skip to main content

Search for: All records

Creators/Authors contains: "Zhang, Yang"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract The matter in an accretion disk must lose angular momentum when moving radially inwards but how this works has long been a mystery. By calculating the trajectories of individual colliding neutrals, ions, and electrons in a weakly ionized 2D plasma containing gravitational and magnetic fields, we numerically simulate accretion disk dynamics at the particle level. As predicted by Lagrangian mechanics, the fundamental conserved global quantity is the total canonical angular momentum, not the ordinary angular momentum. When the Kepler angular velocity and the magnetic field have opposite polarity, collisions between neutrals and charged particles cause: (i) ions to movemore »radially inwards, (ii) electrons to move radially outwards, (iii) neutrals to lose ordinary angular momentum, and (iv) charged particles to gain canonical angular momentum. Neutrals thus spiral inward due to their decrease of ordinary angular momentum while the accumulation of ions at small radius and accumulation of electrons at large radius produces a radially outward electric field. In 3D, this radial electric field would drive an out-of-plane poloidal current that produces the magnetic forces that drive bidirectional astrophysical jets. Because this neutral angular momentum loss depends only on neutrals colliding with charged particles, it should be ubiquitous. Quantitative scaling of the model using plausible disk density, temperature, and magnetic field strength gives an accretion rate of 3 × 10 −8 solar mass per year, which is in good agreement with observed accretion rates.« less
    Free, publicly-accessible full text available May 1, 2023
  2. Free, publicly-accessible full text available April 19, 2023
  3. Photoelectron spectroscopy and quantum chemistry studies are used to investigate the structure and bonding of AuB 8 − . Global minimum sturctural searches show that AuB 8 − possesses a chair-like structure, which can be viewed as Au + bonded to the edge of the doubly-aromatic B 8 2− borozene, Au + [η 2 -B 8 2− ]. Chemical bonding analyses reveal that the AuB 8 − is a novel borozene complex with unique Au–borozene bonding.
    Free, publicly-accessible full text available March 3, 2023
  4. Free, publicly-accessible full text available January 28, 2023
  5. By manipulating the spectral dispersion of detected photons, spectroscopic single-molecule localization microscopy (sSMLM) permits concurrent high-throughput single-molecular spectroscopic analysis and imaging. Despite its promising potential, using discrete optical components and managing the delicate balance between spectral dispersion and spatial localization compromise its performance, including non-uniform spectral dispersion, high transmission loss of grating, high optical alignment demands, and reduced precision. We designed a dual-wedge prism (DWP)-based monolithic imaging spectrometer to overcome these challenges. We optimized the DWP for spectrally dispersing focused beam without deviation and with minimal wavefront error. We integrated all components into a compact assembly, minimizing total transmission lossmore »and significantly reducing optical alignment requirements. We show the feasibility of DWP using ray-tracing and numerical simulations. We validated our numerical simulations by experimentally imaging individual nanospheres and confirmed that DWP-sSMLM achieved much improved spatial and spectral precisions of grating-based sSMLM. We also demonstrated DWP-sSMLM in 3D multi-color imaging of cells.« less
    Free, publicly-accessible full text available January 21, 2023
  6. The COVID-19 pandemic has dramatically increased the use of face masks across the world. Aside from physical distancing, they are among the most effective protection for healthcare workers and the general population. Face masks are passive devices, however, and cannot alert the user in case of improper fit or mask degradation. Additionally, face masks are optimally positioned to give unique insight into some personal health metrics. Recognizing this limitation and opportunity, we present FaceBit: an open-source research platform for smart face mask applications. FaceBit's design was informed by needfinding studies with a cohort of health professionals. Small and easily securedmore »into any face mask, FaceBit is accompanied by a mobile application that provides a user interface and facilitates research. It monitors heart rate without skin contact via ballistocardiography, respiration rate via temperature changes, and mask-fit and wear time from pressure signals, all on-device with an energy-efficient runtime system. FaceBit can harvest energy from breathing, motion, or sunlight to supplement its tiny primary cell battery that alone delivers a battery lifetime of 11 days or more. FaceBit empowers the mobile computing community to jumpstart research in smart face mask sensing and inference, and provides a sustainable, convenient form factor for health management, applicable to COVID-19 frontline workers and beyond.« less
    Free, publicly-accessible full text available December 27, 2022
  7. Free, publicly-accessible full text available December 23, 2022
  8. Free, publicly-accessible full text available September 9, 2022
  9. There has been a booming demand for integrating Convolutional Neural Networks (CNNs) powered functionalities into Internet-of-Thing (IoT) devices to enable ubiquitous intelligent "IoT cameras". However, more extensive applications of such IoT systems are still limited by two challenges. First, some applications, especially medicine-and wearable-related ones, impose stringent requirements on the camera form factor. Second, powerful CNNs often require considerable storage and energy cost, whereas IoT devices often suffer from limited resources. PhlatCam, with its form factor potentially reduced by orders of magnitude, has emerged as a promising solution to the first aforementioned challenge, while the second one remains a bottleneck.more »Existing compression techniques, which can potentially tackle the second challenge, are far from realizing the full potential in storage and energy reduction, because they mostly focus on the CNN algorithm itself. To this end, this work proposes SACoD, a Sensor Algorithm Co-Design framework to develop more efficient CNN-powered PhlatCam. In particular, the mask coded in the Phlat-Cam sensor and the backend CNN model are jointly optimized in terms of both model parameters and architectures via differential neural architecture search. Extensive experiments including both simulation and physical measurement on manufactured masks show that the proposed SACoD framework achieves aggressive model compression and energy savings while maintaining or even boosting the task accuracy, when benchmarking over two state-of-the-art (SOTA) designs with six datasets across four different vision tasks including classification, segmentation, image translation, and face recognition. Our codes are available at:« less
    Free, publicly-accessible full text available October 1, 2022
  10. Free, publicly-accessible full text available August 5, 2022