arXiv subjects

Shuo Liu

Publications and source records attributed to Shuo Liu.

197 records · Page 11Linked to original sources

arXiv2019

N-HANS: Introducing the Augsburg Neuro-Holistic Audio-eNhancement System

N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression. The functionalities are realised based on two neural network models sharing the same architecture, but trained separately. The models are comprised of stacks of residual blocks, each conditioned on additional speech or environmental noise recordings for adapting to different unseen speakers or environments in real life. In addition to a Python API, a command line interface is provided to researchers and developers, both of which are documented at https://github.com/N-HANS/N-HANS. Experimental results indicate that N-HANS achieves outstanding performance, and ensure its reliable usage in real-life audio and speech-related tasks, reaching very high audio and speech quality.

cs.SD↗

arXiv2019

Single-Photon Rydberg Excitation and Trap-Loss Spectroscopy of Cold Cesium Atoms in a Magneto-Optical Trap by Using of a 319-nm Ultra-Violet Laser System

We demonstrate the single-photon Rydberg excitation of cesium atoms in a magneto-optical trap (MOT). We excite atoms directly from ${{6S}_{1/2}}$ ground state to ${{nP}_{3/2}}(n=70-100)$ Rydberg state with a narrow-linewidth 319 nm ultra-violet laser. The detection of Rydberg states is performed by monitoring the reduction of fluorescence signal of the MOT as partial population on ${{6S}_{1/2}} (F = 4)$ ground state are transferred to Rydberg state. We clearly observe Autler-Townes doublet in the trap-loss spectra due to the cooling lights. Utilizing the large electric polarizibility of Rydberg atoms, we observe Stark splitting in the Autler-Townes doublet induced by background DC electric fields. We investigate the dependence of Stark shift on electric fields by theoretical analysis, and then infer the DC electric field from the measured Stark splitting. We find that there is a 44.8(4) mV/cm DC electric field in the vicinity of the cold ensemble. It indicates that high-lying Rydberg atoms can be used as sensors for DC electric fields.

physics.atom-ph↗

arXiv2019

AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition

The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.

cs.HC↗

arXiv2019

Single-Channel Speech Separation with Auxiliary Speaker Embeddings

We present a novel source separation model to decompose asingle-channel speech signal into two speech segments belonging to two different speakers. The proposed model is a neural network based on residual blocks, and uses learnt speaker embeddings created from additional clean context recordings of the two speakers as input to assist in attributing the different time-frequency bins to the two speakers. In experiments, we show that the proposed model yields good performance in the source separation task, and outperforms the state-of-the-art baselines. Specifically, separating speech from the challenging VoxCeleb dataset, the proposed model yields 4.79dB signal-to-distortion ratio, 8.44dB signal-to-artifacts ratio and 7.11dB signal-to-interference ratio.

cs.SD↗

arXiv2019

Fully-control of OAM vortex beam and realization of retro and negative reflection at oblique incidence using dual-band 2-bit coding metasurface

This paper addresses a reflection-type dual-band 2-bit coding metasurface (CM) design to achieve the dual-band functionalities in two different operating bands, independently. We are particularly interested to control linearly-polarized incident waves by encoding the propagation / dynamic phase in order to realize and fully control the orbital angular momentum (OAM) vortex beam (VB) for dual-band, independently. In this regard, we perform digital addition operations to combine the coding sequences of traditional OAM and phase-gradient to construct the proposed CM. The proposed CM generates and steers the OAM-VB with different topological charges in both lower and higher bands, independently. Similarly, the proposed CM is further extended for OAM-VB shaping by combining the coding sequences of traditional OAM and splitting phase unit-cells, and, we discuss two different scenarios. Firstly, OAM-VB splitting is performed to achieve different topological charges in both lower and higher bands, independently. Secondly, quad-beam shaping is realized to verify the proposed concept of this paper. Finally yet importantly, we also examine that the reflected beam directions can be further independently controlled to achieve anomalous, negative and retro-reflection at two different operating bands. The proposed concept will open up possibilities of multi-functional meta-devices and will be useful in many applications such as optical spanners, quantum optics, spatial mode multiplexing for telecommunications, and astronomy etc.

physics.optics↗

arXiv2018

Autler-Townes doublet in single-photon Rydberg spectra of Cesium atomic vapor with a 319 nm UV laser

We demonstrate the single-photon excitation spectra of cesium Rydberg atoms by means of a Doppler-free purely all-optical detection with a room-temperature vapor cell and a 319 nm ultra-violet (UV) laser. We excite atoms directly from 6S1/2 ground state to 71P3/2 Rydberg state with a narrow-linewidth 319 nm UV laser. The detection of Rydberg states is performed by monitoring the absorption of an 852 nm probe beam in a V-type three-level system. With a strong coupling light, we observe the Autler-Townes doublet and investigate experimentally the dependence of the separation and linewidth on the coupling intensity, which is consistent with the prediction based on the dressed state theory. We further investigate the Rydberg spectra with an external magnetic field. The existence of non-degenerate Zeeman sub-levels results in the broadening and shift of the spectra. It has potential application in sensing magnetic field.

physics.atom-ph↗

arXiv2018

A novel EM concentrator with open-concentrator region based on multi-folded transformation optics

Conventional concentrators with inhomogeneous coating materials that fully enclose the destined region pose great challenges for fabrication. In this paper, we propose to design an EM concentrator with homogeneous materials. Distinguished from conventional ones, the elaborately designed EM concentrator features a concentrator region that is open to the outer-world, which is achieved with multi-folded transformation optics method by compressing and folding the coating materials to create window(s). Based on this concept, we also investigate open-rotator and open rotational-concentrator devices, which could simultaneously rotate and store the EM waves in the central destined region. Due to the open nature of our proposed designs, we believe they will find potential applications in remote controlling with impressive new functionalities.

physics.app-ph↗

arXiv2018

Automatic streetlights that glow on detecting night and object using Arduino

Our manuscript aims to develop a system which will lead to energy conservation and by doing so, we would be able to lighten few more homes. The proposed work is accomplished by using Arduino microcontroller and sensors that will control the electricity based on night and object's detection. Meanwhile, a counter is set that will count the number of objects passed through the road. The beauty of the proposed work is that the wastage of unused electricity can be reduced, lifetime of the streetlights gets enhance because the lights do not stay ON during the whole night, and helps to increase safety measurements. We are confident that the proposed idea will be beneficial in the future applications of microcontrollers and sensors etc.

cs.OH↗

arXiv2018

IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation

Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights. In this paper, we present the infrared-to-visual (IR2VI) algorithm, a novel unsupervised thermal-to-visible image translation framework based on generative adversarial networks (GANs). IR2VI is able to learn the intrinsic characteristics from VI images and integrate them into IR images. Since the existing unsupervised GAN-based image translation approaches face several challenges, such as incorrect mapping and lack of fine details, we propose a structure connection module and a region-of-interest (ROI) focal loss method to address the current limitations. Experimental results show the superiority of the IR2VI algorithm over baseline methods.

cs.CV↗

arXiv2018

Controlling spectral energies of all harmonics in programmable way using time-domain digital coding metasurface

Modern wireless communication is one of the most important information technologies, but its system architecture has been unchanged for many years. Here, we propose a much simpler architecture for wireless communication systems based on metasurface. We firstly propose a time-domain digital coding metasurface to reach a simple but efficient method to manipulate spectral distributions of harmonics. Under dynamic modulations of phases on surface reflectivity, we could achieve accurate controls to different harmonics in a programmable way to reach many unusual functions like frequency cloaking and velocity illusion, owing to the temporal gradient introduced by digital signals encoded by '0' and '1' sequences. A theoretical model is presented and experimentally validated to reveal the nonlinear process. Based on the time-domain digital coding metasurface, we propose and realize a new wireless communication system in binary frequency-shift keying (BFSK) frame, which has much more simplified architecture than the traditional BFSK with excellent performance for real-time message transmission. The presented work, from new concept to new system, will find important applications in modern information technologies.

physics.app-ph↗

arXiv2018

Efficient Traffic-Sign Recognition with Scale-aware CNN

The paper presents a Traffic Sign Recognition (TSR) system, which can fast and accurately recognize traffic signs of different sizes in images. The system consists of two well-designed Convolutional Neural Networks (CNNs), one for region proposals of traffic signs and one for classification of each region. In the proposal CNN, a Fully Convolutional Network (FCN) with a dual multi-scale architecture is proposed to achieve scale invariant detection. In training the proposal network, a modified "Online Hard Example Mining" (OHEM) scheme is adopted to suppress false positives. The classification network fuses multi-scale features as representation and adopts an "Inception" module for efficiency. We evaluate the proposed TSR system and its components with extensive experiments. Our method obtains $99.88\%$ precision and $96.61\%$ recall on the Swedish Traffic Signs Dataset (STSD), higher than state-of-the-art methods. Besides, our system is faster and more lightweight than state-of-the-art deep learning networks for traffic sign recognition.

cs.CV↗

arXiv2018

Connecting Complex Electronic Pattern Formation to Critical Exponents

Scanning probes reveal complex, inhomogeneous patterns on the surface of many condensed matter systems. In some cases, the patterns form self-similar, fractal geometric clusters. In this paper, we advance the theory of criticality as it pertains to those geometric clusters (defined as connected sets of nearest-neighbor aligned spins) in the context of Ising models. We show how data from surface probes can be used to distinguish whether electronic patterns observed at the surface of a material are confined to the surface, or whether the patterns originate in the bulk. Whereas thermodynamic critical exponents are derived from the behavior of Fortuin-Kasteleyn (FK) clusters, critical exponents can be similarly defined for geometric clusters. We find that these geometric critical exponents are not only distinct numerically from the thermodynamic and uncorrelated percolation exponents, but that they separately satisfy scaling relations at the critical fixed points discussed in the text. We furthermore find that the two-dimensional (2D) cross-sections of geometric clusters in the three-dimensional (3D) Ising model display critical scaling behavior at the bulk phase transition temperature. In particular, we show that when considered on a 2D slice of a 3D system, the pair connectivity function familiar from percolation theory displays more robust critical behavior than the spin-spin correlation function, and we calculate the corresponding critical exponent. We discuss the implications of these two distinct length scales in Ising models. We also calculate the pair connectivity exponent in the clean 2D case. These results extend the theory of geometric criticality in the clean Ising universality classes, and facilitate the broad application of geometric cluster analysis techniques to maximize the information that can be extracted from scanning image probe data in condensed matter systems.

cond-mat.str-el↗

arXiv2018

Classifying surface probe images in strongly correlated electronic systems via machine learning

Scanning probe experiments such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM) on strongly correlated electronic systems often reveal complex pattern formation on multiple length scales. By studying the universal scaling in these images, we have shown in several distinct correlated electronic systems that the pattern formation is driven by proximity to a disorder-driven critical point, revealing a unification of the pattern formation in these materials. As an alternative approach to this image classification problem of novel materials, here we report the first investigation of the machine learning method to determine which underlying physical model is driving pattern formation in a system. Using a neural network architecture, we are able to achieve 97% accuracy on classifying configuration images from three models with Ising symmetry. This investigation also demonstrates that machine learning can capture the implicit universal behavior of a physical system. This broadens our understanding of what machine learning can do, and we expect more synergy between machine learning and condensed matter physics in the future.

cond-mat.str-el↗

arXiv2017

Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness

Object Detection is critical for automatic military operations. However, the performance of current object detection algorithms is deficient in terms of the requirements in military scenarios. This is mainly because the object presence is hard to detect due to the indistinguishable appearance and dramatic changes of object's size which is determined by the distance to the detection sensors. Recent advances in deep learning have achieved promising results in many challenging tasks. The state-of-the-art in object detection is represented by convolutional neural networks (CNNs), such as the fast R-CNN algorithm. These CNN-based methods improve the detection performance significantly on several public generic object detection datasets. However, their performance on detecting small objects or undistinguishable objects in visible spectrum images is still insufficient. In this study, we propose a novel detection algorithm for military objects by fusing multi-channel CNNs. We combine spatial, temporal and thermal information by generating a three-channel image, and they will be fused as CNN feature maps in an unsupervised manner. The backbone of our object detection framework is from the fast R-CNN algorithm, and we utilize cross-domain transfer learning technique to fine-tune the CNN model on generated multi-channel images. In the experiments, we validated the proposed method with the images from SENSIAC (Military Sensing Information Analysis Centre) database and compared it with the state-of-the-art. The experimental results demonstrated the effectiveness of the proposed method on both accuracy and computational efficiency.

cs.CV↗

arXiv2016

Multi-Fingered Robotic Grasping: A Primer

This technical report presents an introduction to different aspects of multi-fingered robot grasping. After having introduced relevant mathematical background for modeling, form and force closure are discussed. Next, we present an overview of various grasp planning algorithms with the objective of illustrating different approaches to solve this problem. Finally, we discuss grasp performance benchmarking.

cs.RO↗

arXiv2015

Polarization-controlled anisotropic coding metamaterials at terahertz frequencies

Metamaterials based on effective media have achieved a lot of unusual physics (e.g. negative refraction and invisibility cloaking) owing to their abilities to tailor the effective medium parameters that do not exist in nature. Recently, coding metamaterials have been suggested to control electromagnetic waves by designing the coding sequences of digital elements '0' and '1', which possess opposite phase responses. Here, we propose the concept of anisotropic coding metamaterial at terahertz frequencies, in which coding behaviors in different directions are dependent on the polarization status of terahertz waves. We experimentally demonstrate an ultrathin and flexible polarization-controlled anisotropic coding metasurface functioning in the terahertz regime using specially- designed coding elements. By encoding the elements with elaborately-designed digital sequences (in both 1 bit and 2 bits), the x- and y-polarized reflected waves can be deflected or diffused independently in three dimensions. The simulated far-field scattering patterns as well as near-electric-field distributions are given to illustrate the bifunctional performance of the encoded metasurface, which show good agreement to the measurement results. We further demonstrate the abilities of anisotropic coding metasurface to generate beam splitter and realize anomalous reflection and polarization conversion simultaneously, providing powerful controls of differently-polarized terahertz waves. The proposed method enables versatile beam behaviors under orthogonal polarizations using a single metasurface, and hence will promise interesting terahertz devices.

physics.optics↗

arXiv2015

Random Field Driven Spatial Complexity at the Mott Transition in VO2

We report the first application of critical cluster techniques to the Mott metal-insulator transition in vanadium dioxide. We show that the geometric properties of the metallic and insulating puddles observed by scanning near-field infrared microscopy are consistent with the system passing near criticality of the random field Ising model as temperature is varied. The resulting large barriers to equilibrium may be the source of the unusually robust hysteresis phenomena associated with the metal-insulator transition in this system.

cond-mat.str-el↗