Electronics for Low-Size Low-Power Sensors and Systems: From Custom Design to Embedded Solutions II

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 15321

Special Issue Editors


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Guest Editor
Departament of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: design and testing of IoT-based electronic systems; smart remote control of facilities; electronic systems for automation and automotive; energy harvesting systems for sensors nodes; wearable devices for health monitoring; new materials and advanced sensors
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: design of electronic boards; firmware programming of microcontroller-based boards; sensors and energy-harvesting applications; development of wireless sensor networks (WSNs) and body area networks (BANs); wearable devices for health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As smart sensing systems increase in functionality, complexity, and spread, the related electronic sections have to be flexible, reconfigurable, and possibly low-cost. Several electronics issues are still open among researchers, with the aim of minimizing energy consumption, optimizing performance, and reducing dimensions to obtain non-invasive and miniaturized solutions suitable in different applications and scenarios. The Special Issue—“Electronics for Low-Size Low-Power Sensors and Systems: From Custom Design to Embedded Solutions”—will publish innovative developments and synergies in the following topics (but is not limited to them):

  • user-customizable SoC platform applied to sensing and control complex applications;
  • conditioning and interface electronics for smart sensing in real applications;
  • electronic solutions for MEMS devices applied to industrial applications;
  • electronics solutions for IoT-based health monitoring applications;
  • electronic solutions for smart cities, homes and smart workplaces;
  • new sensors for food safety and quality: related interface electronics;
  • wearable systems for biophysical parameters detection: electronic issues;
  • wearable electronic systems for assisting people with physical disabilities, active living, and rehabilitation;
  • applications and innovations of energy harvesting systems: electronics open issues;
  • low-size low-power sensors in embedded SoC: electronics aspects;
  • IoT-based systems for remote process control in different scenarios;
  • low-power Electronic Solutions for signals acquisition/processing from wearable sensors;
  • embedded solutions and platforms for data processing: firmware issues and applications.

Dr. Paolo Visconti
Dr. Fazio Roberto
Prof. Dr. Ramiro Velázquez
Guest Editors

Manuscript Submission Information

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Keywords

  • interface electronics
  • low-power sensors and systems
  • sensor and related electronics
  • wearable systems and energy issues
  • IoT solutions in different scenarios
  • remote process control
  • sensors for industrial applications
  • signals electronic conditioning
  • SoC platforms

Published Papers (3 papers)

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Research

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16 pages, 1194 KiB  
Article
Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification
by Seunghyun Park and Daejin Park
Electronics 2023, 12(11), 2511; https://doi.org/10.3390/electronics12112511 - 2 Jun 2023
Cited by 1 | Viewed by 1737
Abstract
Active noise cancellation (ANC) is the most important function in an audio device because it removes unwanted ambient noise. As many audio devices are increasingly equipped with digital signal processing (DSP) circuits, the need for low-power and high-performance processors has arisen because of [...] Read more.
Active noise cancellation (ANC) is the most important function in an audio device because it removes unwanted ambient noise. As many audio devices are increasingly equipped with digital signal processing (DSP) circuits, the need for low-power and high-performance processors has arisen because of hardware resource restrictions. Low-power design is essential because wireless audio devices have limited batteries. Noise cancellers process the noise in real time, but they have a short secondary path delay in conventional least mean square (LMS) algorithms, which makes implementing high-quality ANC difficult. To solve these problems, we propose a fixed-filter noise cancelling system with a convolutional neural network (CNN) classification algorithm to accommodate short secondary path delay and reduce the noise ratio. The signal-to-noise ratio (SNR) improved by 2.3 dB with CNN noise cancellation compared to the adaptive LMS algorithm. A frequency-domain noise classification and coefficient selection algorithm is introduced to cancel the noise for time-varying systems. Additionally, our proposed ANC architecture includes an even–odd buffer that efficiently computes the fast Fourier transform (FFT) and overlap-save (OLS) convolution. The simulation results demonstrate that the proposed even–odd buffer reduces processing time by 20.3% and dynamic power consumption by 53% compared to the single buffer. Full article
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15 pages, 6706 KiB  
Article
An Implantable Bio-Signal Sensor SoC with Low-Standby-Power 8K-Bit SRAM for Continuous Long-Term Monitoring
by Kyongsu Lee and Jae-Yoon Sim
Electronics 2023, 12(10), 2317; https://doi.org/10.3390/electronics12102317 - 21 May 2023
Viewed by 1237
Abstract
Individualized treatment of chronic diseases opens up great opportunities for implantable biosensor systems capable of tracking vital signals over long periods of time. To this end, low-power techniques in standby mode and the efficient utilization of storage space will be important issues for [...] Read more.
Individualized treatment of chronic diseases opens up great opportunities for implantable biosensor systems capable of tracking vital signals over long periods of time. To this end, low-power techniques in standby mode and the efficient utilization of storage space will be important issues for the implementation of such rechargeable implants with a built-in memory. This paper presents key circuit techniques, including a leakage-current-based clock generator that eliminates the need for an internal reference clock source, a low-standby-power 8Kbit SRAM with negative wordline and dynamic supply voltage scaling, and an adaptive sensing scheme to improve storage space utilization. When implemented with commercial 180 nm CMOS technology for the circuit simulation, approximately 70% (100 nW) of power dissipation was reduced from internal clock source, about 70% of power consumed by 8Kbit SRAM was saved, and the storage space utilization was improved by about 42.8%. In the end, the proposed implantable biosensor SoC consumes about 82.5 nW of standby power, saving about 42% from the previous approach and can last for 2.5 days using a 5 uAh thin-film battery (CYMBET® 1.7 × 2.2 mm2). Full article
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Review

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35 pages, 4860 KiB  
Review
Electromyography Monitoring Systems in Rehabilitation: A Review of Clinical Applications, Wearable Devices and Signal Acquisition Methodologies
by Muhammad Al-Ayyad, Hamza Abu Owida, Roberto De Fazio, Bassam Al-Naami and Paolo Visconti
Electronics 2023, 12(7), 1520; https://doi.org/10.3390/electronics12071520 - 23 Mar 2023
Cited by 24 | Viewed by 11785
Abstract
Recently, there has been an evolution toward a science-supported medicine, which uses replicable results from comprehensive studies to assist clinical decision-making. Reliable techniques are required to improve the consistency and replicability of studies assessing the effectiveness of clinical guidelines, mostly in muscular and [...] Read more.
Recently, there has been an evolution toward a science-supported medicine, which uses replicable results from comprehensive studies to assist clinical decision-making. Reliable techniques are required to improve the consistency and replicability of studies assessing the effectiveness of clinical guidelines, mostly in muscular and therapeutic healthcare. In scientific research, surface electromyography (sEMG) is prevalent but underutilized as a valuable tool for physical medicine and rehabilitation. Other electrophysiological signals (e.g., from electrocardiogram (ECG), electroencephalogram (EEG), and needle EMG) are regularly monitored by medical specialists; nevertheless, the sEMG technique has not yet been effectively implemented in practical medical settings. However, sEMG has considerable clinical promise in evaluating muscle condition and operation; nevertheless, precise data extraction requires the definition of the procedures for tracking and interpreting sEMG and understanding the fundamental biophysics. This review is centered around the application of sEMG in rehabilitation and health monitoring systems, evaluating their technical specifications, including wearability. At first, this study examines methods and systems for tele-rehabilitation applications (i.e., neuromuscular, post-stroke, and sports) based on detecting EMG signals. Then, the fundamentals of EMG signal processing techniques and architectures commonly used to acquire and elaborate EMG signals are discussed. Afterward, a comprehensive and updated survey of wearable devices for sEMG detection, both reported in the scientific literature and on the market, is provided, mainly applied in rehabilitation training and physiological tracking. Discussions and comparisons about the examined solutions are presented to emphasize how rehabilitation professionals can reap the aid of neurobiological detection systems and identify perspectives in this field. These analyses contribute to identifying the key requirements of the next generation of wearable or portable sEMG devices employed in the healthcare field. Full article
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