A novel direct conversion receiver with low cost and low power is implemented in a 0.18 μm 1P6M standard CMOS process for a Mobile UHF RFID reader.A highly linear active mixer with low flicker noise and low noise active load is proposed.An efficient and low cost on-chip DC offset voltage canceling scheme is adopted with a high-input-impedance four-input OPAMP to buffer the output of the DC offset canceller(DCOC) block.The receiver has a measured input 1 dB compression point of 2 dBm and a sensitivity of 72 dBm in the presence of the large leakage signal from the transmitter.Only occupying a silicon area of 2.5 mm 2 and consuming 21 mA from a 1.8 V supply,the receiver makes the mobile UHF RFID reader to communicate with a transponder in a distance of 1 m conveniently. 相似文献
Missing data is a common problem in credit evaluation practice and can obstruct the development and application of an evaluation model. Block-wise missing data is a particularly troublesome issue. Based on multi-task feature selection approach, this paper proposes a method called MMPFS to build a model for credit evaluation that primarily includes two steps: (1) dividing the dataset into several nonoverlapping subsets based on missing patterns, and (2) integrating the multi-task feature selection approach using logistic regression to perform joint feature learning on all subsets. The proposed method has the following advantages: (1) missing data do not need to be managed in advance, (2) available data can be fully used for model learning, (3) information loss or bias caused by general missing data processing methods can be avoided, and (4) overfitting risk caused by redundant features can be reduced. The implementation framework and algorithm principle of the proposed method are described, and three credit datasets from UCI are investigated to compare the proposed method with other commonly used missing data treatments. The results show that MMPFS can produce a better credit evaluation model than data preprocessing methods, such as sample deletion and data imputation.
