470 research outputs found
On Unlimited Sampling
Shannon's sampling theorem provides a link between the continuous and the
discrete realms stating that bandlimited signals are uniquely determined by its
values on a discrete set. This theorem is realized in practice using so called
analog--to--digital converters (ADCs). Unlike Shannon's sampling theorem, the
ADCs are limited in dynamic range. Whenever a signal exceeds some preset
threshold, the ADC saturates, resulting in aliasing due to clipping. The goal
of this paper is to analyze an alternative approach that does not suffer from
these problems. Our work is based on recent developments in ADC design, which
allow for ADCs that reset rather than to saturate, thus producing modulo
samples. An open problem that remains is: Given such modulo samples of a
bandlimited function as well as the dynamic range of the ADC, how can the
original signal be recovered and what are the sufficient conditions that
guarantee perfect recovery? In this paper, we prove such sufficiency conditions
and complement them with a stable recovery algorithm. Our results are not
limited to certain amplitude ranges, in fact even the same circuit architecture
allows for the recovery of arbitrary large amplitudes as long as some estimate
of the signal norm is available when recovering. Numerical experiments that
corroborate our theory indeed show that it is possible to perfectly recover
function that takes values that are orders of magnitude higher than the ADC's
threshold.Comment: 11 pages, 4 figures, copy of initial version to appear in Proceedings
of 12th International Conference on Sampling Theory and Applications (SampTA
Integrated Electronics for Wireless Imaging Microsystems with CMUT Arrays
Integration of transducer arrays with interface electronics in the form of single-chip CMUT-on-CMOS has emerged into the field of medical ultrasound imaging
and is transforming this field. It has already been used in several commercial products such as handheld full-body imagers and it is being implemented by commercial and academic groups for Intravascular Ultrasound and Intracardiac Echocardiography. However, large attenuation of ultrasonic waves transmitted through
the skull has prevented ultrasound imaging of the brain. This research is a prime
step toward implantable wireless microsystems that use ultrasound to image the
brain by bypassing the skull. These microsystems offer autonomous scanning
(beam steering and focusing) of the brain and transferring data out of the brain for
further processing and image reconstruction.
The objective of the presented research is to develop building blocks of an integrated electronics architecture for CMUT based wireless ultrasound imaging systems while providing a fundamental study on interfacing CMUT arrays with their
associated integrated electronics in terms of electrical power transfer and acoustic
reflection which would potentially lead to more efficient and high-performance
systems.
A fully wireless architecture for ultrasound imaging is demonstrated for the
first time. An on-chip programmable transmit (TX) beamformer enables phased
array focusing and steering of ultrasound waves in the transmit mode while its
on-chip bandpass noise shaping digitizer followed by an ultra-wideband (UWB)
uplink transmitter minimizes the effect of path loss on the transmitted image data
out of the brain. A single-chip application-specific integrated circuit (ASIC) is de-
signed to realize the wireless architecture and interface with array elements, each
of which includes a transceiver (TRX) front-end with a high-voltage (HV) pulser,
a high-voltage T/R switch, and a low-noise amplifier (LNA). Novel design techniques are implemented in the system to enhance the performance of its building
blocks.
Apart from imaging capability, the implantable wireless microsystems can include a pressure sensing readout to measure intracranial pressure. To do so, a
power-efficient readout for pressure sensing is presented. It uses pseudo-pseudo
differential readout topology to cut down the static power consumption of the sensor for further power savings in wireless microsystems.
In addition, the effect of matching and electrical termination on CMUT array
elements is explored leading to new interface structures to improve bandwidth
and sensitivity of CMUT arrays in different operation regions. Comprehensive
analysis, modeling, and simulation methodologies are presented for further investigation.Ph.D
CMOS IMAGE SENSORS FOR LAB-ON-A-CHIP MICROSYSTEM DESIGN
The work described herein serves as a foundation for the development of CMOS imaging in lab-on-a-chip microsystems. Lab-on-a-chip (LOC) systems attempt to emulate the functionality of a cell biology lab by incorporating multiple sensing modalidites into a single microscale system. LOC are applicable to drug development, implantable sensors, cell-based bio-chemical detectors and radiation detectors. The common theme across these systems is achieving performance under severe resource constraints including noise, bandwidth, power and size. The contributions of this work are in the areas of two core lab-on-a-chip imaging functions: object detection and optical measurements
A self-powered single-chip wireless sensor platform
Internet of things” require a large array of low-cost sensor nodes, wireless connectivity, low power operation and system intelligence. On the other hand, wireless biomedical implants demand additional specifications including small form factor, a choice of wireless operating frequencies within the window for minimum tissue loss and bio-compatibility This thesis describes a low power and low-cost internet of things system suitable for implant applications that is implemented in its entirety on a single standard CMOS chip with an area smaller than 0.5 mm2. The chip includes integrated sensors, ultra-low-power transceivers, and additional interface and digital control electronics while it does not require a battery or complex packaging schemes. It is powered through electromagnetic (EM) radiation using its on-chip miniature antenna that also assists with transmit and receive functions. The chip can operate at a short distance (a few centimeters) from an EM source that also serves as its wireless link. Design methodology, system simulation and optimization and early measurement results are presented
A 72 Ă— 60 Angle-Sensitive SPAD Imaging Array for Lens-less FLIM
We present a 72 Ă— 60, angle-sensitive single photon avalanche diode (A-SPAD) array for lens-less 3D fluorescence lifetime imaging. An A-SPAD pixel consists of (1) a SPAD to provide precise photon arrival time where a time-resolved operation is utilized to avoid stimulus-induced saturation, and (2) integrated diffraction gratings on top of the SPAD to extract incident angles of the incoming light. The combination enables mapping of fluorescent sources with different lifetimes in 3D space down to micrometer scale. Futhermore, the chip presented herein integrates pixel-level counters to reduce output data-rate and to enable a precise timing control. The array is implemented in standard 180 nm complementary metal-oxide-semiconductor (CMOS) technology and characterized without any post-processing
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Implantable Fluorescence Imager for Deep Neuronal Imaging
This thesis describes the design, fabrication, and characterization of the Implantable Fluorescence Imager (IFI): a camera chip with a needle-like form factor designed for imaging neuronal activity in the deep brain. It is fabricated with a complementary metal oxide semiconductor (CMOS) process, allowing for hundreds or thousands of single- photon-sensitive photodetectors to be densely packed onto a device width comparable to a single-channel fiber optic cannula (~100 ÎĽm). The IFI uses a combination of spectral and temporal filters as a fluorescence emission filter, and per-pixel Talbot gratings for 3D light-field imaging.
The IFI has the potential to overcome the imaging depth limit of multi-photon microscopes imposed by the scattering and absorption of photons in brain tissue, and the resolution limit of noninvasive imaging techniques, such as functional magnetic resonance imaging and photoacoustic imaging. It competes with graded index lens-based miniaturized microscopes in imaging depth, but offers several comparative advantages. First, its cross sectional area is at least an order of magnitude smaller for an equal field of view. Second, the distribution of pixels along its entire length allows the study of multi- layer or multi-region dynamics. Finally, the scalability advantage of silicon integrated circuit technology in system miniaturization and data bandwidth may allow thousands of such imaging shanks to be simultaneously deployed for large-scale volumetric recording
Proceedings of the 11th Annual Deep Brain Stimulation Think Tank: pushing the forefront of neuromodulation with functional network mapping, biomarkers for adaptive DBS, bioethical dilemmas, AI-guided neuromodulation, and translational advancements
The Deep Brain Stimulation (DBS) Think Tank XI was held on August 9–11, 2023 in Gainesville, Florida with the theme of “Pushing the Forefront of Neuromodulation”. The keynote speaker was Dr. Nico Dosenbach from Washington University in St. Louis, Missouri. He presented his research recently published in Nature inn a collaboration with Dr. Evan Gordon to identify and characterize the somato-cognitive action network (SCAN), which has redefined the motor homunculus and has led to new hypotheses about the integrative networks underpinning therapeutic DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers, and researchers (from industry and academia) can freely discuss current and emerging DBS technologies, as well as logistical and ethical issues facing the field. The group estimated that globally more than 263,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: cutting-edge translational neuromodulation, cutting-edge physiology, advances in neuromodulation from Europe and Asia, neuroethical dilemmas, artificial intelligence and computational modeling, time scales in DBS for mood disorders, and advances in future neuromodulation devices
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