arXiv subjects

You Wang

Publications and source records attributed to You Wang.

At least 55 records · Page 3Linked to original sources

arXiv2023

Discrete States-Based Trajectory Planning for Nonholonomic Robots

Due to nonholonomic dynamics, the motion planning of nonholonomic robots is always a difficult problem. This letter presents a Discrete States-based Trajectory Planning(DSTP) algorithm for autonomous nonholonomic robots. The proposed algorithm represents the trajectory as x and y positions, orientation angle, longitude velocity and acceleration, angular velocity, and time intervals. More variables make the expression of optimization and constraints simpler, reduce the error caused by too many approximations, and also handle the gear shifting situation. L-BFGS-B is used to deal with the optimization of many variables and box constraints, thus speeding up the problem solving. Various simulation experiments compared with prior works have validated that our algorithm has an order-of-magnitude efficiency advantage and can generate a smoother trajectory with a high speed and low control effort. Besides, real-world experiments are also conducted to verify the feasibility of our algorithm in real scenes. We will release our codes as ros packages.

cs.RO↗

arXiv2023

Quantum storage of 1650 modes of single photons at telecom wavelength

To advance the full potential of quantum networks one should be able to distribute quantum resources over long distances at appreciable rates. As a consequence, all components in the networks need to have large multimode capacity to manipulate photonic quantum states. Towards this end, a multimode photonic quantum memory, especially one operating at telecom wavelength, remains a key challenge. Here we demonstrate a spectro-temporally multiplexed quantum memory at 1532 nm. Multimode quantum storage of telecom-band heralded single photons is realized by employing the atomic frequency comb protocol in a 10-m-long cryogenically cooled erbium doped silica fibre. The multiplexing encompasses five spectral channels - each 10 GHz wide - and in each of these up to 330 temporal modes, resulting in the simultaneous storage of 1650 modes of single photons. Our demonstrations open doors for high-rate quantum networks, which are essential for future quantum internet.

quant-ph↗

arXiv2022

High-quality multi-wavelength quantum light sources on silicon nitride micro-ring chip

Multi-wavelength quantum light sources, especially at telecom band, are extremely desired in quantum information technology. Despite recent impressive advances, such a quantum light source with high quality remains challenging. Here we demonstrate a multi-wavelength quantum light source using a silicon nitride micro-ring with a free spectral range of 200 GHz. The generation of eight pairs of correlated photons is ensured in a wavelength range of 25.6 nm. With device optimization and noise-rejecting filters, our source enables the generation of heralded single-photons - at a rate of 62 kHz with $g^{(2)}_{h}(0)=0.014\pm0.001$, and the generation of energy-time entangled photons - with a visibility of $99.39\pm 0.45\%$ in the Franson interferometer. These results, at room temperature and telecom wavelength, in a CMOS compatible platform, represent an important step towards integrated quantum light devices for the quantum networks.

quant-ph↗

arXiv2022

Sequence-to-Sequence Voice Reconstruction for Silent Speech in a Tonal Language

Silent Speech Decoding (SSD), based on articulatory neuromuscular activities, has become a prevalent task of Brain-Computer Interface (BCI) in recent years. Many works have been devoted to decoding surface electromyography (sEMG) from articulatory neuromuscular activities. However, restoring silent speech in tonal languages such as Mandarin Chinese is still difficult. This paper proposes an optimized Sequence-to-Sequence (Seq2Seq) approach to synthesize voice from the sEMG-based silent speech. We extract duration information to regulate the sEMG-based silent speech using the audio length. Then, we provide a deep-learning model with an encoder-decoder structure and a state-of-art vocoder to generate the audio waveform. Experiments based on six Mandarin Chinese speakers demonstrate that the proposed model can successfully decode silent speech in Mandarin Chinese and achieve a character error rate (CER) of 6.41% on average with human evaluation.

cs.SD↗

arXiv2022

Direction and Trajectory Tracking Control for Nonholonomic Spherical Robot by Combining Sliding Mode Controller and Model Prediction Controller

Spherical robot is a nonlinear, nonholonomic and unstable system which increases the difficulty of the direction and trajectory tracking problem. In this study, we propose a new direction controller HTSMC, an instruction planning controller MPC, and a trajectory tracking framework MHH. The HTSMC is designed by integrating a fast terminal algorithm, a hierarchical method, the motion features of a spherical robot, and its dynamics. In addition, the new direction controller has an excellent control effect with a quick response speed and strong stability. MPC can obtain optimal commands that are then transmitted to the velocity and direction controller. Since the two torque controllers in MHH are all Lyapunov-based sliding mode controllers, the MHH framework may achieve optimal control performance while assuring stability. Finally, the two controllers eliminate the requirement for MPC's stability and dynamic constraints. Finally, hardware experiments demonstrate the efficacy of the HTSMC, MPC, and MHH.

cs.RO↗

arXiv2022

Amplification of quantum signals by the non-Hermitian skin effect

The non-Hermitian skin effect (NHSE) is a phenomenon whereby certain non-Hermitian lattice Hamiltonians, particularly those with nonreciprocal couplings, can host an extensive number of eigenmodes condensed to the boundary, called skin modes. Although the NHSE has mostly been studied in the classical regime, we show that it can also manifest in quantum systems containing boson number nonconserving processes arising from uniform parametric driving. We study lattices of coupled nonlinear resonators that can function as reciprocal quantum amplifiers. A one-dimensional chain exhibiting NHSE can perform strong photon amplification, aided by the skin modes, that scales exponentially with the chain length and outperforms alternative lattice configurations that lack the NHSE. We show also that two-dimensional nonlinear lattices can perform directional photon amplification between different lattice corners, due to the two-dimensional NHSE.

physics.optics↗

arXiv2022

Towards real-world quantum networks: a review

Quantum networks play an extremely important role in quantum information science, with application to quantum communication, computation, metrology and fundamental tests. One of the key challenges for implementing a quantum network is to distribute entangled flying qubits to spatially separated nodes, at which quantum interfaces or transducers map the entanglement onto stationary qubits. The stationary qubits at the separated nodes constitute quantum memories realized in matter while the flying qubits constitute quantum channels realized in photons. Dedicated efforts around the world for more than twenty years have resulted in both major theoretical and experimental progress towards entangling quantum nodes and ultimately building a global quantum network. Here, we review the development of quantum networks and the experimental progress over the past two decades leading to the current state of the art for generating entanglement of quantum nodes based on various physical systems such as single atoms, cold atomic ensembles, trapped ions, diamonds with Nitrogen-Vacancy centers, solid-state host doped with rare-earth ions, etc. Along the way we discuss the merits and compare the potential of each of these systems towards realizing a quantum network.

quant-ph↗

arXiv2021

Unsupervised cross-user adaptation in taste sensation recognition based on surface electromyography with conformal prediction and domain regularized component analysis

Human taste sensation can be qualitatively described with surface electromyography. However, the pattern recognition models trained on one subject (the source domain) do not generalize well on other subjects (the target domain). To improve the generalizability and transferability of taste sensation models developed with sEMG data, two methods were innovatively applied in this study: domain regularized component analysis (DRCA) and conformal prediction with shrunken centroids (CPSC). The effectiveness of these two methods was investigated independently in an unlabeled data augmentation process with the unlabeled data from the target domain, and the same cross-user adaptation pipeline were conducted on six subjects. The results show that DRCA improved the classification accuracy on six subjects (p < 0.05), compared with the baseline models trained only with the source domain data;, while CPSC did not guarantee the accuracy improvement. Furthermore, the combination of DRCA and CPSC presented statistically significant improvement (p < 0.05) in classification accuracy on six subjects. The proposed strategy combining DRCA and CPSC showed its effectiveness in addressing the cross-user data distribution drift in sEMG-based taste sensation recognition application. It also shows the potential in more cross-user adaptation applications.

q-bio.QM↗

arXiv2021

CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose

In machine learning applications, the reliability of predictions is significant for assisted decision and risk control. As an effective framework to quantify the prediction reliability, conformal prediction (CP) was developed with the CPKNN (CP with kNN). However, the conventional CPKNN suffers from high variance and bias and long computational time as the feature dimensionality increases. To address these limitations, a new CP framework-conformal prediction with shrunken centroids (CPSC) is proposed. It regularizes the class centroids to attenuate the irrelevant features and shrink the sample space for predictions and reliability quantification. To compare CPKNN and CPSC, we employed them in the classification of 12 categories of alternative herbal medicine with electronic nose as a case and assessed them in two tasks: 1) offline prediction: the training set was fixed and the accuracy on the testing set was evaluated; 2) online prediction with data augmentation: they filtered unlabeled data to augment the training data based on the prediction reliability and the final accuracy of testing set was compared. The result shows that CPSC significantly outperformed CPKNN in both two tasks: 1) CPSC reached a significantly higher accuracy with lower computation cost, and with the same credibility output, CPSC generally achieves a higher accuracy; 2) the data augmentation process with CPSC robustly manifested a statistically significant improvement in prediction accuracy with different reliability thresholds, and the augmented data were more balanced in classes. This novel CPSC provides higher prediction accuracy and better reliability quantification, which can be a reliable assistance in decision support.

q-bio.QM↗

arXiv2021

Classifying herbal medicine origins by temporal and spectral data mining of electronic nose

The origins of herbal medicines are important for their treatment effect, which could be potentially distinguished by electronic nose system. As the odor fingerprint of herbal medicines from different origins can be tiny, the discrimination of origins can be much harder than that of different categories. Better feature extraction methods are significant for this task to be more accurately done, but there lacks systematic studies on different feature extraction methods. In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms. With 50 repetitive experiments with bootstrapping, we compared the effectiveness of the extractions with a two-layer neural network w/o dimensionality reduction methods (principal component analysis, linear discriminant analysis) as the three base classifiers. Compared with the conventional aggregated features, the Fast Fourier Transform method and our novel approach (longitudinal-information-in-a-line) showed an significant accuracy improvement(p < 0.05) on all 3 base classifiers and all three herbal medicine categories. Two of the deep learning algorithm we applied also showed partially significant improvement: one-dimensional convolution neural network(1D-CNN) and a novel graph pooling based framework - multivariate time pooling(MTPool).

q-bio.QM↗

arXiv2021

Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose

Electronic nose has been proven to be effective in alternative herbal medicine classification, but due to the nature of supervised learning, previous research heavily relies on the labelled training data, which are time-costly and labor-intensive to collect. To alleviate the critical dependency on the training data in real-world applications, this study aims to improve classification accuracy via data augmentation strategies. The effectiveness of five data augmentation strategies under different training data inadequacy are investigated in two scenarios: the noise-free scenario where different availabilities of unlabelled data were considered, and the noisy scenario where different levels of Gaussian noises and translational shifts were added to represent sensor drifts. The five augmentation strategies, namely noise-adding data augmentation, semi-supervised learning, classifier-based online learning, Inductive Conformal Prediction (ICP) online learning and our novel ensemble ICP online learning proposed in this study, are experimented and compared against supervised learning baseline, with Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) as the classifiers. Our novel strategy, ensemble ICP online learning, outperforms the others by showing non-decreasing classification accuracy on all tasks and a significant improvement on most simulated tasks (25out of 36 tasks,p<=0.05). Furthermore, this study provides a systematic analysis of different augmentation strategies. It shows at least one strategy significantly improved the classification accuracy with LDA (p<=0.05) and non-decreasing classification accuracy with SVM in each task. In particular, our proposed strategy demonstrated both effectiveness and robustness in boosting the classification model generalizability, which can be employed in other machine learning applications.

cs.LG↗

arXiv2021

Giant Enhancement of Unconventional Photon Blockade in a Dimer Chain

Unconventional photon blockade refers to the suppression of multi-photon states in weakly nonlinear optical resonators via the destructive interference of different excitation pathways. It has been studied in a pair of coupled nonlinear resonators and other few-mode systems. Here, we show that unconventional photon blockade can be greatly enhanced in a chain of coupled resonators. Specifically, the strength of the nonlinearity in each resonator needed to achieve unconventional photon blockade is suppressed exponentially with lattice size. The analytic derivation, based on a weak drive approximation, is validated by wavefunction Monte Carlo simulations. These findings show that customized lattices of coupled resonators can be powerful tools for controlling multi-photon quantum states.

quant-ph↗

arXiv2021

Spectrally multiplexed heralded single photon source at telecom-band

Heralded single photon source (HSPS) is an important way in generating genuine single photon, having advantages of experimental simplicity and versatility. However, HSPS intrinsically suffers from the trade-off between the heralded single photon rate and the single photon purity. To overcome this, one can apply multiplexing technology in different degrees of freedom to enhance the performance of HSPS. Here, by employing spectral multiplexing and active feed-forward spectral manipulating, we demonstrate a HSPS at 1.5 μm telecom-band. Our experimental results show that the spectral multiplexing effectively erases the frequency correlation of pair source and significantly improves the heralded single photon rate while keeping the g{^(^2^)}(0) as low as 0.0006{\pm}0.0001. The Hong-Ou-Mandel interference between the heralded single photons and photons from an independent weak coherent source indicates a high indistinguishability. Our results pave a way for scalable HSPS by spectral multiplexing towards deterministic single photon emission.

quant-ph↗

arXiv2021

High-performance quantum entanglement generation via cascaded second-order nonlinear processes

In this paper, we demonstrate the generation of high-performance entangled photon-pairs in different degrees of freedom from a single piece of fiber pigtailed periodically poled LiNbO$_3$ (PPLN) waveguide. We utilize cascaded second-order nonlinear optical processes, i.e. second-harmonic generation (SHG) and spontaneous parametric down conversion (SPDC), to generate photon-pairs. Previously, the performance of the photon pairs is contaminated by Raman noise photons from the fiber pigtails. Here by integrating the PPLN waveguide with noise rejecting filters, we obtain a coincidence-to-accidental ratio (CAR) higher than 52,600 with photon-pair generation and detection rate of 52.3 kHz and 3.5 kHz, respectively. Energy-time, frequency-bin and time-bin entanglement is prepared by coherently superposing correlated two-photon states in these degrees of freedom, respectively. The energy-time entangled two-photon states achieve the maximum value of CHSH-Bell inequality of S=2.708$\pm$0.024 with a two-photon interference visibility of 95.74$\pm$0.86%. The frequency-bin entangled two-photon states achieve fidelity of 97.56$\pm$1.79% with a spatial quantum beating visibility of 96.85$\pm$2.46%. The time-bin entangled two-photon states achieve the maximum value of CHSH-Bell inequality of S=2.595$\pm$0.037 and quantum tomographic fidelity of 89.07$\pm$4.35%. Our results provide a potential candidate for quantum light source in quantum photonics.

quant-ph↗

arXiv2020

Perturbation theory for Maxwell's equations in anisotropic materials with shifting boundaries

Perturbation theory is a kind of estimation method based on theorem of Taylor expansion, and is useful to investigate electromagnetic solutions of small changes. By considering a sharp boundary as a limit of smoothed systems, previous study has solved the problem when applying standard perturbation theory to Maxwell's equations for small shifts in isotropic dielectric interfaces. However, when dealing with anisotropic materials, an approximation is conducted and leads to an unsatisfactory error. Here we develop a modified perturbation theory for small shifts in anisotropically dielectric interfaces. By using optimized smoothing function for each component of permittivity, we obtain a method to calculate the intrinsic frequency shifts of anisotropic permittivity field when boundaries shift, without approximation. Our method shows accurate results when calculating eigenfrequency's shifts in strong-anisotropy materials, and can be widely used for small shifts in anisotropically dielectric interfaces.

physics.comp-ph↗

arXiv2020

Effect of dispersion on indistinguishability between single-photon wave-packets

With propagating through a dispersive medium, the temporal-spectral profile of laser pulses should be inevitably modified. Although such dispersion effect has been well studied in classical optics, its effect on a single-photon wave-packet, i.e., the matter wave of a single-photon, has not yet been entirely revealed. In this paper, we investigate the effect of dispersion on indistinguishability of single-photon wave-packets through the Hong-Ou-Mandel (HOM) interference. By dispersively manipulating two indistinguishable single-photon wave-packets before interfering with each other, we observe that the difference of the second-order dispersion between two optical paths of the HOM interferometer can be mapped to the interference curve, indicating that (1) with the same amount of dispersion effect in both paths, the HOM interference curve must be only determined by the intrinsic indistinguishability between the wave-packets, i.e., dispersion cancellation due to the indistinguishability between Feynman paths; (2) unbalanced dispersion effect in two paths cannot be cancelled and will broaden the interference curve thus providing a way to measure the second-order dispersion coefficient. Our results suggest a more comprehensive understanding of the single-photon wave-packet and pave ways to explore further applications of the HOM interference.

quant-ph↗

arXiv2020

A Multi-Channel Temporal Attention Convolutional Neural Network Model for Environmental Sound Classification

Recently, many attention-based deep neural networks have emerged and achieved state-of-the-art performance in environmental sound classification. The essence of attention mechanism is assigning contribution weights on different parts of features, namely channels, spectral or spatial contents, and temporal frames. In this paper, we propose an effective convolutional neural network structure with a multi-channel temporal attention (MCTA) block, which applies a temporal attention mechanism within each channel of the embedded features to extract channel-wise relevant temporal information. This multi-channel temporal attention structure will result in a distinct attention vector for each channel, which enables the network to fully exploit the relevant temporal information in different channels. The datasets used to test our model include ESC-50 and its subset ESC-10, along with development sets of DCASE 2018 and 2019. In our experiments, MCTA performed better than the single-channel temporal attention model and the non-attention model with the same number of parameters. Furthermore, we compared our model with some successful attention-based models and obtained competitive results with a relatively lighter network.

eess.AS↗

arXiv2020

Image reconstruction through a multimode fiber with a simple neural network architecture

Multimode fibers (MMFs) have the potential to carry complex images for endoscopy and related applications, but decoding the complex speckle patterns produced by mode-mixing and modal dispersion in MMFs is a serious challenge. Several groups have recently shown that convolutional neural networks (CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in terms of training time and computing resources required. The trained networks can accurately reconstruct MMF images collected over a week after the cessation of the training set, with the dense network performing as well as the CNN over the entire period.

eess.IV↗