This paper shows a low power low-noise mixer in RFCMOS 0.18μm technology that operates between 0.3-3.8GHz. The low power mixer has a Gilbert cell configuration which employs low-noise transconductors designed using t...
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A 2 GHz programmable frequency divider is presented in this paper for Chinese and American band WSN applications. The divide ratio can be programmed from 720 to 960. The divider basically consists of a 15/16 dual-modu...
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A 1.8 GHz LC VCO in 1.8-V supply is presented. The VCO achieves low power consumption by optimum selection of inductance in the L-C tank. To increase the tuning range, a three-bit switching capacitor array is used for...
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A 1.8 GHz LC VCO in 1.8-V supply is presented. The VCO achieves low power consumption by optimum selection of inductance in the L-C tank. To increase the tuning range, a three-bit switching capacitor array is used for digital switched tuning. Designed in 0.18μm RF CMOS technology, the proposed VCO achieves a phase noise of -126.2dBc/Hz at 1MHz offset and consumes 1.38mA core current at 1.8-V voltage supply.
Several asynchronous duty cycle MAC protocols have been proposed for low power in the wireless sensornetworks. However, these proposed MACs pay little attention on the performance degradation in many-to-one communica...
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Several asynchronous duty cycle MAC protocols have been proposed for low power in the wireless sensornetworks. However, these proposed MACs pay little attention on the performance degradation in many-to-one communications which is the most common traffic pattern in wireless sensornetworks. In this paper, we propose AB-MAC, an appointment based MAC protocol to overcome the effect of channel contention of multiple senders. In AB-MAC, a fusion appointment scheme is proposed to enable scheduled batch transmission for multiple senders with low overhead. This scheme improves the transmission efficiency of the current asynchronous MACs in many-to-one traffic pattern. The experimental results show that, comparing to X-MAC which is a leading asynchronous MAC, AB-MAC can achieve better performance in terms of throughput, latency and energy efficiency.
A fully integrated 5-GHz PLL frequency synthesizer for WSN applications has been designed and implemented in 0.18μm CMOS technology. With automatic current calibration technique (ACC), the VCO could maintain a good p...
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A fully integrated 5-GHz PLL frequency synthesizer for WSN applications has been designed and implemented in 0.18μm CMOS technology. With automatic current calibration technique (ACC), the VCO could maintain a good performance under a small current consumption of about 0.3mA. Phase switching technique is used in the frequency divider to reduce power consumption. Using a 1.8V supply voltage, the measured power consumption is 12mW and the phase noise is -111.13dBc/Hz at 1MHz offset.
A 0.5 V 4.8 GHz CMOS LC VCO for WSN application was designed. The VCO uses traditional differential negative-resistance structure. With a switched capacitor array, VCO achieves a large tuning range. Thanks to the comb...
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A 0.5 V 4.8 GHz CMOS LC VCO for WSN application was designed. The VCO uses traditional differential negative-resistance structure. With a switched capacitor array, VCO achieves a large tuning range. Thanks to the combination of voltage-boosting circuit, VCO can have a control voltage larger than the supply voltage. Several technologies like width-length-ratio-adjusting of the transistors were used to optimize phase noise performance. The VCO uses 0.13 μm CMOS technology. The proposed VCO achieves a phase noise less than -115 dBc/Hz@1 MHz, and -121.2 dBc/Hz at most and consumes about 2.6 mA core current at 0.5 voltage supply. The performance of VCO meets the requirement of the design target.
When applied to precipitation forecasting, the mean generating function - optimal subset regression (MGF-OSR) model is limited by its low accuracy and high error, while the back propagation (BP) neural network model h...
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When applied to precipitation forecasting, the mean generating function - optimal subset regression (MGF-OSR) model is limited by its low accuracy and high error, while the back propagation (BP) neural network model has difficulty in learning for matrix selection. This paper proposes a new MGF-OSR-BP model, which uses a MGF to extend original data, an OSR to select the best series as the BP neural network input node and learning matrix, and the resultant data for training. The training procedure determines the number of hidden layers and uses an optimal number of hidden layers for model training. This paper uses the MGF-OSR-BP model to analyze precipitation data from Hangzhou, China, for 53 years, from 1956 to 2008. The 1956-2006 precipitation data are used as the training sample, and the 2007-2008 data are used as the test set data to verify the practicality of the forecast system. A fitting verification is performed using the forecasted data against field measurement data, and the results show that the forecast accuracy is better than that of the MGF-OSR model or the MGF stepwise multiple regression model.
When environmental noise keeps to fractional lower order α-stable distribution, the convergence performance of the traditional blind equalization algorithm is unstable. To overcome the deficiency, on the basis of com...
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A method of utilizing active resistance network to calibrate the temperature channel of data-acquisition unit is proposed in this paper. This method changes gate-source voltage by micro-controller so as to simulate th...
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In order to overcome the defects of traditional constant modulus blind equalization algorithm in equalizing higher-order QAM(Quadrature Amplitude Modulation) signals, on the basis of combining the momentum term algori...
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In order to overcome the defects of traditional constant modulus blind equalization algorithm in equalizing higher-order QAM(Quadrature Amplitude Modulation) signals, on the basis of combining the momentum term algorithm and the weighted multi-modulus blind equalization algorithm, Momentum term based Weighted Multi-Modulus blind equalization Algorithm(WMMA) is proposed. In the proposed algorithm, weighted multi-modulus blind equalization algorithm is used to adjust the modulus of the cost function adaptively and momentum term is employed to improve the convergence speed. The simulation results in the underwater acoustic channel show that the proposed algorithm has higher convergence speed and accuracy than Multi-Modulus blind equalization Algorithm(MMA) and Constant Modulus blind equalization Algorithm(CMA).
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