The Experts below are selected from a list of 24528 Experts worldwide ranked by ideXlab platform
W. Su - One of the best experts on this subject based on the ideXlab platform.
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Multi-Sensor Signal fusion-based modulation classification by using wireless Sensor networks
Wireless Communications and Mobile Computing, 2013Co-Authors: Y. Zhang, N. Ansari, W. SuAbstract:Automatic modulation classification AMC is applied as the intermediate step between Signal detection and demodulation to identify modulation schemes. AMC is a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted Signal at the receiver. The proposed modulation classification scheme based on multi-Sensor Signal fusion makes the premise that the combined Signal from multiple Sensors provides a more accurate description than any one of the individual Signals alone. Multi-Sensor Signal fusion offers increased reliability and huge processing gains in overall performance as compared with the single Sensor, thus making AMC of weak Signals in non-cooperative communication environment more reliable and successful. Signal-to-noise ratio improvement through multi-Sensor Signal fusion is studied by using second-order and fourth-order moments method. The classification performance based on multi-Sensor Signal fusion is investigated in the additive white Gaussian noise channel as well as the flat fading channel and is evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset, and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-Sensor Signal fusion-based AMC algorithm can greatly outperform other existing AMC methods.Copyright © 2013 John Wiley & Sons, Ltd.
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Multi-Sensor Signal Fusion Based Modulation Classification by Using Wireless Sensor Networks
2011 IEEE International Conference on Communications (ICC), 2011Co-Authors: Y. Zhang, N. Ansari, W. SuAbstract:Automatic blind modulation classification (MC) is deployed, as the intermediate step between Signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is still a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted Signal at the receiver. The proposed MC scheme based on multi-Sensor Signal fusion makes the premise that the combined Signal from multiple Sensors provides a more accurate description than any one of the individual Signal alone. Multi-Sensor Signal fusion offers increased reliability and huge gains in overall performance as compared to the single Sensor one, thus making automatic modulation classification (AMC) of weak Signals in non-cooperative communication environment more reliable and successful. Modulation constellations improvements using multi-Sensor Signal fusion in the AWGN channel are studied first by using numerical simulations. In order to further study SNR improvement through multi-Sensor Signal fusion, Q-PSK Signal SNR estimations using the M2M4 method after multi-Sensor Signal fusion with 10 Sensors versus SNR are also presented. Finally, classification performances based on multi-Sensor Signal fusion in the AWGN channel are investigated and evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-Sensor Signal fusion based AMC algorithm can greatly outperform other existing AMC schemes.
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ICC - Multi-Sensor Signal Fusion Based Modulation Classification by Using Wireless Sensor Networks
2011 IEEE International Conference on Communications (ICC), 2011Co-Authors: Y. Zhang, N. Ansari, W. SuAbstract:Automatic blind modulation classification (MC) is deployed, as the intermediate step between Signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is still a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted Signal at the receiver. The proposed MC scheme based on multi-Sensor Signal fusion makes the premise that the combined Signal from multiple Sensors provides a more accurate description than any one of the individual Signal alone. Multi-Sensor Signal fusion offers increased reliability and huge gains in overall performance as compared to the single Sensor one, thus making automatic modulation classification (AMC) of weak Signals in non-cooperative communication environment more reliable and successful. Modulation constellations improvements using multi-Sensor Signal fusion in the AWGN channel are studied first by using numerical simulations. In order to further study SNR improvement through multi-Sensor Signal fusion, Q-PSK Signal SNR estimations using the M2M4 method after multi-Sensor Signal fusion with 10 Sensors versus SNR are also presented. Finally, classification performances based on multi-Sensor Signal fusion in the AWGN channel are investigated and evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-Sensor Signal fusion based AMC algorithm can greatly outperform other existing AMC schemes.
Y. Zhang - One of the best experts on this subject based on the ideXlab platform.
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Multi-Sensor Signal fusion-based modulation classification by using wireless Sensor networks
Wireless Communications and Mobile Computing, 2013Co-Authors: Y. Zhang, N. Ansari, W. SuAbstract:Automatic modulation classification AMC is applied as the intermediate step between Signal detection and demodulation to identify modulation schemes. AMC is a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted Signal at the receiver. The proposed modulation classification scheme based on multi-Sensor Signal fusion makes the premise that the combined Signal from multiple Sensors provides a more accurate description than any one of the individual Signals alone. Multi-Sensor Signal fusion offers increased reliability and huge processing gains in overall performance as compared with the single Sensor, thus making AMC of weak Signals in non-cooperative communication environment more reliable and successful. Signal-to-noise ratio improvement through multi-Sensor Signal fusion is studied by using second-order and fourth-order moments method. The classification performance based on multi-Sensor Signal fusion is investigated in the additive white Gaussian noise channel as well as the flat fading channel and is evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset, and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-Sensor Signal fusion-based AMC algorithm can greatly outperform other existing AMC methods.Copyright © 2013 John Wiley & Sons, Ltd.
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Multi-Sensor Signal Fusion Based Modulation Classification by Using Wireless Sensor Networks
2011 IEEE International Conference on Communications (ICC), 2011Co-Authors: Y. Zhang, N. Ansari, W. SuAbstract:Automatic blind modulation classification (MC) is deployed, as the intermediate step between Signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is still a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted Signal at the receiver. The proposed MC scheme based on multi-Sensor Signal fusion makes the premise that the combined Signal from multiple Sensors provides a more accurate description than any one of the individual Signal alone. Multi-Sensor Signal fusion offers increased reliability and huge gains in overall performance as compared to the single Sensor one, thus making automatic modulation classification (AMC) of weak Signals in non-cooperative communication environment more reliable and successful. Modulation constellations improvements using multi-Sensor Signal fusion in the AWGN channel are studied first by using numerical simulations. In order to further study SNR improvement through multi-Sensor Signal fusion, Q-PSK Signal SNR estimations using the M2M4 method after multi-Sensor Signal fusion with 10 Sensors versus SNR are also presented. Finally, classification performances based on multi-Sensor Signal fusion in the AWGN channel are investigated and evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-Sensor Signal fusion based AMC algorithm can greatly outperform other existing AMC schemes.
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ICC - Multi-Sensor Signal Fusion Based Modulation Classification by Using Wireless Sensor Networks
2011 IEEE International Conference on Communications (ICC), 2011Co-Authors: Y. Zhang, N. Ansari, W. SuAbstract:Automatic blind modulation classification (MC) is deployed, as the intermediate step between Signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is still a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted Signal at the receiver. The proposed MC scheme based on multi-Sensor Signal fusion makes the premise that the combined Signal from multiple Sensors provides a more accurate description than any one of the individual Signal alone. Multi-Sensor Signal fusion offers increased reliability and huge gains in overall performance as compared to the single Sensor one, thus making automatic modulation classification (AMC) of weak Signals in non-cooperative communication environment more reliable and successful. Modulation constellations improvements using multi-Sensor Signal fusion in the AWGN channel are studied first by using numerical simulations. In order to further study SNR improvement through multi-Sensor Signal fusion, Q-PSK Signal SNR estimations using the M2M4 method after multi-Sensor Signal fusion with 10 Sensors versus SNR are also presented. Finally, classification performances based on multi-Sensor Signal fusion in the AWGN channel are investigated and evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-Sensor Signal fusion based AMC algorithm can greatly outperform other existing AMC schemes.
N. Ansari - One of the best experts on this subject based on the ideXlab platform.
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Multi-Sensor Signal fusion-based modulation classification by using wireless Sensor networks
Wireless Communications and Mobile Computing, 2013Co-Authors: Y. Zhang, N. Ansari, W. SuAbstract:Automatic modulation classification AMC is applied as the intermediate step between Signal detection and demodulation to identify modulation schemes. AMC is a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted Signal at the receiver. The proposed modulation classification scheme based on multi-Sensor Signal fusion makes the premise that the combined Signal from multiple Sensors provides a more accurate description than any one of the individual Signals alone. Multi-Sensor Signal fusion offers increased reliability and huge processing gains in overall performance as compared with the single Sensor, thus making AMC of weak Signals in non-cooperative communication environment more reliable and successful. Signal-to-noise ratio improvement through multi-Sensor Signal fusion is studied by using second-order and fourth-order moments method. The classification performance based on multi-Sensor Signal fusion is investigated in the additive white Gaussian noise channel as well as the flat fading channel and is evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset, and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-Sensor Signal fusion-based AMC algorithm can greatly outperform other existing AMC methods.Copyright © 2013 John Wiley & Sons, Ltd.
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Multi-Sensor Signal Fusion Based Modulation Classification by Using Wireless Sensor Networks
2011 IEEE International Conference on Communications (ICC), 2011Co-Authors: Y. Zhang, N. Ansari, W. SuAbstract:Automatic blind modulation classification (MC) is deployed, as the intermediate step between Signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is still a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted Signal at the receiver. The proposed MC scheme based on multi-Sensor Signal fusion makes the premise that the combined Signal from multiple Sensors provides a more accurate description than any one of the individual Signal alone. Multi-Sensor Signal fusion offers increased reliability and huge gains in overall performance as compared to the single Sensor one, thus making automatic modulation classification (AMC) of weak Signals in non-cooperative communication environment more reliable and successful. Modulation constellations improvements using multi-Sensor Signal fusion in the AWGN channel are studied first by using numerical simulations. In order to further study SNR improvement through multi-Sensor Signal fusion, Q-PSK Signal SNR estimations using the M2M4 method after multi-Sensor Signal fusion with 10 Sensors versus SNR are also presented. Finally, classification performances based on multi-Sensor Signal fusion in the AWGN channel are investigated and evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-Sensor Signal fusion based AMC algorithm can greatly outperform other existing AMC schemes.
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ICC - Multi-Sensor Signal Fusion Based Modulation Classification by Using Wireless Sensor Networks
2011 IEEE International Conference on Communications (ICC), 2011Co-Authors: Y. Zhang, N. Ansari, W. SuAbstract:Automatic blind modulation classification (MC) is deployed, as the intermediate step between Signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is still a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted Signal at the receiver. The proposed MC scheme based on multi-Sensor Signal fusion makes the premise that the combined Signal from multiple Sensors provides a more accurate description than any one of the individual Signal alone. Multi-Sensor Signal fusion offers increased reliability and huge gains in overall performance as compared to the single Sensor one, thus making automatic modulation classification (AMC) of weak Signals in non-cooperative communication environment more reliable and successful. Modulation constellations improvements using multi-Sensor Signal fusion in the AWGN channel are studied first by using numerical simulations. In order to further study SNR improvement through multi-Sensor Signal fusion, Q-PSK Signal SNR estimations using the M2M4 method after multi-Sensor Signal fusion with 10 Sensors versus SNR are also presented. Finally, classification performances based on multi-Sensor Signal fusion in the AWGN channel are investigated and evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-Sensor Signal fusion based AMC algorithm can greatly outperform other existing AMC schemes.
Huaizhong Li - One of the best experts on this subject based on the ideXlab platform.
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A method of dual-Sensor Signal fusion for DSP-based wide-range vibration detection and control
Measurement: Journal of the International Measurement Confederation, 2015Co-Authors: Huaizhong LiAbstract:This paper presents a unique dual-Sensor Signal fusion technology for DSP (digital Signal processor)-based wide range vibration detection and active vibration control (AVC), which aims to suppress wide range vibration disturbances up to sub-micron level. In this method, two accelerometer Sensors with different measurement ranges and sensitivities are used to detect the vibration disturbances as coarse and fine Sensors respectively, and feed the Signals simultaneously to a DSP controller. Each Sensor is responsible for detecting accelerations in a specific range. By proper incorporation of Signals from the two Sensors, it is possible to achieve a wide detection range of vibrations at low cost. Simulation study shows that in an AVC system with the proposed dual-Sensor Signal fusion approach as the vibration detection component, significant improvement can be achieved comparing with traditional single-Sensor AVC systems.
S.k. Islam - One of the best experts on this subject based on the ideXlab platform.
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Ultra-low-power Sensor Signal processing unit for implantable bioSensor applications
International Conference on Electrical & Computer Engineering (ICECE 2010), 2010Co-Authors: M. R. Haider, S.k. IslamAbstract:In recent years various types of implantable medical devices have been proposed for monitoring various physiological parameters of human body. Long term maintenance-free operation of these devices requires extreme low-power operation to avoid periodic replacement of batteries. For real-time monitoring of health condition, the implantable medical devices also need to transmit the vital information outside of the human body for further diagnostics and processing. This work shows the design and simulation of an ultra-low-power Signal processing unit suitable for implantable bioSensor applications. The proposed unit comprises of a Data Generator Block, an Impulse Generator Block and a Buffer Block. The Data Generator Block takes the Signal from any generic Sensor and transforms the Sensor Signal into frequency modulated digital pulses by employing a relaxation oscillator. The Impulse Generator Block converts the digital pulses into impulse Signals for impulse-radio based wireless telemetry. Finally the Buffer Block drives a standard 200Ω antenna load. Use of weak inversion MOSFET and relaxation oscillator structure inside the Data Generator Block facilitate to achieve extreme low-power operation. Simulation results show the effectiveness of the proposed system.
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A low power Sensor-Signal read-out circuit powered by inductive line
2006 8th International Conference on Solid-State and Integrated Circuit Technology Proceedings, 2006Co-Authors: N. Ericson, W. Qu, S.k. Islam, M.a. Adeeb, M.a. HuqueAbstract:A low-power Sensor Signal processing circuit for implantable bioSensor applications has been proposed. First of all, a read-out circuit is designed to convert the Sensor Signal into a frequency modulated data Signal and then to modulate the data Signal with a high frequency carrier for successful transmission. The chip is fabricated using 0.35mum standard CMOS process and power consumption of the chip is registered to be 100muwatt. The circuit performance meets the desired requirements with a supply voltage as low as 1.5V, and it can be powered up by an external inductive link. To mimic the concept of on-chip power transmission by inductive link, a board level design is implemented with very high link efficiency. Test results emulate the simulation results with good agreement and corroborate the efficacy of the designed system
