Mixed Mode VLSI Implementation of a Neural Associative Memory

A mixed mode digital/analog special purpose VLSI hardware implementation of an associative memory with neural architecture is presented. The memory concept is based on a matrix architecture with binary storage elements holding the connection weights. To enhance the processing speed analog circuit techniques are applied to implement the algorithm for the association. To keep the memory density as high as possible two design strategies are considered. First, the number of transistors per storage element is kept to a minimum. In this paper a circuit technique that uses a single 6-transistor cell for weight storage and analog signal processing is proposed. Second, the device precision has been chosen to a moderate level to save area as much as possible. Since device mismatch limits the performance of analog circuits, the impact of device precision on the circuit performance is explicitly discussed. It is shown that the device precision limits the number of rows activated in parallel. Since the input vector as well as the output vector are considered to be sparsely coded it is concluded, that even for large matrices the proposed circuit technique is appropriate and ultra large scale integration with a large number of connection weights is feasible.     

Sequential Decoding of Convolutional Codes for Rayleigh Fading Channels

The performance of sequential decoding of long constraint length convolutional codes is evaluated for Rayleigh fading channels. Sequential decoding is not practical below a certain theoretical signal-to-noise ratio, and these theoretical limits are calculated for a number of modulation methods and code rates. As an example, with BPSK modulation, soft decisions and code rate 1/2, the theoretical signal-to-noise ratio per information bit is 5.7 dB. Above this limit the bit error rate can be made arbitrarily small by increasing the constraint length at no significant complexity cost. Furthermore, it is shown that with carefully chosen quantization steps, 8 level uniform quantization gives a negligible loss also for sequential decoding on a Rayleigh fading channel. Simulation results using 8 level quantization correspond well with the theoretical performance bounds. Also, the performance on a correlated channel with finite interleaving has been obtained. With an interleaver depth of 50×50 and a normalized doppler frequency equal to 0.01 we are only 0.5 dB away from the performance with perfect interleaving. Finally, bit error rate results show this scheme to compete well with Turbo codes.     

一个可切实服务于运营商的移动无线因特网

本文在简单介绍了移动IP及其路由优化，无线局域网（WLAN）和GPRS的基础上，提出了一个新的利用移动IP来融合WLAN和GPRS的移动无线因特网的网络结构，详细论述了其多连接的路由过程，平滑垂直切换，AAA，并做了简要的性能分析。     

Neural Computation of the MoM Matrix Elements for Planar Configurations

In this paper a new artificial neural network (ANN) based model for the calculation of the method of moments (MoM) matrix elements is presented. Training sets that characterize the matrix elements are first constructed. These sets are then utilized to effectively train two radial basis function (RBF) neural networks to accurately estimate all the elements of the MoM matrix for any mesh used. The potential of the proposed approach is demonstrated in the case of a narrow microstrip line. The current distribution on the microstrip line produced by the trained RBF networks agrees very well with the exact distribution. In addition, the proposed ANN model is much faster than the conventional MoM procedure.     

基于充放电原理实现的微电容测量电路

具有抗分布电容以及简单实用等特性的充放电电路是目前微电容测量中广泛采用的一种测量电路。本文对基于充放电原理的微电容测量电路进行了深入研究，并介绍一种基于充放电原理的实用电路。     

Image processing using cellular neural networks based on multi-valued and universal binary neurons

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partially defined multiple-valued function on the single MVN. An arbitrary mapping described by partially defined or fully defined Boolean function, which can be non-threshold, may be implemented on the single UBN. The quickly converging learning algorithms exist for both types of neurons. Such features of the MVN and UBN may be used for solving the different problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN). It opens the new effective opportunities in nonlinear image filtering and its applications to noise reduction, edge detection and solving of the super resolution problem. A number of experimental results are presented to illustrate the performance of the proposed algorithms.An erratum to this article can be found at     

Fiber-optic reflex sensor for in-line production measurement

The paper describes experiments concerning distance measurement with fiber-optic sensor and improvement of the measurement principle. The sensor probe was well-designed with a small structure and multi-function. The novel optical fiber probe arrangement as well as the possibilities of use for complex measurement problems are explained using the measurement of an internal screw thread as an example. The experimental results show that the measurement uncertainty of the thread minor diameter can reach ±10 μm, and the stability of the measurement system is better than 0.07%.     

Unified Model for Generation Complex Networks with Utility Preferential Attachment

In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics ofthis new network are given.     

Time delay as a key to apoptosis induction in the p53 network

A feedback mechanism that involves the proteins p53 and mdm2, induces cell death as a controlled response to severe DNA damage. A minimal model for this mechanism demonstrates that the response may be dynamic and connected with the time needed to translate the mdm2 protein. The response takes place if the dissociation constant k between p53 and mdm2 varies from its normal value. Although it is widely believed that it is an increase in k that triggers the response, we show that the experimental behaviour is better described by a decrease in the dissociation constant. The response is quite robust upon changes in the parameters of the system, as required by any control mechanism, except for few weak points, which could be connected with the onset of cancer. Received 8 May 2002 / Received in final form 9 July 2002 Published online 17 September 2002     

模糊对向传播网络数据融合目标识别方法

重点研究了模糊对向传播网络 (FCPN)模型。针对数据融合和目标识别的特点 ,提出了基于模糊对向传播网络的融合目标识别方法和改进的模糊对向传播网络 (MFCPN)融合结构。利用仿真数据对网络的训练算法和融合结构进行了实验研究。结果表明 ,模糊对向传播网络较误差后向传播网络 (BPN)能够有效地实现融合识别 ;改进的模糊对向传播网络融合结构是可行的。同时还对FCPN和MFCPN应用于前视红外 (FLIR)和可见光摄像机目标跟踪系统进行了应用研究。