Department of Electronics and Multimedia Communications, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, 040 01 Kosice, Slovakia.
Department of Technologies in Electronics, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, 040 01 Kosice, Slovakia.
Sensors (Basel). 2021 May 19;21(10):3543. doi: 10.3390/s21103543.
A novel method of analog-to-information conversion-the random interval integration-is proposed and studied in this paper. This method is intended primarily for compressed sensing of aperiodic or quasiperiodic signals acquired by commonly used sensors such as ECG, environmental, and other sensors, the output of which can be modeled by multi-harmonic signals. The main idea of the method is based on input signal integration by a randomly resettable integrator before the AD conversion. The integrator's reset is controlled by a random sequence generator. The signal reconstruction employs a commonly used algorithm based on the minimalization of a distance norm between the original measurement vector and vector calculated from the reconstructed signal. The signal reconstruction is performed by solving an overdetermined problem, which is considered a state-of-the-art approach. The notable advantage of random interval integration is simple hardware implementation with commonly used components. The performance of the proposed method was evaluated using ECG signals from the MIT-BIH database, multi-sine, and own database of environmental test signals. The proposed method performance is compared to commonly used analog-to-information conversion methods: random sampling, random demodulation, and random modulation pre-integration. A comparison of the mentioned methods is performed by simulation in LabVIEW software. The achieved results suggest that the random interval integration outperforms other single-channel architectures. In certain situations, it can reach the performance of a much-more complex, but commonly used random modulation pre-integrator.
本文提出并研究了一种新的模拟-信息转换方法——随机区间积分。该方法主要用于压缩感知常用传感器（如 ECG、环境等传感器）获取的非周期或准周期信号，其输出可以用多谐波信号建模。该方法的主要思想是在 AD 转换之前，通过随机可重置积分器对输入信号进行积分。积分器的重置由随机序列发生器控制。信号重构采用常用的基于原始测量向量与重构信号计算的向量之间距离范数最小化的算法。信号重构通过求解超定问题来实现，这被认为是一种最先进的方法。随机区间积分的显著优点是使用常用组件实现简单的硬件。使用来自麻省理工学院-生物医学工程系数据库、多正弦和环境测试信号自己的数据库的 ECG 信号对所提出的方法进行了性能评估。将所提出的方法的性能与常用的模拟-信息转换方法进行了比较：随机采样、随机解调以及随机调制预积分。在 LabVIEW 软件中通过仿真对这些方法进行了比较。所得到的结果表明，随机区间积分优于其他单通道架构。在某些情况下，它可以达到更复杂但常用的随机调制预积分器的性能。
