An innovative analog-to-digital converter (ADC) architecture is proposed, with the aim of acquiring an input signal according to the Compressed Sensing (CS) paradigm and without the need for dedicated active analog blocks. Its core is the capacitive array employed in traditional successive-approximation-register (SAR) ADCs. Introducing only a few additional switches, the array can compute the linear combination of consecutive signal samples, as required by the CS encoding.

To manage the presence of leakage currents, which may impair signal reconstruction, a compensation circuit is considered, allowing close-to-ideal performance of the system when properly designed. A neural network-based decoding strategy is also analyzed, with up to 20 dB of additional reconstruction quality with respect to standard algorithms. Synthetic electrocardiogram signals are used to validate optimizations both at the hardware level in the encoding block and at the software level in the decoder.

提出了一种创新的模数转换器（ADC）架构，其目的是根据压缩传感（CS）范例获取输入信号，而无需专用的有源模拟模块。它的核心是传统逐次逼近寄存器（SAR）ADC中采用的电容阵列。仅引入几个其他开关，该阵列就可以根据CS编码的要求，计算连续信号样本的线性组合。

为了管理可能会影响信号重建的泄漏电流的存在，考虑了一种补偿电路，当设计合理时，该电路可使系统达到接近理想的性能。还分析了基于神经网络的解码策略，相对于标准算法，该策略具有高达20 dB的额外重建质量。合成心电图信号用于在编码块的硬件级别和解码器的软件级别上验证优化。