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
Machine Learning in Sensors and Imaging II
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Machine Learning in Sensors and Imaging II

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 6023

Special Issue Editor

Dr. Hyoungsik Nam

E-Mail Website
Guest Editor
Department of Information Display, Kyung Hee University, Seoul, Korea
Interests: display electronics; low power; machine learning; UI/UX
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning is constantly extending its applications to various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, and management. In particular, since data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement of sensor performances and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging.

The topics of interest may include, but are not limited to, the following:

  • Machine learning for improving sensor performance;
  • New sensor applications using machine learning;
  • Machine learning-based HCI;
  • Machine learning-based localization and object tracking;
  • Machine learning for sensor signal processing;
  • Machine learning-based analysis of the big data collected from sensors

Dr. Hyoungsik Nam
Guest Editor

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.

Related Special Issue

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

16 pages, 3470 KiB  
Article
Stress State Classification Based on Deep Neural Network and Electrodermal Activity Modeling
by Floriana Vasile, Anna Vizziello, Natascia Brondino and Pietro Savazzi
Sensors 2023, 23(5), 2504; https://doi.org/10.3390/s23052504 - 23 Feb 2023
Cited by 2 | Viewed by 1603
Abstract
Electrodermal Activity (EDA) has become of great interest in the last several decades, due to the advent of new devices that allow for recording a lot of psychophysiological data for remotely monitoring patients’ health. In this work, a novel method of analyzing EDA [...] Read more.
Electrodermal Activity (EDA) has become of great interest in the last several decades, due to the advent of new devices that allow for recording a lot of psychophysiological data for remotely monitoring patients’ health. In this work, a novel method of analyzing EDA signals is proposed with the ultimate goal of helping caregivers assess the emotional states of autistic people, such as stress and frustration, which could cause aggression onset. Since many autistic people are non-verbal or suffer from alexithymia, the development of a method able to detect and measure these arousal states could be useful to aid with predicting imminent aggression. Therefore, the main objective of this paper is to classify their emotional states to prevent these crises with proper actions. Several studies were conducted to classify EDA signals, usually employing learning methods, where data augmentation was often performed to countervail the lack of extensive datasets. Differently, in this work, we use a model to generate synthetic data that are employed to train a deep neural network for EDA signal classification. This method is automatic and does not require a separate step for features extraction, as in EDA classification solutions based on machine learning. The network is first trained with synthetic data and then tested on another set of synthetic data, as well as on experimental sequences. In the first case, an accuracy of 96% is reached, which becomes 84% in the second case, thus demonstrating the feasibility of the proposed approach and its high performance. Full article
(This article belongs to the Special Issue Machine Learning in Sensors and Imaging II)
►▼ Show Figures

Figure 1

16 pages, 11726 KiB  
Article
Robust Data Augmentation Generative Adversarial Network for Object Detection
by Hyungtak Lee, Seongju Kang and Kwangsue Chung
Sensors 2023, 23(1), 157; https://doi.org/10.3390/s23010157 - 23 Dec 2022
Cited by 6 | Viewed by 3589
Abstract
Generative adversarial network (GAN)-based data augmentation is used to enhance the performance of object detection models. It comprises two stages: training the GAN generator to learn the distribution of a small target dataset, and sampling data from the trained generator to enhance model [...] Read more.
Generative adversarial network (GAN)-based data augmentation is used to enhance the performance of object detection models. It comprises two stages: training the GAN generator to learn the distribution of a small target dataset, and sampling data from the trained generator to enhance model performance. In this paper, we propose a pipelined model, called robust data augmentation GAN (RDAGAN), that aims to augment small datasets used for object detection. First, clean images and a small datasets containing images from various domains are input into the RDAGAN, which then generates images that are similar to those in the input dataset. Thereafter, it divides the image generation task into two networks: an object generation network and image translation network. The object generation network generates images of the objects located within the bounding boxes of the input dataset and the image translation network merges these images with clean images. A quantitative experiment confirmed that the generated images improve the YOLOv5 model’s fire detection performance. A comparative evaluation showed that RDAGAN can maintain the background information of input images and localize the object generation location. Moreover, ablation studies demonstrated that all components and objects included in the RDAGAN play pivotal roles. Full article
(This article belongs to the Special Issue Machine Learning in Sensors and Imaging II)
►▼ 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