Single Photon Detection Technology in Underwater Wireless Optical Communication:Modulation Modes and Error Correction Coding Analysis

This study explores the application of single photon detection(SPD)technology in underwater wireless optical communication(UWOC)and analyzes the influence of different modulation modes and error correction coding types on communication performance.The study investigates the impact of on-off keying(OOK)and 2-pulse-position modulation(2-PPM)on the bit error rate(BER)in single-channel intensity and polarization multiplexing.Furthermore,it compares the error correction performance of low-density parity check(LDPC)and Reed-Solomon(RS)codes across different error correction coding types.The effects of unscattered photon ratio and depolarization ratio on BER are also verified.Finally,a UWOC system based on SPD is constructed,achieving 14.58 Mbps with polarization OOK multiplexing modulation and 4.37 Mbps with polarization 2-PPM multiplexing modulation using LDPC code error correction.

Approximate error correction scheme for three-dimensional surface codes based reinforcement learning

Quantum error correction technology is an important method to eliminate errors during the operation of quantum computers.In order to solve the problem of influence of errors on physical qubits,we propose an approximate error correction scheme that performs dimension mapping operations on surface codes.This error correction scheme utilizes the topological properties of error correction codes to map the surface code dimension to three dimensions.Compared to previous error correction schemes,the present three-dimensional surface code exhibits good scalability due to its higher redundancy and more efficient error correction capabilities.By reducing the number of ancilla qubits required for error correction,this approach achieves savings in measurement space and reduces resource consumption costs.In order to improve the decoding efficiency and solve the problem of the correlation between the surface code stabilizer and the 3D space after dimension mapping,we employ a reinforcement learning(RL)decoder based on deep Q-learning,which enables faster identification of the optimal syndrome and achieves better thresholds through conditional optimization.Compared to the minimum weight perfect matching decoding,the threshold of the RL trained model reaches 0.78%,which is 56%higher and enables large-scale fault-tolerant quantum computation.

Feedback control and quantum error correction assisted quantum multi-parameter estimation

Quantum metrology provides a fundamental limit on the precision of multi-parameter estimation,called the Heisenberg limit,which has been achieved in noiseless quantum systems.However,for systems subject to noises,it is hard to achieve this limit since noises are inclined to destroy quantum coherence and entanglement.In this paper,a combined control scheme with feedback and quantum error correction(QEC)is proposed to achieve the Heisenberg limit in the presence of spontaneous emission,where the feedback control is used to protect a stabilizer code space containing an optimal probe state and an additional control is applied to eliminate the measurement incompatibility among three parameters.Although an ancilla system is necessary for the preparation of the optimal probe state,our scheme does not require the ancilla system to be noiseless.In addition,the control scheme in this paper has a low-dimensional code space.For the three components of a magnetic field,it can achieve the highest estimation precision with only a 2-dimensional code space,while at least a4-dimensional code space is required in the common optimal error correction protocols.

ADS-B Reception Error Correction Based on the LSTM Neural-Network Model

Standard automatic dependent surveillance broadcast (ADS-B) reception algorithms offer considerable performance at high signal-to-noise ratios (SNRs). However, the performance of ADS-B algorithms in applications can be problematic at low SNRs and in high interference situations, as detecting and decoding techniques may not perform correctly in such circumstances. In addition, conventional error correction algorithms have limitations in their ability to correct errors in ADS-B messages, as the bit and confidence values may be declared inaccurately in the event of low SNRs and high interference. The principal goal of this paper is to deploy a Long Short-Term Memory (LSTM) recurrent neural network model for error correction in conjunction with a conventional algorithm. The data of various flights are collected and cleaned in an initial stage. The clean data is divided randomly into training and test sets. Next, the LSTM model is trained based on the training dataset, and then the model is evaluated based on the test dataset. The proposed model not only improves the ADS-B In packet error correction rate (PECR), but it also enhances the ADS-B In terms of sensitivity. The performance evaluation results reveal that the proposed scheme is achievable and efficient for the avionics industry. It is worth noting that the proposed algorithm is not dependent on conventional algorithms’ prerequisites.

Toward Constructing a Continuous Logical Operator for Error-Corrected Quantum Sensing

Error correction has long been suggested to extend the sensitivity of quantum sensors into the Heisenberg Limit. However, operations on logical qubits are only performed through universal gate sets consisting of finite-sized gates such as Clifford + T. Although these logical gate sets allow for universal quantum computation, the finite gate sizes present a problem for quantum sensing, since in sensing protocols, such as the Ramsey measurement protocol, the signal must act continuously. The difficulty in constructing a continuous logical op-erator comes from the Eastin-Knill theorem, which prevents a continuous sig-nal from being both fault-tolerant to local errors and transverse. Since error correction is needed to approach the Heisenberg Limit in a noisy environment, it is important to explore how to construct fault-tolerant continuous operators. In this paper, a protocol to design continuous logical z-rotations is proposed and applied to the Steane Code. The fault tolerance of the designed operator is investigated using the Knill-Laflamme conditions. The Knill-Laflamme condi-tions indicate that the diagonal unitary operator constructed cannot be fault tolerant solely due to the possibilities of X errors on the middle qubit. The ap-proach demonstrated throughout this paper may, however, find success in codes with more qubits such as the Shor code, distance 3 surface code, [15, 1, 3] code, or codes with a larger distance such as the [11, 1, 5] code.

High Speed Versatile Reed-Solomon Decoder for Correcting Errors and Erasures

A new Chien search method for shortened Reed-Solomon （RS） code is proposed, based on this, a versatile RS decoder for correcting both errors and erasures is designed. Compared with the traditional RS decoder, the weighted coefficient of the Chien search method is calculated sequentially through the three pipelined stages of the decoder. And therefore, the computation of the errata locator polynomial and errata evaluator polynomial needs to be modified. The versatile RS decoder with minimum distance 21 has been synthesized in the Xilinx Virtex-Ⅱ series field programmable gate array （FPGA） xe2v1000-5 and is used by coneatenated coding system for satellite communication. Results show that the maximum data processing rate can be up to 1.3 Gbit/s.

USING ERROR-CORRECTING ENCODERS TO DESIGN LOCAL-RANDOM SEQUENCE GENERATORS

This paper proved the statement that a good linear block encoder is in fact a good local-random sequence generator. Furthermore, this statement discovers the deep relationship between the error-correcting coding theory and the modern cryptography.

Quantum secret sharing based on quantum error-correcting codes

Quantum secret sharing（QSS） is a procedure of sharing classical information or quantum information by using quantum states. This paper presents how to use a [2k- 1, 1, k] quantum error-correcting code （QECC） to implement a quantum （k, 2k-1） threshold scheme. It also takes advantage of classical enhancement of the [2k-1, 1, k] QECC to establish a QSS scheme which can share classical information and quantum information simultaneously. Because information is encoded into QECC, these schemes can prevent intercept-resend attacks and be implemented on some noisy channels.

空间外差遥感数据光谱-干涉图双向校正算法研究

空间外差光谱技术(SHS)作为新型高光谱分析技术,被广泛应用于大气检测、卫星遥感等领域,但由于空间外差光谱仪的制作不理想或工作环境的变化都会改变仪器参数而引入误差,使干涉图数据不准确,需要进行误差校正。在卫星平台下,由于与地面环境的巨大差异,导致地面测定的校正参数对空间外差干涉图数据不再适用,特别是调制项误差(相位误差和非均匀误差)的改变,极大影响了光谱的准确性。针对空间外差遥感数据的调制项误差,从光谱与干涉图两个方面入手,进行误差分离与原因分析,认为空间外差遥感数据误差主要来源于CCD尺寸伸缩导致的光谱频率变化和CCD响应改变导致的干涉图强度变化,提出了光谱-干涉图双向校正方法。校正测试数据选取高分五号搭载检测仪(GMI)实测的12条O 2吸收光谱。取其中一条作为标定光谱,以SCIATRAN仿真的无误差光谱为基准,与标定光谱进行对比,分析两者在光谱维的频率偏差,通过特征峰频率确定两者的频率变换关系。然后根据变换关系将仿真光谱进行频率拉伸,使拉伸后的仿真光谱与实测光谱谱峰重合,计算拉伸仿真光谱与实测光谱干涉图,将两者干涉图相比得到干涉图强度的变化关系。最后将干涉图强度变化关系用于其余11条光谱的校正,得到校正后光谱。为了衡量校正效果,以校正前后光谱的标准差、均方误差和信噪比作为评价指标。结果显示标准差、均方误差都明显降低,同时信噪比显著增大,且标准差基本都在0.07以下,信噪比基本能达到20以上。其中校正效果最好的实测光谱,标准差减少了0.3767,信噪比提高了25.1016,均方误差降低了0.1587,校正效果较差的实测光谱,标准差减少了0.2296,信噪比提高了9.6328,均方误差降低了0.1049。因此所提出的光谱-干涉图双向校正方法对空间外差遥感数据的误差校正起到较好的效果,且处理过程简单,为同类数据的处理提供了一种新思路。

融合DEM与FY-4A数据的ECMWF预报产品深度学习订正方法

精准的数值天气预报是精细化气象公共服务和商业服务的重要前提。欧洲中期天气预报中心(European Center forMedium Weather Forecasting,ECMWF)预报产品在全球被广泛采用,但始终存在系统预报误差。针对数值天气预报中的误差和多源数据融合中的非线性映射等问题,设计了一个ECMWF数值预报产品的深度学习订正模型(Numerical Forecast CorrectionNetwork,NFC-Net)。NFC-Net引入了FY-4A卫星观测数据、数字高程模型数据(Digital Elevation Model,DEM)和ERA5历史实况数据订正预报结果,利用多源数据空间分辨率对齐模块、时空特征提取模块解决多源异构数据特征的提取与融合问题,并通过UNet网络实现ECMWF预报产品的订正。为了评估所提算法的性能,利用NFC-Net对ECMWF产品中的2 m气温和10 m风速两个天气要素开展订正试验,并将试验结果与ECMWF预报结果、ANO方法订正结果、Convlstm方法订正结果、Fuse-CUnet方法订正结果和ERA5实况进行对比。结果显示,NFC-Net模型订正的2 m气温和10 m风速的均方根误差(Root Mean Squared Error,RMSE)分别较ECMWF预报产品下降49.71%和50.86%。表明NFC-Net模型可以充分利用多源数据有效改善复杂地形条件下的订正结果。NFC-Net模型可用于订正ECMWF预报结果,显著提升数值天气预报的精度。

An Empirical Study of Chinese English Learners' Attitudes toward Errors and Error Correction in Question-asking

Based on the questionnaire, this study found that :1) Elementary learners were inclined to commit more global errors compared to their local errors, whilst advanced learners make more local errors; 2) Interlingual factors were more influential than intralingual factors in elementary learners' error making, but for advanced learners, intralingual factors played relatively a much more important role in error making; 3) Elementary learners preferred explicit correction whilst advanced learners favoured im?plicit correction in question-asking.

基于CMA-TRAMS模式地形高度偏差的地面气温误差订正方法研究

采用一元线性方法建立南海台风模式CMA-TRAMS地形高度偏差和地面气温预报误差的回归关系,分别开展不分级、高度偏差分级和地面气温误差分级的三种订正方法的研究,并进行订正效果评估。结果表明,模式地面气温预报误差与地形高度偏差总体呈负的线性相关关系,地面气温预报绝对误差随地形高度偏差绝对值增大而增大(对模式地形高度偏低站点尤为明显),但不同时刻地面气温预报误差特征表现不同,模式对地形高度偏高(即模式地形高于测站高度)和地形高度偏差小于50m的站点,06时地面气温(世界时,下同)预报总体偏低,对地形高度偏低大于50m的站点(即模式地形低于测站高度),06时地面气温预报总体偏高;而无论站点地形高度偏差如何,模式对18时地面气温预报总体偏高。三种订正方法中地面气温误差分级法能有效地减小地面气温预报误差,该方法订正后的分析场准确率可达96%~99%,12~48小时时效预报场准确率总体可提升至90%以上,该方法具有回归关系稳定、效果显著、适用性广、简单易行等特点。

基于双重注意力机制CNN-BiLSTM与LightGBM误差修正的超短期风电功率预测

为了响应国家“双碳”目标,针对风电功率预测误差影响电网安全稳定运行的问题,提出一种基于双重注意力机制改进的CNN-BiLSTM初步预测和LightGBM误差修正的组合预测模型。该模型首先利用卷积神经网络(Convolutional neural network,CNN)与注意力机制结合构成特征注意力模块自适应提取风电功率重要特征,然后利用双向长短期记忆网络(Bi-directional long short-term memory,BiLSTM)与注意力机制结合构成时间注意力模块对风电功率进行初步预测,最后利用LightGBM构造误差修正模型,对初步预测结果进行修正。使用平均绝对误差(Mean absolute error,MAE)、均方根误差(Root mean square error,RMSE)和确定系数(R^(2))作为试验评价指标,结果表明,该组合模型预测效果明显优于BiLSTM、CNN-BiLSTM等模型。

Performance analysis and design of MIMO-OFDM system using concatenated forward error correction codes

This work investigates the performance of various forward error correction codes, by which the MIMO-OFDM system is deployed. To ensure fair investigation, the performance of four modulations, namely, binary phase shift keying(BPSK), quadrature phase shift keying(QPSK), quadrature amplitude modulation(QAM)-16 and QAM-64 with four error correction codes(convolutional code(CC), Reed-Solomon code(RSC)+CC, low density parity check(LDPC)+CC, Turbo+CC) is studied under three channel models(additive white Guassian noise(AWGN), Rayleigh, Rician) and three different antenna configurations(2×2, 2×4, 4×4). The bit error rate(BER) and the peak signal to noise ratio(PSNR) are taken as the measures of performance. The binary data and the color image data are transmitted and the graphs are plotted for various modulations with different channels and error correction codes. Analysis on the performance measures confirm that the Turbo + CC code in 4×4 configurations exhibits better performance.

Application of the Analogue-Based Correction of Errors Method in ENSO Prediction

In this study, a method of analogue-based correction of errors(ACE) was introduced to improve El Ni?o-Southern Oscillation(ENSO) prediction produced by climate models. The ACE method is based on the hypothesis that the flow-dependent model prediction errors are to some degree similar under analogous historical climate states, and so the historical errors can be used to effectively reduce such flow-dependent errors. With this method, the unknown errors in current ENSO predictions can be empirically estimated by using the known prediction errors which are diagnosed by the same model based on historical analogue states. The authors first propose the basic idea for applying the ACE method to ENSO prediction and then establish an analogue-dynamical ENSO prediction system based on an operational climate prediction model. The authors present some experimental results which clearly show the possibility of correcting the flow-dependent errors in ENSO prediction, and thus the potential of applying the ACE method to operational ENSO prediction based on climate models.

基于多种动力-统计方法的中国夏季降水集成预测研究

中国夏季降水预报对中国社会和经济发展具有重要意义,但由于气候系统的不确定性,中国夏季降水预报在当前气象研究中仍然是一个具有挑战性的问题.本文在分析7种动力-统计预报方法对我国夏季降水预报效果的基础上,提出了动态加权集成预报方法.通过比较动态加权集成预报方法与模式系统误差订正、单个动力-统计方法和等权重集成预报的空间距平相关系数(ACC)和预报评分(PS)值,发现动态加权集成预报较系统误差订正、单个动力-统计方法和等权重集成均有更好的预报效果.主要表现为:(1)动态加权集成预报方法2011—2020年夏季降水预报的ACC均值为0.05~0.10,较系统误差订正(-0.08)提高了0.13~0.18,较等权重集成提高了0.02~0.04,且优于绝大多数单个动力-统计预报方法的结果.(2)动态加权集成预报PS均值达到69.3~70.7,明显优于等权重集成预报的67.8~68.5和多数单方案动力-统计预报结果.(3)2021年独立样本检验中,动态加权集成预报中国夏季降水ACC值最高为0.21,明显优于等权重集成预报的最高值0.14,且PS评分值均高于70分.因此,动态加权集成预报可以在单个动力-统计方案预报的基础上,进一步提高中国夏季降水预报的准确率,改进预报技巧稳定性,具有较好的潜在应用价值.

基于遗传-模拟退火算法修正高斯烟羽模型参数

高斯烟羽模型由于受到地形地貌与气象条件等因素的影响,难以准确反映大气的实际扩散过程。为解决上述问题,首先在经验参数作为先验值的基础上,通过遗传算法对实际观测数据进行参数反演修正,根据观测结果调整模型参数,提高模型的准确性;然后,为进一步优化参数修正结果,引入模拟退火算法,通过随机搜索和逐步降温的策略来跳出遗传算法可能陷入的局部最优解,进一步改善模型的性能。为了评估修正效果,建立一个基于权重的模型值与观测值之间差异的适应度函数,通过比较修正前后的误差率来判断参数修正对高斯烟羽模型的影响程度。仿真实验的结果表明,所提出的遗传-模拟退火算法模型能够有效地修正高斯烟羽模型中的扩散参数,修正后的模型在预测污染物浓度方面的误差率下降了89.40%。所提模型可为环境保护和污染防治提供重要的理论支撑和决策依据,具有较大的应用潜力。

Near field 3-D imaging approach for joint high-resolution imaging and phase error correction

This paper combines compressed sensing (CS) imaging theory and range migration algorithm (RMA), and then proposes a near-field three-dimensional (3-D) imaging approach for joint high-resolution imaging and phase error correction. Firstly, a sparse measurement matrix construction method based on a logistic sequence is proposed, which conducts nonlinear transformation for the determined logistic sequence, making it obey uniform distribution, then conducts sign function mapping, and generates the pseudorandom sequence with Bernoulli distribution, thus leading to good signal recovery under down-sampling and easy availability for engineering realization. Secondly, in combination with the RMA imaging approach, the dictionary with all scene information and phase error correction is constructed for CS signal recovery and error correction. Finally, the non-quadratic solution model jointing imaging and phase error correction based on regularization is built, and it is solved by two steps - the separable surrogate functionals (SSF) iterative shrinkage algorithm is adopted to realize target scattering estimate; the iteration mode is adopted for the correction of the dictionary model, so as to achieve the goal of error correction and highly-focused imaging. The proposed approach proves to be effective through numerical simulation and real measurement in anechoic chamber. The results show that, the proposed approach can realize high-resolution imaging in the case of less data; the designed measurement matrix has better non-coherence and easy availability for engineering realization. The proposed approach can effectively correct the phase error, and achieve highly-focused target image. © 2017 Beijing Institute of Aerospace Information.

CHANNEL ERROR CORRECTION FOR WIDEBAND SAR-A JOINT MULTIPLE SUBPULSES PROCESSING METHOD

A novel method,referred to as joint multiple subpulses processing,is developed to calibrate the nonideal transfer function of radio frequency front-end and I/Q imbalance in quadrature modulate/demodulate systems simultaneously,which frequently occur in wideband Synthetic Aperture Radar(SAR) systems.Based on the time-frequency relation of the chirp signal and the analyses of the channel errors in wideband SAR,joint multiple subpulses processing method is adopted to separate the image frequency component due to the I/Q channel error.Then,the complete description of the channel error is acquired for building the correction function,which is used to correct the radar raw echo in frequency domain.The validity and capability of this method are demonstrated by the experiments of the channel error correction on the high resolution SAR system with the effective bandwidth of 500 MHz.

基于SVM-STL-LSTM的区域短期电力负荷预测研究

针对区域电力负荷的时间序列数据随机性强、预测精度低及单一模型的数据特征提取能力差等问题,提出了一种支持向量机(SVM)、STL时序分解法、长短期记忆神经网络(LSTM)组合的电力负荷预测模型。该模型利用SVM对时间序列的电力负荷数据进行初始预测,并通过STL时序分解法对残差序列进行时序分解,从而提高残差序列的稳定性,减小其随机性,最后用LSTM对SVM的预测误差进行修正。试验结果证明,该方法利用误差修正可有效处理随机性强的数据,有利于预测结果的稳定性,提高预测精度。