A Single Image Derain Method Based on Residue Channel Decomposition in Edge Computing

The numerous photos captured by low-price Internet of Things(IoT)sensors are frequently affected by meteorological factors,especially rainfall.It causes varying sizes of white streaks on the image,destroying the image texture and ruining the performance of the outdoor computer vision system.Existing methods utilise training with pairs of images,which is difficult to cover all scenes and leads to domain gaps.In addition,the network structures adopt deep learning to map rain images to rain-free images,failing to use prior knowledge effectively.To solve these problems,we introduce a single image derain model in edge computing that combines prior knowledge of rain patterns with the learning capability of the neural network.Specifically,the algorithm first uses Residue Channel Prior to filter out the rainfall textural features then it uses the Feature Fusion Module to fuse the original image with the background feature information.This results in a pre-processed image which is fed into Half Instance Net(HINet)to recover a high-quality rain-free image with a clear and accurate structure,and the model does not rely on any rainfall assumptions.Experimental results on synthetic and real-world datasets show that the average peak signal-to-noise ratio of the model decreases by 0.37 dB on the synthetic dataset and increases by 0.43 dB on the real-world dataset,demonstrating that a combined model reduces the gap between synthetic data and natural rain scenes,improves the generalization ability of the derain network,and alleviates the overfitting problem.

Single channel signal component separation using Bayesian estimation

A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.

Single channel source separation of radar fuze mixed signal based on phase difference analysis

A new method based on phase difference analysis is proposed for the single-channel mixed signal separation of single-channel radar fuze.This method is used to estimate the mixing coefficients of de-noised signals through the cumulants of mixed signals,solve the candidate data set by the mixing coefficients and signal analytical form,and resolve the problem of vector ambiguity by analyzing the phase differences.The signal separation is realized by exchanging data of the solutions.The waveform similarity coefficients are calculated,and the time鈥攆requency distributions of separated signals are analyzed.The results show that the proposed method is effective.

Single channel blind source separation based on ICA feature extraction

A new technique is proposed to solve the blind source separation (BSS) given only a single channel observation. The basis functions and the density of the coefficients of source signals learned by ICA are used as the prior knowledge. Based on the learned prior information the learning rules of single channel BSS are presented by maximizing the joint log likelihood of the mixed sources to obtain source signals from single observation, in which the posterior density of the given measurements is maximized. The experimental results exhibit a successful separation performance for mixtures of speech and music signals.

Learning a discriminative high-fidelity dictionary for single channel source separation

Sparse-representation-based single-channel source separation,which aims to recover each source’s signal using its corresponding sub-dictionary,has attracted many scholars’attention.The basic premise of this model is that each sub-dictionary possesses discriminative information about its corresponding source,and this information can be used to recover almost every sample from that source.However,in a more general sense,the samples from a source are composed not only of discriminative information but also common information shared with other sources.This paper proposes learning a discriminative high-fidelity dictionary to improve the separation performance.The innovations are threefold.Firstly,an extra sub-dictionary was combined into a conventional union dictionary to ensure that the source-specific sub-dictionaries can capture only the purely discriminative information for their corresponding sources because the common information is collected in the additional sub-dictionary.Secondly,a task-driven learning algorithm is designed to optimize the new union dictionary and a set of weights that indicate how much of the common information should be allocated to each source.Thirdly,a source separation scheme based on the learned dictionary is presented.Experimental results on a human speech dataset yield evidence that our algorithm can achieve better separation performance than either state-of-the-art or traditional algorithms.

Magnesium permeation through mechanosensitive channels:single-current measurements

Compelling evidence shows that intracellular free magnesium [Mg^2＋]i may be a critical regulator of cell activity in eukaryotes. However, membrane transport mechanisms mediating Mg^2＋ influx in mammalian cells are poorly understood. Here, we show that mechanosensitive （MS） cationic channels activated by stretch are permeable for Mg^2＋ ions at different extracellular concentrations including physiological ones. Single-channel currents were recorded from cell-attached and inside-out patches on K562 leukaemia cells at various concentrations of MgCl2 when Mg^2＋ was the only available carrier of inward currents. At 2 mM Mg^2＋, inward mechanogated currents representing Mg^2＋ influx through MS channels corresponded to the unitary conductance of about 5 pS. At higher Mg^2＋ levels, only slight increase of single-channel currents and conductance occurred, implying that Mg^2＋ permeation through MS channels is characterized by strong saturation. At 20 and 90 mM Mg^2＋, mean conductance values for inward currents carried by Mg^2＋ were rather similar, being equal to 6.8 ± 0.5 and 6.4 ± 0.5 pS, respectively. The estimation of the channel-selective permeability according to constant field equation is obviously limited due to saturation effects. We conclude that the detection of single currents is the main evidence for Mg^2＋ permeation through membrane channels activated by stretch. Our single-current measurements document Mg^2＋ influx through MS channels in the plasma membrane of leukaemia cells.

The Reconstructing of Low Signal-noise Ratio Single Ion Channel Signal from Patch-clamp Recordings Sampled in the Colored Background Noise

The single ion channel signal is an ionic current that can be recorded by the patch clamp technique. Hidden Markov model (HMM) algorithm has been used to convert the low signal noise ratio (SNR) noisy recording into an idealized quantal one in the case of white background noise. The traditional HMM algorithm is extended and adapted to the colored background noise. A new algorithm called EHMM (Extended HMM) algorithm is proposed, and mainly validated by simulation. Results show that it’s effective.

The on-line direct fitting of low signal-noise ratio single ion channel recordings based on hidden Markov models

Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct fitting of the patch clamp recording, not of the histogram coming from the recordings, is a desirable way to estimate the transition probabilities. Iterative batch EM algorithm based on hidden markov model has been used in this field but which has the "curse of dimensionality" and besides cant keep tracking the varying of the parameters. A new on line sequential iterative one is proposed here, which needs fewer computational efforts and can adaptively keep tracking the varying of parameters. Simulations suggest its robust, effective and convenient.

Single-Channel Speech Enhancement Using Critical-Band Rate Scale Based Improved Multi-Band Spectral Subtraction

This paper addresses the problem of single-channel speech enhancement in the adverse environment. The critical-band rate scale based on improved multi-band spectral subtraction is investigated in this study for enhancement of single-channel speech. In this work, the whole speech spectrum is divided into different non-uniformly spaced frequency bands in accordance with the critical-band rate scale of the psycho-acoustic model and the spectral over-subtraction is carried-out separately in each band. In addition, for the estimation of the noise from each band, the adaptive noise estimation approach is used and does not require explicit speech silence detection. The noise is estimated and updated by adaptively smoothing the noisy signal power in each band. The smoothing parameter is controlled by a-posteriori signal-to-noise ratio (SNR). For the performance analysis of the proposed algorithm, the objective measures, such as, SNR, segmental SNR, and perceptual evaluations of the speech quality are conducted for the variety of noises at different levels of SNRs. The speech spectrogram and objective evaluations of the proposed algorithm are compared with other standard speech enhancement algorithms and proved that the musical structure of the remnant noise and background noise is better suppressed by the proposed algorithm.

Construction of Low Delay Maximal Rate Single-Symbol Decodable Distributed STBC with Channel Phase Information

Exploiting the source-to-relay channel phase information at the relays can increase the rate upper-bound of distributed orthogonal space-time block codes(STBC)from 2/K to 1/2,where Kis the number of relays.This technique is known as distributed orthogonal space-time block codes with channel phase information(DOSTBC-CPI).However,the decoding delay of existing DOSTBC-CPIs is not optimal.Therefore,based on the rate of 1/2 balanced complex orthogonal design(COD),an algorithm is provided to construct a maximal rate DOSTBC-CPI with only half the decoding delay of existing DOSTBC-CPI.Simulation results show that the proposed method exhibits lower symbol error rate than the existing DOSTBC-CPIs.

Single-Channel Compressive Sensing for DOA Estimation via Sensing Model Optimization

The performance of multi-channel Compressive Sensing (CS)-based Direction-of-Arrival (DOA) estimation algorithm degrades when the gains between Radio Frequency (RF) channels are inconsistent, and when target angle information mismatches with system sensing model. To solve these problems, a novel single-channel CS-based DOA estimation algorithm via sensing model optimization is proposed. Firstly, a DOA sparse sensing model using single-channel array considering the sensing model mismatch is established. Secondly, a new single-channel CS-based DOA estimation algorithm is presented. The basic idea behind the proposed algorithm is to iteratively solve two CS optimizations with respect to target angle information vector and sensing model quantization error vector, respectively. In addition, it avoids the loss of DOA estimation performance caused by the inconsistent gain between RF channels. Finally, simulation results are presented to verify the efficacy of the proposed algorithm.

Fine Single Channel Identification Controlled by Sedimentary Facies—Taking KLA Oilfield as an Example

The Neogene fluvial reservoir in the Bohai oilfield is one of the leading development horizons for increasing reserves and production in the Bohai oilfield. However, the development of offshore fluvial reservoirs is faced with the problems of thin reservoir thickness, narrow plane width, rapid lateral change, and thin well pattern. Taking the KLA oilfield as an example, this paper discusses the nuanced characterization and configuration of a single channel controlled by sedimentary facies to guide developing offshore river facies’ narrow channel main control oilfield. Firstly, based on a large number of core data, the acceptable sedimentary facies identification is realized, the sedimentary model of the study area is established, the delicate calibration of logging facies and seismic facies is realized under the constraint of the sedimentary model, and a set of technical methods for nuanced reservoir characterization guided by seismic sedimentology is summarized, to realize the boundary identification of composite channel configuration and further realize the nuanced characterization of the single narrow channel. Based on this set of technology, it guides the smooth implementation of horizontal wells in the oilfield. The drilling encounter rate of the reservoir in the horizontal section of the single well exceeds 90%, ensuring the injection production connectivity and increasing the reserve production rate by more than 10%.

Channel Estimation for Full-Duplex Relay Transmission in Cloud Radio Access Networks

Relay in full-duplex(FD) mode can achieve higher spectrum efficiency than that in half-duplex mode,while it is crucial to suppress relay self-interference to ensure transmission quality which requires instantaneous channel state information(CSI). In this paper,the channel estimation issue in FD amplify-andforward relay networks is considered,where the training-based estimation technique is adopted. Firstly,the least square(LS) estimation is implemented to obtain composite channel coefficients of source-relay-destination(SRD) channel and relay loop-interference(LI) channel in order to assist destination in performing data detection. Secondly,both LS and maximum likelihood estimation methods are utilized to perform individual channel estimation aiming at supporting successive interference cancelation at destination. Finally,simulation results demonstrate the effectiveness of both composite and individual channel estimation,and the presented ML method can achieve lower MSEs than LS solution.

Deterministic joint remote state preparation of arbitrary single- and two-qubit states

In this paper, two novel schemes for deterministic joint remote state preparation（JRSP） of arbitrary single- and twoqubit states are proposed. A set of ingenious four-particle partially entangled states are constructed to serve as the quantum channels. In our schemes, two senders and one receiver are involved. Participants collaborate with each other and perform projective measurements on their own particles under an elaborate measurement basis. Based on their measurement results,the receiver can reestablish the target state by means of appropriate local unitary operations deterministically. Unit success probability can be achieved independent of the channel＇s entanglement degree.

Single carrier frequency domain equalization and diversity combining for cooperative systems

A low-complexity single carrier frequency-domain equalizer （SC/FDE） and diversity combining method for cooperative systems with demodulate-and-forward relaying over frequency-selective channels is proposed. At the relay nodes, linear SC/FDE is adopted and normalized correlation coefficlent is mtrodueed to derive an equivalent source-to-relay-destination （S-R-D） channel that is highlighted in this study. At the destination, a joint SC/FDE and diversity combining receiver is proposed by utilizing the equivalent S-R-D channel. Simulation results demonstrate the superiority of the proposed SC/FDE scheme over the straightforward SC/FDE which ignores the decisions errors at the intermediate relay nodes.

Clinical Analysis of Minimally Invasive Single-segment Reduction and Internal Fixation in Patients with Thoracolumbar Fractures

Objective:To study the clinical effect of minimally invasive single-segment reduction and internal fixation in patients with thoracolumbar fractures.Methods:From June 2013 to June 2014,100 patients with thoracolumbar fractures were selected as the subjects and they were randomly divided into observation group(50 cases)and control group(50 cases).The patients in the observation group were treated with minimally invasive singlesegment reduction and internal fixation.The patients in the control group were treated with short segmental fixation.The clinical effects of the two groups were compared.Results:There was no significant difference in the compression rate and Cobb angle between the two groups before and after operation(P>0.05).For all patients who were followed up for the last time,the Cobb angle was significantly lower in the observation group than in the control group(P<0.05).The social function,affective function and physical pain score of the observation group were significantly better than the control group(P<0.05).The amount of bleeding in the observation group was(250.4±41.0)ml,which was significantly lower than that in the control group(267.5±32.8)ml.The time required for the operation was(90.2±35.4)min,which was significantly lower than that of the control group(104.5±22.6)min(P<0.05).After treatment,the prognosis was 70.00%and the excellent and good rate was 98.00%,which was significantly higher than that of the control group(46.00%)and 78.00%(P<0.05).Conclusion:Thoracolumbar fractures in patients with dilated channel minimally invasive single-segment reduction and internal fixation treatment can effectively repair the patient's vertebral height and Cobb angle and the degree of correction after surgery was significantly better,safer and worthy of clinical recommended use.

用于多通道单分子定位的高精度图像配准方法

单分子定位技术可以绕过光学系统的衍射限制,在生物样品的单粒子追踪和超分辨显微成像中得到了广泛应用.多通道单分子定位采用多个成像通道,可以实现对不同目标的同时追踪或多色超分辨成像,也可以提升单粒子追踪的轴向深度或实现更高的定位精度和密度.但各通道图像间的差异会影响协同定位或定量分析,因此图像配准是其图像数据预处理的关键环节;且由于单分子定位精度高,其对多通道图像配准精度的要求也很高.现有技术一般采用基于控制点的配准方法,且多采用复杂而精密的方式来获取基准物网格图像用于定位得到控制点对,以实现高精度图像配准,对样品或实验设备要求高,难以直接推广.为此,本文基于局部非线性变换和误匹配点剔除,发展了一种可以直接采用随机分布荧光珠样品作为基准物的高精度图像配准方法,通过在特征匹配和变换模型参数估计的过程中对控制点进行监测和迭代筛选,以剔除因单分子定位不准确或精度差而导致未精确匹配的控制点对,从而消除以随机分布荧光珠样品作为基准物时对于控制点准确获取和精确匹配所带来的不良影响,同时采用基于局部加权平均的二阶多项式拟合进行变换模型参数估计,以更好地适用于不同通道间存在局部非线性形变的情形.结果表明,采用该方法只需要3次迭代,就可以将未准确定位和精确匹配的控制点对找到并剔除,从而实现更准确的变换模型参数估计,将配准精度提高一个数量级,在图像局部非线性形变情况严重的正交像散双通道单分子定位成像系统中实现了约6 nm的配准精度.

基于双分支注意力U-Net的语音增强方法

针对语音增强网络对全局语音相关特征提取困难、对语音局部上下文信息的捕捉效果不佳的问题,提出了一种基于双分支注意力U-Net的时域语音增强方法,该方法使用U-Net编码器-解码器结构,将单通道带噪语音经过一维卷积后得到的高维时域特征作为输入。首先利用残差连接设计了基于Conformer的残差卷积来增强网络降噪的能力。其次设计了双分支注意力机制结构,利用全局和局部注意力获取带噪语音中更丰富的上下文信息,同时有效表示长序列特征,提取更多样的特征信息。最后结合时域频域损失函数构建了加权损失函数对网络进行训练,提高网络的语音增强性能。使用了多个指标对增强语音的质量和可懂度等进行评价,在公开数据集Voice Bank+DEMAND上的增强后的语音感知质量(PESQ)为3.11,短时可懂度(STOI)为95%,信号失真度(CSIG)为4.44,噪声失真测(CBAK)为3.60,综合质量测度(COVL)为3.81,其中PESQ相较于SE-Conformer提高了7.6%,相较于TSTNN提高了5.1%。实验结果表明,所提方法在语音降噪的各个指标都表现出更优的实验结果,能够完成语音增强任务的相关要求。

GNSS-R外辐射源雷达微弱目标回波盲检测方法

针对传统全球导航卫星系统反射(global navigation satellite system-reflected, GNSS-R)外辐射源雷达系统所采用的双通道配置结构在信号处理上运算量较大且在工程实现上硬件成本较高的问题,提出一种基于高阶循环累积量的单通道GNSS-R外辐射源雷达微弱目标回波盲检测方法。利用主成分分析方法将强直达波信号从单通道接收的混合信号中抽取出来;通过直达波信号的高阶循环频率估计出微弱回波信号的高阶循环频率;基于微弱回波信号高阶循环频率与其他信号不同的特性将其从单通道混合信号中提取出来,通过匹配滤波实现目标检测。实验结果表明,所提方法在没有先验信息的情况下能有效提取出微弱目标的回波信号,且与传统二维匹配滤波目标检测方法相比有更好的目标检测性能。

单通道脊柱内镜与单边双通道脊柱内镜技术治疗腰椎管狭窄症临床疗效分析

目的探讨单通道脊柱内镜与单边双通道脊柱内镜技术在腰椎管狭窄症的手术减压情况、术后疗效和手术安全性。方法收集该科自2020年7月~2022年7月开展脊柱内镜手术治疗的79例LSS患者资料(包括单通道脊柱内镜手术33例,纳入单通道组;单边双通道脊柱内镜手术46例,纳入双通道组)。对两组患者的围手术期指标和疗效指标、减压节段椎间隙硬膜囊横截面积进行分组比较。结果两组患者的手术时间、住院时间和手术并发症情况比较,差异均不显著(P>0.05)。与术前相比较,两组患者术后1个月的腰痛、下肢痛VAS和ODI评分均明显改善(P<0.05),术后12个月这3项指标均进一步改善(P<0.05);上述3项指标的两组间比较,差异均不显著(P>0.05)。术后12个月时,单通道组的优良率90.1%(30/33),双通道组为91.3%(42/46),两组差异不显著(P>0.05)。与术前相比,两组患者术后1个月的硬膜囊横截面积均明显增加(P<0.05),且双通道组的硬膜囊横截面积和术后增加值均显著大于单通道组(P<0.05)。结论单通道与单边双通道脊柱内镜减压术治疗腰椎管狭窄症均可获得满意的减压效果,单通道脊柱内镜手术仅需单一切口,创伤相对更少;而单边双通道脊柱内镜手术则操作更为灵活,减压更为彻底。