scholarly journals An Optimal Digital Filtering Technique for Incremental Delta-Sigma ADCs Using Passive Integrators

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 213
Author(s):  
Hyunmin Park ◽  
Hyungil Chae ◽  
Jintae Kim
Keyword(s):  
Optimization Problem ◽  
Filter Design ◽  
Digital Filtering ◽  
Test Vehicle ◽  
Convex Optimization Problem ◽  
Gain Compression ◽  
Digital Filter Design ◽  
Delta Sigma ◽  
Filtering Technique ◽  
Oversampling Ratio

This paper presents an optimal digital filtering technique to enhance the resolution of incremental delta-sigma modulators (incremental DSMs, IDSMs) using a low-power passive integrator. We first describe a link between a passive integrator and its impact on the output of the IDSM. We then show that the optimal digital filter design can be cast as a convex optimization problem, which can be efficiently solved. As a test vehicle of the proposed technique, we use a behavioral 2nd-order IDSM model that captures critical non-idealities of the integrator, such as gain compression and output saturation. The effectiveness of the presented technique is verified using extensive simulations. The result shows that the presented filtering technique improves signal-to-noise and distortion ratio (SNDR) by 15 dB–20 dB, achieving SNDR over 90 dB when the oversampling ratio (OSR) = 256, and this corresponds to best-in-class performance when compared to previously published DSM designs using passive integrators.

scholarly journals Inverse reinforcement learning in contextual MDPs

2021 ◽  
Author(s):  
Stav Belogolovsky ◽  
Philip Korsunsky ◽  
Shie Mannor ◽  
Chen Tessler ◽  
Tom Zahavy
Keyword(s):  
Reinforcement Learning ◽  
Optimization Problem ◽  
Decision Processes ◽  
Inverse Reinforcement Learning ◽  
Convex Optimization Problem ◽  
Reward Function ◽  
Dynamic Treatment Regime ◽  
Markov Decision ◽  
Dynamic Treatment ◽  
Recorded Data

AbstractWe consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent. Instead, the agent observes demonstrations from an optimal policy. The goal is to learn the reward mapping, such that the agent will act optimally even when encountering previously unseen contexts, also known as zero-shot transfer. We formulate this problem as a non-differential convex optimization problem and propose a novel algorithm to compute its subgradients. Based on this scheme, we analyze several methods both theoretically, where we compare the sample complexity and scalability, and empirically. Most importantly, we show both theoretically and empirically that our algorithms perform zero-shot transfer (generalize to new and unseen contexts). Specifically, we present empirical experiments in a dynamic treatment regime, where the goal is to learn a reward function which explains the behavior of expert physicians based on recorded data of them treating patients diagnosed with sepsis.

Tables for Butterworth-digital-filter design

1970 ◽  
Vol 6 (6) ◽  
pp. 157
Author(s):  
R. Genesio ◽  
A. Laurentini
Keyword(s):  
Digital Filter ◽  
Filter Design ◽  
Digital Filter Design

IIR Based Digital Filter Design for Denoising the Photoplethysmography Signal

2021 ◽  
Author(s):  
Omer Eris ◽  
Seval Bulut Eris ◽  
Cahit Bilgin ◽  
Mehmet Recep Bozkurt
Keyword(s):  
Digital Filter ◽  
Filter Design ◽  
Digital Filter Design
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