Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

基于SAOR的Massive MIMO系统信号检测算法

大规模多输入多输出(Massive multiple input multiple output, Massive MIMO)系统采用最小均方误差(Minimum mean square error, MMSE)接收检测方法时存在矩阵求逆复杂度高的问题，已有较多降低复杂度的研究。在降低检测算法复杂度的同时，如何提高算法收敛速度和检测性能一直是人们关注的焦点。本文将对称加速超松弛(Symmetric accelerated over-relaxation， SAOR)迭代算法应用于Massive MIMO系统信号检测中，避免了复杂的矩阵求逆计算，实现了复杂度较最小均方误差算法降低了一个数量级。仿真结果表明，基于SAOR的检测方法通过较少的迭代次数就能逼近最小均方误差（Minimum mean square error, MMSE）算法的检测性能，为Massive MIMO系统中接收信号的快速检测提供了较好的实现方法。     

三塘湖盆地中—下侏罗统煤层甲烷风化带划分

以三塘湖盆地中、低煤阶煤层含气量和主要气体成分为基础,结合盆地构造、沉积及水文地质特征,对汉水泉凹陷和条湖凹陷煤层甲烷风化带进行了划分,并对库木苏凹陷、马朗凹陷、淖毛湖凹陷和苏鲁克凹陷煤层甲烷风化带进行了预测。结果表明,三塘湖盆地甲烷风化带深度为400~1000 m.由于受东北冲断隆起带的影响,三塘湖盆地北部甲烷风化带浅于南部。沉积方面,盆地东部淖毛湖凹陷、马朗凹陷和条湖凹陷深湖—半深湖和辫状河三角洲成煤环境优于盆地西部汉水泉凹陷和库木苏凹陷扇三角洲成煤环境,盆地东部甲烷风化带普遍浅于西部;水文地质方面,条湖凹陷和马朗凹陷中开启性局部滞留水文地质单元形成的甲烷风化带浅于其他凹陷封闭性弱径流水文地质单元的甲烷风化带。初步预测马朗凹陷北部为下一步煤层气勘探开发的优势区块,条湖凹陷北部为下一步煤系气勘探开发的优势区块。     

基于DoG检测图像特征点的快速二进制描述子

针对SIFT描述子实时性差和传统二进制描述子对尺度、旋转和视角变化鲁棒性差的问题,本文通过优化采样模式和添加灰度差分不变量比较测试进行改进,提出了一种鲁棒性更高的二进制描述子。首先,设计了一种尺度关联、编号标记的采样模式;然后,旋转采样模式中各采样点到特定位置,确保描述子尺度、旋转不变性;接着,分析了采样点点对模式对描述子的影响,选择使用机器学习训练后的128对采样点对;最后,选择灰度值比较测试及梯度绝对值和比较测试构建二进制描述子。实验中采用DoG检测图像关键点,结果表明:本文提出的描述子在描述子构建和描述子匹配上比SIFT描述子分别快84%和67%;在有视角变化的图像匹配上,准确率比传统的二进制描述子高3%~5%,召回率平均要高30%以上。本文提出的特征点描述方法适用于时间要求高的图像匹配领域。     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

不同热解工艺下半焦的微观结构特点及其对燃烧性能的影响

摘要：对比研究了3种直立内热炉半焦(熄焦方式分别为水泡熄焦、喷水熄焦及烟气干熄焦)及一种流化床热解半焦的微观结构及燃烧性能。通过对半焦组成、孔隙结构、有机官能团、碳化学结构及半焦微晶结构分析，获得了不同低温热解工艺半焦的组成及微观结构特点，明确了组成及微观结构与燃烧性能之间的关系。结果表明，不同热解工艺半焦的组成和微观结构差异明显，其对半焦的燃烧性能影响显著。干熄焦碳化学结构缺陷比例大，快速热解半焦孔隙结构发0达、挥发分高，与水喷焦相比水泡焦石墨化程度低；水喷焦燃烧性能优于水泡焦，干法所得半焦的燃烧性优于湿法；半焦的挥发分含量决定其燃点，在快升温速率条件下孔隙结构对半焦的燃烧性能影响较大，而慢升温速率条件下有序化度对半焦的燃烧性能影响较大。     

Individual-based multi-objective optimal structured treatment interruption for HIV infection

In this paper, based on the measurable quantities from an individual patient that has infection to human immunodeficiency virus (HIV) and his/her condition is near to acquired immune deficiency syndrome (AIDS), individual-based multi-objective optimal treatments have been proposed. Firstly, the most effective parameters of the patient in computing Long-term non-progressor (LTNP) equilibrium are derived using global sensitivity analysis (GSA). To accomplish GSA effectively, Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) are utilized to rank each of the parameters based on each state of the 5-dimensional model. Then, these results are used by Dempster–Shafer (D–S) evidence theory (DSET) to rank the most effective parameters comprehensively. Now, these effective identified parameters are estimated using extended Kalman filter (EKF), which its covariance matrices are optimized based on particle swarm optimization (PSO) algorithm. Thus, the proposed methodology gives a calibrated model corresponding to the individual patient. Based on this calibrated model, the LTNP equilibrium related to the individual patient is derived. Using the derived individual-based LTNP equilibrium optimal structured treatment interruption (STI) strategies are extracted by defining suitable multi-objective optimization problem and solving it through using non-dominated sorting genetic algorithm-II (NSGA-II). The results demonstrate that the proposed optimal treatments are able to effectively reach LTNP equilibrium with using the minimum and maximum drug usage of 3.6% and 35.1% of full drug usage treatment. Meanwhile, the different optimal treatments give the decision-makers enough flexibility to choose the suitable treatment based on existing facilities and necessities.     

An EWMA signed ranks control chart with reliable run length performances

During the design phase of a control chart, the determination of its exact run length properties plays a vital role for its optimal operation. Markov chain or integral equation methods have been extensively applied in the design phase of conventional control charts. However, for distribution-free schemes, due to the discrete nature of the statistics being used (such as the sign or the Wilcoxon signed rank statistics, for instance), it is impossible to accurately compute their run length properties. In this work, a modified distribution-free phase II exponentially weighted moving average (EWMA)-type chart based on the Wilcoxon signed rank statistic is considered and its exact run length properties are discussed. A continuous transformation of the Wilcoxon signed rank statistic, combined with the classical Markov chain method, is used for the determination of the average run length in the in- and out-of control cases. Moreover, its exact performance is derived without any knowledge of the distribution of sample observations. Finally, an illustrative example is provided showing the practical implementation of our proposed chart.     

菱铁矿直接还原-低阶煤提质一体化扩大试验

摘要：菱铁矿作为中国重要的铁矿资源，因具有“贫、细、杂”的特点，常规选矿工艺无法对其进行有效分选；此外，中国低阶煤通常被用作低级燃料直接燃烧使用，导致大气污染严重。因此，开发复杂难选铁矿石及低阶煤资源高效清洁利用新技术，将具有重要现实意义。以难选的低品位菱铁矿和难利用低阶煤为对象，开展菱铁矿直接还原与低阶煤提质一体化扩大试验研究。以神府烟煤为还原剂，在直接还原温度为1050℃，时间为120min，C/Fe质量比为250，脱硫剂配比为6%，干式磁选磁场强度为015 T，一段磨矿细度小于0074mm占50%左右，湿式磁选磁场强度为008 T，二段磨矿细度小于0074mm占90%左右，磁选磁场强度为0043 T的条件下，可获得直接还原铁（DRI）粉铁品位9103%、铁总回收率8133%的良好指标；同时每吨DRI固定碳质量分数为7611%的半焦产品产出率为1340kg，其质量达到工业及民用燃料兰炭Ⅰ级品标准。在煤基直接还原工艺中一步成功实现低阶煤半焦化提质和铁矿直接还原，不仅可制备电炉炼钢原料，还可以生产新型的清洁燃料半焦。