Wireless Implantable ICs for Energy-Efficient Long-Term Ambulatory EEG Monitoring

This thesis presents the design, development, and experimental characterization of wireless subcutaneous implantable integrated circuits and systems for long-term ambulatory EEG monitoring. Application-, system- and circuit-level requirements for such a device are discussed and a critical review of the state-of-the-art academic and currently available commercial solutions are provided. Two prototypes are presented: The first prototype presented in Chapter 2 is an 8-channel wireless implantable device with a 2.5×1.5 mm2 custom-designed integrated circuit implemented using CMOS 180nm technology at its core. The microchip is fabricated and the measurement results showing its efficacy in EEG signal recording in terms of input-referred noise, voltage gain, signal-to-noise ratio, and power consumption are presented. The chip is implemented together with a BLE 5.0 module on the same platform. Our vision and discussions on biocompatible encapsulation of this system, as well as its integration with a microelectrode array as also provided. The second prototype, also implemented in CMOS 180nm technology and presented in Chapter 3, employs a novel EEG recording channel architecture that enables long-term implantation of EEG monitoring devices through significant improvement of their energy efficiency. The channel leverages the inherent sparsity of the EEG signals and conducts recording in an activity-dependent adaptive manner. Thanks to the proposed fully dynamic spectral-compressing architecture, the recording channels power consumption is drastically reduced. More importantly, the proposed architecture reduces the required wireless transmission throughput by more than an order of magnitude. Our test results on 10 different patients’ pre-recorded human EEG data shows an average of 12.6× improvement in the device’s energy efficiency

Nonlinear interferometric vibrational imaging

A method of examining a sample, which includes: exposing a reference to a first set of electromagnetic radiation, to form a second set of electromagnetic radiation scattered from the reference; exposing a sample to a third set of electromagnetic radiation to form a fourth set of electromagnetic radiation scattered from the sample; and interfering the second set of electromagnetic radiation and the fourth set of electromagnetic radiation. The first set and the third set of electromagnetic radiation are generated from a source; at least a portion of the second set of electromagnetic radiation is of a frequency different from that of the first set of electromagnetic radiation; and at least a portion of the fourth set of electromagnetic radiation is of a frequency different from that of the third set of electromagnetic radiation

Distinguishing non-resonant four-wave-mixing noise in coherent stokes and anti-stokes Raman scattering

A method of examining a sample comprises exposing the sample to a pump pulse of electromagnetic radiation for a first period of time, exposing the sample to a stimulant pulse of electromagnetic radiation for a second period of time which overlaps in time with at least a portion of the first exposing, to produce a signal pulse of electromagnetic radiation for a third period of time, and interfering the signal pulse with a reference pulse of electromagnetic radiation, to determine which portions of the signal pulse were produced during the exposing of the sample to the stimulant pulse. The first and third periods of time are each greater than the second period of time

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Enhanced frequency management for automatic HF radio communication systems

The work described in this thesis aims to enhance the frequency management of automatic high frequency (HF) radio communication systems. During the research programme two new frequency management tools were developed; a chirpsounder monitoring tool to provide accuracy enhancement information for propagation prediction programs and an algorithm designed to allow optimisation of signal formats, so that in-band interference is avoided and the overall system throughput rate is increased. Two new HF communication system architectures are presented, which use system design and programming methodologies derived from the fields of artificial intelligence and computer networks.The characteristics of the HF band are presented from a communicator's viewpoint, rather than the generalised, technical approach normally associated with such reviews. The methods employed by current HF communication systems to overcome the inherent time and frequency variability of HF channels are presented in the form of reviews of propagation, natural noise and co-channel interference prediction methods, embedded real-time channel evaluation algorithms and HF communications system architectures. The inadequacies of these current techniques are analysed. The eradication of their shortcomings is the main objective of the work described in the thesis.The short-term inaccuracies associated with current propagation analysis procedures can limit the performance of automatic HF communication systems. An accuracy enhancement methodology is proposed which makes use of measurements made on oblique chirpsounder transmitters. In order to provide accuracy enhancement data, a chirpsounder-based, propagation monitor was constructed. Its implementation and trials are described and methods of using its output to enhance prediction model accuracy are discussed. Ways in which its performance may be improved are detailed.The theory of a technique, termed "template correlation", which provides automatic HF communication systems with signal format adaptation data in order to enable them to avoid in-band interference, is presented. The objective of this work is to enhance the error-free capacity of a channel via adaptation of the signal. The results of computer simulations and laboratory bench trials of template correlation are presented. Enhancements of the technique in the light of the trials results are included.Two proposed design methodologies for automatic HF communication systems are described. The first uses many of the frequency management tools associated with current automatic systems and it combines the information from these using a blackboard-based expert system architecture. The second proposed design is more conceptual than the first. An inductive expert system is employed to produce rules describing the ways in which an automatic HF system should respond to certain path conditions. Examples of how such a system might function are given.The single, most important factor which has enabled the techniques described in this thesis to be feasible is the availability of cheap but powerful microprocessors. Thus the overall philosophy of the work is to improve the performance of automatic HF communication systems via the incorporation of processing power and "intelligent software" into the communication system's terminals

Area- and Energy- Efficient Modular Circuit Architecture for 1,024-Channel Parallel Neural Recording Microsystem.

This research focuses to develop system architectures and associated electronic circuits for a next generation neuroscience research tool, a massive-parallel neural recording system capable of recording 1,024 channels simultaneously. Three interdependent prototypes have been developed to address major challenges in realization of the massive-parallel neural recording microsystems: minimization of energy and area consumption while preserving high quality in recordings. First, a modular 128-channel Δ-ΔΣ AFE using the spectrum shaping has been designed and fabricated to propose an area-and energy efficient solution for neural recording AFEs. The AFE achieved 4.84 fJ/C−s·mm2 figure of merit that is the smallest the area-energy product among the state-of-the-art multichannel neural recording systems. It also features power and area consumption of 3.05 µW and 0.05 mm2 per channel, respectively while exhibiting 63.3 dB signal-to-noise ratio with 3.02 µVrms input referred noise. Second, an on-chip mixed signal neural signal compressor was built to reduce the energy consumption in handling and transmission of the recorded data since this occupies a large portion of the total energy consumption as the number of parallel recording increases. The compressor reduces the data rates of two distinct groups of neural signals that are essential for neuroscience research: LFP and AP without loss of informative signals. As a result, the power consumptions for the data handling and transmissions of the LFP and AP were reduced to about 1/5.35 and 1/10.54 of the uncompressed cases, respectively. In the total data handling and transmission, the measured power consumption per channel is 11.98 µW that is about 1/9 of 107.5 µW without the compression. Third, a compact on-chip dc-to-dc converter with constant 1 MHz switching frequency has been developed to provide reliable power supplies and enhance energy delivery efficiency to the massive-parallel neural recording systems. The dc-to-dc converter has only predictable tones at the output and it exhibits > 80% power conversion efficiency at ultra-light loads, < 100 µW that is relevant power most of the multi-channel neural recording systems consume. The dc-to-dc converter occupies 0.375 mm2 of area which is less than 1/20 of the area the first prototype consumes (8.64 mm2).PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133244/1/sungyun_1.pd

Statistical assessment on Non-cooperative Target Recognition using the Neyman-Pearson statistical test

Electromagnetic simulations of a X-target were performed in order to obtain its Radar Cross Section (RCS) for several positions and frequencies. The software used is the CST MWS©. A 1 : 5 scale model of the proposed aircraft was created in CATIA© V5 R19 and imported directly into the CST MWS© environment. Simulations on the X-band were made with a variable mesh size due to a considerable wavelength variation. It is intended to evaluate the Neyman-Pearson (NP) simple hypothesis test performance by analyzing its Receiver Operating Characteristics (ROCs) for two different radar detection scenarios - a Radar Absorbent Material (RAM) coated model, and a Perfect Electric Conductor (PEC) model for recognition purposes. In parallel the radar range equation is used to estimate the maximum range detection for the simulated RAM coated cases to compare their shielding effectiveness (SE) and its consequent impact on recognition. The AN/APG-68(V)9’s airborne radar specifications were used to compute these ranges and to simulate an airborne hostile interception for a Non-Cooperative Target Recognition (NCTR) environment. Statistical results showed weak recognition performances using the Neyman-Pearson (NP) statistical test. Nevertheless, good RCS reductions for most of the simulated positions were obtained reflecting in a 50:9% maximum range detection gain for the PAniCo RAM coating, abiding with experimental results taken from the reviewed literature. The best SE was verified for the PAniCo and CFC-Fe RAMs.Simulações electromagnéticas do alvo foram realizadas de modo a obter a assinatura radar (RCS) para várias posições e frequências. O software utilizado é o CST MWS©. O modelo proposto à escala 1:5 foi modelado em CATIA© V5 R19 e importado diretamente para o ambiente de trabalho CST MWS©. Foram efectuadas simulações na banda X com uma malha de tamanho variável devido à considerável variação do comprimento de onda. Pretende-se avaliar estatisticamente o teste de decisão simples de Neyman-Pearson (NP), analisando as Características de Operação do Receptor (ROCs) para dois cenários de detecção distintos - um modelo revestido com material absorvente (RAM), e outro sendo um condutor perfeito (PEC) para fins de detecção. Em paralelo, a equação de alcance para radares foi usada para estimar o alcance máximo de detecção para ambos os casos de modo a comparar a eficiência de blindagem electromagnética (SE) entre os diferentes revestimentos. As especificações do radar AN/APG-68(V)9 do F-16 foram usadas para calcular os alcances para cada material, simulando uma intercepção hostil num ambiente de reconhecimento de alvos não-cooperativos (NCTR). Os resultados mostram performances de detecção fracas usando o teste de decisão simples de Neyman-Pearson como detector e uma boa redução de RCS para todas as posições na gama de frequências selecionada. Um ganho de alcance de detecção máximo 50:9 % foi obtido para o RAM PAniCo, estando de acordo com os resultados experimentais da bibliografia estudada. Já a melhor SE foi verificada para o RAM CFC-Fe e PAniCo

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa