Digitally-assisted, ultra-low power circuits and systems for medical applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 219-225).In recent years, trends in the medical industry have created a growing demand for a variety of implantable medical devices. At the same time, advances in integrated circuits techniques, particularly in CMOS, have opened possibilities for advanced implantable systems that are very small and consume minimal energy. Minimizing the volume of medical implants is important as it allows for less invasive procedures and greater comfort to patients. Minimizing energy consumption is imperative as batteries must last at least a decade without replacement. Two primary functions that consume energy in medical implants are sensor interfaces that collect information from biomedical signals, and radios that allow the implant to communicate with a base-station outside of the body. The general focus of this work was the development of circuits and systems that minimize the size and energy required to carry out these two functions. The first part of this work focuses on laying down the theoretical framework for an ultra-low power radio, including advances to the literature in the area of super-regeneration. The second part includes the design of a transceiver optimized for medical implants, and its implementation in a CMOS process. The final part describes the design of a sensor interface that leverages novel analog and digital techniques to reduce the system's size and improve its functionality. This final part was developed in conjunction with Marcus Yip.by Jose L. Bohorquez.Ph.D

A Novel Power-Efficient Wireless Multi-channel Recording System for the Telemonitoring of Electroencephalography (EEG)

This research introduces the development of a novel EEG recording system that is modular, batteryless, and wireless (untethered) with the supporting theoretical foundation in wireless communications and related design elements and circuitry. Its modular construct overcomes the EEG scaling problem and makes it easier for reconfiguring the hardware design in terms of the number and placement of electrodes and type of standard EEG system contemplated for use. In this development, portability, lightweight, and applicability to other clinical applications that rely on EEG data are sought. Due to printer tolerance, the 3D printed cap consists of 61 electrode placements. This recording capacity can however extend from 21 (as in the international 10-20 systems) up to 61 EEG channels at sample rates ranging from 250 to 1000 Hz and the transfer of the raw EEG signal using a standard allocated frequency as a data carrier. The main objectives of this dissertation are to (1) eliminate the need for heavy mounted batteries, (2) overcome the requirement for bulky power systems, and (3) avoid the use of data cables to untether the EEG system from the subject for a more practical and less restrictive setting. Unpredictability and temporal variations of the EEG input make developing a battery-free and cable-free EEG reading device challenging. Professional high-quality and high-resolution analog front ends are required to capture non-stationary EEG signals at microvolt levels. The primary components of the proposed setup are the wireless power transmission unit, which consists of a power amplifier, highly efficient resonant-inductive link, rectification, regulation, and power management units, as well as the analog front end, which consists of an analog to digital converter, pre-amplification unit, filtering unit, host microprocessor, and the wireless communication unit. These must all be compatible with the rest of the system and must use the least amount of power possible while minimizing the presence of noise and the attenuation of the recorded signal A highly efficient resonant-inductive coupling link is developed to decrease power transmission dissipation. Magnetized materials were utilized to steer electromagnetic flux and decrease route and medium loss while transmitting the required energy with low dissipation. Signal pre-amplification is handled by the front-end active electrodes. Standard bio-amplifier design approaches are combined to accomplish this purpose, and a thorough investigation of the optimum ADC, microcontroller, and transceiver units has been carried out. We can minimize overall system weight and power consumption by employing battery-less and cable-free EEG readout system designs, consequently giving patients more comfort and freedom of movement. Similarly, the solutions are designed to match the performance of medical-grade equipment. The captured electrical impulses using the proposed setup can be stored for various uses, including classification, prediction, 3D source localization, and for monitoring and diagnosing different brain disorders. All the proposed designs and supporting mathematical derivations were validated through empirical and software-simulated experiments. Many of the proposed designs, including the 3D head cap, the wireless power transmission unit, and the pre-amplification unit, are already fabricated, and the schematic circuits and simulation results were based on Spice, Altium, and high-frequency structure simulator (HFSS) software. The fully integrated head cap to be fabricated would require embedding the active electrodes into the 3D headset and applying current technological advances to miniaturize some of the design elements developed in this dissertation

Prediction of nonlinear nonstationary time series data using a digital filter and support vector regression

Volatility is a key parameter when measuring the size of the errors made in modelling returns and other nonlinear nonstationary time series data. The Autoregressive Integrated Moving- Average (ARIMA) model is a linear process in time series; whilst in the nonlinear system, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Markov Switching GARCH (MS-GARCH) models have been widely applied. In statistical learning theory, Support Vector Regression (SVR) plays an important role in predicting nonlinear and nonstationary time series data. We propose a new class model comprised of a combination of a novel derivative Empirical Mode Decomposition (EMD), averaging intrinsic mode function (aIMF) and a novel of multiclass SVR using mean reversion and coefficient of variance (CV) to predict financial data i.e. EUR-USD exchange rates. The proposed novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates. Our simulation results show that our model significantly outperforms simulations by state-of-art ARIMA, GARCH, Markov Switching generalised Autoregressive conditional Heteroskedasticity (MS-GARCH), Markov Switching Regression (MSR) models and Markov chain Monte Carlo (MCMC) regression.Open Acces

Millimetre-Resolution Photonics-Assisted Radar

Radar is essential in applications such as anti-collision systems for driving, airport security screening, and contactless vital sign detection. The demand for high-resolution and real-time recognition in radar applications is growing, driving the development of electronic radars with increased bandwidth, higher frequency, and improved reconfigurability. However, conventional electronic approaches are challenging due to limitations in synthesising radar signals, limiting performance. In contrast, microwave photonics-enabled radars have gained interest because they offer numerous benefits compared to traditional electronic methods. Photonics-assisted techniques provide a broad fractional bandwidth at the optical carrier frequency and enable spectrum manipulation, producing wideband and high-resolution radar signals in various formats. However, photonic-based methods face limitations like low time-frequency linearity due to the inherent nonlinearity of lasers, restricted RF bandwidth, limited stability of the photonic frequency multipliers, and difficulties in achieving extended sensing with dispersion-based techniques. In response to these challenges, this thesis presents approaches for generating broadband radar signals with high time-frequency linearity using recirculated unidirectional optical frequency-shifted modulation. The photonics-assisted system allows flexible bandwidth tuning from sub-GHz to over 30 GHz and requires only MHz-level electronics. Such a system offers millimetre-level range resolution and a high imaging refresh rate, detecting fast-moving objects using the ISAR technique. With millimetre-level resolution and micrometre accuracy, this system supports contactless vital sign detection, capturing precise respiratory patterns from simulators and a living body using a cane toad. In the end, we highlight the promise of merging radar and LiDAR, foreshadowing future advancements in sensor fusion for enhanced sensing performance and resilience

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion