The Bayesian Approach to Signal Modelling

In this paper, an introduction to Bayesian methods in signal processing will be given. The paper starts by considering the important issues of model selection and parameter estimation and derives analytic expressions for the model probabilities of two simple models. The idea of marginal estimation of certain model parameter is then introduced and expressions are derived for the marginal probabilitiy densities for frequencies in white Gaussian noise and a Bayesian approach to general changepoint analysis is given. Numerical integration methods are introduced based on Markov chain Monte Carlo techniques and the Gibbs sampler in particular.

Blind equalization and sequence estimation using Markov chain Monte Carlo methods

This paper discusses the problem of restoring a digital input signal that has been degraded by an unknown FIR filter in noise, using the Gibbs sampler. A method for drawing a random sample of a sequence of bits is presented; this is shown to have faster convergence than a scheme by Chen and Li, which draws bits independently. ©1998 IEEE.

Water quality assessment of Sonbolrood River by using hylsenhof index

This research investigates the quality of sonbolrood river by using Hylsenhof HFBI indicators and identified Macroinvertebrates invertebrates community in the family level. This study took place during 1388-1389 with four sampling season in four stations respectively in the forests of Kalyj kheyl village in Savadkuh (first station), industrial area of Islamabad (second Station), earth dam of Sonbolrood (third station) and the Place crosses Sonbolrood with Babolrood river (fourth Station). Macroinvertebrates invertebrates collected by quantitative sampler of Sorbr and they were isolated in laboratory by loop and they were identified in the family level. Generally, Macroinvertebrates of Sonbolrood river were formed three branches: Arthropods and flat worms and mollusks, including 3 tiers, 6 orders and 14 families that showed the maximum diversity and density in autumn and the least diversity and density in summer at all stations, also the third and fourth stations respectively were highest and lowest diversity and density. The water quality of Sonbolrood river based on the water quality Guide(Hylsenhof) is evaluated with excellent condition for all stations except third station. Sonbolrood river with having high slope, rocky and sandy bed, with self-refining act, completely is a proper ecosystem for aquatic organisms, but it is done due to increased organic matter and sewage factory located in industrial zone in the third station and then the increased water pollution caused by nurturing the water warm fish in the earth dam of Sonbolrood. (because of this, the water quality at third station based on the water quality Guide(Hylsenhof) are evaluated in a fairly good condition) and adding domestic sewages of adjacent villages like Seyedkola village and Shirdarkola caused increased pollution and increased trophy of Macroinvertebrates that are resistant to pollution and affect upon Macroinvertebrates community.

Bayesian interpolation in a dynamic sinusoidal model with application to packet-loss concealment

In this paper, we consider Bayesian interpolation and parameter estimation in a dynamic sinusoidal model. This model is more flexible than the static sinusoidal model since it enables the amplitudes and phases of the sinusoids to be time-varying. For the dynamic sinusoidal model, we derive a Bayesian inference scheme for the missing observations, hidden states and model parameters of the dynamic model. The inference scheme is based on a Markov chain Monte Carlo method known as Gibbs sampler. We illustrate the performance of the inference scheme to the application of packet-loss concealment of lost audio and speech packets. © EURASIP, 2010.

Bayesian Learning of Noisy Markov Decision Processes

We consider the inverse reinforcement learning problem, that is, the problem of learning from, and then predicting or mimicking a controller based on state/action data. We propose a statistical model for such data, derived from the structure of a Markov decision process. Adopting a Bayesian approach to inference, we show how latent variables of the model can be estimated, and how predictions about actions can be made, in a unified framework. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from the posterior distribution. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.

高效毛细管电泳安培检测的研究与应用

安培电化学检测与高效毛细管电泳分离技术联用的应用范围很广，涉及到诸多分析对象。本论文着重在以下四个方面开展研究工作：(1) 碳纤维电极的应用，(2) 金属Pd超微粒碳纤维修饰电极的应用，(3) 氢醌类衍生物自组装修饰Pt电极的应用，(4) 在柱毛细管电极的制作。◆ 碳纤维工作电极用于柱端安培检测我们将碳纤维工作电极用于毛细管电泳电化学安培检测褪黑素、磺胺类药物、中药秦皮、噻唑衍生物和巴比妥类化合物，考察了检测电位、缓冲液、进样电压和时间及分离电压对分离和检测的影响，优化条件，对被测物进行快速高效的分离分析。在最佳条件下，线性范围较宽、灵敏度高、检测限低，而且重现性好。并用于实际样品的分离和检测，有一定的实际意义。◆ 金属Pd超微粒碳纤维修饰电极检测肼类物质和羟胺我们用一种新型的方法，获得了高分散纳米级金属钯超微粒碳纤维阵列修饰电极。该修饰电极对于肼类化合物及羟胺有很好的电催化活性，能很大程度地降低过电位，可在中性pH值下进行快速、灵敏地分离检测，而且在静态和流动体系中的稳定性高、重现性好。◆ 氢醌类衍生物自组装修饰Pt电极电催化氧化检测肼类化合物使用4-吡啶基氢醌自组装修饰铂电极，展示了极优异的表面电化学特征，修饰层有着极好的稳定性。这一修饰电极对肼类化合物有极好的电催化氧化性能，极大地降低了肼类化合物的氧化过电位，在0.0V即可实现对它们的检测。并利用这一特性成功地将其应用在毛细管电泳电化学快速、灵敏地检测肼类化合物，而且重现性好。◆ 在柱毛细管电极的制作在柱毛细管电极的制作为得到最佳CEEC检测灵敏度，分离毛细管柱出口与工作电极必须安置于一条直线上，并且要在整个实验过程中位置保持不变。经典的毛细管电泳安培型电化学检测池与电泳体系的连接主要有离柱(Off-colunm)和柱端(End-colunm)两种方式。前者是将电极直接插入毛细管内，要求电极直径要比毛细管柱的内径小，或者腐蚀毛细管柱出口，增大出口处内径。电极的安装要在显微镜下进行。为获得重现的结果，电极必须保持插入毛细管内的位置相同。这给安装电极带来不方便，由于种种原因需要重新安装电极时，很难固定在同一位置。后者可以将电极直接放置在毛细管出口处。这两种安装电极的方式都要在显微镜下进行。为了获得重现的结果，电极每次都应固定在相对于毛细管柱相同的位置。这给安装电极带来了很大不便，在使用和安装电极方面都需要操作者有熟练的技巧，需要使用微调节器，令初学者感到困难，检测结果也因人而异，限制了毛细管电泳安培型检测器的商品化。我们首次用真空喷镀的方法将毛细管的出口端形成Au膜，使毛细管与电极连为一体。解决了毛细管电泳安培检测时电极的安装问题。极大地提高了分析结果的重现性。这一技术将对毛细管电泳安培检测的应用产生重大影响。

CSR数字高频分析系统的同步采样器HDL设计

本文主要阐述了CSR数字高频分析系统的同步采样器的研究、设计和实现，该分析系统是基于软件无线电技术构建的。 本文的创新点主要在于提出了一种很好的正交同步欠采样方法，这种基于软件无线电原理的I、Q两路正交同步的采样方法，主要利用了平方律部件、一阶环路滤波器和NCO来对I、Q两路采样触发脉冲上升沿的时间差进行闭环控制，从而实现对高频信号的数据采集和处理。这种方法降低了对AD芯片采样速率要求，同时也为后续的基带信号处理提供了方便。 在整个同步采样系统的实现过程中我采用了功能强大的Matlab7.0作为通信算法的仿真平台，选用了AD6645、StratixIIEP2S60和DSP6416作为主要的硬件平台，并在SynaptiCAD和ModelSim中做出了FPGA的HDL程序设计及波形实现

A QUANTITATIVE ASSESSMENT OF SOURCE CONTRIBUTIONS TO INHALABLE PARTICULATE MATTER POLLUTION IN METROPOLITAN BOSTON

In this paper, source apportionment techniques are employed to identify and quantify the major particle pollution source classes affecting a monitoring site in metropolitan Boston, MA. A Principal Component Analysis (PCA) of paniculate elemental data allows the estimation of mass contributions for five fine mass panicle source classes (soil, motor vehicle, coal related, oil and salt aerosols), and six coarse panicle source classes (soil, motor vehicle, refuse incineration, residual oil, salt and sulfate aerosols). Also derived are the elemental characteristics of those source aerosols and their contributions to the total recorded elemental concentrations (i.e. an elemental mass balance). These are estimated by applying a new approach to apportioning mass among various PCA source components: the calculation of Absolute Principal Component Scores, and the subsequent regression of daily mass and elemental concentrations on these scores.

一种无缆自动返回式采水器的设计与研究

海洋环境污染监测、海水化学与地质调查和生物采样，大多数情况下需要现场采集水样。有时需要在不同深度依次采样，获取梯度剖面的现场水体信息，以保证分析结果的统一性、规范性和类比性。近几十年来，水体取样和保存技术迅速发展，各式各样的采水器纷纷问世并获得成功应用。常规CTD葵花式采水器（CTD rosette sampler）（配备尼斯金采样瓶（Niskin bottle）或郭福洛采样瓶（Go-Flo bottle））和船用吸泵均为目前广泛使用的梯度水样采集装置。但其应用各有一定缺陷。因此，设计一种既适用于大型科考船又适用于小型船只、采样深度可精确控制、采水操作简便、无沾污的采水器在海洋科学研究中有较大实用意义。 本课题所设计的无缆自动返回式采水器（a New Auto-Returned Column Water Sampling Device-‘ARWAD’）是一种新型垂直剖面采水器，该采水器外挂四个1.5L卡盖式结构的HOUSKIN采水瓶。为采水器加载适当重量的配置体，使其在水中受到负浮力下潜。采水是在释放配置体后，采水器受到正浮力上升的过程中依次完成的。 本文的主要内容为：（1） 本课题详述了该无缆自动返回式采水器的机械机构与密封方案。采水器的主壳体为硬铝合金圆柱状防水耐压设计，外挂四个卡盖式结构的采水瓶，其机械机构主要包括①配置体的悬挂与释放机构；②采水瓶关闭采水机构；（2） 详述该采水器的控制系统，包括硬件设计方案和软件设计，并对控制系统进行了调试。控制系统装置于主体的防水耐压仓内，以单片机为核心，装载压力传感器获知水深。下位机程序控制在到达指定深度时释放配置体和关闭各采样瓶，上位机程序实现在甲板上的自检、设定配置体的释放深度和各采水瓶的关闭采样深度；（3） 用解析法和经验公式对该采水器承压零件尺寸进行设计，同时用数值计算方法对其强度校核；（4） 为保证采水器的采水精度和确保其工作安全，对采水器进行水动力学计算，确定阻力及阻力系数等参数，使采水器在上升采水过程中最大速度小于2.0m/s，以为外形设计提供参考；（5） 采水器采样性能验证。现场采集水样后，在实验室测定悬浮颗粒物的质量浓度和体积浓度分布。在采集水样的同时，应用激光粒度仪获取现场悬浮物粒径分布信息。通过与激光粒度仪现场测定结果对比，证明该自动返回式采水器采水深度准确，质量较好。 该采水器使用时无需钢丝绳，而是装载压力传感器获知水深，由此避免了使用绞车和由钢丝绳计量深度带来的深度误差；采样时也无须连接电缆，节省电力；自由下潜和上升，无需配备绞车深度补偿设施，避免出现深度逆变现象。主要应用于驾驶小规模船只在较浅水域（湖泊或河口）采水，亦可用于大型科考船，其结构轻便、造价低廉以及操作方便等性能使其成为CTD葵花式采水器和泵采系统的有益补充。

机器人化月表采样器的研制

首先介绍了月表采样的新特点与存在的困难;根据月表环境、月表样品的特点以及嫦娥三期工程中对月表采样的要求,合理地选择了采样方式,研制出了一台六自由度机器人化月表采样器,并且对采样器的采样原理,机械系统和运动原理做了详细的分析;最后在石灰粉上做了采样实验,验证了多功能的机器人化月表采样器的基本功能与可行性。

机器人化月表采样器控制系统研究

在未来的深空探测任务中迫切需要采样任务的机器人化。为突破这一关键的技术,我们研制了一台六自由度的机器人化月表采样器原理样机,为其选择合理的控制方式并搭建了一套基于CAN总线的分布式控制系统。本文将详细介绍该机器人化月表采样器的工作原理及其控制系统的基本结构,并将阐述在操作采样器执行采样任务时采用的控制方式。最后搭建试验平台进行采样试验,验证采样器和控制系统的基本功能及其有效性。

机器人化月表采样器的研究

月球是离地球最近的行星，了解开发月球是人类认识太空的第一步。月壤是月球的重要组成部分，也是人类认识月球的基本切入点，现在许多国家都在开展无人探月活动，因此设计一种无人操作的能够适应月表环境的机器人化月表采样器将是我们认识月球地质状况的关键。应用于月表探测的机器人化月表采样器在作业时往往面对极为复杂的苛刻的月表环境。传统的采样器受其自身的机械结构限制，一般不能够适应这么苛刻的月表环境，或者是不能达到预定的采样目标。具有多种采样形式的月表采样器的出现，解决了这一问题。国外自50年代开始研究机器人化行星采样器，并且已经研制出了许多样机。目前已有的行星采样器存在着只具有单种采样形式，或者受着陆舱体积的限制不能够进行深层采样等不足，因此都不能满足探月活动中要求越来越高的月表样品采集活动。本文叙述了行星采样器的研究和发展现状，并设计了一种用于月表探测时进行月壤采集的多功能机器人化月表采样器。该采样器具有三种针对不同月表样品的采样形式：夹取、铲取和钻取，根据采样的对象的不同能够自动改变采样方式，整个机械系统由仿生蚌式铲和转塔式钻机两大部分组成，仿生蚌式铲具有多种采样形式，转塔式钻机解决了空间受限的情况下采集深层样品的问题，整个系统具有六个运动副。机器人化月表采样器采用了基于CAN总线的集中式控制系统。本文在详细介绍机器人化月表采样器机械系统设计的基础上，对机器人化月表采样器对月壤的采样过程进行了数学建模，以便于将来对采样器的可靠性进行改进，并且减少设计费用和提高了终端产品的性能。通过采样实验验证了机器人化月表采样器机械结构确实满足预期采样目的。最后，本文对机器人化月表采样器的发展和后续研究工作进行了展望。

Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models.

We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.

THE STEAM-JET AEROSOL COLLECTOR

A new principle of sampling aerosol particles by means of steam injection with the consequent collection of grown droplets has been established. An air stream free of water-soluble gases is rapidly mixed with steam. The resulting supersaturation causes aerosol particles to grow into droplets. The droplets containing dissolved aerosol species are then collected by two cyclones in series. The solution collected in the cyclones is constantly pumped out and can be on- or off-line analysed by means of ion chromatography or flow injection analysis. On the basis of the new sampling principle a prototype of an aerosol sampler was designed which is capable of sampling particles quantitatively down to several nanometres in diameter. The mass sampling efficiency of the instrument was found to be 99\%. The detection limit of the sampler for ammonium, sulphate, nitrate and chloride ions is below 0.7 mu g m(-3). By reduction of an already identified source of contamination, much lower detection limits can be achieved. During measurements the sampler proved to be stable, working without any assistance for extended periods of time. Comparison of the sampler with filter packs during measurements of ambient air aerosols showed that the sampler gives good results.

Nested dictionary learning for hierarchical organization of imagery and text

A tree-based dictionary learning model is developed for joint analysis of imagery and associated text. The dictionary learning may be applied directly to the imagery from patches, or to general feature vectors extracted from patches or superpixels (using any existing method for image feature extraction). Each image is associated with a path through the tree (from root to a leaf), and each of the multiple patches in a given image is associated with one node in that path. Nodes near the tree root are shared between multiple paths, representing image characteristics that are common among different types of images. Moving toward the leaves, nodes become specialized, representing details in image classes. If available, words (text) are also jointly modeled, with a path-dependent probability over words. The tree structure is inferred via a nested Dirichlet process, and a retrospective stick-breaking sampler is used to infer the tree depth and width.