Dong-Woo Jee

Bio: Dong-Woo Jee is an academic researcher from Ajou University. The author has contributed to research in topics: Phase noise & Phase-locked loop. The author has an hindex of 11, co-authored 39 publications receiving 539 citations. Previous affiliations of Dong-Woo Jee include Pohang University of Science and Technology & Katholieke Universiteit Leuven.

A 345 µW Multi-Sensor Biomedical SoC With Bio-Impedance, 3-Channel ECG, Motion Artifact Reduction, and Integrated DSP

Abstract: This paper presents a MUlti-SEnsor biomedical IC (MUSEIC). It features a high-performance, low-power analog front-end (AFE) and fully integrated DSP. The AFE has three biopotential readouts, one bio-impedance readout, and support for general-purpose analog sensors The biopotential readout channels can handle large differential electrode offsets ( ${\pm} $ 400 mV), achieve high input impedance ( ${>}$ 500 M $\Omega$ ), low noise ( ${ 620 nVrms in 150 Hz), and large CMRR ( ${>}$ 110 dB) without relying on trimming while consuming only 31 $\mu$ W/channel. In addition, fully integrated real-time motion artifact reduction, based on simultaneous electrode-tissue impedance measurement, with feedback to the analog domain is supported. The bio-impedance readout with pseudo-sine current generator achieves a resolution of 9.8 m $\Omega$ / $\surd$ Hz while consuming just 58 $\mu$ W/channel. The DSP has a general purpose ARM Cortex M0 processor and an HW accelerator optimized for energy-efficient execution of various biomedical signal processing algorithms achieving 10 $\times$ or more energy savings in vector multiply-accumulate executions.

A Multi(bio)sensor Acquisition System With Integrated Processor, Power Management, $8 \times 8$ LED Drivers, and Simultaneously Synchronized ECG, BIO-Z, GSR, and Two PPG Readouts

Abstract: A battery-powered multisensor acquisition system with five dedicated channels [electrocardiograph (50 $\mu \text{W}$ ), bioimpedance (46 $\mu \text{W}$ ), galvanic skin response (15 $\mu \text{W}$ ), and 2 $\times $ photoplethysmogram (134 $\mu \text{W}$ ) is presented. It includes an ARM Cortex M0, analog and digital filters, timestamp converter and sample rate converter (SRC), and generic interfaces to support additional sensor modalities. The timestamp module makes precise synchronization between the data streams possible. The SRC module makes the sample rates of data from the internal and external sensor readouts compatible with each other, and is up to a factor 35 more energy efficient compared with a software solution. These modules enable performing accurate and reliable (correlation) techniques. The power management includes two buck converters, an LDO, and eight LED drivers, supporting up to 64 LEDs in an $8 \times 8$ matrix organization. It makes this system the most complete and versatile sensor readout system with state-of-the-art performance (1073 $\mu \text{W}$ with all channels enabled).

18.3 A multi-parameter signal-acquisition SoC for connected personal health applications

Abstract: Connected personal healthcare, or Telehealth, requires smart, miniature wearable devices that can collect and analyze physiological and environmental parameters during a user's daily routine. To truly support emerging applications (Fig. 18.3.1), a generic platform is needed that can acquire a multitude of sensor modalities and has generic energy-efficient signal processing capabilities. SoC technology gives significant advantages for miniaturization. But meeting low-power, medical grade signal quality, multi-sensor support and generic signal processing is still a challenge. For instance, [1] demonstrated a multi-sensor interface but it lacks support for efficient on-chip signal processing and doesn't have a high performance AFE. [2] showed a very low power signal processor but without support for multi-sensor interfacing. [3] presented a highly integrated SoC but lacking power efficiency. This paper demonstrates a highly integrated low-power SoC with enough flexibility to support many emerging applications. A wide range of sensor modalities are supported including 3-lead ECG and bio-impedance via high-performance and low-power AFE. The ARM Cortex™ M0 processor and matrix-multiply-accumulate accelerator can execute numerous biomedical signal processing algorithms (e.g. Independent Component Analysis (ICA), Principal Component Analysis (PCA,) CWT, feature extraction/classification, etc.) in an energy efficient way without sacrificing flexibility. The diversity in supported modalities and the generic processing capabilities, all provided in a single-chip low-power solution, make the proposed SoC a key enabler for emerging personal health applications (Fig. 18.3.1).

A 13 $\mu {\rm A}$ Analog Signal Processing IC for Accurate Recognition of Multiple Intra-Cardiac Signals

Abstract: A low-power analog signal processing IC is presented for the low-power heart rhythm analysis. The ASIC features 3 identical, but independent intra-ECG readout channels each equipping an analog QRS feature extractor for low-power consumption and fast diagnosis of the fatal case. A 16-level digitized sine-wave synthesizer together with a synchronous readout circuit can measure bio-impedance in the range of 0.1-4.4 kΩ with 33 mΩrms resolution and higher than 97% accuracy. The proposed 25 mm2 ASIC consumes only 13 μA from 2.2 V. It is a highly integrated solution offering all the functionality of acquiring multiple high quality intra-cardiac signals, requiring only a few limited numbers of external passives.

A 2 GHz Fractional-N Digital PLL with 1b Noise Shaping $\Delta\Sigma$ TDC

Abstract: This paper presents a low-power noise-shaping ΔΣ time-to-digital converter (TDC) and its application to a fractional-N digital PLL. With a simple structure of single-delay-stage Δ modulator followed by a charge pump based Σ modulator, a wide range of TDC input is converted to ΔΣ modulated single bit stream without loss of signal information. The ΔΣ architecture of TDC effectively improves the conversion performance of linearity and resolution while handling a large input range due to the operation of the dual-modulus divider. In addition, with a downscaling of the amount of the single delay in Δ modulator, the signal and noise transfer characteristics of TDC can be profiled to suppress the out-band noises at the input to the loop filter, resulting in easy filtering without any extra noise cancelling scheme. The DPLL is fabricated with a 0.13 μm CMOS technology. With a loop bandwidth of 1 MHz, DPLL shows an in-band phase noise of - 107 dBc/Hz at 500 kHz offset and an out-of-band phase noise of -118.5 dBc/Hz at 3 MHz offset. The TDC consumes 1 mA.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Wearables in Medicine

Abstract: Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches.

Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint Devices

Abstract: Endpoint devices for Internet-of-Things not only need to work under extremely tight power envelope of a few milliwatts, but also need to be flexible in their computing capabilities, from a few kOPS to GOPS. Near-threshold (NT) operation can achieve higher energy efficiency, and the performance scalability can be gained through parallelism. In this paper, we describe the design of an open-source RISC-V processor core specifically designed for NT operation in tightly coupled multicore clusters. We introduce instruction extensions and microarchitectural optimizations to increase the computational density and to minimize the pressure toward the shared-memory hierarchy. For typical data-intensive sensor processing workloads, the proposed core is, on average, $3.5\times $ faster and $3.2\times $ more energy efficient, thanks to a smart L0 buffer to reduce cache access contentions and support for compressed instructions. Single Instruction Multiple Data extensions, such as dot products, and a built-in L0 storage further reduce the shared-memory accesses by $8\times $ reducing contentions by $3.2\times $ . With four NT-optimized cores, the cluster is operational from 0.6 to 1.2 V, achieving a peak efficiency of 67 MOPS/mW in a low-cost 65-nm bulk CMOS technology. In a low-power 28-nm FD-SOI process, a peak efficiency of 193 MOPS/mW (40 MHz and 1 mW) can be achieved.

Medical Instrumentation Application And Design

Abstract: Thank you for reading medical instrumentation application and design. Maybe you have knowledge that, people have search hundreds times for their favorite books like this medical instrumentation application and design, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious bugs inside their laptop. medical instrumentation application and design is available in our digital library an online access to it is set as public so you can get it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the medical instrumentation application and design is universally compatible with any devices to read.

A 345 µW Multi-Sensor Biomedical SoC With Bio-Impedance, 3-Channel ECG, Motion Artifact Reduction, and Integrated DSP

Abstract: This paper presents a MUlti-SEnsor biomedical IC (MUSEIC). It features a high-performance, low-power analog front-end (AFE) and fully integrated DSP. The AFE has three biopotential readouts, one bio-impedance readout, and support for general-purpose analog sensors The biopotential readout channels can handle large differential electrode offsets ( ${\pm} $ 400 mV), achieve high input impedance ( ${>}$ 500 M $\Omega$ ), low noise ( ${ 620 nVrms in 150 Hz), and large CMRR ( ${>}$ 110 dB) without relying on trimming while consuming only 31 $\mu$ W/channel. In addition, fully integrated real-time motion artifact reduction, based on simultaneous electrode-tissue impedance measurement, with feedback to the analog domain is supported. The bio-impedance readout with pseudo-sine current generator achieves a resolution of 9.8 m $\Omega$ / $\surd$ Hz while consuming just 58 $\mu$ W/channel. The DSP has a general purpose ARM Cortex M0 processor and an HW accelerator optimized for energy-efficient execution of various biomedical signal processing algorithms achieving 10 $\times$ or more energy savings in vector multiply-accumulate executions.