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Advancements in Low-Power Ubiquitous Sensing, Computing, and Communication Interfaces for...
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Advancements in Low-Power Ubiquitous Sensing, Computing, and Communication Interfaces for IoT: Circuits, Systems, and Applications

A special issue of Journal of Low Power Electronics and Applications (ISSN 2079-9268).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5595

Special Issue Editors

Dr. Mohammad Rafiqul Haider

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, The University of Alabama at Birmingham (UAB), Birmingham, AL 35294, USA
Interests: low-power electronics; neuromorphic architecture; inkjet-printed sensors; short-range wireless transmitter; analog reservoir-based edge computing; oscillatory neural network; machine learning; inductive link; wearable devices
Prof. Dr. Syed Kamrul Islam

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, The University of Missouri-Columbia (MIZZOU), Columbia, MO 65211, USA
Interests: analog/mixed-signal integrated circuits; monolithic sensors; semiconductor devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proliferation of sensors and sensor networks requires innovative approaches to sensing, computing, and communication. Sensors are integral in various aspects of our lives, from everyday activities to advanced healthcare, industrial automation, transportation, agriculture, security, and defense. These devices range from simple in-home temperature sensors to sophisticated brain–machine interfaces for prosthetic limbs. As technology continues to advance, we are entering an era of internet-connected devices, commonly referred to as the Internet of Things (IoT), that is reshaping our lifestyles and work environments.

Projections indicate that over 41.0 billion IoT devices will be connected to existing networks by 2027, gathering data on our daily activities, work routines, movements, and the maintenance of essential machinery. IoT edge devices require key design features, including energy efficiency, distributed processing, security, and wireless connectivity. These features are crucial for enabling high-density data collection, real-time monitoring, assessment, and control operations. Addressing the challenges posed by contemporary cyber–physical systems will necessitate a multidisciplinary and transformative approach.

Dr. Mohammad Rafiqul Haider
Prof. Dr. Syed Kamrul Islam
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Low Power Electronics and Applications is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • IoT edge device
  • energy efficiency
  • local computing
  • security
  • wireless telemetry
  • cyber–physical system

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Published Papers (3 papers)

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Research

15 pages, 1313 KiB  
Article
mTanh: A Low-Cost Inkjet-Printed Vanishing Gradient Tolerant Activation Function
by Shahrin Akter and Mohammad Rafiqul Haider
J. Low Power Electron. Appl. 2025, 15(2), 27; https://doi.org/10.3390/jlpea15020027 - 2 May 2025
Viewed by 202
Abstract
Inkjet-printed circuits on flexible substrates are rapidly emerging as a key technology in flexible electronics, driven by their minimal fabrication process, cost-effectiveness, and environmental sustainability. Recent advancements in inkjet-printed devices and circuits have broadened their applications in both sensing and computing. Building on [...] Read more.
Inkjet-printed circuits on flexible substrates are rapidly emerging as a key technology in flexible electronics, driven by their minimal fabrication process, cost-effectiveness, and environmental sustainability. Recent advancements in inkjet-printed devices and circuits have broadened their applications in both sensing and computing. Building on this progress, this work has developed a nonlinear computational element coined as mTanh to serve as an activation function in neural networks. Activation functions are essential in neural networks as they introduce nonlinearity, enabling machine learning models to capture complex patterns. However, widely used functions such as Tanh and sigmoid often suffer from the vanishing gradient problem, limiting the depth of neural networks. To address this, alternative functions like ReLU and Leaky ReLU have been explored, yet these also introduce challenges such as the dying ReLU issue, bias shifting, and noise sensitivity. The proposed mTanh activation function effectively mitigates the vanishing gradient problem, allowing for the development of deeper neural network architectures without compromising training efficiency. This study demonstrates the feasibility of mTanh as an activation function by integrating it into an Echo State Network to predict the Mackey–Glass time series signal. The results show that mTanh performs comparably to Tanh, ReLU, and Leaky ReLU in this task. Additionally, the vanishing gradient resistance of the mTanh function was evaluated by implementing it in a deep multi-layer perceptron model for Fashion MNIST image classification. The study indicates that mTanh enables the addition of 3–5 extra layers compared to Tanh and sigmoid, while exhibiting vanishing gradient resistance similar to ReLU. These results highlight the potential of mTanh as a promising activation function for deep learning models, particularly in flexible electronics applications. Full article
(This article belongs to the Special Issue Advancements in Low-Power Ubiquitous Sensing, Computing, and Communication Interfaces for IoT: Circuits, Systems, and Applications)
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18 pages, 6129 KiB  
Article
OptimalNN: A Neural Network Architecture to Monitor Chemical Contamination in Cancer Alley
by Uchechukwu Leo Udeji and Martin Margala
J. Low Power Electron. Appl. 2024, 14(2), 33; https://doi.org/10.3390/jlpea14020033 - 10 Jun 2024
Cited by 1 | Viewed by 2074
Abstract
The detrimental impact of toxic chemicals, gas, and oil spills in aquatic environments poses a severe threat to plants, animals, and human life. Regions such as Cancer Alley exemplify the profound consequences of inadequately controlled chemical spills, significantly affecting the local community. Given [...] Read more.
The detrimental impact of toxic chemicals, gas, and oil spills in aquatic environments poses a severe threat to plants, animals, and human life. Regions such as Cancer Alley exemplify the profound consequences of inadequately controlled chemical spills, significantly affecting the local community. Given the far-reaching effects of these spills, it has become imperative to devise an efficient method for early monitoring, estimation, and cleanup, utilizing affordable and effective techniques. In this research, we explore the application of U-shaped neural Network (UNET) and U-shaped neural network transformer (UNETR) neural network models designed for the image segmentation of chemical and oil spills. Our models undergo training using the Commonwealth Scientific and Industrial Research Organization (CSIRO) dataset and the Oil Spill Detection dataset, employing a specialized filtering technique to enhance detection accuracy. We achieved training accuracies of 95.35% and 91% by applying UNET on the Oil Spill and the CSIRO datasets after 50 epochs of training, respectively. We also achieved a training accuracy of 75% by applying UNETR to the Oil Spill dataset. Additionally, we integrated mixed precision to expedite the model training process, thus maximizing data throughput. To further accelerate our implementation, we propose the utilization of the Field Programmable Gate Array (FPGA) architecture. The results obtained from our study demonstrate improvements in inference latency on FPGA. Full article
(This article belongs to the Special Issue Advancements in Low-Power Ubiquitous Sensing, Computing, and Communication Interfaces for IoT: Circuits, Systems, and Applications)
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15 pages, 1776 KiB  
Article
Vehicle Detection in Adverse Weather: A Multi-Head Attention Approach with Multimodal Fusion
by Nujhat Tabassum and Mohamed El-Sharkawy
J. Low Power Electron. Appl. 2024, 14(2), 23; https://doi.org/10.3390/jlpea14020023 - 13 Apr 2024
Cited by 2 | Viewed by 2638
Abstract
In the realm of autonomous vehicle technology, the multimodal vehicle detection network (MVDNet) represents a significant leap forward, particularly in the challenging context of weather conditions. This paper focuses on the enhancement of MVDNet through the integration of a multi-head attention layer, aimed [...] Read more.
In the realm of autonomous vehicle technology, the multimodal vehicle detection network (MVDNet) represents a significant leap forward, particularly in the challenging context of weather conditions. This paper focuses on the enhancement of MVDNet through the integration of a multi-head attention layer, aimed at refining its performance. The integrated multi-head attention layer in the MVDNet model is a pivotal modification, advancing the network’s ability to process and fuse multimodal sensor information more efficiently. The paper validates the improved performance of MVDNet with multi-head attention through comprehensive testing, which includes a training dataset derived from the Oxford Radar RobotCar. The results clearly demonstrate that the multi-head MVDNet outperforms the other related conventional models, particularly in the average precision (AP) of estimation, under challenging environmental conditions. The proposed multi-head MVDNet not only contributes significantly to the field of autonomous vehicle detection but also underscores the potential of sophisticated sensor fusion techniques in overcoming environmental limitations. Full article
(This article belongs to the Special Issue Advancements in Low-Power Ubiquitous Sensing, Computing, and Communication Interfaces for IoT: Circuits, Systems, and Applications)
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