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Upsampling01:22

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics.

Aosen Wang, Feng Lin, Zhanpeng Jin

    IEEE Transactions on Biomedical Circuits and Systems
    |January 23, 2016
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    Summary
    This summary is machine-generated.

    This study introduces a dynamic knob (DK) for adaptive compressed sensing (CS) architectures. The DK improves biosignal reconstruction quality by over 70% in low-power wireless sensing applications.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Computer Engineering

    Background:

    • Compressed sensing (CS) enables efficient data acquisition by simultaneously sensing and compressing data.
    • Existing CS architectures lack flexibility and adaptivity for dynamic data sensing, particularly for biosignals.
    • There is a need for adaptive CS systems that can optimize performance under energy constraints.

    Purpose of the Study:

    • To propose and evaluate a novel dynamic knob (DK) design for reconfigurable compressed sensing architectures.
    • To enhance the adaptivity and efficiency of CS systems for biosignal processing.
    • To demonstrate the performance improvements of the DK design in terms of signal reconstruction quality and energy consumption.

    Main Methods:

    • Developed a template-based dynamic knob (DK) structure incorporating supervised learning and a look-up table.
    • Modeled DK performance using a closed analytic form and optimized the design via dynamic programming.
    • Implemented the DK design on a 130 nm process, measuring its physical footprint and energy consumption.
    • Benchmarked the DK's performance using a publicly available biosignal dataset.

    Main Results:

    • The proposed adaptive CS architecture with the DK design achieved over 70% improvement in signal reconstruction quality compared to traditional CS.
    • The implemented DK design operates at ultra-low power (187.88 nJ/event) with a small footprint (0.058 mm²).
    • The DK design demonstrated effective adaptivity to biosignal dynamics, enhancing signal quality under energy constraints.

    Conclusions:

    • The dynamic knob (DK) provides an effective solution for adaptive compressed sensing, particularly for biosignal applications.
    • The ultra-low power and adaptive nature of the DK design are crucial for energy-constrained wireless sensing.
    • This work advances the field of compressed sensing by enabling more flexible and efficient dynamic data acquisition for biosignals.