arXiv subjects

Rui Yang

Publications and source records attributed to Rui Yang.

At least 325 records · Page 18Linked to original sources

arXiv2020

Ergodic-localized junctions in a periodically-driven spin chain

We report the analogue simulation of an ergodiclocalized junction by using an array of 12 coupled superconducting qubits. To perform the simulation, we fabricated a superconducting quantum processor that is divided into two domains: a driven domain representing an ergodic system, while the second is localized under the effect of disorder. Due to the overlap between localized and delocalized states, for small disorder there is a proximity effect and localization is destroyed. To experimentally investigate this, we prepare a microwave excitation in the driven domain and explore how deep it can penetrate the disordered region by probing its dynamics. Furthermore, we performed an ensemble average over 50 realizations of disorder, which clearly shows the proximity effect. Our work opens a new avenue to build quantum simulators of driven-disordered systems with applications in condensed matter physics and material science

quant-ph↗

arXiv2019

Fabrication of the impedance-matched Josephson parametric amplifier and the study of the gain profile

We designed and fabricated an impedance matched Josephson junction parametric amplifier (JPA) working in the flux-pump mode for the broadband amplification of microwave signals. We developed a very simple fabrication method suitable for a small lab. We studied the phase response, as well as the gain, as a function of frequency and pump power at various pump frequencies. The phase response can be explained with the behavior of the non-linear Duffing oscillator. The observed decrease of the resonance frequency as the pump power increases, as well as the emergency of an unstable bifurcation zone, are the characteristic non-linear behavior of the Duffing oscillator. The gain profile in the stable zone can be explained with a model adapted from the theoretical model for the two-dimensional gain profile of an impedance-matched current-pumped JPA. With an appropriate environmental impedance, the theoretical model captures the features and morphology of the gain profile, such as the emergence of a gain hot zone with two branches around the resonance frequency of the JPA. Based on the gain profile, we propose that the best working zone is the merging point of the two branches of the gain hot zone before the emergence of the bifurcation zone, which gives a large bandwidth and a good gain. Over 17dB gain with a bandwidth larger than 300MHz was observed. The impedance matched JPA is used in our superconducting quantum computers for improving the fast readout fidelity of the transmon qubits.

physics.app-ph↗

arXiv2019

A Locating Model for Pulmonary Tuberculosis Diagnosis in Radiographs

Objective: We propose an end-to-end CNN-based locating model for pulmonary tuberculosis (TB) diagnosis in radiographs. This model makes full use of chest radiograph (X-ray) for its improved accessibility, reduced cost and high accuracy for TB disease. Methods: Several specialized improvements are proposed for detection task in medical field. A false positive (FP) restrictor head is introduced for FP reduction. Anchor-oriented network heads is proposed in the position regression section. An optimization of loss function is designed for hard example mining. Results: The experimental results show that when the threshold of intersection over union (IoU) is set to 0.3, the average precision (AP) of two test data sets provided by different hospitals reaches 0.9023 and 0.9332. Ablation experiments shows that hard example mining and change of regressor heads contribute most in this work, but FP restriction is necessary in a CAD diagnose system. Conclusion: The results prove the high precision and good generalization ability of our proposed model comparing to previous works. Significance: We first make full use of the feature extraction ability of CNNs in TB diagnostic field and make exploration in localization of TB, when the previous works focus on the weaker task of healthy-sick subject classification.

eess.IV↗

arXiv2019

Robust Matrix Completion State Estimation in Distribution Systems

Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.

cs.LG↗

arXiv2019

Low-PAPR Multi-channel OOK Waveform for IEEE 802.11ba Wake-up Radio

The peak-to-average-power ratio (PAPR) of the frequency domain multiplexed wake-up signals (WUSs) specified in IEEE P802.11ba can be very large and difficult to manage since it depends on the number and allocation of the active channels, and the data rate on each channel. To address this issue, we propose a transmission scheme based on complementary sequences (CSs) for multiple WUSs multiplexed in the frequency domain. We discuss how to construct CSs compatible with the framework of IEEE P802.11ba by exploiting a recursive Golay complementary pair (GCP) construction to reduce the instantaneous power fluctuations in time. We compare the proposed scheme with the other options under a non-linear power amplifier (PA) distortion. Numerical results show that the proposed scheme can lower the PAPR of the transmitted signal in frequency division multiple access (FDMA) scenarios more than 3 dB and yields a superior error rate performance under severe PA distortion.

cs.IT↗

arXiv2019

Learning Target-oriented Dual Attention for Robust RGB-T Tracking

RGB-Thermal object tracking attempt to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust feature representation learning or adaptive modal weighting. However, how to integrate dual attention mechanism for visual tracking is still a subject that has not been studied yet. In this paper, we propose two visual attention mechanisms for robust RGB-T object tracking. Specifically, the local attention is implemented by exploiting the common visual attention of RGB and thermal data to train deep classifiers. We also introduce the global attention, which is a multi-modal target-driven attention estimation network. It can provide global proposals for the classifier together with local proposals extracted from previous tracking result. Extensive experiments on two RGB-T benchmark datasets validated the effectiveness of our proposed algorithm.

cs.CV↗

arXiv2019

Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity

In this paper, we propose Hard Person Identity Mining (HPIM) that attempts to refine the hard example mining to improve the exploration efficacy in person re-identification. It is motivated by following observation: the more attributes some people share, the more difficult to separate their identities. Based on this observation, we develop HPIM via a transferred attribute describer, a deep multi-attribute classifier trained from the source noisy person attribute datasets. We encode each image into the attribute probabilistic description in the target person re-ID dataset. Afterwards in the attribute code space, we consider each person as a distribution to generate his view-specific attribute codes in different practical scenarios. Hence we estimate the person-specific statistical moments from zeroth to higher order, which are further used to calculate the central moment discrepancies between persons. Such discrepancy is a ground to choose hard identity to organize proper mini-batches, without concerning the person representation changing in metric learning. It presents as a complementary tool of hard example mining, which helps to explore the global instead of the local hard example constraint in the mini-batch built by randomly sampled identities. Extensive experiments on two person re-identification benchmarks validated the effectiveness of our proposed algorithm.

cs.CV↗

arXiv2019

Matrix Completion for Low-Observability Voltage Estimation

With the rising penetration of distributed energy resources, distribution system control and enabling techniques such as state estimation have become essential to distribution system operation. However, traditional state estimation techniques have difficulty coping with the low-observability conditions often present on the distribution system due to the paucity of sensors and heterogeneity of measurements. To address these limitations, we propose a distribution system state estimation algorithm that employs matrix completion (a tool for estimating missing values in low-rank matrices) augmented with noise-resilient power flow constraints. This method operates under low-observability conditions where standard least-squares-based methods cannot operate, and flexibly incorporates any network quantities measured in the field. We empirically evaluate our method on the IEEE 33- and 123-bus test systems, and find that it provides near-perfect state estimation performance (within 1\% mean absolute percent error) across many low-observability data availability regimes.

math.OC↗

arXiv2019

A Reliable Uplink Control Channel Design with Complementary Sequences

In this study, we propose two schemes for uplink control channels based on non-contiguous complementary sequences (CSs) where the peak-to-average-power ratio (PAPR) of the resulting orthogonal frequency division multiplexing (OFDM) signal is always less than or equal to 3 dB. To obtain the proposed schemes, we extend Golay's concatenation and interleaving methods by considering extra upsampling and shifting parameters. The proposed schemes enable a flexible non-contiguous resource allocation in frequency, e.g., an arbitrary number of null symbols between the occupied resource blocks (RBs). The first scheme separates the PAPR minimization and the inter-cell interference minimization problems. While the former is solved by spreading the sequences in a Golay complementary pair (GCP) with the sequences in another GCP, the latter is managed by designing a set of GCPs with low cross-correlation. The second scheme generates reference symbols (RSs) and data symbols on each RB as parts of an encoded CS. Therefore, it enables coherent detection at the receiver side. The numerical results show that the proposed schemes offer significantly improved PAPR and cubic metric (CM) results in case of non-contiguous resource allocation as compared to the sequences defined in 3GPP New Radio (NR) and Zadoff-Chu (ZC) sequences.

cs.IT↗

arXiv2018

Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking

The tracking-by-detection framework requires a set of positive and negative training samples to learn robust tracking models for precise localization of target objects. However, existing tracking models mostly treat different samples independently while ignores the relationship information among them. In this paper, we propose a novel structure-aware deep neural network to overcome such limitations. In particular, we construct a graph to represent the pairwise relationships among training samples, and additionally take the natural language as the supervised information to learn both feature representations and classifiers robustly. To refine the states of the target and re-track the target when it is back to view from heavy occlusion and out of view, we elaborately design a novel subnetwork to learn the target-driven visual attentions from the guidance of both visual and natural language cues. Extensive experiments on five tracking benchmark datasets validated the effectiveness of our proposed method.

cs.CV↗

arXiv2018

Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning

Incorporating various modes of information into the machine learning procedure is becoming a new trend. And data from various source can provide more information than single one no matter they are heterogeneous or homogeneous. Existing deep learning based algorithms usually directly concatenate features from each domain to represent the input data. Seldom of them take the quality of data into consideration which is a key issue in related multimodal problems. In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL). Specifically, we take the weighting of each domain as a decision-making problem and teach an agent learn to interact with the environment. The agent can tune the weight of each domain through discrete action selection and obtain a positive reward if the saliency results are improved. The target of the agent is to achieve maximum rewards after finished its sequential action selection. We validate the proposed algorithms on multimodal saliency detection in a coarse-to-fine way. The coarse saliency maps are generated from an encoder-decoder framework which is trained with content loss and adversarial loss. The final results can be obtained via adaptive weighting of maps from each domain. Experiments conducted on two kinds of salient object detection benchmarks validated the effectiveness of our proposed quality-aware deep neural network.

cs.CV↗

arXiv2018

Sequence-Based OOK for Orthogonal Multiplexing of Wake-up Radio Signals and OFDM Waveforms

In this study, we propose an approach to constructing on-off keying (OOK) symbols for wake-up radios (WURs) by using sequences in the frequency domain. The proposed method enables orthogonal multiplexing of wake-up signals (WUSs) and orthogonal frequency division multiplexing (OFDM) waveforms. We optimize the sequences with a tractable algorithm by considering the reliability of WUSs in fading channels. The proposed algorithm relies on an alternating minimization technique, i.e. cyclic algorithm-new (CAN), which was originally proposed for obtaining a unimodular sequence with good aperiodic correlation properties. In this study, we extend CAN to generate OOK waveforms with Manchester coding. We demonstrate the performance of four optimized sequences and compare with state-of-the-art approaches. We show that the proposed scheme improves the wake-up radio receiver (WURx) performance by controlling the energy distribution in frequency domain while removing the interference-floor at the OFDM receiver.

eess.SP↗

arXiv2018

The stability of exfoliated FeSe nanosheets during in-air device fabrication process

We studied the stability and superconductivity of FeSe nanosheets during an in-air device fabrication process. Methods were developed to improve the exfoliation yield and to maintain the superconductivity of FeSe. Raman spectroscopy, atomic force microscopy, optical microscopy and time-of-flight-secondary-ion-mass-spectroscopy measurements show that FeSe nanosheets decayed in air. Precipitation of Se particles and iron oxidation likely occurred during the decay process. Transport measurements revealed that the superconductivity of FeSe disappeared during a conventional electron beam lithography process. Shadow mask evaporation and transfer onto pre-defined electrodes methods were shown to be effective in maintaining the superconductivity after the in-air device fabrication process. These methods developed provide a way of making high quality FeSe nano-devices.

cond-mat.mtrl-sci↗

arXiv2018

Interlayer coupling effect in twisted stacked few layer black phosphorus revealed by abnormal blue shifts in Raman spectra

Twisted stacked few layer black phosphorus heterostructures were successfully fabricated in this work. Abnormal blue shifts in their Ag1 and Ag2 Raman peaks and unique optical reflections were observed in these samples. The phonon behavior difference can be explained by our density functional theory calculations, which suggest that interlayer coupling has a significant effect in twisted bilayer black phosphorus. According to the calculations, the interlayer interactions are not simply van der Waals interactions. Additional interactions, such as weak valence bonding between the top and bottom flakes, are considered to be the cause of the blue shifts in their Raman spectra.

cond-mat.mtrl-sci↗

arXiv2018

Timing Channel in IaaS: How to Identify and Investigate

Recently, the IaaS (Infrastructure as a Service) Cloud (e.g., Amazon EC2) has been widely used by many organizations. However, some IaaS security issues create serious threats to its users. A typical issue is the timing channel. This kind of channel can be a cross-VM information channel, as proven by many researchers. Because it is covert and traceless, the traditional identification methods cannot build an accurate analysis model and obtain a compromised result. We investigated the underlying behavior of the timing channel from the perspective of the memory activity records and summarized the signature of the timing channel in the underlying memory activities. An identification method based on long-term behavior signatures was proposed. We proposed a complete set of forensics steps including evidence extraction, identification, record reserve, and evidence reports. We studied four typical timing channels, and the experiments showed that these channels can be detected and investigated, even with disturbances from normal processes.

cs.CR↗

arXiv2018

A Comparison of SC-FDE and UW DFT-s-OFDM for Millimeter Wave Communications

In this study, we compare the single-carrier (SC) waveform adopted in IEEE 802.11ad and unique word discrete Fourier transform spread orthogonal frequency division multiplexing (UW DFT-s-OFDM) waveform. We provide equivalent representations of up-sampling and down-sampling operations of the SC waveform by using discrete Fourier transform (DFT) and inverse DFT to enable explicit comparison of these two similar waveforms. By using this representation, we discuss why the IEEE 802.11ad SC waveform can cause suboptimal performance in multipath channel and discuss how to improve it with UW DFT-s-OFDM. With comprehensive link-level simulations, we show that replacing the 802.11ad SC waveform with UW DFT-spread OFDM can result in 1 dB gain in peak throughput without affecting the IEEE 802.11ad packet structure. We also evaluate the cross links where the transmitter is UW-DFT-s-OFDM and the receiver is traditional SC-FDE or vice versa. We demonstrate that UW DFT-s-OFDM receiver can decode an IEEE 802.11ad SC waveform with a slight SNR loss while IEEE 802.11ad SC receiver can decode a UW DFT-spread OFDM waveform with an interference floor.

cs.IT↗

arXiv2017

DFT-Spread OFDM with Frequency Domain Reference Symbols

The fifth generation (5G) wireless standard will support several new use cases and 10 to 100 times the performance of fourth generation (4G) systems. Because of the diverse applications for 5G, flexible solutions which can address conflicting requirements will be needed. In this paper, we propose a solution which enables the use of discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) and OFDM, which address different requirements, using a common reference symbol (RS) design. In this solution, the DFT-s-OFDM symbol contains RSs in the frequency domain that may be shared by a subsequent OFDM symbol. The proposed scheme is generated by puncturing the output of a DFT-spread block and replacing the punctured samples with RSs in frequency. We prove that puncturing the interleaved samples at the output of the DFT-spread operation equivalently introduces a periodic interference to the data symbols at the input of the DFT-spread operation. We show that the interference due to the puncturing can be removed with a low-complexity receiver by exploiting the zeros inserted to certain locations before the DFT-spread block at the transmitter. Simulation results support that the proposed scheme removes the error floor caused by the puncturing and achieves lower peak-to-average-power ratio than OFDM.

cs.IT↗

arXiv2017

Thermal Sublimation: a Scalable and Controllable Thinning Method for the Fabrication of Few-Layer Black Phosphorus

In this work, we reported uniform layer-by-layer sublimation of black phosphorus under heating below 600 K. The uniformity and crystallinity of BP samples after thermal thinning were confirmed by Raman spectra and 2D Raman imaging. A uniform and crystalline bilayer black phosphorus flake with an area of 180 um^2 was prepared with this method. No micron scale defects were observed. The sublimation rate of BP was around 0.18 nm / min at 500 K and 1.5 nm / min at 550 K. Both room and high temperature Raman peak intensity ratio Si/A2g vs. BP thickness relations were established for in-situ thickness determination. The sublimation thinning method was shown to be a controllable and scalable approach to prepare few-layer black phosphorus.

cond-mat.mtrl-sci↗