhance the resiliency of urban communities, arrays of wireless sensors and actuators have been proposed to monitor and control infrastructure systems in order to limit damage, speed emergency response, and make post-disaster decisions more efficiently. While great advances in the use of wireless sensor networks (WSNs) for the purposes of monitoring and control of civil infrastructure have been made, significant technological barriers have hindered their ability to be reliably used in the field for long durations. Some of these limitations include: reliance on finite, portable power supplies, limited radio bandwidth for data communication, and limited computational capacity. To resolve current bottlenecks, paradigm-altering approaches to the design of wireless monitoring and control systems are required. Through the process of evolution, biological central nervous systems (CNS) have evolved into highly adaptive and robust systems whose sensing and actuation capabilities far surpass the current capabilities of engineered (i.e., man-made) monitoring and control systems. In this dissertation, the mechanisms employed by biological sensory systems serve as sources of inspiration for overcoming the current challenges faced by wireless nodes for structural monitoring and control. The basic, yet elegant, methods of signal processing and data transmission used by the CNS are mimicked in this thesis to enable highly compressed communication with real-time data processing for WSNs engaged in infrastructure monitoring. Specifically, the parallelized time-frequency decomposition of the mammalian cochlea is studied, modeled, and recreated in an ultra-low power analog circuit. In lieu of transmitting data, the cochlea-inspired wireless sensors emulate the neurons by encoding filtered outputs into binary electrical spike trains for highly efficient wireless transmission. These transmitted spike train signals are processed for pattern classification of sensor data to identify structural damage and to perform feedback control in real-time. A key contribution of this thesis is the development and experimental validation of a bio-inspired wireless sensor node that exhibits large energy savings while employing real-time processing techniques, thus overcoming many of the current challenges of traditional wireless sensor nodes.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107302/1/cpeckens_1.pd