A CMOS 100 MHz continuous-time seventh order 0.05° equiripple linear phase leapfrog multiple loop feedback Gm-C filter

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”A novel 100 MHz CMOS Gm-C seventh-order 0.05° equiripple linear phase low-pass multiple loop feedback (MLF) filter based on leapfrog (LF) topology is presented. The filter is implemented using a fully-differential linear, high performance operational transconductance amplifier (OTA) based on cross-coupled pairs. PSpice simulations in a standard TSMC 0.25 μm CMOS process and with a single 5 V power supply have shown that the cut-off frequency of the filter without and with gain boost ranges from 8-32 MHz and 15-100 MHz, respectively. With gain boost, total harmonic distortion (THD) for a differential input voltage Vid of 315 mVpp at 1 MHz is less than -40 dB, dynamic range at 1% THD is over 55 dB, output noise with bandwidth 500 MHz is only 300 μVRMS, and power consumption is 322 mW

Z-->bb-bar in U(1)R symmetric supersymmetry.

We compute the one-loop corrections to the $Z \to b\bar{b}$ vertex in the $U(1)_R$ symmetric minimal supersymmetric extension of the standard model. We find that the predicted value of $R_b$ is consistent with experiment if the mass of the lighter top squark is no more than 180 GeV. Furthermore, other data combines to place a lower bound of 88 GeV on the mass of the light top squark. A top squark in this mass range should be accessible to searches by experiments at FNAL and LEP

Single-amplifier integrator-based low power CMOS filter for video frequency applications

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This paper describes a new low power fully differential second-order continuous-time low pass filter for use at video frequencies. The filter uses a single active device in combination with MOSFET resistors and grounded capacitors to achieve very low power consumption, small chip area and large dynamic range. The ideal integrator is realised using an internally compensated opamp consisting of only current mirrors and voltage buffers, whilst the lossy integrator is implemented by a single passive RC circuit. The filter has been simulated using a CMOS process. Results show that with a single 5 V power supply, cut-off frequency can be tuned from 3.5 MHz to 8 MHz, dynamic range is better than 67 dB, and power consumption is less than 1.7 mW

An incremental approach to MSE-based feature selection

Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training. A neural network classifier is implemented with an incremental training approach to detect and discard irrelevant features. By learning attributes one after another, our classifier can find directly the attributes that make no contribution to classification. These attributes are marked and considered for removal. Incorporated with a Minimum Squared Error (MSE) based feature ranking scheme, four batch removal methods based on classifier error rate have been developed to discard irrelevant features. These feature selection methods reduce the computational complexity involved in searching among a large number of possible solutions significantly. Experimental results show that our feature selection methods work well on several benchmark problems compared with other feature selection methods. The selected subsets are further validated by a Constructive Backpropagation (CBP) classifier, which confirms increased classification accuracy and reduced training cost