High-frequency characterization of packaging network in to-can photodiode modules

A simple method for analyzing the effects of TO packaging network on the high-frequency response of photodiode modules is presented. This method is established based on the relations of the scattering parameters of the packaging network, photodiode chip, and module. It is shown that the results obtained by this method agree well with those obtained by the conventional comparison method. The proposed method is much more convenient since only the electrical domain measurements are required. (C) 2008 Wiley Periodicals, Inc.

Injection locked Fabry-Perot laser diodes for WDM passive optical network spare function

In this paper, a protection scheme for transmitters in wavelength-division-multiplexing passive optical network (WDM-PON) has been proposed and demonstrated. If any downstream transmitter encounters problems at the central office (CO), the interrupted communication can be restored immediately by injecting a Fabry-Perot laser diode (FP-LD) with the upstream lightwave corresponding to the failure transmitter. Compared with the conventional methods, this proposed architecture provides a cost-effective and reliable protection scheme employing a common FP-LD. In the experiment, a 1 36 protection capability was implemented with a 2.5 Gbit/s downstream transmission capability. (C) 2009 Elsevier B.V. All rights reserved.

Determining of the delay time for a heating ventilating and air-conditioning plant using two weighted neural network approach

This paper presents an two weighted neural network approach to determine the delay time for a heating, ventilating and air-conditioning (HVAC) plan to respond to control actions. The two weighted neural network is a fully connected four-layer network. An acceleration technique was used to improve the General Delta Rule for the learning process. Experimental data for heating and cooling modes were used with both the two weighted neural network and a traditional mathematical method to determine the delay time. The results show that two weighted neural networks can be used effectively determining the delay time for AVAC systems.

Calibration of microwave network analyzer

This paper presents a systematic description of the methods for calibrating microwave network analyzer and test fixtures, and discusses the problems arising in the calibration. The general criteria for choosing calibration standards and corresponding algorithms are discussed and suggestions to overcome these problems and improve the calibration accuracy are also given. It has been found that for reciprocal test fixtures, the four equations obtained with the thru standard can be used at the same time. Meanwhile, the calibration accuracy can be improved. It has been shown that using the same calibration procedures but different algorithms may lead to the occurrence of frequency limitation.

Iris recognition algorithm based on point covering of high-dimensional space and neural network

In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

A new method based on the construction of hypothetical symmetrical networks for fixture calibration in network analyzers

At least three known standards are normally required for the full two-port test fixture calibration in vector network analyzers (VNA). In this paper, a calibration procedure using only one standard, based on establishing two hypothetical symmetrical fixtures using triple-through method, is shown. The results using the calibrating method to subtract the influence of fixtures are in accord with the directly measured data of the device-under-test (DUT) without the fixtures very well, which shows that the proposed method is very simple and accurate.

Method for improving classification performance of neural network based on fuzzy input and network inversion

In order to effectively improve the classification performance of neural network, first architecture of fuzzy neural network with fuzzy input was proposed. Next a cost function of fuzzy outputs and non-fuzzy targets was defined. Then a learning algorithm from the cost function for adjusting weights was derived. And then the fuzzy neural network was inversed and fuzzified inversion algorithm was proposed. Finally, computer simulations on real-world pattern classification problems examine the effectives of the proposed approach. The experiment results show that the proposed approach has the merits of high learning efficiency, high classification accuracy and high generalization capability.

The design algorithm of an associative memory neural network

A design algorithm of an associative memory neural network is proposed. The benefit of this design algorithm is to make the designed associative memory model can implement the hoped situation. On the one hand, the designed model has realized the nonlinear association of infinite value pattern from n dimension space to m dimension space. The result has improved the ones of some old associative memory neural network. On the other hand, the memory samples are in the centers of the fault-tolerant. In average significance the radius of the memory sample fault-tolerant field is maximum.

The image coding scheme based on neural network and SVM

This paper applies data coding thought, which based on the virtual information source modeling put forward by the author, to propose the image coding (compression) scheme based on neural network and SVM. This scheme is composed by "the image coding (compression) scheme based oil SVM" embedded "the lossless data compression scheme based oil neural network". The experiments show that the scheme has high compression ratio under the slightly damages condition, partly solve the contradiction which 'high fidelity' and 'high compression ratio' cannot unify in image coding system.

Lossless data compression scheme based on neural network and SVM

First, the compression-awaited data are regarded Lis character strings which are produced by virtual information source mapping M. then the model of the virtual information source M is established by neural network and SVM. Last we construct a lossless data compression (coding) scheme based oil neural network and SVM with the model, an integer function and a SVM discriminant. The scheme differs from the old entropy coding (compressions) inwardly, and it can compress some data compressed by the old entropy coding.