Quantum Noise of Kramers-Kronig Receiver

Abstrac--Kramers-Kronig (KK) receiver, which is equivalent to heterodyne detection with one single photodetector, provides an efficient method to reconstruct the complex-valued optical field by means of intensity detection given a minimum-phase signal. In this paper, quantum noise of the KK receiver is derived analytically and compared with that of the balanced heterodyne detection. We show that the quantum noise of the KK receiver keeps the radical fluctuation of the measured signal the same as that of the balanced heterodyne detection, while compressing the tangential noise to 1/3 times the radical one using the information provided by the Hilbert transform. In consequence, the KK receiver has 3/2 times the signal-to-noise ratio of balanced heterodyne detection while presenting an asymmetric distribution of fluctuations, which is also different from that of the latter. More interestingly, the projected in-phase and quadrature field operators of the retrieved signal after down conversion have a time dependent quantum noise distribution depending on the time-varying phase. This property provides a feasible scheme for controlling the fluctuation distribution according to the requirements of measurement accuracy in the specific direction. Under the condition of strong carrier wave, the fluctuations of the component requiring to be measured more accurately can be compressed to 1 / 6, which is even lower than 1/4 by measuring a coherent state. Finally, we prove the analytic conclusions by simulation results

StepNet: Spatial-temporal Part-aware Network for Sign Language Recognition

Sign language recognition (SLR) aims to overcome the communication barrier for the people with deafness or the people with hard hearing. Most existing approaches can be typically divided into two lines, i.e., Skeleton-based and RGB-based methods, but both the two lines of methods have their limitations. RGB-based approaches usually overlook the fine-grained hand structure, while Skeleton-based methods do not take the facial expression into account. In attempts to address both limitations, we propose a new framework named Spatial-temporal Part-aware network (StepNet), based on RGB parts. As the name implies, StepNet consists of two modules: Part-level Spatial Modeling and Part-level Temporal Modeling. Particularly, without using any keypoint-level annotations, Part-level Spatial Modeling implicitly captures the appearance-based properties, such as hands and faces, in the feature space. On the other hand, Part-level Temporal Modeling captures the pertinent properties over time by implicitly mining the long-short term context. Extensive experiments show that our StepNet, thanks to Spatial-temporal modules, achieves competitive Top-1 Per-instance accuracy on three widely-used SLR benchmarks, i.e., 56.89% on WLASL, 77.2% on NMFs-CSL, and 77.1% on BOBSL. Moreover, the proposed method is compatible with the optical flow input, and can yield higher performance if fused. We hope that this work can serve as a preliminary step for the people with deafness

FoVR: Attention-based VR Streaming through Bandwidth-limited Wireless Networks

Consumer Virtual Reality (VR) has been widely used in various application areas, such as entertainment and medicine. In spite of the superb immersion experience, to enable high-quality VR on untethered mobile devices remains an extremely challenging task. The high bandwidth demands of VR streaming generally overburden a conventional wireless connection, which affects the user experience and in turn limits the usability of VR in practice. In this paper, we propose FoVR, attention-based hierarchical VR streaming through bandwidth-limited wireless networks. The design of FoVR stems from the insight that human's vision is hierarchical, so that different areas in the field of view (FoV) can be served with VR content of different qualities. By exploiting the gaze tracking capacity of the VR devices, FoVR is able to accurately predict the user's attention so that the streaming of hierarchical VR can be appropriately scheduled. In this way, FoVR significantly reduces the bandwidth cost and computing cost while keeping high quality of user experience. We implement FoVR on a commercial VR device and evaluate its performance in various scenarios. The experiment results show that FoVR reduces the bandwidth cost by 88.9% and 76.2%, respectively compared to the original VR streaming and the state-of-the-art approach

The Research Progress of Oil Sand Separation Technology in China

From 2007 to 2008, Research Institute of Petroleum Exploration & Development, Langfang Branch launched oil sand resource exploration and the study of hot water separation technology in Fengcheng area, Northwest of Junggar Basin, and the recoverable oil-sand oil resource is 54.98 million tons with the oil content in 7.1-10%, which is distributed in Cretaceous and Jurassic with the thickness of 80-140 meters, the cover depth of oil sand is 50-90 meters. Combining with the characteristics of the oil sand in this area and based on the research of hot water separation mechanism in oil sand, the hot water separation reagent for the oil sand in this area has been successfully developed, and its separation rate reaches 90%, provided that the concentrations of the agent is 4% and the separation temperature is 85 °C. Based on series of study, the construction of testing site, which is capable of processing 10,000 tons oil sand in this area, is completed, and the on-site separation tests of oil sand are launched with the recovery rate of 90% in normal operation, and the hot water separation technology and equipment research & development are successful.Key words: Oil sand; Hot water separation technology; Separation reagent; Test

Stress analysis of rigid hanger of railway arch bridge based on vehicle-bridge coupling vibration

In order to study the stress of two new types of rigid hangers (circular steel and flat-plate rigid hangers) on the railway arch bridges, a finite element model of a railway through arch bridge was established. The influences of different types and sizes of hangers on the dynamic characteristics of the bridge were compared. Based on the established vehicle-bridge coupling vibration model, the influences of circular steel and flat-plate hanger sizes on the stress amplitude of hanger were discussed when the train passes through the bridge. The results show that when the flexible hanger of arch bridge was replaced by the rigid hanger, the symmetrical vertical bending frequency of bridge significantly increased. With the change of the size of flat-plate hanger, the torsional mode of the bridge was doped with the local vibration of the flat-plate hanger. With the increase of circular steel hanger diameter, the maximum stress amplitude of the hanger decreases as a whole. As for the flat-plate hanger, when the long side size b is the same, the maximum stress amplitude of the hanger decreases with the increase of the short side size d. When the short side size d is the same, with the increase of the long side size b, the maximum stress amplitude of the shorter hanger decreases, and the maximum stress amplitude of the longer hanger increases. When the size of the flat-plate hanger is too small or too large, the maximum stress amplitude is large

Distillation of Gaussian Einstein-Podolsky-Rosen steering with noiseless linear amplification

Einstein-Podolsky-Rosen (EPR) steering is one of the most intriguing features of quantum mechanics and an important resource for quantum communication. The inevitable loss and noise in the quantum channel will lead to decrease of the steerability and turn it from two-way to one-way. Despite an extensive research on protecting entanglement from decoherence, it remains a challenge to protect EPR steering due to its intrinsic difference from entanglement. Here, we experimentally demonstrate the distillation of Gaussian EPR steering in lossy and noisy environment using measurement-based noiseless linear amplification. Our scheme recovers the two-way steerability from one-way in certain region of loss and enhances EPR steering for both directions. We also show that the distilled EPR steering allows to extract secret key in one-sided device-independent quantum key distribution. Our work paves the way for quantum communication exploiting EPR steering in practical quantum channels

Low complexity Reed–Solomon-based low-density parity-check design for software defined optical transmission system based on adaptive puncturing decoding algorithm

AbstractWe propose and demonstrate a low complexity Reed–Solomon-based low-density parity-check (RS-LDPC) code with adaptive puncturing decoding algorithm for elastic optical transmission system. Partial received codes and the relevant column in parity-check matrix can be punctured to reduce the calculation complexity by adaptive parity-check matrix during decoding process. The results show that the complexity of the proposed decoding algorithm is reduced by 30% compared with the regular RS-LDPC system. The optimized code rate of the RS-LDPC code can be obtained after five times iteration

Deep Causal Learning: Representation, Discovery and Inference

Causal learning has attracted much attention in recent years because causality reveals the essential relationship between things and indicates how the world progresses. However, there are many problems and bottlenecks in traditional causal learning methods, such as high-dimensional unstructured variables, combinatorial optimization problems, unknown intervention, unobserved confounders, selection bias and estimation bias. Deep causal learning, that is, causal learning based on deep neural networks, brings new insights for addressing these problems. While many deep learning-based causal discovery and causal inference methods have been proposed, there is a lack of reviews exploring the internal mechanism of deep learning to improve causal learning. In this article, we comprehensively review how deep learning can contribute to causal learning by addressing conventional challenges from three aspects: representation, discovery, and inference. We point out that deep causal learning is important for the theoretical extension and application expansion of causal science and is also an indispensable part of general artificial intelligence. We conclude the article with a summary of open issues and potential directions for future work