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Volume 15, September
 
 

J. Low Power Electron. Appl., Volume 15, Issue 4 (December 2025) – 2 articles

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18 pages, 2888 KB  
Article
Data Analysis of Electrical Impedance Spectroscopy-Based Biosensors Using Artificial Neural Networks for Resource Constrained Devices
by Marco Grossi and Martin Omaña
J. Low Power Electron. Appl. 2025, 15(4), 56; https://doi.org/10.3390/jlpea15040056 - 26 Sep 2025
Abstract
Portable and wearable sensors have gained attention in recent years to perform measurements in many different applications. Sensors based on Electrical Impedance Spectroscopy (EIS) are particularly promising, because they can make accurate measurements with minimum perturbation to the sample under test. Electrochemical biosensors [...] Read more.
Portable and wearable sensors have gained attention in recent years to perform measurements in many different applications. Sensors based on Electrical Impedance Spectroscopy (EIS) are particularly promising, because they can make accurate measurements with minimum perturbation to the sample under test. Electrochemical biosensors are devices that use electrochemical techniques to measure a target analyte. In the case of electrochemical biosensors based on EIS, the measured impedance spectrum is fitted to that of an equivalent electrical circuit, whose component values are then used to estimate the concentration of the target analyte. Fitting EIS data is usually carried out by sophisticated algorithms running on a PC. In this paper, we have evaluated the feasibility to perform EIS data fitting using simple Artificial Neural Networks (ANNs) that can be run on resource constrained microcontrollers, which are typically used for portable and wearable sensors. We considered a typical case of an impedance spectrum in the range 0.1–10 kHz, modeled by using the simplified Randles equivalent circuit. Our analyses have shown that simple ANNs can be a low power alternative to perform EIS data fitting on low-cost microcontrollers with a memory occupation in the order of kilo bytes and a measurement accuracy between 1% and 3%. Full article
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37 pages, 14177 KB  
Review
Wake-Up Receivers: A Review of Architectures Analysis, Design Techniques, Theories and Frontiers
by Suhao Chen, Xiaopeng Yu and Xiongchun Huang
J. Low Power Electron. Appl. 2025, 15(4), 55; https://doi.org/10.3390/jlpea15040055 - 23 Sep 2025
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Abstract
The rapid growth of the Internet of Things (IoT) has driven the need for ultra-low-power wireless communication systems. Wake-up receivers (WuRXs) have emerged as a key technology to enable energy-efficient, near-always-on operation for IoT devices. This review explores the state of the art [...] Read more.
The rapid growth of the Internet of Things (IoT) has driven the need for ultra-low-power wireless communication systems. Wake-up receivers (WuRXs) have emerged as a key technology to enable energy-efficient, near-always-on operation for IoT devices. This review explores the state of the art in WuRXs design, focusing on low-power architectures, key trade-offs, and recent advancements. We discuss the challenges in achieving low power consumption while maintaining sensitivity, power consumption, and interference resilience. The review highlights the evolution from radio frequency (RF) envelope detection architectures to more complex heterodyne and subthreshold designs and concludes with future directions for WuRXs research. Full article
(This article belongs to the Topic Advanced Integrated Circuit Design and Application)
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