Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Signal Processing for Sensors
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Signal Processing for Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 2862

Special Issue Editors

Dr. Yanni Yang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Shandong University, Qingdao 266237, China
Interests: wireless sensing; intelligent sensing; human sensing and behavior analysis; pervasive computing; mobile computing
Prof. Dr. Jiannong Cao

E-Mail Website
Guest Editor
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
Interests: distributed systems and blockchain; wireless sensing and networking; big data and machine learning; mobile cloud and edge computing
Prof. Dr. Pengfei Hu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Shandong University, Qingdao 266237, China
Interests: security and privacy for IoT systems; machine learning security & privacy; mobile/wearable sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenlin An

E-Mail Website
Guest Editor
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
Interests: wireless localization and sensing; novel backscatter communication and sensing system

Special Issue Information

Dear Colleagues,

In the last decade, we have witnessed the rapid development of sensors and an increasing number of studies on making full use of sensory data for various applications, e.g., human sensing, environment sensing, and underwater sensing. Dealing with the collected signal which bears inherent noises due to the hardware imperfections or external interference from the environment is fundamental to achieve a high-performance sensing result. Recent advances in model-driven and data-driven signal processing have made great efforts in tackling the noisy and error-prone signals.

This Special Issue aims to collect original research and review articles on technologies, solutions, applications, and new challenges that are related to signal and sensory data processing. Potential topics include, but are not limited to, the following:

  • Model-driven signal processing methods;
  • Data-driven signal processing methods;
  • Radio frequency signal processing;
  • Acoustic signal processing;
  • Wearable sensor signal processing;
  • Mobile sensor signal processing;
  • Time-series signal processing;
  • Signal processing for human sensing applications;
  • Signal processing for environment sensing applications;
  • Signal processing in security-related applications;
  • Sensory data management and analytics, including quality, integrity, and trustworthiness;
  • Sensor signal processing for resource-constrained and mobile platforms;
  • Novel embedded machine learning algorithms on sensor data.

Dr. Yanni Yang
Prof. Dr. Jiannong Cao
Prof. Dr. Pengfei Hu
Dr. Zhenlin An
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • signal processing
  • sensory data
  • sensing applications
  • noise deduction
  • interference cancellation
  • high-performance sensing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 43650 KiB  
Article
The Realization of a Three-Dimensional Temperature Measurement System with a Two-Dimensional Sensor Array and the Demonstration of the Deformation Effect of Gravity on the Heating Patterns
by Dogan Can Samuk and Oguzhan Cakir
Sensors 2025, 25(1), 198; https://doi.org/10.3390/s25010198 - 1 Jan 2025
Viewed by 305
Abstract
Electric heaters are widely used owing to their portability, fast heating, single-focus heating, and energy efficiency advantages. Manufacturers provide customers with information on the power consumption and energy efficiency classes of heaters but do not provide any information on heating patterns. Knowing the [...] Read more.
Electric heaters are widely used owing to their portability, fast heating, single-focus heating, and energy efficiency advantages. Manufacturers provide customers with information on the power consumption and energy efficiency classes of heaters but do not provide any information on heating patterns. Knowing the heating pattern enables users to select the correct heater, which has a significant effect on comfort, health, energy efficiency, industrial process performance, plant growth, and climate change. In previous studies, two-dimensional temperature measurements were performed using sensor arrays. However, the three-dimensional heating patterns of the heaters have not been extracted, and the deformation effect of gravity on the heating patterns has not been demonstrated. In this study, a temperature measurement system with 64 temperature sensors placed at equal intervals in the xz-plane was designed and implemented. Then, the fan heater was moved along the y-axis at intervals of 10 cm from 0 to 100 cm, and three-dimensional heating patterns were obtained for different fan voltages. As part of the research objectives, the deformation effect of gravity on the heating pattern was revealed, and the shift in the maximum temperature point on the +z-axis was measured. The mathematical formula for the maximum temperature value was derived based on the fan voltage and the distance between the heater and the sensor array. The goodness-of-fit statistical values for the derived mathematical formula for the 55 temperature measurements were calculated as the root mean square error of 1.9543 and R-squared of 99.43%, demonstrating the accuracy of the presented model. Full article
(This article belongs to the Special Issue Signal Processing for Sensors)
►▼ Show Figures

Figure 1

22 pages, 4923 KiB  
Article
Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory
by Thi-Thu-Huong Le, Yustus Eko Oktian, Uk Jo and Howon Kim
Sensors 2023, 23(17), 7647; https://doi.org/10.3390/s23177647 - 4 Sep 2023
Cited by 1 | Viewed by 1654
Abstract
Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional [...] Read more.
Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss. Full article
(This article belongs to the Special Issue Signal Processing for Sensors)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop