Digital elevation models and GIS for watershed modelling and flood prediction-A case study of Accra Ghana

Geographical Information Systems (GIS) and Digital Elevation Models (DEM) can be used to perform many geospatial and hydrological modelling including drainage and watershed delineation, flood prediction and physical development studies of urban and rural settlements. This paper explores the use of contour data and planimetric features extracted from topographic maps to derive digital elevation models (DEMs) for watershed delineation and flood impact analysis (for emergency preparedness) of part of Accra, Ghana in a GIS environment. In the study two categories of DEMs were developed with 5 m contour and planimetric topographic data; bare earth DEM and built environment DEM. These derived DEMs were used as terrain inputs for performing spatial analysis and obtaining derivative products. The generated DEMs were used to delineate drainage patterns and watershed of the study area using ArcGIS desktop and its ArcHydro extension tool from Environmental Systems Research Institute (ESRI). A vector-based approach was used to derive inundation areas at various flood levels. The DEM of built-up areas was used as inputs for determining properties which will be inundated in a flood event and subsequently generating flood inundation maps. The resulting inundation maps show that about 80% areas which have perennially experienced extensive flooding in the city falls within the predicted flood extent. This approach can therefore provide a simplified means of predicting the extent of inundation during flood events for emergency action especially in less developed economies where sophisticated technologies and expertise are hard to come by. © 2009 Springer Netherlands.

Bayesian parameter identification for vibro-acoustic models with nonparametric uncertainty

Vibration and acoustic analysis at higher frequencies faces two challenges: computing the response without using an excessive number of degrees of freedom, and quantifying its uncertainty due to small spatial variations in geometry, material properties and boundary conditions. Efficient models make use of the observation that when the response of a decoupled vibro-acoustic subsystem is sufficiently sensitive to uncertainty in such spatial variations, the local statistics of its natural frequencies and mode shapes saturate to universal probability distributions. This holds irrespective of the causes that underly these spatial variations and thus leads to a nonparametric description of uncertainty. This work deals with the identification of uncertain parameters in such models by using experimental data. One of the difficulties is that both experimental errors and modeling errors, due to the nonparametric uncertainty that is inherent to the model type, are present. This is tackled by employing a Bayesian inference strategy. The prior probability distribution of the uncertain parameters is constructed using the maximum entropy principle. The likelihood function that is subsequently computed takes the experimental information, the experimental errors and the modeling errors into account. The posterior probability distribution, which is computed with the Markov Chain Monte Carlo method, provides a full uncertainty quantification of the identified parameters, and indicates how well their uncertainty is reduced, with respect to the prior information, by the experimental data. © 2013 Taylor & Francis Group, London.

Genetic Stability of Progeny from an Artificial Allotetraploid Carp Using Sperm from Five Fish Species

Microsatellite markers and D-loop sequences of mtDNA from a female allotetraploid parent carp and her progenies of generations 1 and 2 induced by sperm of five distant fish species were analyzed. Eleven microsatellite markers were used to identify 48 alleles from the allotetraploid female. The same number of alleles (48) appeared in the first and second generations of the gynogenetic offspring, regardless of the source of the sperm used as an activator. The mtDNA D-loop analysis was performed on the female tetraploid parent, 25 gynogenetic offspring, and 5 sperm-donor species. Fourteen variable sites from the 1,018 bp sequences were observed in the offspring as compared to the female tetraploid parent. Results from D-loop sequence and microsatellite marker analysis showed exclusive maternal transmission, and no genetic information was derived from the father. Our study suggests that progenies of artificial tetraploid carp are genetically stable, which is important for genetic breeding of this tetraploid fish.

Automatic construction and natural-language description of nonparametric regression models

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural- language text. Our approach treats unknown regression functions non- parametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state- of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.

Digital Elevation Models and GIS for watershed modelling and flood prediction - A case study of Accra Ghana

Geographical Information Systems (GIS) and Digital Elevation Models (DEM) can be used to perform many geospatial and hydrological modelling including drainage and watershed delineation, flood prediction and physical development studies of urban and rural settlements. This paper explores the use of contour data and planimetric features extracted from topographic maps to derive digital elevation models (DEMs) for watershed delineation and flood impact analysis (for emergency preparedness) of part of Accra, Ghana in a GIS environment. In the study two categories of DEMs were developed with 5 m contour and planimetric topographic data; bare earth DEM and built environment DEM. These derived DEMs were used as terrain inputs for performing spatial analysis and obtaining derivative products. The generated DEMs were used to delineate drainage patterns and watershed of the study area using ArcGIS desktop and its ArcHydro extension tool from Environmental Systems Research Institute (ESRI). A vector-based approach was used to derive inundation areas at various flood levels. The DEM of built-up areas was used as inputs for determining properties which will be inundated in a flood event and subsequently generating flood inundation maps. The resulting inundation maps show that about 80% areas which have perennially experienced extensive flooding in the city falls within the predicted flood extent. This approach can therefore provide a simplified means of predicting the extent of inundation during flood events for emergency action especially in less developed economies where sophisticated technologies and expertise are hard to come by. © Springer Science + Business Media B.V. 2009.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

Information geometry theory of high-dimension space and application for speaker independent continuous digit speech recognition

In the light of descriptive geometry and notions in set theory, this paper re-defines the basic elements in space such as curve and surface and so on, presents some fundamental notions with respect to the point cover based on the High-dimension space (HDS) point covering theory, finally takes points from mapping part of speech signals to HDS, so as to analyze distribution information of these speech points in HDS, and various geometric covering objects for speech points and their relationship. Besides, this paper also proposes a new algorithm for speaker independent continuous digit speech recognition based on the HDS point dynamic searching theory without end-points detection and segmentation. First from the different digit syllables in real continuous digit speech, we establish the covering area in feature space for continuous speech. During recognition, we make use of the point covering dynamic searching theory in HDS to do recognition, and then get the satisfying recognized results. At last, compared to HMM (Hidden Markov models)-based method, from the development trend of the comparing results, as sample amount increasing, the difference of recognition rate between two methods will decrease slowly, while sample amount approaching to be very large, two recognition rates all close to 100% little by little. As seen from the results, the recognition rate of HDS point covering method is higher than that of in HMM (Hidden Markov models) based method, because, the point covering describes the morphological distribution for speech in HDS, whereas HMM-based method is only a probability distribution, whose accuracy is certainly inferior to point covering.

Computational information geometry and its applications

Digitization is the main feature of modern Information Science. Conjoining the digits and the coordinates, the relation between Information Science and high-dimensional space is consanguineous, and the information issues are transformed to the geometry problems in some high-dimensional spaces. From this basic idea, we propose Computational Information Geometry (CIG) to make information analysis and processing. Two kinds of applications of CIG are given, which are blurred image restoration and pattern recognition. Experimental results are satisfying. And in this paper, how to combine with groups of simple operators in some 2D planes to implement the geometrical computations in high-dimensional space is also introduced. Lots of the algorithms have been realized using software.

Quantum computation using artificial molecules

Two kinds of quantum computation systems using artificial molecules: quantum computer and quantum analog computer are described. The artificial molecule consists of two or three coupled quantum dots stacked along z direction and one single electron, In quantum computer, one-qubit and two-qubit gates are constructed by one molecule and two molecules, respectively. The coupling between two qubits in a quantum gate can be controlled by thin film electrodes. We also constructed a quantum analog computer by designing a three-dot molecule network and mapping a graph 3-colorability problem onto the network. The ground-state configuration of the single electrons in the network corresponds to one of the problem solutions, We numerically study the operations of the two kinds of the quantum computers and demonstrate that they quantum gates can perform the quantum computation and solve complex problems.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

三维物体识别及姿态测定的推理系统

本文介绍了三维物体识别及姿态测定的一种新技术，从物体空间域模型出发，通过约束推理及几何推理，在物体三维信息部分给定的条件下，推断预测图象模型，并通过实测的图象数据反馈，推断出隐含在图象中未给定的三维信息，最终实现三维物体识别及姿态测定。整个系统在ＶＩＣＯＭ机上用Ｃ语言完成。

地震数据波动方程反演方法研究

Geophysical inversion is a theory that transforms the observation data into corresponding geophysical models. The goal of seismic inversion is not only wave velocity models, but also the fine structures and dynamic process of interior of the earth, expanding to more parameters such as density, aeolotropism, viscosity and so on. As is known to all, Inversion theory is divided to linear and non-linear inversion theories. In rencent 40 years linear inversion theory has formed into a complete and systematic theory and found extensive applications in practice. While there are still many urgent problems to be solved in non-linear inversion theory and practice. Based on wave equation, this dissertation has been mainly involved in the theoretical research of several non-linear inversion methods: waveform inversion, traveltime inversion and the joint inversion about two methods. The objective of gradient waveform inversion is to find a geologic model, thus synthetic seismograms generated by this geologic model are best fitted to observed seismograms. Contrasting with other inverse methods, waveform inversion uses all characteristics of waveform and has high resolution capacity. But waveform inversion is an interface by interface method. An artificial parameter limit should be provided in each inversion iteration. In addition, waveform information will tend to get stuck in local minima if the starting model is too far from the actual model. Based on velocity scanning in traditional seismic data processing, a layer-by-layer waveform inversion method is developed in this dissertation to deal with weaknesses of waveform inversion. Wave equation is used to calculate the traveltime and derivative (perturbation of traveltime with respect to velocity) in wave-equation traveltime inversion (WT). Unlike traditional ray-based travetime inversion, WT has many advantages. No ray tracing or traveltime picking and no high frequency assumption is necessary and good result can be got while starting model is far from real model. But, comparing with waveform inversion, WT has low resolution. Waveform inversion and WT have complementary advantages and similar algorithm, which proves that the joint inversion is a better inversion method. And another key point which this dissertation emphasizes is how to give fullest play to their complementary advantages on the premise of no increase of storage spaces and amount of calculation. Numerical tests are implemented to prove the feasibility of inversion methods mentioned above in this dissertation. Especially for gradient waveform inversion, field data are inversed. This field data are acquired by our group in Wali park and Shunyi district. Real data processing shows there are many problems for waveform inversion to deal with real data. The matching of synthetic seismograms with observed seismograms and noise cancellation are two primary problems. In conclusion, on the foundation of the former experiences, this dissertation has implemented waveform inversions on the basis of acoustic wave equation and elastic wave equation, traveltime inversion on the basis of acoustic wave equation and traditional combined waveform traveltime inversion. Besides the traditional analysis of inversion theory, there are two innovations: layer by layer inversion of seimic reflection data inversion and rapid method for acoustic wave-equation joint inversion.

Contextual models for object detection using boosted random fields

We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local evidence of a conditional random field (CRF). The graph structure is learned by assembling graph fragments in an additive model. The connections between individual pixels are not very informative, but by using dense graphs, we can pool information from large regions of the image; dense models also support efficient inference. We show how contextual information from other objects can improve detection performance, both in terms of accuracy and speed, by using a computational cascade. We apply our system to detect stuff and things in office and street scenes.