Single-electron tunneling depressing synapse for cellular neural networks

In this paper, a cellular neural network with depressing synapses for contrast-invariant pattern classification and synchrony detection is presented, starting from the impulse model of the single-electron tunneling junction. The results of the impulse model and the network are simulated using simulation program with integrated circuit emphasis (SPICE). It is demonstrated that depressing synapses should be an important candidate of robust systems since they exhibit a rapid depression of excitatory postsynaptic potentials for successive presynaptic spikes.

Multi-channel micro neural probe fabricated with SOI

Silicon-on-insulator (SOI) substrate is widely used in micro-electro-mechanical systems (MEMS). With the buried oxide layer of SOI acting as an etching stop, silicon based micro neural probe can be fabricated with improved uniformity and manufacturability. A seven-record-site neural probe was formed by inductive-coupled plasma (ICP) dry etching of an SOI substrate. The thickness of the probe is 15 mu m. The shaft of the probe has dimensions of 3 mmx100 mu mx15 mu m with typical area of the record site of 78.5 mu m(2). The impedance of the record site was measured in-vitro. The typical impedance characteristics of the record sites are around 2 M Omega at 1 kHz. The performance of the neural probe in-vivo was tested on anesthetic rat. The recorded neural spike was typically around 140 mu V. Spike from individual site could exceed 700 mu V. The average signal noise ratio was 7 or more.

FBG pressure sensor based on the double shell cylinder with temperature compensation

A novel fiber Bragg grating (FBG) pressure sensor based on the double shell cylinder with temperature compensation is presented. in the sensing scheme, a sensing FBG is affixed in the tangential direction on the outer surface of the inner cylinder, and another FBG is affixed in the axial direction to compensate the temperature fluctuation. Based on the theory of elasticity, the theoretical analysis of the strain distribution of the sensing shell is presented. Experiments are carried out to test the performance of the sensor. A pressure sensitivity of 0.0937 nm/MPa has been achieved. The experimental results also demonstrate that the two FBGs have the same temperature sensitivity, which can be utilized to compensate the temperature induced wavelength shift during the pressure measurement. (C) 2008 Elsevier Ltd. 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.

The application of DBF neural networks for object recognition

On the basis of DBF nets proposed by Wang Shoujue, the model and properties of DBF neural network were discussed in this paper. When applied in pattern recognition, the algorithm and implement on hardware were presented respectively. We did experiments on recognition of omnidirectionally oriented rigid objects on the same level, using direction basis function neural networks, which acts by the method of covering the high dimensional geometrical distribution of the sample set in the feature space. Many animal and vehicle models (even with rather similar shapes) were recognized omnidirectionally thousands of times. For total 8800 tests, the correct recognition rate is 98.75%, the error rate and the rejection rate are 0.5% and 1.25% respectively. (C) 2003 Elsevier Inc. All rights reserved.

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.

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.

Influence of deep level defects on electrical compensation in semi-insulating InP materials

In this paper, we analyze and compare electrical compensation and deep level defects in semi-insulating ( SI) materials prepared by Fe-doping and high temperature annealing of undoped InP. Influence of deep level defects in the SI-InP materials on the electrical compensation has been studied thermally stimulated current spectroscopy (TSC). Electrical property of the Fe-doped SI-InP is deteriorated due to involvement of a high concentration of deep level defects in the compensation. In contrast, the concentration of deep defects is very low in high temperature annealed undoped SI-InP in which Fe acceptors formed by diffusion act as the only compensation centre to pin the Fermi level, resulting in excellent electrical performance. A more comprehensive electrical compensation model of SI-InP has been given based on the research results.

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.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79, 1.45, 1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.

A novel face recognition method based on hypersausage neural networks

In this paper, we firstly give the nature of 'hypersausages', study its structure and training of the network, then discuss the nature of it by way of experimenting with ORL face database, and finally, verify its unsurpassable advantages compared with other means.

A micromachined SiO2/silicon probe for neural signal recordings

The development of an implantable five channel microelectrode array is presented for neural signal recordings. The detailed fabrication process is outlined with four masked used. The SEM images show that the probe shank is 1.2mm long, 100 mu m wide and 30 mu m thick with the recording sites spaced 200 mu m apart for good signal isolation. The plot of the single recording site impedance versus frequency is shown by test in vitro and the ompedence declines with the increasing frequency. Experiment in vivo using this probe is under way.

Growth of high quality semi-insulating InP single crystal by suppression of compensation defects

Deep level defects in as-grown and annealed SI-InP samples were investigated by thermally stimulated current spectroscopy. Correlations between electrical property, compensation ratio, thermal stability and deep defect concentration in SI-InP were revealed. An optimized crystal growth condition for high quality SI-InP was demonstrated based on the experimental results.