The Experts below are selected from a list of 87384 Experts worldwide ranked by ideXlab platform
Sudhakar Pamarti - One of the best experts on this subject based on the ideXlab platform.
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CICC - A 50MHz bandwidth, 10-b ENOB, 8.2mW VCO-based ADC enabled by filtered-dithering based linearization
Proceedings of the IEEE 2013 Custom Integrated Circuits Conference, 2013Co-Authors: Abhishek Ghosh, Sudhakar PamartiAbstract:A dithering technique for linearization of VCO-based ADCs is proposed. The proposed technique conditions the Signal to the VCO input to appear as white noise thereby eliminating spurious Signal Content arising out of the VCO non-linearity. The technique, thus obviates the need for power-hungry digital calibration techniques or expensive front-end loop-filters. A prototype implementation (in 65nm CMOS) based on the technique achieves 10-b ENOB in digitizing Signals with 50MHz bandwidth consuming 8.2mW at an FoM of 90fJ/conv.step.
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ISCAS - Mitigating timing errors in time-interleaved ADCs: A Signal conditioning approach
2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), 2013Co-Authors: Abhishek Ghosh, Sudhakar PamartiAbstract:Novel techniques based on Signal-conditioning are presented to mitigate timing errors in time-interleaved ADCs. A theoretical bound on the achievable spurious Signal Content, on applying the techniques, is also derived. Behavioral simulations corroborating the same are presented.
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Mitigating Timing Errors in Time-Interleaved ADCs: a Signal conditioning approach
arXiv: Information Theory, 2012Co-Authors: Abhishek Ghosh, Sudhakar PamartiAbstract:Novel techniques based on Signal-conditioning are presented to mitigate timing errors in time-interleaved ADCs. A theoretical bound on the achievable spurious Signal Content, on applying the techniques, is also derived. Behavioral simulations corroborating the same are presented.
Lei Wang - One of the best experts on this subject based on the ideXlab platform.
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Research on Communication Network Structure Mining Based on Spectrum Monitoring Data
IEEE Access, 2020Co-Authors: Xinrong Wu, Lu Yu, Lei WangAbstract:The physical characteristics of the massive spectrum Signals carrying the communication information and the statistical laws of these characteristics also potentially reflect the communication behavior of the communication individuals and the intelligence information related to the communication behavior. Intercepting and cracking Signal Content usually faces enormous difficulties and costs, and more often, we are not able to crack the encrypted Signal Content. However, by studying the physical features extracted from the spectrum monitoring Signals and the statistical laws of these features, it is also possible to dig out the hidden relationships between communication individuals and even the communication network structure, so as to analyze the communication behaviors of the communication individuals. Based on the characteristics of carrier frequency, bandwidth, power, Signal monitoring time and direction information of spectrum monitoring Signals, this paper identifies each spectrum Signal and studies the distribution characteristics and statistical laws of massive spectrum monitoring Signals in the column coordinate system. Due to the clustering of the spectrum Signals generated by the sources in the power, monitoring time and direction, and the correlation of the spectrum Signals generated by the two parties in the communication process, based on the improved density clustering algorithm, this paper proposes a method for mining the communication relationship between communication individuals from the spectrum monitoring data, and guesses and constructs the communication network structure by matching the communication individual with the communication relationship. Finally, we analyze the communication network structure mined from the spectrum monitoring data.
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The Communication Relationship Discovery Based on the Spectrum Monitoring Data by Improved DBSCAN
IEEE Access, 2019Co-Authors: Xinrong Wu, Lu Yu, Lei Wang, Wei TongAbstract:The communication relationship can reflect the behavior relationship between different communication targets. The in-depth analysis of the communication relationship can obtain the behaviors of communication individuals, and speculate their hierarchical positions in the communication network, so as to provide a basis for further speculation on the structure of the communication network. For massive spectrum Signals, we can also obtain important information such as communication relationships and behaviors of communication individuals, without cracking the Signal Content, but by analyzing the physical characteristics and statistical laws of the spectrum Signals. In order to overcome the difficulties and costs of analyzing communication behaviors from cracking the Signal Content in existing research, this paper studies the physical characteristics and statistical laws of spectrum Signals based on the features of frequency hopping period, average power and time of Signal occurrence. Because the spectrum Signals generated by the communication individuals show clustering characteristics, this paper proposes a communication relationship mining method based on improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The method can accurately discover the communication relationship of the radio station from the incomplete spectrum monitoring data, without cracking the Content carried by the spectrum Signals, which provides a new idea for the mining and analysis of mass spectrum monitoring Signals.
Xinrong Wu - One of the best experts on this subject based on the ideXlab platform.
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Research on Communication Network Structure Mining Based on Spectrum Monitoring Data
IEEE Access, 2020Co-Authors: Xinrong Wu, Lu Yu, Lei WangAbstract:The physical characteristics of the massive spectrum Signals carrying the communication information and the statistical laws of these characteristics also potentially reflect the communication behavior of the communication individuals and the intelligence information related to the communication behavior. Intercepting and cracking Signal Content usually faces enormous difficulties and costs, and more often, we are not able to crack the encrypted Signal Content. However, by studying the physical features extracted from the spectrum monitoring Signals and the statistical laws of these features, it is also possible to dig out the hidden relationships between communication individuals and even the communication network structure, so as to analyze the communication behaviors of the communication individuals. Based on the characteristics of carrier frequency, bandwidth, power, Signal monitoring time and direction information of spectrum monitoring Signals, this paper identifies each spectrum Signal and studies the distribution characteristics and statistical laws of massive spectrum monitoring Signals in the column coordinate system. Due to the clustering of the spectrum Signals generated by the sources in the power, monitoring time and direction, and the correlation of the spectrum Signals generated by the two parties in the communication process, based on the improved density clustering algorithm, this paper proposes a method for mining the communication relationship between communication individuals from the spectrum monitoring data, and guesses and constructs the communication network structure by matching the communication individual with the communication relationship. Finally, we analyze the communication network structure mined from the spectrum monitoring data.
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Communication Behavior Structure Mining Based on Electromagnetic Spectrum Analysis
2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2019Co-Authors: Ting Pan, Changhua Yao, Yindong Zhou, Xinrong Wu, Xiaowen LuAbstract:In order to guarantee the emergency communication of anti-terrorism and safeguard stability, owing to the needs of several works such as ensuring the security for major events and maintaining social stability, it is of great significance to master the communication situation of the target area through electromagnetic spectrum analysis for the problem that Signal Content is difficult to capture. From the perspective of electromagnetic analysis, this paper simulates spectrum monitoring data of the monitoring station, analyzes the communication relationship by statistical analysis of electromagnetic Signals, and uses the path loss model to locate the communication nodes. Experimental results show that this method can effectively obtain the communication behavior structure of the object region.
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The Communication Relationship Discovery Based on the Spectrum Monitoring Data by Improved DBSCAN
IEEE Access, 2019Co-Authors: Xinrong Wu, Lu Yu, Lei Wang, Wei TongAbstract:The communication relationship can reflect the behavior relationship between different communication targets. The in-depth analysis of the communication relationship can obtain the behaviors of communication individuals, and speculate their hierarchical positions in the communication network, so as to provide a basis for further speculation on the structure of the communication network. For massive spectrum Signals, we can also obtain important information such as communication relationships and behaviors of communication individuals, without cracking the Signal Content, but by analyzing the physical characteristics and statistical laws of the spectrum Signals. In order to overcome the difficulties and costs of analyzing communication behaviors from cracking the Signal Content in existing research, this paper studies the physical characteristics and statistical laws of spectrum Signals based on the features of frequency hopping period, average power and time of Signal occurrence. Because the spectrum Signals generated by the communication individuals show clustering characteristics, this paper proposes a communication relationship mining method based on improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The method can accurately discover the communication relationship of the radio station from the incomplete spectrum monitoring data, without cracking the Content carried by the spectrum Signals, which provides a new idea for the mining and analysis of mass spectrum monitoring Signals.
Abhishek Ghosh - One of the best experts on this subject based on the ideXlab platform.
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CICC - A 50MHz bandwidth, 10-b ENOB, 8.2mW VCO-based ADC enabled by filtered-dithering based linearization
Proceedings of the IEEE 2013 Custom Integrated Circuits Conference, 2013Co-Authors: Abhishek Ghosh, Sudhakar PamartiAbstract:A dithering technique for linearization of VCO-based ADCs is proposed. The proposed technique conditions the Signal to the VCO input to appear as white noise thereby eliminating spurious Signal Content arising out of the VCO non-linearity. The technique, thus obviates the need for power-hungry digital calibration techniques or expensive front-end loop-filters. A prototype implementation (in 65nm CMOS) based on the technique achieves 10-b ENOB in digitizing Signals with 50MHz bandwidth consuming 8.2mW at an FoM of 90fJ/conv.step.
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ISCAS - Mitigating timing errors in time-interleaved ADCs: A Signal conditioning approach
2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), 2013Co-Authors: Abhishek Ghosh, Sudhakar PamartiAbstract:Novel techniques based on Signal-conditioning are presented to mitigate timing errors in time-interleaved ADCs. A theoretical bound on the achievable spurious Signal Content, on applying the techniques, is also derived. Behavioral simulations corroborating the same are presented.
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Mitigating Timing Errors in Time-Interleaved ADCs: a Signal conditioning approach
arXiv: Information Theory, 2012Co-Authors: Abhishek Ghosh, Sudhakar PamartiAbstract:Novel techniques based on Signal-conditioning are presented to mitigate timing errors in time-interleaved ADCs. A theoretical bound on the achievable spurious Signal Content, on applying the techniques, is also derived. Behavioral simulations corroborating the same are presented.
Thomas Laepple - One of the best experts on this subject based on the ideXlab platform.
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Empirical estimate of the Signal Content of Holocene temperature proxy records
Climate of the Past, 2019Co-Authors: Maria Reschke, Kira Rehfeld, Thomas LaeppleAbstract:Abstract. Proxy records from climate archives provide evidence about past climate changes, but the recorded Signal is affected by non-climate-related effects as well as time uncertainty. As proxy-based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy record Signal Content as well as the uncertainties involved. In this study, we empirically estimate Signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the middle to late Holocene (last 6000 years). This is achieved through a comparison of the correlation of proxy time series from nearby sites of three compilations and model time series extracted at the proxy sites from two transient climate model simulations: a Holocene simulation of the ECHAM5/MPI-OM model and the Holocene part of the TraCE-21ka simulation. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial timescales to be low ( R ), even for nearby sites, which resulted in low SNR estimates. The estimated SNRs depend on the assumed time uncertainty of the proxy records, the timescale analysed, and the model simulation used. Using the spatial correlation structure of the ECHAM5/MPI-OM simulation, the estimated SNRs on centennial timescales ranged from 0.05 – assuming no time uncertainty – to 0.5 for a time uncertainty of 400 years. On millennial timescales, the estimated SNRs were generally higher. Use of the TraCE-21ka correlation structure generally resulted in lower SNR estimates than for ECHAM5/MPI-OM. As the number of available high-resolution proxy records continues to grow, a more detailed analysis of the Signal Content of specific proxy types should become feasible in the near future. The estimated low Signal Content of Holocene temperature compilations should caution against over-interpretation of these multi-proxy and multisite syntheses until further studies are able to facilitate a better characterisation of the Signal Content in paleoclimate records.
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Empirical estimate of the Signal Content of Holocene temperature proxy records
2018Co-Authors: Maria Reschke, Kira Rehfeld, Thomas LaeppleAbstract:Abstract. Proxy records from climate archives provide evidence about past climate changes, but the recorded Signal is affected by non-climate related effects as well as time uncertainty. As proxy based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy records’ Signal Content as well as the uncertainties involved. In this study, we empirically estimate Signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the mid to late Holocene. This is achieved through a comparison of proxy time series from close-by sites of three compilations and model time series data at the proxy sites from two transient Holocene climate model simulations. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial time scales to be rather low (R
