A Low Power Fully CMOS Integrated RF Transceiver IC for Wireless Sensor Networks

Seo, Heejin; Moon, Yeon-Kug; Park, Yong-Kuk; Kim, Dong–Su; Kim, Dong–Sun; Lee, Youn-Sung; Won, Kwang-Ho; Kim, Seong-Dong; Choi, Pyung

doi:10.1109/tvlsi.2007.893586

Cited by 28 publications

(11 citation statements)

References 8 publications

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“…The energy consumption of the transmitter depends on various parameters including the transmission distance and transmission medium [21]. A range between 20 nJ per effectively transmitted bit as a low but realistic value [22,23] and a more conventional 5 lJ/S [24] is selected for this comparison. Similar power on the order of 280 nJ/bit is required to store data in non-volatile bulk memory of the NAND-flash type [25].…”

Section: Measured Results and Comparisonmentioning

confidence: 99%

Integrated control circuit for adaptive sampling

Rieger

Nian

2011

Analog Integr Circ Sig Process

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A fully integrated controller for non-uniform data sampling suitable for the pre-processing of signals in a power-and size-restricted sensor front-end is presented. The sample rate is dynamically varied based on signal activity determined by the 2nd derivative of the input voltage. The derivative is realized with high-pass filters having 647 Hz cut-off frequency. A digital circuit generates the time-stamps and the trigger for an external analogto-digital converter. Measured results of a 0.35 lm CMOS implementation show a sample rate variation of 7:1 and a system power advantage compared to conventional frontends. The circuit dissipates 48 lW from ±1.5 V supplies and consumes an active area of 0.068 mm 2 .

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Section: Measured Results and Comparisonmentioning

confidence: 99%

Integrated control circuit for adaptive sampling

Rieger

Nian

2011

Analog Integr Circ Sig Process

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“…Thus, placing the neural network before the VCO helps in the non-linearity compensation. The ADC and DAC used for the purpose are the standard high precision temperature stable convertors [17]. The hardware requirements for the neural network is obtained from MODELSIM simulation which is then integrated with the SPICE environment using the mixed signal tool of SYNOPSYS.…”

Section: Nonlinearity Compensation Using Neural Networkmentioning

confidence: 99%

A high performance 1 GHz voltage controlled oscillator using neural system architecture

RoyChaudhuri

Barma

2010

Analog Integr Circ Sig Process

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In this paper, an improved voltage controlled oscillator scheme with temperature compensated frequency, a wide linear range, low phase noise and low power dissipation is presented for application in transmitting signals of gas sensors from mines using RF communication. The basic unit of the oscillator is a ring based differential amplifier incorporating temperature compensated voltage dependent PMOS capacitors and standard PMOS triode connected load with temperature compensated biasing scheme. The increased nonlinearity in the presence of PMOS capaciors is reduced to 0.001% using a digitally programmable neural architecture which is simulated in the mixed signal domain of SYNOPSYS. Additionally, the temperature compensated PMOS capacitor improves the sensitivity of the VCO and the standard temperature compensated biasing scheme of the PMOS triode connected load reduces the drift in amplitude with temperature variation. The PMOS varactor lowers the phase noise of the VCO compared to parasitic capacitors without increasing the power dissipation. The entire VCO is designed using 0.18 lm typical technology of TSMC with 1.8 V power supply. The tuning range of the VCO is 0.3-1.7 V, maximum frequency is 1 GHz with a linear change of around 750 MHz, temperature sensitivity and power consumption are around 50 ppm/°C and 2 mW respectively. The phase noise is obtained to be around123dBc/Hz at 1 MHz offset frequency.

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“…It can be widely used in environmental monitoring, military, and anti-terrorism area [1][2] . It also can be used in agriculture management to monitor farmland data, service for the food production and pest control.…”

Section: Introductionmentioning

confidence: 99%

Hole patching strategy with the least mobile nodes in wireless sensor network of agriculture

Feng

Liu

et al. 2013

SPIE Proceedings

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Wireless Sensor Network (WSN) can be used in agriculture management. But hole can be produced in WSN due to the factors such as fault, energy depletion and so on, which can cause agricultural data incomplete. This fault can be repaired by deploying the mobile sensor nodes to patch the hole. The main optimization goal of this paper is to deploy the least mobile sensor nodes to make the cost of hole patching minimization. A hole patching strategy with the least mobile sensor nodes in WSN is proposed in this paper. Firstly, analyze geometric characteristics of the hole through detecting the hole. Next, deploy the mobile sensor nodes on the appropriate location to patch the hole according to the geometric characteristics. The strategy given in this paper includes two steps: (1) Choose the key intersection points from the set of intersection points of arc. (2) Determine the patching position of the mobile nodes to patch the hole. Finally, verify the effective of the strategy using a simulation experiment and analyze the performance based on the comparison of the experimental results. Using the hole patching strategy with the least mobile nodes in WSN can patch the hole completely and make the reparation cost minimization.

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A Low Power Fully CMOS Integrated RF Transceiver IC for Wireless Sensor Networks

Cited by 28 publications

References 8 publications

Integrated control circuit for adaptive sampling

Integrated control circuit for adaptive sampling

A high performance 1 GHz voltage controlled oscillator using neural system architecture

Hole patching strategy with the least mobile nodes in wireless sensor network of agriculture

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