arXiv subjects

Jianing Chen

Publications and source records attributed to Jianing Chen.

27 records · Page 2Linked to original sources

arXiv2020

Agile Reactive Navigation for A Non-Holonomic Mobile Robot Using A Pixel Processor Array

This paper presents an agile reactive navigation strategy for driving a non-holonomic ground vehicle around a preset course of gates in a cluttered environment using a low-cost processor array sensor. This enables machine vision tasks to be performed directly upon the sensor's image plane, rather than using a separate general-purpose computer. We demonstrate a small ground vehicle running through or avoiding multiple gates at high speed using minimal computational resources. To achieve this, target tracking algorithms are developed for the Pixel Processing Array and captured images are then processed directly on the vision sensor acquiring target information for controlling the ground vehicle. The algorithm can run at up to 2000 fps outdoors and 200fps at indoor illumination levels. Conducting image processing at the sensor level avoids the bottleneck of image transfer encountered in conventional sensors. The real-time performance of on-board image processing and robustness is validated through experiments. Experimental results demonstrate that the algorithm's ability to enable a ground vehicle to navigate at an average speed of 2.20 m/s for passing through multiple gates and 3.88 m/s for a 'slalom' task in an environment featuring significant visual clutter.

cs.RO↗

arXiv2020

Fully Embedding Fast Convolutional Networks on Pixel Processor Arrays

We present a novel method of CNN inference for pixel processor array (PPA) vision sensors, designed to take advantage of their massive parallelism and analog compute capabilities. PPA sensors consist of an array of processing elements (PEs), with each PE capable of light capture, data storage and computation, allowing various computer vision processing to be executed directly upon the sensor device. The key idea behind our approach is storing network weights "in-pixel" within the PEs of the PPA sensor itself to allow various computations, such as multiple different image convolutions, to be carried out in parallel. Our approach can perform convolutional layers, max pooling, ReLu, and a final fully connected layer entirely upon the PPA sensor, while leaving no untapped computational resources. This is in contrast to previous works that only use a sensor-level processing to sequentially compute image convolutions, and must transfer data to an external digital processor to complete the computation. We demonstrate our approach on the SCAMP-5 vision system, performing inference of a MNIST digit classification network at over 3000 frames per second and over 93% classification accuracy. This is the first work demonstrating CNN inference conducted entirely upon the processor array of a PPA vision sensor device, requiring no external processing.

cs.CV↗

arXiv2020

Investigation of plasmonic evolution of atomically size-selected Au clusters by electron energy loss spectrum--from solid state to molecular scale

Versatile quantum modes emerge for plasmon describing the collective oscillations of free electrons in metallic nanoparticles when the particle sizes are greatly reduced. Rather than traditional nanoscale study, the understanding of quantum plasmon desires extremal atomic control of the nanoparticles, calling for size dependent plasmon measurement over a series of nanoparticles with atomically adjustable atom number over several orders of magnitude. Here we report the N dependent plasmonic evolution of atomically size selected gold particles with N= 100 70000 using electron energy loss (EEL) spectroscopy in a scanning transmission electron microscope. The EEL mapping assigns a feature at 2.7 eV as the bulk plasmon and another at 2.4 eV as surface plasmon, which evolution reveals three regimes. When N decreases from 70000 to 887, the bulk plasmon stays unchanged while the surface plasmon exhibits a slight red shift from 2.4 to 2.3 eV. It can be understood by the dominance of classical plasmon physics and electron boundary scattering induced retardation. When N further decreases from 887 to 300, the bulk plasmon disappears totally and the surface plasmon shows a steady blueshift, which indicates that the quantum confinement emerges and modifies the intraband transition. When N 100 300, the plasmon is split to three fine features, which is attributed to superimposed single electron transitions between the quantized molecular like energy level by the time dependent density functional theory calculations. The surface plasmon's excitation ratio has a scaling law with an exponential dependence on N ( N^0.669), essentially the square of the radius. A unified evolution picture from the classical to quantum, molecular plasmon is thus demonstrated.

physics.atm-clus↗

arXiv2019

A Camera That CNNs: Towards Embedded Neural Networks on Pixel Processor Arrays

We present a convolutional neural network implementation for pixel processor array (PPA) sensors. PPA hardware consists of a fine-grained array of general-purpose processing elements, each capable of light capture, data storage, program execution, and communication with neighboring elements. This allows images to be stored and manipulated directly at the point of light capture, rather than having to transfer images to external processing hardware. Our CNN approach divides this array up into 4x4 blocks of processing elements, essentially trading-off image resolution for increased local memory capacity per 4x4 "pixel". We implement parallel operations for image addition, subtraction and bit-shifting images in this 4x4 block format. Using these components we formulate how to perform ternary weight convolutions upon these images, compactly store results of such convolutions, perform max-pooling, and transfer the resulting sub-sampled data to an attached micro-controller. We train ternary weight filter CNNs for digit recognition and a simple tracking task, and demonstrate inference of these networks upon the SCAMP5 PPA system. This work represents a first step towards embedding neural network processing capability directly onto the focal plane of a sensor.

cs.CV↗

arXiv2018

A mid-infrared biaxial hyperbolic van der Waals crystal

Hyperbolic media have attracted much attention in the photonics community, thanks to their ability to confine light to arbitrarily small volumes and to their use for super-resolution applications. The 2D counterpart of these media can be achieved with hyperbolic metasurfaces, which support in-plane hyperbolic guided modes thanks to nanopatterns which, however, pose significant fabrication challenges and limit the achievable confinement. We show that thin flakes of the van der Waals material α-MoO3 can support naturally in-plane hyperbolic polariton guided modes at mid-infrared frequencies without any patterning. This is possible because α-MoO3 is a biaxial hyperbolic crystal, with three different Restrahlen bands, each for a different crystal axis. Our findings can pave the way towards new paradigm to manipulate and confine light in planar photonic devices.

physics.optics↗

arXiv2015

Ultralong Lifetime Plasmons on Picosecond Time Scale Enabled by Hybrid Plasmon-Phonon Polaritons

Graphene plasmonics is of great interest for compact optical devices working in broad frequency domains with ultrahigh speed and very low energy consumption. However, graphene plasmons damp out quickly on most substrates mainly due to scattering loss from substrate surface phonons and impurities. Here we discover a new hybridized plasmon-phonon polariton mode in graphene/h-BN van der Waals heterostructures, which enables ultralong hybrid plasmon lifetime up to 1.6 picosecond, the longest plasmon lifetime ever demonstrated. Such remarkably long lifetime arises from the coupling of long-lifetime h-BN transverse optical phonon with graphene plasmons, which uniquely exists in monolayer heterostructures. Our findings and understanding of this unexploited hybrid mode offer a novel approach to tune the plasmon behaviours in the frequency, time and space domains. This can potentially introduce a new paradigm to generate highly-confined plasmons with ultra-long lifetime for various applications, such as deep-subwavelength metamaterials, ultra-low-loss waveguides, and ultrafast optical switches.

cond-mat.mtrl-sci↗

arXiv2014

Effects of Electric Field Gradient on Sub-nanometer Spatial Resolution of Tip-enhanced Raman Spectroscopy

Tip-enhanced Raman spectroscopy (TERS) with sub-nanometer spatial resolution has been recently demonstrated experimentally. However, the physical mechanism underlying is still under discussion. Here, we theoretically investigate the electric field gradient of a coupled tip-substrate system. Our calculations suggest that the ultra-high spatial resolution of TERS can be partially attributed to the electric field gradient effect owning to its tighter spatial confinement and sensitivity to the infrared (IR)-active of molecules.

cond-mat.mes-hall↗

arXiv2013

Strong plasmon reflection at nanometer-size gaps in monolayer graphene on SiC

We employ tip-enhanced infrared near-field microscopy to study the plasmonic properties of epitaxial quasi-free-standing monolayer graphene on silicon carbide. The near-field images reveal propagating graphene plasmons, as well as a strong plasmon reflection at gaps in the graphene layer, which appear at the steps between the SiC terraces. When the step height is around 1.5 nm, which is two orders of magnitude smaller than the plasmon wavelength, the reflection signal reaches 20% of its value at graphene edges, and it approaches 50% for step heights as small as 5 nm. This intriguing observation is corroborated by numerical simulations, and explained by the accumulation of a line charge at the graphene termination. The associated electromagnetic fields at the graphene termination decay within a few nanometers, thus preventing efficient plasmon transmission across nanoscale gaps. Our work suggests that plasmon propagation in graphene-based circuits can be tailored using extremely compact nanostructures, such as ultra-narrow gaps. It also demonstrates that tip-enhanced near-field microscopy is a powerful contactless tool to examine nanoscale defects in graphene.

cond-mat.mes-hall↗

arXiv2012

Optical nano-imaging of gate-tuneable graphene plasmons

The ability to manipulate optical fields and the energy flow of light is central to modern information and communication technologies, as well as quantum information processing schemes. However, as photons do not possess charge, controlling them efficiently by electrical means has so far proved elusive. A promising way to achieve electric control of light could be through plasmon polaritons - coupled excitations of photons and charge carriers - in graphene. In this two-dimensional sheet of carbon atoms, it is expected that plasmon polaritons and their associated optical fields can be readily tuned electrically by varying the graphene carrier density. While optical graphene plasmon resonances have recently been investigated spectroscopically, no experiments so far have directly resolved propagating plasmons in real space. Here, we launch and detect propagating optical plasmons in tapered graphene nanostructures using near-field scattering microscopy with infrared excitation light. We provide real-space images of plasmonic field profiles and find that the extracted plasmon wavelength is remarkably short - over 40 times smaller than the wavelength of illumination. We exploit this strong optical field confinement to turn a graphene nanostructure into a tunable resonant plasmonic cavity with extremely small mode volume. The cavity resonance is controlled in-situ by gating the graphene, and in particular, complete switching on and off of the plasmon modes is demonstrated, thus paving the way towards graphene-based optical transistors. This successful alliance between nanoelectronics and nano-optics enables the development of unprecedented active subwavelength-scale optics and a plethora of novel nano-optoelectronic devices and functionalities, such as tunable metamaterials, nanoscale optical processing and strongly enhanced light-matter interactions for quantum devices and (bio)sensors.

cond-mat.mtrl-sci↗