Parallel Processing Design for LTE PUSCH Demodulation and Decoding Based on Multi-Core Processor

The Long Term Evolution (LTE) system imposes high requirements for dispatching delay.Moreover,very large air interface rate of LTE requires good processing capability for the devices processing the baseband signals.Consequently,the single-core processor cannot meet the requirements of LTE system.This paper analyzes how to use multi-core processors to achieve parallel processing of uplink demodulation and decoding in LTE systems and designs an approach to parallel processing.The test results prove that this approach works quite well.

Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm

The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes.Because of its operation,the application of this classification may be limited to problems with a certain number of instances,particularly,when run time is a consideration.However,the classification of large amounts of data has become a fundamental task in many real-world applications.It is logical to scale the k-Nearest Neighbor method to large scale datasets.This paper proposes a new k-Nearest Neighbor classification method(KNN-CCL)which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts.The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters.The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets.Finally,sets of experiments are conducted on the UCI datasets.The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance.

Analysis of sea level changes in the Caspian Sea related to Cosmo-geophysical processes based on satellite and terrestrial data

Analysis results of the average annual sea levels in the Caspian Sea obtained from ground and satellite observations, corresponding to solar activity characteristics, magnetic field data, and length of day are presented. Spectra of the indicated processes were investigated and their approximation models were also built. Previously assumed statistical relationships between space-geophysical processes and Caspian Sea level(CSL) changes were confirmed. A close connection was revealed between the low-frequency models of the solar and geomagnetic activity parameters and the CSL changes. Predictions extending into the next decades showed a high probability of an increase in the CSL and a decrease of the compared space-geophysical parameters.

A new data acquisition and processing system for profiling sonar

A multi-beam chirp sonar based on IP connections and DSP processing nodes was proposed and designed to provide an expandable system with high-speed processing and mass-storage of real-time signals for multi-beam profiling sonar.The system was designed for seabed petroleum pipeline detection and orientation,and can receive echo signals and process the data in real time,refreshing the display 10 times per second.Every node of the chirp sonar connects with data processing nodes through TCP/IP. Merely by adding nodes,the system’s processing ability can be increased proportionately without changing the software.System debugging and experimental testing proved the system to be practical and stable.This design provides a new method for high speed active sonar.

Image Processing on Geological Data in Vector Format and Multi-Source Spatial Data Fusion

The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly.

Fast Web - Based Data Transmission

Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.

A Granularity-Aware Parallel Aggregation Method for Data Streams

This paper focuses on the parallel aggregation processing of data streams based on the shared-nothing architecture. A novel granularity-aware parallel aggregating model is proposed. It employs parallel sampling and linear regression to describe the characteristics of the data quantity in the query window in order to determine the partition granularity of tuples, and utilizes equal depth histogram to implement partitio ning. This method can avoid data skew and reduce communi cation cost. The experiment results on both synthetic data and actual data prove that the proposed method is efficient, practical and suitable for time-varying data streams processing.

Fast modeling of gravity gradients from topographic surface data using GPU parallel algorithm

The gravity gradient is a secondary derivative of gravity potential,containing more high-frequency information of Earth’s gravity field.Gravity gradient observation data require deducting its prior and intrinsic parts to obtain more variational information.A model generated from a topographic surface database is more appropriate to represent gradiometric effects derived from near-surface mass,as other kinds of data can hardly reach the spatial resolution requirement.The rectangle prism method,namely an analytic integration of Newtonian potential integrals,is a reliable and commonly used approach to modeling gravity gradient,whereas its computing efficiency is extremely low.A modified rectangle prism method and a graphical processing unit(GPU)parallel algorithm were proposed to speed up the modeling process.The modified method avoided massive redundant computations by deforming formulas according to the symmetries of prisms’integral regions,and the proposed algorithm parallelized this method’s computing process.The parallel algorithm was compared with a conventional serial algorithm using 100 elevation data in two topographic areas(rough and moderate terrain).Modeling differences between the two algorithms were less than 0.1 E,which is attributed to precision differences between single-precision and double-precision float numbers.The parallel algorithm showed computational efficiency approximately 200 times higher than the serial algorithm in experiments,demonstrating its effective speeding up in the modeling process.Further analysis indicates that both the modified method and computational parallelism through GPU contributed to the proposed algorithm’s performances in experiments.

Optical A/D Quantizer Scheme Based on Parallel Phase Mod ulators

A high-speed and high-res ol ution optical A/D quantizer is proposed. Its architecture is discussed. Bit circ uits are built by using the phase modulators in parallel. Based on the different character of the half-wave voltage for every phase modulator and the polari zed bias design of incident light, the RF input signal is coded and transmitted in the form of optical digital signal. According to the principle of the archit ecture, the high-resolution quantizers with 8-bit and 12-bit, et al. are bui lt, which operate at 100 GS/ s. Their quantization noise is invariable almost w ith bit circuits increasing. The simulation result of 4-bit A/D quantizer is al so given.

超大规模数据处理中并行计算技术的应用研究

随着人工智能和大数据时代的到来,超大规模数据处理成了一个重要的研究领域。该文主要探讨并行计算技术在超大规模数据处理中的应用,首先详细阐述并行计算和超大规模数据处理的基本理论与概念,特别是并行计算的编程模型与工具,最后通过分析并行计算在搜索引擎、气象预报和金融分析等中的实际案例,阐述并行计算技术在超大规模数据处理中的实际应用。

OBN地震数据成像处理基本逻辑与关键方法技术

海洋油气勘探逐渐进入深水深层勘探领域,地下地质构造复杂(横向变速剧烈)、目标油藏复杂(由以构造油气藏为主转向构造与地层岩性油气藏并重),同时还可能伴随海底地形及附近岩性的复杂变化,所有因素促使海洋油气地震勘探技术不断变革。提高海洋油气勘探效益的首要问题是发展尽可能满足高精度地震波成像需求的地震数据采集技术及对应的高精度地震波成像技术。当前,无论海上和陆上油气地震勘探,“两宽一高”地震数据采集技术和全波形反演(FWI)/最小二乘逆时偏移(LS_RTM)为代表的地震波成像技术是标志性的领先技术。海上油气地震勘探中,海底节点(OBN)地震数据采集是目前业界公认的、最有可能真正实现“两宽一高”地震数据采集的技术。与拖缆数据采集相比,OBN数据采集具有宽方位照明、数据信噪比高、无检端鬼波、存在实测的(至少一阶自由表面相关)下行波场、四分量观测等优点。尤其是宽方位照明和存在至少一阶自由表面下行波场的特点,使得OBN数据具备了对中深层复杂构造和近海底介质进行高精度成像的能力。着重讨论了高精度地震波成像对地震数据采集的要求,指出OBN数据采集在海洋油气勘探中的必要性;分析了OBN数据采集的地震波场的特点,据此提出OBN数据地震波成像处理的基本逻辑及相应的关键技术;认为海洋油气勘探中地震波成像处理的特殊问题主要由特征反射层引起,海水面、海底面和地下介质中若干强反射层构成了这些特征反射层,提出了模型驱动波动理论特征反射层相关多次波预测与压制的技术路线,并对比了几种代表性的多次波预测的基础理论;指出对应当前的线性化偏移成像算子叠前数据域与叠前成像域是等价的,据此以成像道集后处理为中心,给出期望成像道集的定义,将弱旁瓣、定量的反射系数作为保真高分辨地震波成像的目标,在两个域中尽可能完美实现地下同一反(绕/散)射点、不同炮检距反(绕/散)射子波的同相位叠加,尽可能好地实现保真高分辨带限反射系数的成像;提出最好把带限反射系数成像推进到宽带波阻抗成像的技术路线;结合OBN数据的特点,给出了OBN数据地震波成像处理的基本技术流程,指出各环节的关键方法技术。最后,针对OBN数据四分量观测的特点,指出是实际观测的多波地震波场中的波现象(主要是P_SV波)与地震波传播及模拟理论不匹配导致了当前多波成像结果达不到预期,建议重点研究实际观测的多波地震波场中的波现象与地震波传播及模拟理论不匹配的物理根源,而不是发展更高端的矢量波成像算法。期望本文的思想观点对OBN地震勘探在海洋油气勘探中的进一步应用产生积极的促进作用。

基于OpenMP的遥感影像并行ISODATA聚类研究

针对传统影像分类算法执行效率较低,无法满足海量高分辨率遥感数据实时处理需求的问题,对资源三号卫星专题产品中遥感影像的迭代自组织数据分析算法进行分析与研究,设计一种基于OpenMP的并行ISODATA聚类算法(PIsodata Omp)。采用OpenMP技术优化ISODATA算法中的样本点聚类、聚类样本中心标准差计算,实现基于共享内存的单机多核并行化处理。实验结果表明,PIsodata Omp算法能在保证分类精度不变的情况下,明显提高资源三号卫星影像数据的处理速度。

用于VSLAM系统的CNN在FPGA平台上的加速

为实现视觉同步定位与建图系统中卷积神经网络在FPGA上的加速,基于SuperPoint模型设计一种低功耗高效CNN加速器及相应的SoC系统。采用循环分块、数据复用、计算单元展开和双缓冲策略充分利用加速器的片上资源;为提高突发传输效率,预先对权重参数重排;提出Pack模块和Unpack模块,设计多通道数据传输,用于提高传输带宽。在Ultra96-V2 FPGA平台上部署整个SoC系统,在仅3 W左右的功耗下实现25.63 GOPS的吞吐量,其BRAM效率、DSP效率、性能密度和功耗效率相比之前的文献有明显优势。

基于FPGA的大动态范围数据采集系统设计

针对传统数据采集系统受动态范围限制,进行测量时对大信号易过载、小信号易丢失等问题,设计了一种基于FPGA的大动态范围多通道数据采集系统,讨论了噪声相关性与放大器增益对动态范围的影响。该系统采用一种分立式架构的双ADC同步采集单元,结合FPGA高速并行处理的特点进行数据融合,对前端可变增益放大电路实时控制,实现多通道数据采集系统对微小信号采集时的高分辨率以及对大信号采集时的高容差。对采用动态范围最大为108 dB的AD7768芯片研制的实验样机进行测试,结果表明其在64 kHz的采样频率下可达到160 dB以上的动态范围,系统采样精度达到0.1%,线性度优于0.005%,通道间相位精度达到±0.05°。

基于QT的多型号弹载记录仪回读系统设计

弹载记录仪在军事和航天领域发挥着重要作用。然而,不同的弹载记录仪在数据上传方式、编帧结构和回读指令等方面可能存在差异,这导致传统的回读系统无法满足其多样化的需求。为解决这一问题,设计了一种基于QT的多型号多功能数据回读系统。该系统支持多种接口传输方式,用于读取不同型号的弹载记录仪数据。同时,它可以根据不同型号的记录仪设置相应的读取指令,并实时绘制回读过程中的数据曲线。回读完成后,系统能够分离处理不同编帧方式的数据。试验验证表明,该系统能够有效实现针对不同型号记录仪的数据回读、数据绘制和数据分离等功能。该系统的设计显著提高了回读过程的效率和灵活性,具有较强的工程实用价值。

基于流计算和大数据平台的实时交通流预测

目前交通流预测实时性差,很难满足在线分析和预测任务的需求,基于此提出一种Flink流计算框架和大数据平台结合的实时交通流预测方法。基于流计算框架实时捕捉和预处理数据,包括采用Flink的transform算子对数据进行校验和处理,将处理后的数据sink到大数据的HDFS文件系统,交由下一步的大数据并行框架进行分析建模与训练,实现基于流计算和大数据平台的实时交通流预测。实验结果表明,Flink能够实时捕捉和预处理交通流数据,把数据准时无误送入分布式文件系统中,在此基础上借助大数据框架下的并行分析和建模优势,在实时性数据分析与预测方面取得了较好的效果。

知识和数据驱动的污水处理反硝化脱氮过程协同优化控制

为有效提升城市污水处理过程的脱氮效果,提出一种知识和数据驱动的反硝化脱氮过程协同优化控制(Knowledge-data-driven cooperative optimal control,KDDCOC).所提方法主要有以下两个方面:首先,建立一种基于自适应知识核函数的协同优化控制目标模型,动态描述出水水质(Effluent quality,EQ)以及泵送能耗(Pumping energy consumption,PE)、关键变量的协同关系;其次,提出一种知识引导的协同优化算法(Knowledge guide-based cooperative optimization algorithm,KGCO),快速准确求解硝态氮(Nitrate nitrogen,SNO)优化设定值,提高KDDCOC的响应速度.KDDCOC利用比例−积分−微分(Proportional-integral-derivative,PID)控制器对硝态氮优化设定值进行跟踪,将提出的KDDCOC应用于城市污水处理过程基准仿真模型1号(Benchmark simulation model No.1,BSM1),实验结果表明,该方法能够提高出水水质,降低运行能耗,有效改善脱氮效果.

基于MapReduce的并行化电网运行数据处理方法研究

在大规模新能源的新型电力系统中,由于云端电力数据存在结构复杂、数据量大及多维度的特点,因此不利于发挥数据对运行的指导作用,甚至有可能危害电力系统的安全、稳定运行。针对上述问题,文中提出了一种基于MapReduce的电网数据分析方法。其将云计算应用于新型电力系统,并构建了基于MapReduce云计算模型的并行化处理算法,进而提升了系统的响应速度。通过将该方法应用于电网的数据处理结果表明,所提方法可以有效地提高电网运行数据处理的准确性和工作效率。在海量数据的工况下,其处理效率约为30 min,且随着数据量的增加仍可保持稳定性与准确性,实现了网格化的并行分析。

基于随机森林的电网GIS数据分布式存储方法

内部电网地理信息系统(Geographic Information Systern,GIS)数据体量增加,对电网数据存储性能造成了极大的困难,为此,提出一种基于随机森林的电网GIS数据分布式存储方法。以跨域资源共享(Cross-Origin Resource Sharing,CORS)技术在电网GIS空间信息服务平台中获取的电网GIS数据为基础,根据类区分度数值选择电网GIS数据特征,引入随机森林算法分类处理电网GIS数据,将其合理分发给不同的服务器,采用并行处理手段存储分类数据,从而实现了电网GIS数据的分布式存储。实验数据显示:应用所提方法后,电网GIS数据分类精度达到了96.8%,电网GIS数据分布式存储时间最小值为5.2 s,充分证实了所提方法数据存储性能更佳。

数据手套用柔性传感系统及信号特征分析技术

对数据手套采集信息的各组成部件进行归纳,总结数据手套中的传感系统,重点探讨柔性传感器的制备和传感阵列在手套中的集成。综述数据手套在不同应用场景下对采集信号的处理模式,特别介绍深度学习及神经网络计算方法在手势识别中的研究进展。最后,总结数据手套需突破的问题及未来发展趋势。