Wireless surface acoustic wave (SAW) sensors have some unique features that make them promising for industrial metrology. Their decisive advantage lies in their purely passive operation and the wireless readout capability allowing the installation also at particularly inaccessible locations. Furthermore, they are small, low-cost and rugged components on highly stable substrate materials and thus particularly suited for harsh environments. Nevertheless, a sensor itself does not carry out any measurement but always requires a suitable excitation and interrogation circuit: a reader. A variety of different architectures have been presented and investigated up to now. This review paper gives a comprehensive survey of the present state of reader architectures such as time domain sampling (TDS), frequency domain sampling (FDS) and hybrid concepts for both SAW resonators and reflective SAW delay line sensors. Furthermore, critical performance parameters such as measurement accuracy, dynamic range, update rate, and hardware costs of the state of the art in science and industry are presented, compared and discussed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022187 | PMC |
| http://dx.doi.org/10.3390/s18061734 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Unsupervised deep learning-based medical image registration: a survey.
Phys Med Biol
December 2024
Guangxi University, School of Computer and Electronic Information, Nanning, 530005, CHINA.
In recent decades, medical image registration technology has undergone significant development, becoming one of the core technologies in medical image analysis. With the rise of deep learning, deep learning-based medical image registration methods have achieved revolutionary improvements in processing speed and automation, showing great potential, especially in unsupervised learning. This paper briefly introduces the core concepts of deep learning-based unsupervised image registration, followed by an in-depth discussion of innovative network architectures and a detailed review of these studies, highlighting their unique contributions.
View Article and Find Full Text PDFLatest advancements and prospects in the next-generation of Internet of Things technologies.
PeerJ Comput Sci
October 2024
Universidad Europea del Atlántico, Santander, Spain.
The Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology.
View Article and Find Full Text PDFDeanthropomorphising NLP: Can a language model be conscious?
PLoS One
December 2024
LaSTUS, Universitat Pompeu Fabra, Barcelona, Spain.
This work is intended as a voice in the discussion over previous claims that a pretrained large language model (LLM) based on the Transformer model architecture can be sentient. Such claims have been made concerning the LaMDA model and also concerning the current wave of LLM-powered chatbots, such as ChatGPT. This claim, if confirmed, would have serious ramifications in the Natural Language Processing (NLP) community due to wide-spread use of similar models.
View Article and Find Full Text PDFBeyond White as a Neutral Color.
Stud Health Technol Inform
November 2024
School of Civil Engineering, University of Leeds, UK.
Article Synopsis
- This discussion paper examines the rise of white surfaces in healthcare design, highlighting the historical, sociopolitical, and environmental implications, including the ecological degradation caused by mining for white pigments.
- It critiques the dominance of neutral color palettes in design, linking it to a rejection of colorful local traditions and questioning the latent racism associated with the preference for white, rooted in 20th-century modernism.
- The paper calls for a more inclusive approach to color in healthcare spaces that considers diverse cultural sensitivities and experiences, while also encouraging designers to rethink their aesthetic choices beyond a purely white aesthetic.
Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.
Eur J Nucl Med Mol Imaging
November 2024
Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
Purpose: Deep convolutional neural networks (CNN) hold promise for assisting the interpretation of dopamine transporter (DAT)-SPECT. For improved communication of uncertainty to the user it is crucial to reliably discriminate certain from inconclusive cases that might be misclassified by strict application of a predefined decision threshold on the CNN output. This study tested two methods to incorporate existing label uncertainty during the training to improve the utility of the CNN sigmoid output for this task.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!