A CMOS front-end circuit for SONET OC-96 receiver

An optical receiver front-end for SONET OC-96 receivers was analyzed and designed in 0.18 mu m CMOS process. It consists of a transimpedance amplifier (TIA) and a limiting amplifier (LA). The TIA takes a fully differential configuration, and regulated cascode (RGC) input stage is implemented. The LA was realized by five cascaded identical gain stages with active inductor load. The TIA achieved 4.2GHz bandwidth for 0.5pF photodiode (PD) capacitance and 1.2k 0 transimpedance gain. The LA achieved 5.4GHz bandwidth and 29dB voltage gain. The optical sensitivity is -19dBm at 5-Gb/s for a bit-error rate of 10(-12), and it dissipates 45.5mW for I.8V supply.

The wideband high-linearity mixers for cable television tuner application

The wideband high-linearity mixers for a double conversion cable TV tuner is presented. The up-conversion mixer converts the input signal from 100MHz to 1000 MHz to the intermediate frequency (IF) of I GHz above. And the down-conversion mixer converts the frequency back. The degeneration resistors are used to Improve the linearity. The tuner is implemented in a 0.35 mu m SiGe technology. Input power at 1dB compression point can reach +14.23dBm. The lowest noise figure is 17.5dB. The two mixers consume 103mW under a supply voltage of 5 V.

A high-gain high-linearity wide-band low noise amplifier for terrestrial and cable receptions

This paper presents the design of a wide-band low-noise amplifier (LNA) implemented in a 0.35 mu m SiGe BiCMOS technology for cable (DVB-C) and terrestrial (DVB-T) tuner applications. The LNA utilizes current injection to achieve high linearity. Without using inductors, the LNA achieves 0.1-1GHz wide bandwidth and 18.8-dB gain with less than 1.4-dB gain variation. The noise figure(NF) of the wideband LNA is 5dB, its 1-dB compression point is -2dBm and IIP3 is 8dBm. The LNA dissipates 120mW power with a 5-V supply.

An ambient proof and wide tuning range LC VCO with automatic amplitude control for tuner applications

This paper represents a LC VCO with AAC (Auto Amplitude Control), in which PMOS FETs are used as active components, and the varactors are directly connected to ground to widen Kvco linear range. The AAC circuitry adds little noise to the VCO and provides it with robust performance over a wide temperature and carrier frequency range. The VCO is fabricated in 50-GHz 0.35-mu m SiGe BiCMOS process. The measurement results show that it has -127.27-dBc/Hz phase noise at 1-MHz offset and a linear gain of 32.4-MHz/V between 990-MHz and 1.14-GHz. The whole circuit draws 6.6-mA current from 5.0-V supply.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.

maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams

IEEE

三维物体识别及姿态测定的推理系统

本文介绍了三维物体识别及姿态测定的一种新技术，从物体空间域模型出发，通过约束推理及几何推理，在物体三维信息部分给定的条件下，推断预测图象模型，并通过实测的图象数据反馈，推断出隐含在图象中未给定的三维信息，最终实现三维物体识别及姿态测定。整个系统在ＶＩＣＯＭ机上用Ｃ语言完成。

Performing Feature Selection with ACO

R. Jensen, 'Performing Feature Selection with ACO. Swarm Intelligence and Data Mining,' A. Abraham, C. Grosan and V. Ramos (eds.), Studies in Computational Intelligence, vol. 34, pp. 45-73. 2006.

Tolerance-based and Fuzzy-Rough Feature Selection.

R. Jensen and Q. Shen, 'Tolerance-based and Fuzzy-Rough Feature Selection,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 877-882, 2007.

Methods to accelerate the learning of bayesian network structures

R. Daly and Q. Shen. Methods to accelerate the learning of bayesian network structures. Proceedings of the Proceedings of the 2007 UK Workshop on Computational Intelligence.

A Framework for the Scoring of Operators on the Search Space of Equivalence Classes of Bayesian Network Structures

R. Daly and Q. Shen. A Framework for the Scoring of Operators on the Search Space of Equivalence Classes of Bayesian Network Structures. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 67-74.

Preserving Piece-wise Linearity in Fuzzy Interpolation

Z. Huang and Q. Shen. Preserving Piece-wise Linearity in Fuzzy Interpolation. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 105-112.

FRANTIC - A system for inducing accurate and comprehensible fuzzy rules

M. Galea and Q. Shen. FRANTIC - A system for inducing accurate and comprehensible fuzzy rules. Proceedings of the 2004 UK Workshop on Computational Intelligence, pages 136-143.

Fuzzy interpolation with generalized representative values

Z. Huang and Q. Shen. Fuzzy interpolation with generalized representative values. Proceedings of the 2004 UK Workshop on Computational Intelligence, pages 161-171.

Subsethood-based fuzzy modelling and classification

K. Rasmani and Q. Shen. Subsethood-based fuzzy modelling and classification. Proceedings of the 2004 UK Workshop on Computational Intelligence, pages 181-188.