Manipulating the electron dynamics in the non-sequential double ionization process of Ar atoms by an orthogonal two-color laser field

Electron dynamics during non-sequential double ionization(NSDI) is one of the most attractive areas of research in the field of laser–atom or laser–molecule interaction. Based on the classic two-dimensional model, we study the process of NSDI of argon atoms driven by a few-cycle orthogonal two-color laser field composed of 800 nm and 400 nm laser pulses. By changing the relative phase of the two laser pulses, a localized enhancement of NSDI yield is observed at 0.5πand 1.5π, which could be attributed to a rapid and substantial increase in the number of electrons returning to the parent ion within extremely short time intervals at these specific phases. Through the analysis of the electron–electron momentum correlations within different time windows of NSDI events and the angular distributions of emitted electrons in different channels, we observe a more pronounced electron–electron correlation phenomenon in the recollision-induced ionization(RII) channel. This is attributed to the shorter delay time in the RII channel.

Non-sequential narrow band imaging for targeted biopsy and monitoring of gastric intestinal metaplasia

AIM： To evaluate the efficacy of non-sequential narrow band imaging （NBI） for a better recognition of gastric intestinal metaplasia （GIM）. METHODS： Previously diagnosed GIM patients underwent targeted biopsy from areas with and without GIM, as indicated by NBI, twice at an interval of 1 year. The authors compared the endoscopic criteria such as light blue crest （LBC）, villous pattern （VP）, and large long crest （LLC） with standard histology. The results from two surveillance endoscopies were compared with histology results for sensitivity, specificity, positive predic-tive value （PPV）, negative predictive value （NPV）, and likelihood ratio of positive test （LR＋）. The number of early gastric cancer cases detected was also reported. RESULTS： NBI targeted biopsy was performed in 38 and 26 patients during the first and second surveillance endoscopies, respectively. There were 2 early gastric cancers detected in the first endoscopy. No cancer was detected from the second study. Surgical and endoscopic resections were successfully performed in each patient. Sensitivity, specificity, PPV, NPV, and LR＋ of all 3 endoscopic criteria during the first/second surveillances were 78.8%/91.3%, 82.5%/89.1%, 72.8%/77.8%, 86.8%/96.1, and 4.51/8.4, respectively. LBC provided the highest LR＋ over VP and LLC. CONCLUSION： Nonequential NBI is useful for GIM targeted biopsy. LBC provides the most sensitive reading. However, the optimal duration between two surveillances requires further study.

基于DCNv2和Transformer Decoder的隧道衬砌裂缝高效检测模型研究

为解决因衬砌裂缝性状随机、分布密集、标注框分辨率低所导致的现有模型识别精度低、检测速度慢及参数量庞大等问题,以第2版可变形卷积网络(DCNv2)和端到端变换器解码器(Transformer Decoder)为基础对YOLOv8网络框架进行改进,提出了面向衬砌裂缝的检测模型DTD-YOLOv8。首先,通过引入DCNv2对YOLOv8主干卷积网络C2f进行融合以实现模型对裂缝形变特征的准确快速感知,同时采用Transformer Decoder对YOLOv8检测头进行替换以实现端到端框架内完整目标检测流程,从而消除因Anchor-free处理模式所带来的计算消耗。采用自建裂缝数据集对SSD,Faster-RCNN,RT-DETR,YOLOv3,YOLOv5,YOLOv8和DTD-YOLOv8的7种检测模型进行对比验证。结果表明:改进模型F1分数和mAP@50值分别为87.05%和89.58%;其中F1分数相较其他6种模型分别提高了14.16%,7.68%,1.55%,41.36%,8.20%和7.40%;mAP@50分别提高了28.84%,15.47%,1.33%,47.65%,10.14%和10.84%。改进模型参数量仅为RT-DETR的三分之一,检测单张图片的速度为16.01 ms,FPS为65.46帧每秒,对比其他模型检测速度得到提升。该模型在面向运营隧道裂缝检测任务需求时能够表现出高效的性能。

The effect of electron initial longitudinal velocity on the non-sequential double ionization process in an elliptically polarized laser field

The effect of initial longitudinal velocity of the tunnelled electron on the non-sequential double ionization （NSDI） process in an elliptically polarized laser field is studied by a semiclassical model. We find that the non-zero initial longitudinal velocity has a suppressing effect on single-return collision （SRC） events in the double ionization process, more specifically, it results in an obvious reduction in the center part of the correlation momentum distributions in the direction of the major polarization axis （z axis） and makes the distribution of single-return collision in the minor polarization axis （x axis） become narrower.

Theoretical study on non-sequential double ionization of carbon disulfide with different bond lengths in linearly polarizedlaser fields

By using a two-dimensional Monte-Carlo classical ensemble method, we investigate the double ionization（DI） process of the CS_2 molecule with different bond lengths in an 800-nm intense laser field. The double ionization probability presents a ＂knee＂ structure with equilibrium internuclear distance R = 2.9245 a.u.（a.u. is short for atomic unit）. As the bond length of CS increases, the DI probability is enhanced and the ＂knee＂ structure becomes less obvious. In addition,the momentum distribution of double ionized electrons is also investigated, which shows the momentum mostly distributed in the first and third quadrants with equilibrium internuclear distance R = 2.9245 a.u. As the bond length of CS increases,the electron momentum becomes evenly distributed in the four quadrants. Furthermore, the energy distributions and the corresponding trajectories of the double-ionized electrons versus time are also demonstrated, which show that the bond length of CS in the CS_2 molecule plays a key role in the DI process.

Parallel Implementation of the CCSDS Turbo Decoder on GPU

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)standard.However,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism design.To mitigate this issue,we propose a padding method that inserts several bits before the information frame header.To obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are considered.The presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small modifications.The proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

A Denoiser for Correlated Noise Channel Decoding: Gated-Neural Network

This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).

Quantized Decoders that Maximize Mutual Information for Polar Codes

In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar codes.Based on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized symbols.The nested structure of polar codes ensures that the MMI quantization can be implemented stage by stage.Simulation results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization bits.Furthermore,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.

A highly reliable encoding and decoding communication framework based on semantic information

Increasing research has focused on semantic communication,the goal of which is to convey accurately the meaning instead of transmitting symbols from the sender to the receiver.In this paper,we design a novel encoding and decoding semantic communication framework,which adopts the semantic information and the contextual correlations between items to optimize the performance of a communication system over various channels.On the sender side,the average semantic loss caused by the wrong detection is defined,and a semantic source encoding strategy is developed to minimize the average semantic loss.To further improve communication reliability,a decoding strategy that utilizes the semantic and the context information to recover messages is proposed in the receiver.Extensive simulation results validate the superior performance of our strategies over state-of-the-art semantic coding and decoding policies on different communication channels.

Improved Segmented Belief Propagation List Decoding for Polar Codes with Bit-Flipping

Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) decoding.In this work, an improved segmented belief propagation list decoding based on bit flipping(SBPL-BF) is proposed. On the one hand, the proposed algorithm makes use of the cooperative characteristic in BPL decoding such that the codeword is decoded in different BP decoders. Based on this characteristic, the unreliable bits for flipping could be split into multiple subblocks and could be flipped in different decoders simultaneously. On the other hand, a more flexible and effective processing strategy for the priori information of the unfrozen bits that do not need to be flipped is designed to improve the decoding convergence. In addition, this is the first proposal in BPL decoding which jointly optimizes the bit flipping of the information bits and the code bits. In particular, for bit flipping of the code bits, a H-matrix aided bit-flipping algorithm is designed to enhance the accuracy in identifying erroneous code bits. The simulation results show that the proposed algorithm significantly improves the errorcorrection performance of BPL decoding for medium and long codes. It is more than 0.25 d B better than the state-of-the-art BPL decoding at a block error rate(BLER) of 10^(-5), and outperforms CA-SCL decoding in the low signal-to-noise(SNR) region for(1024, 0.5)polar codes.

The"Decoding Zhonghua"International Conference on Dialogue among Civilisations Held in Beijing

The"Decoding Zhonghua"International Conference on Dialogue among Civilisations,hosted by China International Public Relations Association,China Ethnic News and Academy of Contemporary China and World Studies was held in Beijing on January 17th.With the theme"Pursing Harmonious Coexistence of Civilisations through Dialogue".

基于ML-Decoder多分量雷达信号脉内调制识别方法

在现代电子侦察领域,由于电磁环境复杂,脉冲流密度较大,存在同时接收多个雷达信号的情况,多个雷达信号会在时域和频域出现重叠问题,使得雷达信号的特征变得混淆复杂。雷达信号的脉冲调制识别研究在单分量信号中取得了较好的效果,而在多分量雷达信号领域中,需要更多创新方法。为了解决上述问题,提出基于多标签解码器网络(Multi-Lable Decoder Network)框架。该网络框架首先用Choi-Williams分布(Choi-Williams Distribution,CWD)将一维信号转变为时频图。然后通过卷积神经网络提取特征,将提取的特征和查询向量一起送进decoder分类器中。decoder分类器通过标签查询的方法匹配特征信息,有效地避免传统卷积神经网络通过全局池化而淹没丰富的特征。用该方法对由六种典型雷达信号随机组成的多分量雷达信号经行调制识别分析,平均识别准确率达到93.9%,优于所对比的其他深度学习算法。

Area optimization of parallel Chien search architecture for Reed-Solomon(255,239) decoder

A global optimization algorithm （GOA） for parallel Chien search circuit in Reed-Solomon （RS） （255,239） decoder is presented. By finding out the common modulo 2 additions within groups of Galois field （GF） multipliers and pre-computing the common items, the GOA can reduce the number of XOR gates efficiently and thus reduce the circuit area. Different from other local optimization algorithms, the GOA is a global one. When there are more than one maximum matches at a time, the best match choice in the GOA has the least impact on the final result by only choosing the pair with the smallest relational value instead of choosing a pair randomly. The results show that the area of parallel Chien search circuits can be reduced by 51% compared to the direct implementation when the group-based GOA is used for GF multipliers and by 26% if applying the GOA to GF multipliers separately. This optimization scheme can be widely used in general parallel architecture in which many GF multipliers are involved.

A Total Dose Radiation Hardened PDSOI CMOS 3-Line to 8-Line Decoder

The first domestic total dose hardened 2μm partially depleted silicon-on-insulator （PDSOI） CMOS 3-line to 8- line decoder fabricated in SIMOX is demonstrated. The radiation performance is characterized by transistor threshold voltage shifts,circuit static leakage currents,and I-V curves as a function of total dose up to 3× 10^5rad（Si）. The worst case threshold voltage shifts of the front channels are less than 20mV for nMOS transistors at 3 × 10^5rad（Si） and follow-up irradiation and less than 70mV for the pMOS transistors. Furthermore, no significant radiation induced leakage currents and functional degeneration are observed.

Soft Decoding Scheme of Convolution Code Combined with Huffman Coding

This paper proposes a modification of the soft output Viterbi decoding algorithm (SOVA) which combines convolution code with Huffman coding. The idea is to extract the bit probability information from the Huffman coding and use it to compute the a priori source information which can be used when the channel environment is bad. The suggested scheme does not require changes on the transmitter side. Compared with separate decoding systems, the gain in signal to noise ratio is about 0 5-1.0 dB with a limi...

Low complexity iterative differential decoding algorithm for multiband UWB communication systems

Due to not requiring channel state information （CSI） at both the transmitter and the receiver, noncoherent ultra-wideband （UWB） incurs a performance penalty of approximately 3 dB in the required signal to noise ratio （SNR） compared to the coherent case. To overcome the gap, an effective differential encoding and decoding scheme for multiband UWB systems is proposed. The proposed scheme employs the parallel concatenation of two recursive differential unitary space-frequency encoders at the transmitter. At the receiver, two component decoders iteratively decode information bits by interchanging soft metric values between each other. To reduce the computation complexity, a decoding algorithm which only uses transition probability to calculate the log likelihood ratios （LLRs） for the decoded bits is given. Simulation results show that the proposed scheme can dramatically outperform the conventional differential and even coherent detection at high SNR with a few iterations.

Low complexity suboptimal decode algorithms for quasi- orthogonal space time block codes

Due to the high complexity of the pairwise decoding algorithm and the poor performance of zero forcing（ ZF） /minimum mean square error（ MMSE） decoding algorithm, two low-complexity suboptimal decoding algorithms, called pairwisequasi-ZF and pairwise-quasi-MMSE decoders, are proposed. First,two transmit signals are detected by the quasi-ZF or the quasiMMSE algorithm at the receiver. Then, the two detected signals as the decoding results are substituted into the two pairwise decoding algorithm expressions to detect the other two transmit signals. The bit error rate（ BER） performance of the proposed algorithms is compared with that of the current known decoding algorithms.Also, the number of calculations of ZF, MMSE, quasi-ZF and quasi-MMSE algorithms is compared with each other. Simulation results showthat the BER performance of the proposed algorithms is substantially improved in comparison to the quasi-ZF and quasiMMSE algorithms. The BER performance of the pairwise-quasiZF（ pairwise-quasi-MMSE） decoder is equivalent to the pairwiseZF（ pairwise-MMSE） decoder, while the computational complexity is significantly reduced.

Low complexity joint source-channel decoding for transmission of wavelet compressed images

To utilize residual redundancy to reduce the error induced by fading channels and decrease the complexity of the field model to describe the probability structure for residual redundancy, a simplified statistical model for residual redundancy and a low complexity joint source-channel decoding（JSCD） algorithm are proposed. The complicated residual redundancy in wavelet compressed images is decomposed into several independent 1-D probability check equations composed of Markov chains and it is regarded as a natural channel code with a structure similar to the low density parity check （LDPC） code. A parallel sum-product （SP） and iterative JSCD algorithm is proposed. Simulation results show that the proposed JSCD algorithm can make full use of residual redundancy in different directions to correct errors and improve the peak signal noise ratio （PSNR） of the reconstructed image and reduce the complexity and delay of JSCD. The performance of JSCD is more robust than the traditional separated encoding system with arithmetic coding in the same data rate.

Achievability of Chong-Motani-Garg relay channel capacity bounds using forward decoding

To reduce decoding delay of a communication scheme which is backward-decoding-based and achievable Chong Motani-Garg capacity bounds, a novel forward-sliding-window decoding-based communication scheme is proposed. In this scheme, if w = （w1, w2） is the message to be sent in block b, the relay will decode message w1 and generate a new message z at the end of block b, and the receiver will decode message w1 at the end of block b ＋ 1 and decode message z and w2 at the end of block b ＋ 2. Analysis results show that this new communication scheme can achieve the same Chong-Motani-Garg bounds and the decoding delay is only two blocks which is much shorter than that of backward decoding. Therefore, Chong-Motani-Garg bounds can be achieved by a forward decoding-based communication scheme with short decoding delay.

基于encoder-decoder框架的城镇污水厂出水水质预测

由于污水厂的出水水质指标繁多、污水处理过程中反应复杂、时序非线性程度高,基于机理模型的预测方法无法取得理想效果。针对此问题,提出基于深度学习的污水厂出水水质预测方法,并以吉林省某污水厂监测水质为来源数据,利用多种结合encoder-decoder结构的神经网络预测水质。结果显示,所提结构对LSTM和GRU网络预测能力都有一定提升,对长期预测能力提升更加显著,ED-GRU模型效果最佳,短期预测中的4个出水水质指标均方根误差(RMSE)为0.7551、0.2197、0.0734、0.3146,拟合优度(R2)为0.9013、0.9332、0.9167、0.9532,可以预测出水质局部变化,而长期预测中的4个指标RMSE为1.7204、1.7689、0.4478、0.8316,R2为0.4849、0.5507、0.4502、0.7595,可以预测出水质变化趋势,与顺序结构相比,短期预测RMSE降低10%以上,R2增加2%以上,长期预测RMSE降低25%以上,R2增加15%以上。研究结果表明,基于encoder-decoder结构的神经网络可以对污水厂出水水质进行准确预测,为污水处理工艺改进提供技术支撑。