arXiv subjects

Xin He

Publications and source records attributed to Xin He.

At least 181 records · Page 10Linked to original sources

arXiv2020

Hybrid colour filters for multispectral imaging

Multispectral cameras capture images in multiple wavelengths in narrow spectral bands. They offer advanced sensing well beyond normal cameras and many single sensor based multispectral cameras have been commercialized aimed at a broad range of applications, such as agroforestry research, medical analysis and so on. However, the existing single sensor based multispectral cameras require accurate alignment to overlay each filter on image sensor pixels, which makes their fabrication very complex, especially when the number of bands is large. This paper demonstrates a new filter technology using a hybrid combination of single plasmonic layer and dielectric layers by computational simulations. A filter mosaic of various bands with narrow spectral width can be achieved with single run manufacturing processes (i.e., exposure, development, deposition and other minor steps), regardless of the number of bands.

physics.optics↗

arXiv2020

A new CMY camera technology using Al-TiO2-Al nanorod filter mosaic integrated on a CMOS image sensor

A CMY colour camera differs from its RGB counterpart in that it employs a subtractive colour space of cyan, magenta and yellow. CMY cameras tend to performs better than RGB cameras in low light conditions due to their much higher transmittance. However, conventional CMY colour filter technology made of pigments and dyes are limited in performance for the next generation image sensors with submicron pixel sizes. These conventional filters are difficult to fabricate at nanoscale dimensions as they use their absorption properties to subtract colours. This paper presents a CMOS compatible nanoscale thick CMY colour mosaic made of Al-TiO2-Al nanorods forming an array 0.82 million colour pixels of 4.4 micron each, arranged in a CMYM pattern. The colour mosaic was then integrated onto a MT9P031 monochrome image sensor to make a CMY camera and the colour imaging demonstrated using a 12 colour Macbeth chart. The developed technology will have applications in astronomy, low exposure time imaging in biology and photography.

physics.ins-det↗

arXiv2020

Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training

Deep learning has become widely used in complex AI applications. Yet, training a deep neural network (DNNs) model requires a considerable amount of calculations, long running time, and much energy. Nowadays, many-core AI accelerators (e.g., GPUs and TPUs) are designed to improve the performance of AI training. However, processors from different vendors perform dissimilarly in terms of performance and energy consumption. To investigate the differences among several popular off-the-shelf processors (i.e., Intel CPU, NVIDIA GPU, AMD GPU, and Google TPU) in training DNNs, we carry out a comprehensive empirical study on the performance and energy efficiency of these processors by benchmarking a representative set of deep learning workloads, including computation-intensive operations, classical convolutional neural networks (CNNs), recurrent neural networks (LSTM), Deep Speech 2, and Transformer. Different from the existing end-to-end benchmarks which only present the training time, We try to investigate the impact of hardware, vendor's software library, and deep learning framework on the performance and energy consumption of AI training. Our evaluation methods and results not only provide an informative guide for end-users to select proper AI accelerators, but also expose some opportunities for the hardware vendors to improve their software library.

cs.DC↗

arXiv2020

TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes

Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance records on various standard benchmarks. However, limited by the representations (axis-aligned rectangles, rotated rectangles or quadrangles) adopted to describe text, existing methods may fall short when dealing with much more free-form text instances, such as curved text, which are actually very common in real-world scenarios. To tackle this problem, we propose a more flexible representation for scene text, termed as TextSnake, which is able to effectively represent text instances in horizontal, oriented and curved forms. In TextSnake, a text instance is described as a sequence of ordered, overlapping disks centered at symmetric axes, each of which is associated with potentially variable radius and orientation. Such geometry attributes are estimated via a Fully Convolutional Network (FCN) model. In experiments, the text detector based on TextSnake achieves state-of-the-art or comparable performance on Total-Text and SCUT-CTW1500, the two newly published benchmarks with special emphasis on curved text in natural images, as well as the widely-used datasets ICDAR 2015 and MSRA-TD500. Specifically, TextSnake outperforms the baseline on Total-Text by more than 40% in F-measure.

cs.CV↗

arXiv2020

Scene Text Detection and Recognition: The Deep Learning Era

With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inescapably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, approach and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.

cs.CV↗

arXiv2020

Convergence Rates of Inertial Primal-Dual Dynamical Methods for Separable Convex Optimization Problems

In this paper, we propose a second-order continuous primal-dual dynamical system with time-dependent positive damping terms for a separable convex optimization problem with linear equality constraints. By the Lyapunov function approach, we investigate asymptotic properties of the proposed dynamical system as the time $t\to+\infty$. The convergence rates are derived for different choices of the damping coefficients. We also show that the obtained results are robust under external perturbations.

math.OC↗

arXiv2020

Computational Study of Defect variant Perovskites A2BX6 for Photovoltaic Applications

A comprehensive study of the structural, electronic, and optical properties of lead-free perovskites has been carried out by means of first principles method based on DFT. The calculations are performed for the compound of the type A2BX6 with A=Rb, and Cs; B=Sn, Pd, and Pt; and X=Cl, Br, and I. The calculated structural parameters (lattice constants and bond lengths) agree well with the experiments. The computed band gap reveals a semiconducting profile for all these compounds showing a decreasing trend of the band gap energy by changing the halide ions consecutively from Cl to Br and Br to I. However, for variation in the B-site cation, the band gap increases by changing the cation from Pd to Pt via Sn. The most likely compounds, Rb2PdBr6 and Cs2PtI6, exhibit a band gap within the optimal range of 0.9-1.6 eV for single-junction photovoltaic applications. The optical properties in terms of the optimal value of the dielectric constant, optical conductivity, and absorption coefficient are also investigated upto the photon energy of 10 eV. Our results indicate that upon changing the halogen ions (Cl by Br and Br by I) the optical properties altered significantly. Maximum dielectric constants and high optical absorption are found for Rb2PdI6 and Cs2PtI6. The unique optoelectronic properties such as ideal band gap, high dielectric constants, and optimum absorption of A2BX6 perovskites could be efficiently utilized in designing high performance single and multi-junction perovskite solar cells.

cond-mat.mtrl-sci↗

arXiv2019

GuardRider: Towards Sustainable Backscattering System over WiFi in the Wild

The WiFi backscatter communications offer ultra-low power and ubiquitous connections for IoT systems. Caused by the intermittent-nature of the WiFi traffics, state-of-the-art WiFi backscatter communications are not reliable for backscatter link or simple for tag to do adaptive transmission. In order to build sustainable (reliable and simple) WiFi backscatter communications, we present GuardRider, a WiFi backscatter system that enables backscatter communications riding on WiFi signals in the wild. The key contribution of GuardRider is an optimization algorithm of designing RS codes to follow the statistical knowledge of WiFi traffics and adjust backscatter transmission. With GuardRider, the reliable baskscatter link is guaranteed and a backscatter tag is able to adaptively transmit information without heavily listening the excitation channel. We built a hardware prototype of GuardRider using a customized tag with FPGA implementation. Both the simulations and field experiments verify that GuardRider could achieve a notably gains in bit error rate and frame error rate, which are hundredfold reduction in simulations and around 99% in filed experiments.

eess.SP↗

arXiv2019

COOK: Chirp-OOK Communication with Self-reliant Bitrate Adaptation in Backscatter Networks

For large-scale Internet of Things (IoT), backscatter communication is a promising technology to reduce power consumption and simplify deployment. However, backscatter communication lacks stability, along with limited communication range within a few meters. Due to the limited computation ability of backscatter tags, it is burdensome to effectively adapt the bitrate for the time-varying channel. Thus, backscatter tags are failed to fully utilize the optimal transmission rate. In this paper, we design a system named COOK with self-reliant bitrate adaptation in backscatter communication. Channel symmetry allows backscatter tags to adjust bitrate depending on the received signal strength of the excitation source (ES) without feedback. In addition, the chirp spreading signal is exploited as the ES signal to enable backscatter tags to work under noise floor. Our modulation approach is denoted as Chirp-OOK since the tags reflect the chirp signal by employing the on-off keying modulation. It allows that receiver can decode under the noise floor and the bitrate varies flexibly as the communication range changes. We have implemented the prototype system based on the universal software radio peripheral (USRP) platform. Extensive experiment results demonstrate the effectiveness of the proposed system. Our system provides valid communication distance up to $27m$, which is 7 times as compared with normal backscatter system. The system significantly increases the backscatter communication stability, by supporting bitrate adaptation ranges from 0.33kbps to 1.2Mbps, and guaranteeing the bit error rate (BER) is below 1%.

eess.SP↗

arXiv2019

Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of experienced dermatologists in many countries. To alleviate this problem, computer-aided diagnosis with state-of-the-art (SOTA) machine learning techniques would be a promising solution. In this paper, we aim at understanding the performance of convolutional neural network (CNN) based approaches. We first build two versions of skin disease datasets from Internet images: (a) Skin-10, which contains 10 common classes of skin disease with a total of 10,218 images; (b) Skin-100, which is a larger dataset that consists of 19,807 images of 100 skin disease classes. Based on these datasets, we benchmark several SOTA CNN models and show that the accuracy of skin-100 is much lower than the accuracy of skin-10. We then implement an ensemble method based on several CNN models and achieve the best accuracy of 79.01\% for Skin-10 and 53.54\% for Skin-100. We also present an object detection based approach by introducing bounding boxes into the Skin-10 dataset. Our results show that object detection can help improve the accuracy of some skin disease classes.

eess.IV↗

arXiv2019

Attacking Vision-based Perception in End-to-End Autonomous Driving Models

Recent advances in machine learning, especially techniques such as deep neural networks, are enabling a range of emerging applications. One such example is autonomous driving, which often relies on deep learning for perception. However, deep learning-based perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images. Nevertheless, the vast majority of such demonstrations focus on perception that is disembodied from end-to-end control. We present novel end-to-end attacks on autonomous driving in simulation, using simple physically realizable attacks: the painting of black lines on the road. These attacks target deep neural network models for end-to-end autonomous driving control. A systematic investigation shows that such attacks are easy to engineer, and we describe scenarios (e.g., right turns) in which they are highly effective. We define several objective functions that quantify the success of an attack and develop techniques based on Bayesian Optimization to efficiently traverse the search space of higher dimensional attacks. Additionally, we define a novel class of hijacking attacks, where painted lines on the road cause the driver-less car to follow a target path. Through the use of network deconvolution, we provide insights into the successful attacks, which appear to work by mimicking activations of entirely different scenarios. Our code is available at https://github.com/xz-group/AdverseDrive

cs.LG↗

arXiv2019

Implementation of on-chip multi-channel focusing wavelength demultiplexing with regularized digital metamaterials

Adiabatic waveguide taper and on-chip wavelength demultiplexer are the key components of photonic integrated circuits. However, these two kinds of devices which designed by traditional semi-analytic methods or brute-force search methods usually have large size. Here, based on regularized digital metamaterials, we have designed, fabricated and characterized a two-channel focused wavelength demultiplexer with a footprint of 2.4 x 10 um2. The designed demultiplexer can directly connect to a grating coupler under the absence of an adiabatic waveguide taper. The objective first method and modified steepest descent method are used to design the demultiplexer which splits 1520 nm and 1580 nm light from a 10-um-wide input waveguide into two 0.48-um-wide output waveguides. Experimental results show that the insertion loss of the upper (lower) channel of the demultiplexer is -1.77 dB (-2.10 dB) and the crosstalk is -25.17 dB (-12.14 dB). Besides, the simulation results indicate that the fabrication tolerance of our devices can reach 20 nm in etching depth and 10 nm in plane size changing. Benefit From the extensibility of our design method, we can design other types of ultra-compact 'focused' devices, like mode splitters, mode converters and power splitters, and we can also design devices with more complicated functionalities, for example, we have designed a three-channel focused wavelength demultiplexer.

physics.app-ph↗

arXiv2019

Symmetry-constrained Rectification Network for Scene Text Recognition

Reading text in the wild is a very challenging task due to the diversity of text instances and the complexity of natural scenes. Recently, the community has paid increasing attention to the problem of recognizing text instances with irregular shapes. One intuitive and effective way to handle this problem is to rectify irregular text to a canonical form before recognition. However, these methods might struggle when dealing with highly curved or distorted text instances. To tackle this issue, we propose in this paper a Symmetry-constrained Rectification Network (ScRN) based on local attributes of text instances, such as center line, scale and orientation. Such constraints with an accurate description of text shape enable ScRN to generate better rectification results than existing methods and thus lead to higher recognition accuracy. Our method achieves state-of-the-art performance on text with both regular and irregular shapes. Specifically, the system outperforms existing algorithms by a large margin on datasets that contain quite a proportion of irregular text instances, e.g., ICDAR 2015, SVT-Perspective and CUTE80.

cs.CV↗

arXiv2019

Strong optical coupling through superfluid Brillouin lasing

Brillouin scattering has applications ranging from signal processing, sensing and microscopy, to quantum information and fundamental science. Most of these applications rely on the electrostrictive interaction between light and phonons. Here we show that in liquids optically-induced surface deformations can provide an alternative and far stronger interaction. This allows the demonstration of ultralow threshold Brillouin lasing and strong phonon-mediated optical coupling for the first time. This form of strong coupling is a key capability for Brillouin-reconfigurable optical switches and circuits, for photonic quantum interfaces, and to generate synthetic electromagnetic fields. While applicable to liquids quite generally, our demonstration uses superfluid helium. Configured as a Brillouin gyroscope this provides the prospect of measuring superfluid circulation with unprecedented precision, and to explore the rich physics of quantum fluid dynamics, from quantized vorticity to quantum turbulence.

physics.optics↗

arXiv2019

All-optical switch and logic gates based on all-dielectric hybrid silicon-Ge2Sb2Te5 metamaterials

We numerically propose an all-dielectric hybrid metamaterial (MM) to realize all-optical switch and logic gates in shortwave infrared (SWIR) band. Such MM consists of one silicon rod and one Ge2Sb2Te5 (GST) rod pair. Utilizing the transition from amorphous to crystalline state of GST, such MM can produce electromagnetically induced transparency (EIT) analogue with active control. Based on this, we realized all-optical switching at 1500 nm with a modulation depth 84%. Besides, three different logic gates, NOT, NOR and OR, can also be achieved in this device simultaneously. Thanks to the reversible and fast phase transition process of GST, this device possesses reconfigurable ability as well as fast response time, and has potential applications in future optical networks.

physics.optics↗

arXiv2019

Chipscale plasmonic modulators and switches based on metal-insulator-metal waveguides with Ge2Sb2Te5

We introduce phase-change material Ge2Sb2Te5 (GST) into metal-insulator-metal (MIM) waveguide systems to realize chipscale plasmonic modulators and switches in the telecommunication band. Benefitting from the high contrast of optical properties between amorphous and crystalline GST, the three proposed structures can act as reconfigurable and non-volatile modulators and switches with excellent modulation depth 14 dB and fast response time in nanosecond, meanwhile possessing small footprints, simple frameworks and easy fabrication. This work provides new solutions to design active devices in MIM waveguide systems, and can find potential applications in more compact all-optical circuits for information processing and storage.

physics.app-ph↗

arXiv2019

Actively tunable terahertz electromagnetically induced transparency analogue based on vanadium-oxide-assisted metamaterials

Recently, phase-change materials (PCMs) have drawn more attention due to the dynamically tunable optical properties. Here, we investigate the active control of electromagnetically induced transparency (EIT) analogue based on terahertz (THz) metamaterials integrated with vanadium oxide (VO2). Utilizing the insulator-to-metal transition of VO2, the amplitude of EIT peak can be actively modulated with a significant modulation depth. Meanwhile the group delay within the transparent window can also be dynamically tuned, achieving the active control of slow light effect. Furthermore, we also introduce independently tunable transparent peaks as well as group delay based on a double-peak EIT with good tuning performance. Finally, based on broadband EIT, the active tuning of quality factor of the EIT peak is also realized. This work introduces active EIT control with more degree of freedom by employing VO2, and can find potential applications in future wireless and ultrafast THz communication systems as multi-channel filters, switches, spacers, logic gates and modulators.

physics.optics↗

arXiv2019

A plasmonic ellipse resonator possessing hybrid modes for ultracompact chipscale application

We propose a plasmonic ellipse resonator possessing hybrid modes based on metal-insulator-metal (MIM) waveguide system. Specially, this nanocavity has hybrid characteristic of rectangle and disk resonator, therefore supporting both Fabry-Perot modes (FPMs) and whispering-gallery modes (WGMs). Besides, by changing the length of major and minor radius of the ellipse, the resonant wavelengths of FPMs and WGMs can be independently tuned and close to each other, thus constructing a plasmon-induced transparency (PIT) - like spectrum profile. Benefitting from this, a dual-band slow light is achieved with one single resonator. Furthermore, this component can also act as a multi-band color filter and refractive index sensor. We believe such multi-mode resonator with ultra-small footprint will play an important role in more compact on-chip optical circuits in the future.

physics.optics↗