Single-shot Method Research Articles

A 0.59 nW/kHz Clock Circuit with High-Precision Clock Calibration for Passive Internet of Things Chips

The high precision and low power consumption of the clock generator are critical in passive RFID transponders and passive IoT chips, but fluctuations in PVT can cause considerable degradation in the precision of the chip’s internal clocks. This paper proposes a high-precision clock circuit with a single-shot calibration method to addresses this issue in a low-power clock solution. Based on the reference timespan in the preamble of the down-link RF envelope, a TDIF (Time-digital to current-frequency) calibration method was implemented with both a streamlined procedure and customized circuits. By computing the difference between the time counts and applying it to an ultra-low-power, current-starved oscillator, the current change ratio can be linearly controlled. Compared to the traditional integer frequency division scheme used by passive tags for a 160 k bits up-link data rate, the required frequency for the clock generator was reduced from 960 kHz to 320 kHz, the calibration error was reduced from ±10% to ±3% for ±25% frequency deviation, the calibration time was 133.3 μs for a single shot in this work, and the power consumption was 158 nW after the calibration was completed. This leads to an excellent power efficiency of 0.59 nW/kHz and meets the requirements of low power, low cost, and PVT robustness in the RF-powered passive IoT chips. By appropriately increasing the number of calibration digits and the duration, this calibration approach could also be used for other ultra-low-power passive IoT chips that require higher-precision clocking without the use of off-chip crystals.

Extended depth of field for Fresnel zone aperture camera via fast passive depth estimation.

The lensless camera with incoherent illumination has gained significant research interest for its thin and flexible structure. However, it faces challenges in resolving scenes with a wide depth of field (DoF) due to its depth-dependent point spread function (PSF). In this paper, we present a single-shot method for extending the DoF in Fresnel zone aperture (FZA) cameras at visible wavelengths through passive depth estimation. The improved ternary search method is utilized to determine the depth of targets rapidly by evaluating the sharpness of the back propagation reconstruction. Based on the depth estimation results, a set of reconstructed images focused on targets at varying depths are derived from the encoded image. After that, the DoF is extended through focus stacking. The experimental results demonstrate an 8-fold increase compared with the calibrated DoF at 130 mm depth. Moreover, our depth estimation method is five times faster than the traversal method, while maintaining the same level of accuracy. The proposed method facilitates the development of lensless imaging in practical applications such as photography, microscopy, and surveillance.

Single shot detection of alterations across multiple ionic currents from assimilation of cell membrane dynamics

The dysfunction of ion channels is a causative factor in a variety of neurological diseases, thereby defining the implicated channels as key drug targets. The detection of functional changes in multiple specific ionic currents currently presents a challenge, particularly when the neurological causes are either a priori unknown, or are unexpected. Traditional patch clamp electrophysiology is a powerful tool in this regard but is low throughput. Here, we introduce a single-shot method for detecting alterations amongst a range of ion channel types from subtle changes in membrane voltage in response to a short chaotically driven current clamp protocol. We used data assimilation to estimate the parameters of individual ion channels and from these we reconstructed ionic currents which exhibit significantly lower error than the parameter estimates. Such reconstructed currents thereby become sensitive predictors of functional alterations in biological ion channels. The technique correctly predicted which ionic current was altered, and by approximately how much, following pharmacological blockade of BK, SK, A-type K+ and HCN channels in hippocampal CA1 neurons. We anticipate this assay technique could aid in the detection of functional changes in specific ionic currents during drug screening, as well as in research targeting ion channel dysfunction.

Single-shot fast cinematic imaging during merging process of multiple electron filaments in electrostatic potential well.

For the first time, details of the spatial and temporal acceptable evolution of the merging process of co-rotating electron vortices in a potential well are successfully captured using a "single-shot method" with a high temporal resolution of 10 µs. Four-electron filaments are trapped inside the Beam eXperiment-Upgrade linear trap [H. Himura, Nucl. Instrum. Methods Phys. Res. A 811, 100 (2016)] with a uniform axial magnetic field and co-axial multi-ring electrodes. Images of non-emitting electron filaments are captured using a high-speed camera with up to 1 000 000fps, a microchannel plate, a fast-decay phosphor screen of which fluorescence duration is 0.15 µs, and a super fine metallic mesh with an open area ratio of 89%. Images captured every 10 µs clearly show the growth of multiple short-wave instabilities in the wing trailing electron vortices. The experimental methods and measurement techniques presented in this paper can contribute to revealing exactly how small vortices evolve into a large structure or turbulence in a potential well through complex processes.

Slimming Neural Networks Using Adaptive Connectivity Scores.

In general, deep neural network (DNN) pruning methods fall into two categories: 1) weight-based deterministic constraints and 2) probabilistic frameworks. While each approach has its merits and limitations, there are a set of common practical issues such as trial-and-error to analyze sensitivity and hyper-parameters to prune DNNs, which plague them both. In this work, we propose a new single-shot, fully automated pruning algorithm called slimming neural networks using adaptive connectivity scores (SNACS). Our proposed approach combines a probabilistic pruning framework with constraints on the underlying weight matrices, via a novel connectivity measure, at multiple levels to capitalize on the strengths of both approaches while solving their deficiencies. In SNACS, we propose a fast hash-based estimator of adaptive conditional mutual information (ACMI), that uses a weight-based scaling criterion, to evaluate the connectivity between filters and prune unimportant ones. To automatically determine the limit up to which a layer can be pruned, we propose a set of operating constraints that jointly define the upper pruning percentage limits across all the layers in a deep network. Finally, we define a novel sensitivity criterion for filters that measures the strength of their contributions to the succeeding layer and highlights critical filters that need to be completely protected from pruning. Through our experimental validation, we show that SNACS is faster by over 17× the nearest comparable method and is the state-of-the-art single-shot pruning method across four standard Dataset-DNN pruning benchmarks: CIFAR10-VGG16, CIFAR10-ResNet56, CIFAR10-MobileNetv2, and ILSVRC2012-ResNet50.

Single-shot local measurement of terahertz correlated second harmonic generation in laser air plasma filaments.

We present a single-shot detection method of terahertz correlated second harmonic generation in plasma-based sources by directly mixing an optical probe into femtosecond laser-induced plasma filaments in air. The single-shot second harmonic trace is obtained by measuring a second harmonic generation on a conventional CCD with a spatiotemporally distorted probe beam. The system shows a spectrometer resolution of 22 fs/pixel on the CCD and a true resolution on the order of the probe pulse duration. With considerable THz peak electric field strength, this formalism can open the door to single-shot THz detection without bandwidth limitations.

Single-shot non-line-of-sight imaging based on chromato-axial differential correlography

Non-line-of-sight (NLOS) imaging is a challenging task aimed at reconstructing objects outside the direct view of the observer. Nevertheless, traditional NLOS imaging methods typically rely on intricate and costly equipment to scan and sample the hidden object. These methods often suffer from restricted imaging resolution and require high system stability. Herein, we propose a single-shot high-resolution NLOS imaging method via chromato-axial differential correlography, which adopts low-cost continuous-wave lasers and a conventional camera. By leveraging the uncorrelated laser speckle patterns along the chromato-axis, this method can reconstruct hidden objects of diverse complexity using only one exposure measurement. The achieved background stability through single-shot acquisition, along with the inherent information redundancy in the chromato-axial differential speckles, enhances the robustness of the system against vibration and colored stain interference. This approach overcomes the limitations of conventional methods by simplifying the sampling process, improving system stability, and achieving enhanced imaging resolution using available equipment. This work serves as a valuable reference for the real-time development and practical implementation of NLOS imaging.

A Y-shaped network based single-shot absolute phase recovery method for fringe projection profilometry

Fringe projection profilometry (FPP) is a widely used non-contact 3D measurement method. Though maturing in the last decade, it remains a significant challenge when facing the phase unwrapping of measured object surfaces in a single-shot measurement setting. With the rapid development of deep learning techniques, the adoption of a data-driven approach is gaining popularity in the field of optical metrology. This study proposes a new absolute phase recovery method based on the devised single-stage deep learning network. The aim is to ensure high-quality absolute phase recovery from a single-shot fringe projection measurement. Unlike most existing approaches, where the numerators and denominators of the wrapped phases and the fringe orders are predicted in various stages, the proposed method acquires the wrapped phases and the corresponding fringe orders within a single network, i.e. it can predict both wrapped phases and the corresponding fringe orders directly and simultaneously from the single fringe pattern projected in the single-shot mode based on a unified Y-shaped network. Experiments on benchmark datasets and models have demonstrated the effectiveness and efficiency of the technique, especially in terms of high-quality recovery of absolute phase information by using the lightweight single-stage network, and enabling the FPP-based phase 3D measurements in an online manner.

Solution-State 2D NMR Spectroscopy of Mixtures HyperpolarizedUsing Optically Polarized Crystals.

The HYPNOESYS method (Hyperpolarized NOE System), which relies on the dissolution of optically polarized crystals, has recently emerged as a promising approach to enhance the sensitivity of NMR spectroscopy in the solution state. However, HYPNOESYS is a single-shot method that is not generally compatible with multidimensional NMR. Here we show that 2D NMR spectra can be obtained from HYPNOESYS-polarized samples, using single-scan acquisition methods. The approach is illustrated with a mixture of terpene molecules and a benchtop NMR spectrometer, paving the way to a sensitive, information-rich and affordable analytical method.

Single-shot measurement of the Jones matrix for anisotropic media using four-channel digital polarization holography.

Dynamic measurement of the Jones matrix is crucial in investigating polarization light fields, which have wide applications in biophysics, chemistry, and mineralogy. However, acquiring the four elements of the Jones matrix instantly is difficult, hindering the characterization of random media and transient processes. In this study, we propose a single-shot measurement method of the Jones matrix for anisotropic media called "four-channel digital polarization holography" (FC-DPH). The FC-DPH system is created by a slightly off-axis superposition of reference light waves, which are modulated by a spatial light modulator (SLM), and signal light waves that pass through a Ronchi grating. The SLM enables flexible adjustment of the spatial carrier frequency, which can be adapted to different anisotropic media. The four elements of the Jones matrix can be obtained from the interferogram through the inverse Fourier transform. Optical experiments on anisotropic objects validate the feasibility and accuracy of the proposed method.

One-Shot Adaptation of GAN in Just One CLIP.

There are many recent research efforts to fine-tune a pre-trained generator with a few target images to generate images of a novel domain. Unfortunately, these methods often suffer from overfitting or under-fitting when fine-tuned with a single target image. To address this, here we present a novel single-shot GAN adaptation method through unified CLIP space manipulations. Specifically, our model employs a two-step training strategy: reference image search in the source generator using a CLIP-guided latent optimization, followed by generator fine-tuning with a novel loss function that imposes CLIP space consistency between the source and adapted generators. To further improve the adapted model to produce spatially consistent samples with respect to the source generator, we also propose contrastive regularization for patchwise relationships in the CLIP space. Experimental results show that our model generates diverse outputs with the target texture and outperforms the baseline models both qualitatively and quantitatively. Furthermore, we show that our CLIP space manipulation strategy allows more effective attribute editing. Our Github source is as follows: https://github.com/cyclomon/OneshotCLIP.

Improvement in Signal Phase Detection Using Deep Learning with Parallel Fully Connected Layers

We report a single-shot phase-detection method using deep learning in a holographic data-storage system. The error rate was experimentally confirmed to be reduced by up to three orders of magnitude compared with that in the conventional phase-determination algorithm by learning the light-intensity distribution around a target signal pixel. In addition, the output speed of a signal phase could be shortened by devising a network and arranging the fully connected layers in parallel. In our environment, the phase-output time of a single-pixel classification was approximately 18 times longer than that in our previous method, with the minimum-finding algorithm. However, it could be reduced to 1.7 times or less when 32 pixels were simultaneously classified. Therefore, the proposed method can significantly reduce the error rates and suppress the phase-output time to almost the same level as that in the previous method. Thus, our proposed method can be a promising phase-detection method for realizing a large-density data-storage system.

Serial and multi-level proteome analysis for microscale protein samples

Post-translational modifications (PTMs), such as phosphorylation and ubiquitination, play key roles in signal transduction and protein homeostasis. The crosstalk of PTMs greatly expands the components of proteome and protein functions. Multi-level proteome analysis, which involves proteome investigations of total lysate and PTMs in this context, provides a comprehensive approach to explore the PTM crosstalk of a biological system under diverse disturbances. However, multi-level proteome practice remains technically challenging. Here we intended to build a strategy for multi-level proteome analysis, in which we focus on the serial profiling the total proteome, ubiquitinome and phosphoproteome from the microscale of starting material. We started by evaluating five common lysis buffers and found that the sodium deoxycholate buffer provided the best overall performance. We then developed an approach for serial enrichment and profiling of the multi-level proteome. To expand the depth of identification, we customized the variable windows to perform data-independent acquisition (DIA) sequencing for each proteome. In total, we identified 6465 proteins, ∼20,000 GlyGly sites (class 1), and ∼ 19,000 phosphosites (class 1) sequentially using 1 mg of HeLa digest by three DIA measurements. We applied this strategy to analyze MG132-treated HeLa cells and observed the crosstalk between ubiquitination and phosphorylation. Our method can be referenced for other multi-level proteome studies with microscale samples. SignificanceLysis buffer containing sodium deoxycholate provided the best overall performance in multi-level proteome analysis. One step of ubiquitination enrichment before phosphorylation enrichment does not reduce the reproducibility of phosphoproteome. Customized isolation windows were established for DIA analysis on each level of proteome. Combined the serial enrichment approach and the customized single-shot DIA method enabled the multi-level proteome of microscale protein samples.

Accurate decoding of data pages in an amplitude- and phase-modulated signal beam detected by the single-shot transport of intensity equation method with convolutional neural network-based classifiers

We propose an accurate method for the classification and decoding of data pages in complex-amplitude-modulated signal beams detected via the transport of the intensity equation (TIE) method using simple classifiers with a convolutional neural network (CNN) for holographic data storage (HDS). The classifiers allow a single-shot TIE method using two cameras to detect the complex-amplitude-modulated signal beam in HDS. Although the phase distribution detected using the single-shot TIE method tends to be superimposed with strong phase noise, we demonstrate experimentally that the CNN-based classifiers can classify/decode data pages in the complex-amplitude-modulated signal beam accurately without phase noise removal.

High-Fidelity Multiplexed Single-Shot Readout for Transmon Qubits With High-Power Measurement

Single-shot readout of a quantum state is an essential component in building a large-scale quantum processor, andreduc-ing the readout error is the key challenge. To achieve a high signal-to-noiseratio,quantum-limitedlow-noiseamplifierssuchasJoseph-son parametric amplifiers are typically used. However, a high-fidel-ity single-shot readout method without a parametric amplifier would be advantageous, as it would reduce system complexity. In this paper, we present a high-fidelity result of frequency-multi-plexed, high-power single-shot readout for superconducting qubits without using a parametric amplifier. We achieved a high readout fidelity of 98.3 % with a 400 ns pulse and confirmed that the high-power readout can be usedintomography of anentangledstate. We havedemonstratedthepotentialusefulnessofthistechniqueinchar-acterizing the final quantum state at the end of quantum opera-tions in a superconducting quantum processor.

Single-shot quantitative differential phase contrast imaging combined with programmable polarization multiplexing illumination.

We propose a single-shot quantitative differential phase contrast method with polarization multiplexing illumination. In the illumination module of our system, a programmable LED array is divided into four quadrants and covered with polarizing films of four different polarization angles. We use a polarization camera with polarizers before the pixels in the imaging module. By matching the polarization angle between the polarizing films over the custom LED array and the polarizers in the camera, two sets of asymmetric illumination acquisition images can be calculated from a single-shot acquisition image. Combined with the phase transfer function, we can calculate the quantitative phase of the sample. We present the design, implementation, and experimental image data demonstrating the ability of our method to obtain quantitative phase images of a phase resolution target, as well as Hela cells.

Single-shot quantitative phase contrast imaging based on deep learning.

Quantitative differential phase-contrast (DPC) imaging is one of the commonly used methods for phase retrieval. However, quantitative DPC imaging requires several pairwise intensity measurements, which makes it difficult to monitor living cells in real-time. In this study, we present a single-shot quantitative DPC imaging method based on the combination of deep learning (DL) and color-encoded illumination. Our goal is to train a model that can generate an isotropic quantitative phase image (i.e., target) directly from a single-shot intensity measurement (i.e., input). The target phase image was reconstructed using a linear-gradient pupil with two-axis measurements, and the model input was the measured color intensities obtained from a radially asymmetric color-encoded illumination pattern. The DL-based model was trained, validated, and tested using thirteen different cell lines. The total number of training, validation, and testing images was 264 (10 cells), 10 (1 cell), and 40 (2 cells), respectively. Our results show that the DL-based phase images are visually similar to the ground-truth phase images and have a high structural similarity index (>0.98). Moreover, the phase difference between the ground-truth and DL-based phase images was smaller than 13%. Our study shows the feasibility of using DL to generate quantitative phase imaging from a single-shot intensity measurement.

Single-shot quantitative amplitude and phase imaging based on a pair of all-dielectric metasurfaces

Quantitative amplitude and phase imaging (QAPI) has been an effective technique to examine label-free biomedical samples. Simple and reliable QAPI techniques realized by replacing conventional bulky optical elements with planar structures will improve the system portability and facilitate in vivo imaging applications. Here, we propose a single-shot QAPI method realized by simply inserting a pair of all-dielectric geometric phase metasurfaces into a traditional microscope. The first metasurface splits a linearly polarized incident beam into two circularly polarized components and the following metasurface deflects the two beams back toward their initial directions. The metasurface pair generates two laterally displaced replicas of the input object, of which the interference forms a retardance image with a bias retardation controlled by an analyzer. The amplitude and phase information of the object is reconstructed from four retardance images simultaneously recorded by a polarized camera. The metasurface pair can be placed near any conjugate plane of the object, which provides a flexible and robust configuration for QAPI, demonstrating its wide usage in live imaging.

Sensitive, High-Throughput HLA-I and HLA-II Immunopeptidomics Using Parallel Accumulation-Serial Fragmentation Mass Spectrometry

Comprehensive and in-depth identification of the human leukocyte antigen class I (HLA-I) and class II (HLA-II) tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is a powerful technology for direct identification of HLA peptides from patient-derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare and clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high-throughput, sensitive, and single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight MS on the Bruker timsTOF single-cell proteomics system (SCP). We demonstrate greater than twofold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation, and reduces input requirements to as few as 1e6 A375cells for >800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen and noncanonical proteins. We also apply our optimized single-shot SCP acquisition methods to tumor-derived samples, enabling sensitive, high-throughput, and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15mg wet weight tissue.

Single-shot 3D measurement of highly reflective objects with deep learning.

Three-dimensional (3D) measurement methods based on fringe projection profilometry (FPP) have been widely applied in industrial manufacturing. Most FPP methods adopt phase-shifting techniques and require multiple fringe images, thus having limited application in dynamic scenes. Moreover, industrial parts often have highly reflective areas leading to overexposure. In this work, a single-shot high dynamic range 3D measurement method combining FPP with deep learning is proposed. The proposed deep learning model includes two convolutional neural networks: exposure selection network (ExSNet) and fringe analysis network (FrANet). The ExSNet utilizes self-attention mechanism for enhancement of highly reflective areas leading to overexposure problem to achieve high dynamic range in single-shot 3D measurement. The FrANet consists of three modules to predict wrapped phase maps and absolute phase maps. A training strategy directly opting for best measurement accuracy is proposed. Experiments on a FPP system showed that the proposed method predicted accurate optimal exposure time under single-shot condition. A pair of moving standard spheres with overexposure was measured for quantitative evaluation. The proposed method reconstructed standard spheres over a large range of exposure level, where prediction errors for diameter were 73 µm (left) and 64 µm (right) and prediction error for center distance was 49 µm. Ablation study and comparison with other high dynamic range methods were also conducted.