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In this paper an iterative method for recovery of the missing samples of signals is investigated in detail, and some novel linear, nonlinear, and adaptive extrapolation techniques are proposed to be used along with it to increase the convergence rate of the recovery system. The proposed methods would remarkably speed up the convergence rate, save processing power, and reduce the delay of the system compared to some well known accelerated versions of the aforementioned iterative algorithm. © 2007 Elsevier B.V. All rights reserved  

Level Crossing (LC) Analog-to-Digital converters have been suggested as an asynchronous alternative to conventional schemes. It is our intention to improve the performance of these LC converters. In this paper, we also suggest alternative adaptive and multi-level adaptive LC schemes and use an iterative method to drastically improve the performance of LC converters. The impressive improvement of these schemes make LC converters a potential competitor to other conventional A/D converters such as Sigma Delta Modulators (SDM). ©2007 IEEE  

Sensors are among essential building blocks of any Cyber-Physical Systems (CPSs). Acquisition and processing of their sensory data contribute to the power consumption and computation load of the overall CPSs. For data acquisition, the conventional fixed frequency sampling in many such systems is sub-optimal since a sizable number of samples do not contain important information. In this work, we propose a Signal-Dependent Sampling (SDS) method and present its associated circuit implementation. Using the proposed SDS method, the number of retained samples is significantly reduced with little or negligible compromise in the quality of the (reconstructed) signal. The associated error and added... 

Given the emergence of the Internet of Things (IoT) Cyber-Physical Systems (CPSs) and their omnipresence, reducing their power consumption is among the major design priorities. To reduce the power consumption of such systems, we propose the use of a signal-dependent sampling method in a bottom-up fashion, which can lead to up to a 94% reduction in the overall system power with negligible or no loss in performance. Moreover, the proposed technique provides further flexibility for self-aware CPSs to dynamically adjust the number of data samples that are needed for processing (and consequently reduce the power consumption) based on the application at hand and the desired trade-off between...