High-Dimensional Space Geometrical Informatics and its applications to image restoration

With a view to solve the problems in modern information science, we put forward a new subject named High-Dimensional Space Geometrical Informatics (HDSGI). It builds a bridge between information science and point distribution analysis in high-dimensional space. A good many experimental results certified the correctness and availability of the theory of HDSGI. The proposed method for image restoration is an instance of its application in signal processing. Using an iterative "further blurring-debluring-further blurring" algorithm, the deblured image could be obtained.

Small-signal and large-signal performance test of high-speed optoelectronics devices

This paper presents measurement methods for determining the reflection coefficients and frequency responses of semiconductor laser diodes, photodiodes, and EA modulator chips. A novel method for determining the intrinsic frequency responses of laser diodes is also proposed, and applications of the developed measurement methods are discussed. We demonstrate the compensation of bonding wire on the capacitances of both the submount and the laser diode, and present a method for estimating the potential modulation bandwidth of TO packaging technique. Initial study on removing the effects of test fixture on large-signal performances of optoelectronic devices at high data rate is also given.

Large-Signal Performance of 1.3 mu m InAs/GaAs quantum-dot lasers

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on string languages generated by spiking neural p systems

We continue the study of spiking neural P systems by considering these computing devices as binary string generators: the set of spike trains of halting computations of a given system constitutes the language generated by that system. Although the "direct" generative capacity of spiking neural P systems is rather restricted (some very simple languages cannot be generated in this framework), regular languages are inverse-morphic images of languages of finite spiking neural P systems, and recursively enumerable languages are projections of inverse-morphic images of languages generated by spiking neural P systems.

measuring acoustic wave transit time in furnace based on active acoustic source signal

Accurate measurement of transit time for acoustic wave between two sensors installed on two sides of a furnace is a key to implementing the temperature field measurement technique based on acoustical method. A new method for measuring transit time of acoustic wave based on active acoustic source signal is proposed in this paper, which includes the followings: the time when the acoustic source signal arrives at the two sensors is measured first; then, the difference of two arriving time arguments is computed, thereby we get the transit time of the acoustic wave between two sensors installed on the two sides of the furnace. Avoiding the restriction on acoustic source signal and background noise, the new method can get the transit time of acoustic wave with higher precision and stronger ability of resisting noise interference.

Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks

Enzymatic hydrolysis of cellulose was highly complex because of the unclear enzymatic mechanism and many factors that affect the heterogeneous system. Therefore, it is difficult to build a theoretical model to study cellulose hydrolysis by cellulase. Artificial neural network (ANN) was used to simulate and predict this enzymatic reaction and compared with the response surface model (RSM). The independent variables were cellulase amount X-1, substrate concentration X-2, and reaction time X-3, and the response variables were reducing sugar concentration Y-1 and transformation rate of the raw material Y-2. The experimental results showed that ANN was much more suitable for studying the kinetics of the enzymatic hydrolysis than RSM. During the simulation process, relative errors produced by the ANN model were apparently smaller than that by RSM except one and the central experimental points. During the prediction process, values produced by the ANN model were much closer to the experimental values than that produced by RSM. These showed that ANN is a persuasive tool that can be used for studying the kinetics of cellulose hydrolysis catalyzed by cellulase.

SBS-based slow light in optical fibers: optimum design considerations for undistorted slow-light signal propagation in steady-state and transient regimes

SPIE

Optimization of Global Signal Networks for Island-Style FPGAs

We present a staggered buffer connection method that provides flexibility for buffer insertion while designing global signal networks using the tile-based FPGA design methodology. An exhaustive algorithm is used to analyze the trade-off between area and speed of the global signal networks for this staggered buffer insertion scheme, and the criterion for determining the design parameters is presented. The comparative analytic result shows that the methods in this paper are proven to be more efficient for FPGAs with a large array size.

Simulation of a Monolithic Integrated CMOS Preamplifier for Neural Recordings

A monolithic integrated CMOS preamplifier is presented for neural recording applications. Two AC-coupied capacitors are used to eliminate the large and random DC offsets existing in the electrode-electrolyte interface. Diode-connected nMOS transistors with a negative voltage between the gate and source are candidates for the large resistors necessary for the preamplifier. A novel analysis is given to determine the noise power spectral density. Simulation results show that the two-stage CMOS preamplifier in a closed-loop capacitive feedback configuration provides an AC in-band gain of 38.8dB,a DC gain of 0,and an input-referred noise of 277nVmax, integrated from 0. 1Hz to 1kHz. The preamplifier can eliminate the DC offset voltage and has low input-referred noise by novel circuit configuration and theoretical analysis.

Small Signal Equivalent Circuit Model and Modulation Properties of Vertical Cavity-Surface Emitting Lasers

Small signal equivalent circuit model and modulation properties of vertical cavity-surface emitting lasers (VCSEL's) are presented. The modulation properties both in analytic-equation calculation and in circuit model simulation are studied. The analytic-equation calculation of the modulation properties is calculated by using Mathcad program and the circuit model simulation is simulation is simulated by using Pspice program respectively. The results of calculation and the simulation are in good agreement with each other. Experiment is performed to testify the circuit model.

Architecture research and hardware implementation on simplified neural computing system for face identification

This paper describes a special-purpose neural computing system for face identification. The system architecture and hardware implementation are introduced in detail. An algorithm based on biomimetic pattern recognition has been embedded. For the total 1200 tests for face identification, the false rejection rate is 3.7% and the false acceptance rate is 0.7%.

Small signal equivalent circuit model of vertical cavity surface emitting lasers

Small signal equivalent circuit model of vertical cavity surface emitting lasers (VCSEL's) is given in this paper. The modulation properties of VCSEL are simulated using this model in Pspice program. The simulation results are good agree with experiment data. Experiment is performed to testify the circuit model.

Artificial neural networks expecting more advanced microelectronics technology