윤영규 교수

A voltage-controlled oscillator (VCO) based analog-to-digital converter (ADC) is a time-based architecture with a first-order noise-shaping property, which can be implemented using a VCO and digital circuits. This paper analyzes the performance of VCO-based ADCs in the presence of nonidealities such as jitter, nonlinearity, mismatch, and the metastability of D flip-flops. Based on this analysis, design criteria for determining parameters for VCO-based ADCs are described. In addition, a digital c

Ultra-multiplexed fluorescence imaging requires the use of spectrally overlapping fluorophores to label proteins and then to unmix the images of the fluorophores. However, doing this remains a challenge, especially in highly heterogeneous specimens, such as the brain, owing to the high degree of variation in the emission spectra of fluorophores in such specimens. Here, we propose PICASSO, which enables more than 15-color imaging of spatially overlapping proteins in a single imaging round without

One of the major challenges in large scale optical imaging of neuronal activity is to simultaneously achieve sufficient temporal and spatial resolution across a large volume. Here, we introduce sparse decomposition light-field microscopy (SDLFM), a computational imaging technique based on light-field microscopy (LFM) that takes algorithmic advantage of the high temporal resolution of LFM and the inherent temporal sparsity of spikes to improve effective spatial resolution and signal-to-noise rati

Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatiotemporal neighboring pixels, even when its temporally adjacent frames alone do not provide useful information for statistical prediction. Such dependency is captured and used by a conv

In this paper, a bandpass analog-to-digital converter (ADC) based on time-interleaved oversampled ADC is introduced. Unlike previous delta-sigma bandpass ADCs that require accurate digital-to-analog converters and high-speed analog circuits, the proposed architecture provides bandpass function by time-interleaving first-order voltage-controlled-oscillator (VCO)-based ADCs. The use of VCO-based ADC has the advantage that its resolution is determined by the time resolution rather than the voltage

ABSTRACT Nanoscale resolution imaging of whole vertebrates is required for a systematic understanding of human diseases, but this has yet to be realized. Expansion microscopy (ExM) is an attractive option for achieving this goal, but the expansion of whole vertebrates has not been demonstrated due to the difficulty of expanding hard body components. Here, we demonstrate whole-body ExM, which enables nanoscale resolution imaging of anatomical structures, proteins, and endogenous fluorescent prote

This paper presents a bandpass ADC which exploits enhanced time-resolution of a deep submicron CMOS process. Unlike conventional bandpass ADCs that rely on voltage resolution and Gm-LC filters, the proposed ADC employs time-interleaved voltage-controlled oscillators that enable frequency tunable bandstop noise shaping property without a feedback loop. The ADC implemented in 65nm CMOS achieves SNR of 63.3dB for 1MHz signal located at 1.5GHz, while consuming 19.6mW from 1.2V supply.

We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and <i>in-situ</i> molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-mergi

In this paper, a linearization technique for voltage-controlled oscillator (VCO)-based analog-to-digital converter (ADC) is presented. Even order harmonics are canceled by using pseudo-differential architecture with two identical VCO-based ADCs. The effect of cancelation technique is verified through simulation with a prototype designed in 0.18μm CMOS technology and verilog. The CMOS ring VCO consumes about 280μW of power in average and the digital counter is implemented in verilog. With the sam

In this paper, an entropy based method for quantifying the depth of anesthesia from rat EEG is presented. The proposed index for the depth of anesthesia called modified Shannon entropy (MShEn) is based on Shannon entropy (ShEn) and spectral entropy (SpEn) which are widely used for analyzing non-stationary signals. Discrimination power (DP), as a performance indicator for indexes, is defined and used to derive the final index for the depth of anesthesia. For experiment, EEG from anesthetized rats

Nanoscale imaging of whole vertebrates is essential for the systematic understanding of human diseases, yet this goal has not yet been achieved. Expansion microscopy (ExM) is an attractive option for accomplishing this aim; however, the expansion of even mouse embryos at mid- and late-developmental stages, which have fewer calcified body parts than adult mice, is yet to be demonstrated due to the challenges of expanding calcified tissues. Here, we introduce a state-of-the-art ExM technique, term