Drift-Free Indoor Navigation Using Simultaneous Localization and Mapping of the Ambient Heterogeneous Magnetic Field

In the absence of external reference position information (e.g. GNSS) SLAM has proven to be an effective method for indoor navigation. The positioning drift can be reduced with regular loop-closures and global relaxation as the backend, thus achieving a good balance between exploration and exploitation. Although vision-based systems like laser scanners are typically deployed for SLAM, these sensors are heavy, energy inefficient, and expensive, making them unattractive for wearables or smartphone applications. However, the concept of SLAM can be extended to non-optical systems such as magnetometers. Instead of matching features such as walls and furniture using some variation of the ICP algorithm, the local magnetic field can be matched to provide loop-closure and global trajectory updates in a Gaussian Process (GP) SLAM framework. With a MEMS-based inertial measurement unit providing a continuous trajectory, and the matching of locally distinct magnetic field maps, experimental results in this paper show that a drift-free navigation solution in an indoor environment with millimetre-level accuracy can be achieved. The GP-SLAM approach presented can be formulated as a maximum a posteriori estimation problem and it can naturally perform loop-detection, feature-to-feature distance minimization, global trajectory optimization, and magnetic field map estimation simultaneously. Spatially continuous features (i.e. smooth magnetic field signatures) are used instead of discrete feature correspondences (e.g. point-to-point) as in conventional vision-based SLAM. These position updates from the ambient magnetic field also provide enough information for calibrating the accelerometer and gyroscope bias in-use. The only restriction for this method is the need for magnetic disturbances (which is typically not an issue indoors); however, no assumptions are required for the general motion of the sensor.Comment: ISPRS Workshop Indoor 3D 201

Stochastic dynamics of a finite-size spiking neural network

We present a simple Markov model of spiking neural dynamics that can be analytically solved to characterize the stochastic dynamics of a finite-size spiking neural network. We give closed-form estimates for the equilibrium distribution, mean rate, variance and autocorrelation function of the network activity. The model is applicable to any network where the probability of firing of a neuron in the network only depends on the number of neurons that fired in a previous temporal epoch. Networks with statistically homogeneous connectivity and membrane and synaptic time constants that are not excessively long could satisfy these conditions. Our model completely accounts for the size of the network and correlations in the firing activity. It also allows us to examine how the network dynamics can deviate from mean-field theory. We show that the model and solutions are applicable to spiking neural networks in biophysically plausible parameter regimes

Rural Poverty in China: Problem and Policy

This paper describes the economic conditions of rural China regarding poverty. By dividing the problem of rural poverty into three components it explains why rural poverty is China’s No. 1 economic problem in spite of the significant improvement in the living standard of the rural population. After discussing the solution proposed by the Chinese government it raises two policy questions, one concerning a proposal to eliminate the operational functions of township governments in the streamlining of the local government structure and the second on the possibility of controlling the abuse of power by local party officials that infringes on the rights of the farmers. A comparison with the conditions in India is provided.

Accounting for Economic Growth in Taiwan and Mainland China: A Comparative Analysis

This paper provides a statistical summary of aggregate economic growth in Taiwan and mainland China using the standard national income accounting framework by decomposing aggregate growth into components due to growths in capital, labor and total factor productivity. For Taiwan, new series of capital stock and of human capital are constructed. The major findings include (a) the stability of input coefficients (under the assumption of constant returns) and of the rate of increase in TFP for the entire period 1951-1999, (b) a labor exponent of about 0.7 and a rate of annual increase in TFP of about 0.03, and (c) a slower exponential rate of growth of real gdp since 1987 to about 0.065 from the 1951-1999 average of 0.081 mainly as a result of the large reduction in the growth rate of labor input to half. For mainland China, a capital stock series of Chow (1993) was extended to 1998. The major findings include (a) the stability of the relative input coefficients for the entire period 1952-1998 but TFP did not increase until 1979, (b) a labor exponent of about 0.35 and a rate of increase in TFP of about 0.027 after 1979, and (c) the absence of equally large reduction in the rate of increase in labor input as in Taiwan and the smaller exponent of labor leading to a prospect of only a moderate reduction in future growth rate.Growth

Duplicating Contingent Claims by the Lagrange Method

The problem of investing y(0) dollars at time 0 to duplicate a contigent claim is formulated as a dynamic optimization problem and solved by the Langrange method. If the function defining dy(t) is concave in y(t), owing to costs of trading in incomplete markets, there is an economy of scale in producing many claims simultaneously, thus explaining the profitability of institutions in providing such financial services.Finance

A Time-Series Analysis of the Shanghai and New York Stock Price Indices

A time series analysis of the Shanghai and New York Stock Exchange composite price indices is provided to compare the weekly rates of return and volatilities of these two markets and to study their co-movement in 1992-2002. The rate of return and volatility of the Shanghai market were higher. The rates of returns in the two markets were approximately serially uncorrelated and mutually uncorrelated. Volatility, as measured by the absolute change in the rate of return, has positive serially correlations in both markets as expected, but the autoregressions are temporarily unstable. Most surprisingly the volatility measures of the two markets are significantly negatively correlated. Volatility in each market was found to Granger cause volatility in the other market negatively. This spurious correlation is explained by the negative correlations of macroeconomic fundamentals in the United States and China as indicated by a negative correlation between the rates of change in their GDP while their capital markets are not integrated. The analysis has implications for the use of autoregressions and Granger causality tests, and the interpretation of spurious correlation.Time series analysis; Rate of return; Volatility; Autogressions; Granger causality; Spurious correlation; Shanghai stock price; New York stock price

A Model for National Income Determination in Taiwan

Following Chow (1985 and 2010) and using annual data from 1951 to 2010 for Taiwan this paper estimates a consumption function based on the permanent income hypothesis and an investment function based on the accelerations principle. The data support the permanent income hypothesis Friedman (1957) whereas the permanent income hypothesis of Hall (1978) was supported in Chow (1985 and 2010). The accelerations principle is strongly supported, as in the case of China. An explanation why the two economies have different consumption functions is given.consumption, investment, permanent income, Taiwan, macroeconomics

Important Lessons from Studying the Chinese Economy

In 1979 the United States and China established normal diplomatic relations, allowing me to visit China and study the Chinese economy. After doing so for thirty years since and advising the government of Taiwan in the 1960s and the 1970s and the government of the People’s Republic of China in the 1980s and the 1990s this is an opportune moment for me to summarize the important lessons that I have learned. The lessons will be summarized in four parts: on economic science, on formulating economic policy and providing economic advice, on the special characteristics of the Chinese economy and on the experience of China’s economic reform. At the beginning I should comment on the quality of Chinese official data on which almost all quantitative studies referred to in this article were based. Chow (2006(a)) has presented the view that by and large the official data are useful and fairly accurate. The main justification is that every time I tested an economic hypothesis or estimated an economic relation using the official data the result confirmed the well-established economic theory. It would be a miracle if I had the power to make the Chinese official statisticians fabricate data to support my hypotheses. Even if I had had the power, most of the data had already been published for years before I conceived the ideas of the studies reported in this article.China, Chinese economy, Taiwan, economic reforms, data

Equity Premium and Consumption Sensitivity When the Consumer- Investor Allows for Unfavorable Circumstances

Introducing one additional element due to possible misfortune to the return of each of two assets in the basic model of Samuelson (Rev.Econom.Statist.51 (1969)239)on optimum portfolio and consumption decisions,this paper resolves both the excess equity premium and the excess consumption sensitivity puzzles.This uni ed treatment provides a framework to study how important state variables will a ect the change in aggregate consumption which is consid- ered unpredictable in one formulation of the permanent income hypothesis.The implications of the theory agree with empirical results reported here and elsewhere.The theoretical framework appears to be simple and powerful as compared with alternative theories to explain the two puzzles.Optimum consumption and investment;Asset pricing;Consumption sensitivity;Robust control; The Lagrange method