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Quantum diamond microscope for narrowband magnetic imaging with high spatial and spectral resolution

Physical Review Applied 2024年第5期22卷 054050-054050页

作者： Zechuan Yin Jiashen Tang Connor A. Hart John W. Blanchard Xinyan Xiang Saipriya Satyajit Smriti Bhalerao Tao Tao Stephen J. DeVience Department of Electrical Engineering and Computer Science University of Maryland College Park Maryland 20742 USA Quantum Technology Center University of Maryland College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA Fischell Department of Bioengineering University of Maryland College Park Maryland 20742 USA

The quantum diamond microscope (QDM) is a recently developed technology for near-field imaging of magnetic fields with micron-scale spatial resolution, under ambient conditions. In the present work, we integrate a QDM with a narrowband measurement protocol and a lock-in camera, and we demonstrate imaging of radio-frequency (rf) magnetic field patterns produced by microcoils, with a spectral resolution of approximately 1Hz. This rf QDM provides multifrequency imaging with a central detection frequency that is easily tunable over the megahertz scale, allowing spatial discrimination of both crowded spectral peaks and spectrally well-separated signals. The present instrument has a spatial resolution of approximately 2μm, a field of view of approximately 300×300μm2, and a per-pixel sensitivity to narrowband fields of about 1nTHz−1/2. Spatial noise can be reduced to the picotesla scale by signal averaging and/or spatial binning. The rf QDM enables simultaneous imaging of the amplitude, frequency, and phase of narrowband magnetic field patterns at the micron scale, with potential applications in real-space NMR imaging, ac susceptibility mapping, impedance tomography, analysis of electronic circuits, and spatial eddy-current-based inspection.

关键词： Magneto-optics Magnetometry Nuclear & electron resonance Quantum sensing Nitrogen vacancy centers in diamond Solid-state detectors Optically detected magnetic resonance Photoluminescence

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Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs

arXiv

引用

arXiv 2023年

作者： Li, Zehui Zhao, Xiangyu Shen, Mingzhu Stan, Guy-Bart Liò, Pietro Zhao, Yiren Department of Bioengineering Imperial College London United Kingdom Department of Electrical and Electronic Engineering Imperial College London United Kingdom Department of Computer Science and Technology University of Cambridge United Kingdom

Graphs are widely used to encapsulate a variety of data formats, but real-world networks often involve complex node relations beyond only being pairwise. While hypergraphs and hierarchical graphs have been developed and employed to account for the complex node relations, they cannot fully represent these complexities in practice. Additionally, though many Graph Neural Networks (GNNs) have been proposed for representation learning on higher-order graphs, they are usually only evaluated on simple graph datasets. Therefore, there is a need for a unified modelling of higher-order graphs, and a collection of comprehensive datasets with an accessible evaluation framework to fully understand the performance of these algorithms on complex graphs. In this paper, we introduce the concept of hybrid graphs, a unified definition for higher-order graphs, and present the Hybrid Graph Benchmark (HGB). HGB contains 23 real-world hybrid graph datasets across various domains such as biology, social media, and e-commerce. Furthermore, we provide an extensible evaluation framework and a supporting codebase to facilitate the training and evaluation of GNNs on HGB. Our empirical study of existing GNNs on HGB reveals various research opportunities and gaps, including (1) evaluating the actual performance improvement of hypergraph GNNs over simple graph GNNs;(2) comparing the impact of different sampling strategies on hybrid graph learning methods;and (3) exploring ways to integrate simple graph and hypergraph information. We make our source code and full datasets publicly available at https://***/hybrid-graph-benchmark/. © 2023, CC BY.

关键词： Graph neural networks

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Unidirectional Image Magnification and Demagnification Using Pyramid Diffractive Optical Networks

Unidirectional Image Magnification and Demagnification Using...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Bijie Bai Xilin Yang Tianyi Gan Jingxi Li Deniz Mengu Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of California Los Angeles California USA Bioengineering Department University of California Los Angeles California USA California NanoSystems Institute (CNSI) University of California Los Angeles California USA

We introduce a pyramid-structured deep diffractive optical network (P-D 2 NN) design that performs unidirectional image magnification and demagnification using reduced degrees of freedom. Experimental validation at te... 详细信息

ISBN: (纸本)9798350369311

关键词： Optical fiber networks Lasers and electrooptics Electro-optical waveguides

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PAINDECOG: MACHINE LEARNING-BASED IDENTIFICATION OF PAIN BIOMARKERS FROM SEEG SIGNALS

arXiv

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arXiv 2025年

作者： Sidharth, Sidharth Sathish, Vishwas Bansal, Shweta Sun, Samantha Pham, Timmy Weaver, Kurt Rao, Rajesh P.N. Herron, Jeffrey Department of Electrical and Computer Engineering University of Washington Seattle United States Paul G. Allen School of Computer Science and Engineering University of Washington Seattle United States Department of Bioengineering University of Washington Seattle United States Department of Radiology University of Washington Seattle United States Department of Neurological Surgery University of Washington Seattle United States

This study presents a systematic machine-learning approach for classifying acute pain from raw electrophysiological signals. We address binary and ternary classification tasks, leveraging Power-In-Band (PIB) and signal coherence—a functional connectivity metric—as distinguishing features. Our method evaluates the effectiveness of traditional machine learning algorithms on a manually curated electrophysiological dataset obtained from intracranial electroencephalography (iEEG), offering valuable insights into model performance for pain detection. Furthermore, we identify critical electrode pairings associated with acute pain, providing a clearer understanding of the neural markers that differentiate pain states. This work highlights the potential of targeted feature engineering in advancing pain classification, setting the stage for future enhancements in real-time and personalized pain assessment tools. Additionally, these findings have promising applications in neuromodulation and Deep Brain Stimulation (DBS), where adaptive and closed-loop systems could leverage identified pain markers to modulate pain-related brain regions more precisely, offering improved therapeutic options for chronic pain management Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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Unidirectional focusing of light using structured diffractive surfaces

arXiv

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arXiv 2024年

作者： Li, Yuhang Gan, Tianyi Li, Jingxi Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA United States Bioengineering Department University of California Los Angeles90095 United States

Unidirectional optical systems enable selective control of light through asymmetric processing of radiation, effectively transmitting light in one direction while blocking unwanted propagation in the opposite direction. Here, we introduce a reciprocal diffractive unidirectional focusing design based on linear and isotropic diffractive layers that are structured. Using deep learning-based optimization, a cascaded set of diffractive layers are spatially engineered at the wavelength scale to focus light efficiently in the forward direction while blocking it in the opposite direction. The forward energy focusing efficiency and the backward energy suppression capabilities of this unidirectional architecture were demonstrated under various illumination angles and wavelengths, illustrating the versatility of our polarization-insensitive design. Furthermore, we demonstrated that these designs are resilient to adversarial attacks that utilize wavefront engineering from outside. Experimental validation using terahertz radiation confirmed the feasibility of this diffractive unidirectional focusing framework. Diffractive unidirectional designs can operate across different parts of the electromagnetic spectrum by scaling the resulting diffractive features proportional to the wavelength of light and will find applications in security, defense, and optical communication, among others. Copyright © 2024, The Authors. All rights reserved.

关键词： Terahertz waves

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Lying mirror

arXiv

引用

arXiv 2024年

作者： Li, Yuhang Chen, Shiqi Bai, Bijie Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA United States Bioengineering Department University of California Los Angeles90095 United States

We introduce an all-optical system, termed the "lying mirror", to hide input information by transforming it into misleading, ordinary-looking patterns that effectively camouflage the underlying image data and deceive the observers. This misleading transformation is achieved through passive light-matter interactions of the incident light with an optimized structured diffractive surface, enabling the optical concealment of any form of secret input data without any digital computing. These lying mirror designs were shown to camouflage different types of input image data, exhibiting robustness against a range of adversarial manipulations, including random image noise as well as unknown, random rotations, shifts, and scaling of the object features. The feasibility of the lying mirror concept was also validated experimentally using a structured micro-mirror array along with multi-wavelength illumination at 480, 550 and 600 nm, covering the blue, green and red image channels. This framework showcases the power of structured diffractive surfaces for visual information processing and might find various applications in defense, security and entertainment. Copyright © 2024, The Authors. All rights reserved.

关键词： Mirrors

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Source-Free Domain Adaptation for SSVEP-based Brain-computer Interfaces

arXiv

引用

arXiv 2023年

作者： Guney, Osman Berke Kucukahmetler, Deniz Ozkan, Huseyin The Department of Electrical and Computer Engineering Boston University BostonMA United States The Department of Bioengineering Imperial College London London United Kingdom The Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey

This paper presents a source free domain adaptation method for steady-state visually evoked potentials (SSVEP) based brain-computer interface (BCI) spellers. SSVEP-based BCI spellers assist individuals experiencing speech difficulties by enabling them to communicate at a fast rate. However, achieving a high information transfer rate (ITR) in most prominent methods requires an extensive calibration period before using the system, leading to discomfort for new users. We address this issue by proposing a novel method that adapts a powerful deep neural network (DNN) pre-trained on data from source domains (data from former users or participants of previous experiments) to the new user (target domain), based only on the unlabeled target data. This adaptation is achieved by minimizing our proposed custom loss function composed of self-adaptation and local-regularity terms. The self-adaptation term uses the pseudo-label strategy, while the novel local-regularity term exploits the data structure and forces the DNN to assign similar labels to adjacent instances. The proposed method priorities user comfort by removing the burden of calibration while maintaining an excellent character identification accuracy and ITR. In particular, our method achieves striking 201.15 bits/min and 145.02 bits/min ITRs on the benchmark and BETA datasets, respectively, and outperforms the state-of-the-art alternatives. Our code is available at https://***/osmanberke/SFDA-SSVEP-BCI Copyright © 2023, The Authors. All rights reserved.

关键词： Brain computer interface

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Snapshot multi-spectral imaging through defocusing and a Fourier imager network

arXiv

引用

arXiv 2025年

作者： Yang, Xilin Fanous, Michael John Chen, Hanlong Lee, Ryan Costa, Paloma Casteleiro Li, Yuhang Huang, Luzhe Zhang, Yijie Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

Multi-spectral imaging, which simultaneously captures the spatial and spectral information of a scene, is widely used across diverse fields, including remote sensing, biomedical imaging, and agricultural monitoring. Here, we introduce a snapshot multi-spectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components. Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multi-spectral information;this encoded image information is rapidly decoded via a deep learning-based multi-spectral Fourier Imager Network (mFIN). We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 92.98% for predicting the illumination channels at the input and achieved a robust multi-spectral image reconstruction on various test objects. This deep learning-powered framework achieves high-quality multi-spectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine, industrial quality control, and agriculture, among others. Copyright © 2025, The Authors. All rights reserved.

关键词： Aberrations

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All-Optical Phase Conjugation Using a Diffractive Visual Processor

All-Optical Phase Conjugation Using a Diffractive Visual Pro...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Che-Yung Shen Jingxi Li Tianyi Gan Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of California Los Angeles CA USA Bioengineering Department University of California Los Angeles CA USA California NanoSystems Institute (CNSI) University of California Los Angeles CA USA

We present a diffractive visual processor that can approximate optical phase conjugation operation by linear optical processing without any digital computing or external power sources, which can be used for turbidity ... 详细信息

ISBN: (纸本)9798350369311

关键词： Visualization Optical diffraction Turbidity Lasers and electrooptics Adaptive optics Electro-optical waveguides

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SpiralVortex Superresolution Localization With a Stable Excitation Beam

SpiralVortex Superresolution Localization With a Stable Exci...

引用

2024 Frontiers in Optics, FiO 2024

作者： Reynolds, Samantha Fowler, Patrick Voitiv, Andrew A. Restrepo, Diego Gibson, Emily A. Gopinath, Juliet T. Siemens, Mark Department of Physics and Astronomy University of Denver DenverCO United States University of Colorado Anschutz Medical Campus Dept. of Cell and Developmental Biology AuroraCO United States University of Colorado Anschutz Medical Campus Department of Bioengineering AuroraCO United States University of Colorado Boulder Dept. of Electrical Computer and Energy Engineering BoulderCO United States University of Colorado Boulder Department of Physics BoulderCO United States

We propose and demonstrate SpiralVortex superresolution localization, in which fluorescence is measured while a dark spot spirals out from the center of a stationary excitation beam. The stable beam offers potential f...

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