A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

新型实用布线算法──L─M算法

本文提出新型布线算法，集李氏迷宫法与线搜索法的长处为一体，以饱和带法进行动态排序，以线搜索法确定借孔位置，然后用李氏法进行单层布线，获得最佳路径，从而达到线型好，布通率高的效果。     

Depth of intergranular oxygen diffusion during environment-dependent fatigue crack growth in alloy 718

The elevated-temperture fatigue crack growth behavior in alloy 718, when subjected to a loading frequency lower than the transitional frequency of this alloy, is viewed as fully environment dependent. In this process, the crack growth increment per loading cycle is assumed to be equal to the intergranular oxygen diffusion depth at the crack tip during the cycle effective oxidation time. In order to identify the trend of this diffusion depth an experimental program was carried out on compact tension specimens made of alloy 718 at 650 °C in which fatigue crack growth measurements were made for cyclic load conditions with and without hold time periods at minimum load level. This work resulted in establishing a relationship correlating the intergranular oxygen diffusion depth and the value of the stress intensity factor range ΔK. This relationship, when integrated over the cycle effective oxidation time, results in a closed-form solution describing the environment-dependent fatigue crack growth rate. A comparison is made between the results of this solution when applied to different loading frequencies and the corresponding experimental results. This comparison shows good agreement between the two sets of results. Furthermore, by combining the parabolic rate law of diffusion and the equation for the intergranular oxygen diffusion depth, an explicit expression for the oxygen diffusivity of grain boundaries is derived. It is found that this diffusivity is both a ΔK- and a frequency-dependent parameter.     

Data mining of resilience indicators

In recent years, the Asia-Pacific region has experienced several financial setbacks, including speculative attacks in 1998 and the SARS outbreak in 2003. Financial stresses of this nature are unanticipated, and not all of the dangers can be predicted by the examination of market information and macroeconomic indicators. The Early Warning System (EWS) that has been adopted by the International Monetary Fund may not be able to predict future financial crises for all possible scenarios, because shocks come in many different forms. To supplement the EWS, this paper proposes a data mining framework to measure the resilience of an economy. The resilience framework does not predict a crisis, but rather assesses the current state of health of an economy and its ability to withstand a financial shock should one occur. The framework is based on a feedback system consisting of two stages. The first stage assigns a resilience score to each economy based on a fuzzy logic scoring scheme that is built on the ambiguous reasoning of experts. The second stage uses the classification tree approach to estimate thresholds for each economic indicator, and examines the quality of the fuzzy score. The result from the second stage is then passed back to the first stage as feedback. The final result is obtained when the feedback system reaches its equilibrium state. The proposed resilience framework is applied to the external-sector and the public-sector economies of several countries to illustrate its applicability.     

Inter- and intra-molecular ionic interactions of polyampholyte: Carboxymethyl-2-diethylaminoethylcellulose

Carboxymethyl-2-diethylaminoethylcellulose (CM-DEAE cellulose) was prepared by etherification of carboxymethylcellulose with diethylaminoethyl chloride in a NaOH solution. The behaviour of CM-DEAE cellulose in aqueous solution was studied by viscosity and GPC measurements. The degree of substitution (DS) and existing states of DEAE substituents were examined by the use of proton NMR, and those of the CM substituents were observed with FT-IR. The results reveal that the reduced viscosity and apparent molecular size of CM-DEAE cellulose vary with the concentration of sodium chloride and changes in pH and DS. These phenomena can be explained in terms of inter- and intra-molecular ionic interactions.     

我的家

我生于东京的银座,那是个靠近传统花街柳巷、歌舞升平的地方,在童年的记忆里那里随时随地都是一片华美景象。而在这些景象的背后有一排排美丽的临街二层长屋建筑,大都是料亭,还有颇有风致的砖结构剧场。那时父母在这里经营着咖啡馆,街上的行人总是把店里挤得满满的。数年前父亲     

Exploratory analysis of brain connectivity with ICA.

Covariance-based methods of exploration of functional connectivity of the brain from functional magnetic resonance imaging (fMRI) experiments, such as principal component analysis (PCA) and structural equation modeling (SEM), require a priori knowledge such as an anatomical model to infer functional connectivity. In this research, a hybrid method, combining independent component analysis (ICA) and SEM, which is capable of deriving functional connectivity in an exploratory manner without the need of a prior model is introduced. The spatial ICA (SICA) derives independent neural systems or sources involved in task-related brain activation, while an automated method based on the SEM finds the structure of the connectivity among the elements in independent neural systems. Unlike second-order approaches used in earlier studies, the task-related neural systems derived from the ICA provide brain connectivity in the complete statistical sense. The use and efficacy of this approach is illustrated on two fMRI datasets obtained from a visual task and a language reading task.     

Comparisons of PWM and one-cycle control for power amplifier with multilevel converter

Based on the concepts of cascade multilevel converters and one-cycle technique, comparisons are conducted to characterize the distortion of the pulsewidth modulation (PWM) and one-cycle control methods that were applied to converters. Simulation results for the different control schemes are obtained in PSIM software initially to see the effect of one-cycle control different from that of PWM control. Through the comparisons, the advantages and disadvantages are identified for each method. The one-cycle scheme is better than PWM control in reducing undesirable harmonics and tracing dynamic waveforms. Simulation and experimental results are also provided to verify the conclusions.