A mixed-signal demodulator for a low-complexity IR-UWB receiver: Methodology, simulation and design

Crepaldi, Marco; Casu, Mario R.; Graziano, Mariagrazia; Zamboni, Maurizio

doi:10.1016/j.vlsi.2008.07.005

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…In the field of communication, signals are considered to be temporal data, which can be learned with deep learning to recognize its patterns inside [24]. Xuming Lin And Ruifang Liu [25] proposed a Deep Convolutional Network Demodulator (DCND) model and it can successfully demodulate symbol sequences from mixed signals.…”

Section: Introductionmentioning

confidence: 99%

Mixing Position Estimation of AIS Mixed Signal Based on Double-Sliding Window Method

Zhang

Meng

et al. 2019

IEEE Access

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With the development of the shipping industry, Automatic Identification System (AIS) used for ship communication becomes more and more important. Aiming at the problem of mixing position estimation of AIS mixed signals, this paper improves the double sliding-window detection algorithm to estimate the mixing position of miscellaneous signals accurately. There is a significant difference in energy between the aliased and unmixed parts of the mixed signal. When unmixed parts just enter one of the energy detections windows, the decision function reaches its peak value by establishing a proper decision function, that is to say, the position of the beginning and end of the mixing part is estimated. The simulation results show that the proposed algorithm which is compared with the frequency and amplitude detection algorithm can achieve the mixing position estimation with low complexity and strong robustness, and the estimation accuracy is close to the Cramer-Rao Bound under the condition of the high signal-to-noise ratio.

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Section: Introductionmentioning

confidence: 99%

Mixing Position Estimation of AIS Mixed Signal Based on Double-Sliding Window Method

Zhang

Meng

et al. 2019

IEEE Access

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“…Our aim is to demonstrate the feasibility of an integrated and compact system, which we outline in Section 2. Our previous works were focused on the UWB probe system, particularly on the transmitter and the receiver [5][6][7][8]. Here, we focus instead on the processing part, which we refer to as Imaging Unit (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Hardware Acceleration of Beamforming in a UWB Imaging Unit for Breast Cancer Detection

et al. 2013

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The Ultrawideband (UWB) imaging technique for breast cancer detection is based on the fact that cancerous cells have different dielectric characteristics than healthy tissues. When a UWB pulse in the microwave range strikes a cancerous region, the reflected signal is more intense than the backscatter originating from the surrounding fat tissue. A UWB imaging system consists of transmitters, receivers, and antennas for the RF part, and of a digital back-end for processing the received signals. In this paper we focus on the imaging unit, which elaborates the acquired data and produces 2D or 3D maps of reflected energies. We show that one of the processing tasks, Beamforming, is the most timing critical and cannot be executed in software by a standard microprocessor in a reasonable time. We thus propose a specialized hardware accelerator for it. We design the accelerator in VHDL and test it in an FPGA-based prototype. We also evaluate its performance when implemented on a CMOS 45 nm ASIC technology. The speed-up with respect to a software implementation is on the order of tens to hundreds, depending on the degree of parallelism permitted by the target technology.

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Simulation and design of an UWB imaging system for breast cancer detection

et al. 2014

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not only for their insightful guidance, patience, support and invaluable assistance throughout this work, but also for their help in my daily life over the past three years. I would also like to show my gratitude to Marco and Azzurra for their help in reviewing my thesis, as well as Marco Crepaldi for his technical advice and help during my circuit design work. I have received much help and support from numerous past and present members of the UWB group, including Alessandro, Stefano, Matteo, Paolo, Franscesco and Antonio. I want to say thank you so much guys, I am so happy to work with you. My appreciation extends to all the students in the VLSI Lab, Awais, Ruiyu, Juanchi, Manoj, Ali, Azhar, Abduwali and Matteo who kept me company with their discussions and cheerful chats during the coffee break, and the rest of my fellows whose names are missing due to space limitation. Moreover, I would like to thank all my lovely friends, especially my roommates and "lunch group" members, for their companion, encouragement and support. Finally, I would like to thank my family for their unconditional support and love along these years. Thanks Mom, thanks Dad, your support means so much to me. I have to say thank you to Yu Jian, for those moments of peace and well-being that only you can give me.

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A mixed-signal demodulator for a low-complexity IR-UWB receiver: Methodology, simulation and design

Cited by 5 publications

References 36 publications

Mixing Position Estimation of AIS Mixed Signal Based on Double-Sliding Window Method

Mixing Position Estimation of AIS Mixed Signal Based on Double-Sliding Window Method

Hardware Acceleration of Beamforming in a UWB Imaging Unit for Breast Cancer Detection

Simulation and design of an UWB imaging system for breast cancer detection

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