Special Issue "Signal Processing and Machine Learning for Smart Sensing Applications"
Deadline for manuscript submissions: 20 October 2022 | Viewed by 10068
Interests: adaptive signal processing; machine learning; IoT; noise cancellation
Special Issues, Collections and Topics in MDPI journals
Interests: wireless localization and sensing; signal processing and detection; machine learning and information fusion
Special Issues, Collections and Topics in MDPI journals
Interests: GNSS; multi-Source positioning; radio navigation
Interests: multisensory fusion for indoor/outdoor pedestrian positioning; GNSS positioning in urban environments; integrity monitoring
Interests: neural networks; pattern recognition; machine learning; image processing; outdoor robotics; artificial intelligence; indoor localization and positioning
Special Issues, Collections and Topics in MDPI journals
Special Issue in Data: Wireless Localization: Tracking and Navigation Data Set
Special Issue in Journal of Sensor and Actuator Networks: Indoor Positioning and Navigation of Sensor Networks
Special Issue in Sensors: Applications and Innovations on Sensor-Enabled Wearable Devices
Special Issue in AI: Artificial Intelligence in Robotics Navigation
Special Issue in AI: Artificial Intelligence in the Smart Everything and Everywhere Era
Special Issue in Data: Data from Smartphones and Wearables
Special Issue in Data: Measurements of User and Sensor Data from the Internet of Things (IoT) Devices
Special Issue in Sensors: Advances in Indoor Positioning and Indoor Navigation
Special Issue in Data: Computer Vision Datasets for Positioning, Tracking and Wayfinding
Special Issue in Sensors: Computer Vision for Positioning, Tracking and Wayfinding: State of the Art Solutions
This Special Issue focuses on advanced signal processing and machine learning technologies for smart sensing applications. Successful examples include radio navigation, indoor/outdoor positioning, mm-wave sensing, speech denoising, noise cancellation, etc. One of the objectives of this Special Issue is to present smart sensing applications that leverage state-of-the-art signal processing and machine learning technologies. The other main purpose is to promote interdisciplinary collaborations between researchers in the fields of signal processing and machine learning technologies for smart sensing applications.
The emerging trends for smart sensing include: (1) the integration of sensors with low-power embedded signal processing into one system, (2) the integration of multiple sensors in the same system to extract more useful data, and (3) the use of compressive sensing techniques to extract the useful information from original sensor output. To achieve these goals, sophisticated signal processing and machine learning technologies are required.
Prof. Dr. Ying-Ren Chien
Prof. Dr. Mu Zhou
Dr. Ao Peng
Dr. Ni Zhu
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 2400 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.
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Automatic Sleep Arousal Detection with Single Lead EEG Using Stacking Ensemble Learning
Authors: Ying-Ren Chien; Cheng-Hsuan Wu; Hen-Wai Tsao
Affiliation: Department of Electrical Engineering, National Ilan University, No. 1, Sec. 1, Shen-Lung Rd., I-Lan City, 26041, Taiwan, R.O.C Graduate Institute of Communication Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, 10617, Taiwan Graduate Institute of Communication Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, 10617, Taiwan
Abstract: The quality of sleeping could affect human performance in their work and their emotions. Many studies have shown that sleep arousals can induce various sleep disorders. Thus, arousals are a good marker of sleep disruption representing a harmful feature for sleep. Nowadays, the method of detecting sleep arousals is to collect patient’s physiological data such as electroencephalography, electrocardiography, and electromyography through overnight polysomnography (PSG) test. Even though sleep arousal detection could achieve better performance by exploiting much or complete information of the PSG signals, the procedure of collecting PSG signals is time-consuming and cumbersome. Even worse, many cables with contact sensors have to attach to the user. It could make the user feels uncomfortable. In this work, we focus on using a single-lead EEG signal, which is possibly and easily to be collected by using a headphone-like device, to design an automatic sleep arousal detector based on stacking ensemble learning.
Title: Effectiveness of Neural Networks for Solving Inverse Problems in Magnetic-Field-Based Positioning
Authors: Ai-ichiro Sasaki
Affiliation: Kindai Univ.
Abstract: Magnetic-field based positioning is a feasible technology for establishing accurate indoor positioning systems. For realizing the systems, the position of a target device must be calculated from information of magnetic fields detected by multiple sensors. This calculation is not easy because it is a nonlinear inverse problem. We previously proposed a machine-learning approach for solving the inverse problem and demonstrated that the target position can be estimated fairly accurately by using the nearest neighbor methods. However, it was desirable to enhance the estimation accuracy for realizing accurate positioning systems. In this study, we discuss the machine-learning approach by using neural network algorithms. It is demonstrated that the estimation accuracy is enhanced by replacing the nearest neighbor method with the neural network algorithms. The estimation accuracy can be further improved by using predictor functions obtained by considering characteristics of the magnetic-field spatial distribution.
Title: A New Multimedia System Based on Interactive Tangible Interfacing and Computer Vision Techniques for Traditional Rituals
Authors: Chao-Ming Wang, Shih-Mo Tseng and Yu-Sheng Lin
Affiliation: Department of Digital Media Design, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C.
Abstract: To promote the sustainability of the traditional rituals of the popular Mazu religious culture in Taiwan and Southeast Asia, a new digital system named “i-Ritual,” which combines interactive multimedia technology with the ritual process for worshipping the deity Mazu, is proposed for a worshipper to experience the ritual tradition interactively. Three major steps of the Mazu ritual process, namely, “incense waving,” “moon-block casting,” and “fortune-telling poem drawing,” are carried out by multimedia technology using interactive tangible interfacing and computer vision techniques. Interviews with experts and a questionnaire survey of participants’ opinions were carried out to evaluate the effectiveness of the system, yielding the following findings: (1) the proposed system can bring the participants to experience the ritual process effectively; (2) the proposed three major ritual steps not only attracts more participations but also promotes general people’s learning of the religious ritual process; (3) the proposed system can attract participants by integrating multimedia effects with good usability; (4) the uses of tangible interfacing and computer vision techniques bring the users new experiences of the traditional ritual; and (5) the proposed system was affirmed to be good for religious education and can be extended for use by other religions.
Title: Development of Rim Defect Inspection System Using Deep Learning Network and Robot Arm
Authors: Wei-Lung Mao, Yu-Ying Chiu, Bing-Hong Lin, Chun-Chi Wang, Yi-Ting Wu, Cheng-Yu You
Affiliation: Graduate School of Engineering Science and Technology and Department of Electrical Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C.
Abstract: In this research, the eye-in-hand vision architecture, deep learning and convolutional neural networks are imported to construct an automatic defect detection system. It is a more efficient defect detection way for forged aluminum rims of electric vehicles. The detection system takes ABB robot arm as the core of the motion path planning, and outputs a 3D drawing of the rim using Robotstudio tool, and it can simulate the environment layout and path trajectory. The generative adversarial networks (GAN) and deep convolution generative adversarial networks (DCGAN) are used to generate a large number of defective images to expand the number of training data sets. The defect detection algorithm developed by YOLO achieves a fast and high-performance defect detection, which is better than current methods. The graphical user interface with C# language can find and mark the defect patterns in the detection images. The experimental results prove the accuracy and efficiency of our proposed automatic inspection system.
Title: An MQTT-based Automatic Fire Alarm System for Psychiatric Ward
Authors: Lan-Ying Chang1,3, Chen-Kuei Li1, Shan-Ju Wang1, Wei-Lung Mao2*, Yu-Chen Chang2,Yao-Teng Yang2, Yun-Yi Li2 , Chun-Chi Wang2
Affiliation: 1Department of Nursing, Wanqiao Branch, Taichung Veterans General Hospital, Taiwan 2Graduate School of Engineering Science and Technology and Department of Electrical Engineering, National Yunlin University of Science and Technology, Taiwan 3Graduate Institute of International Business and Administration and Department of Business Administration, National Yunlin University of Science and Technology, Taiwan
Abstract: Keywords: Smoke Sensors, Message Queuing Telemetry Transport (MQTT) Protocol, Wired and Wireless Communication, Hospital Fire Alarm System, Psychiatric Ward. Corresponding Author: Wei-Lung Mao, Professor, Graduate School of Engineering Science and Technology and Department of Electrical Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C. Tel: +886-5-534-2601 Ext 4222 Fax: +886-5-532-1719 E-mail: [email protected], [email protected] As society gradually relies on technologies to prevent dangers and detect the environment, the processing of data transmission and analysis becomes important. In recent years, firefighters are sacrificed during rescue missions. Even with the high-tech equipment in the current era, the lives of firefighters are not prevented in the fire scene. The psychiatric ward is equipped with access control management. When a fire occurs, the main key is to quickly extinguish the fire and evacuate the patient. In the fire drill research, when the alarm bell rings, it takes 9 minutes for the staff to find the fire source, and 19 minutes for reporting, extinguishing, and evacuating to a safe place.Thus, this research designs and develops an alarm system for hospitals and schools. If the fire source can be found in the early stage of the fire and put out the fire in time, the property loss of the hospital can be reduced. Therefore, the effect of intervening scientific and intelligent equipment to shorten the emergency response time is discussed. In fact, there are many addressable fire alarm systems on the market, whether wireless or wired, in fact, the research has a certain scale, and because the implementation site of this project is located in the Wanqiao Branch of Taichung Veterans General Hospital, in order to maintain the hospital. The internal equipment operates normally and reduces interference, and wired transmission is used as the planned method. However, if the wired products on the market cannot rely on the original transmission line, the wiring of the building must be remodeled, which will increase the time. In order to reduce this situation, network transmission is a very good choice, and this project will also take this as the main axis. Relying on the network line or WIFI, the Arduino microprocessor obtains the alarm data of the sensor and uploads it to the network, and adopts the MQTT communication protocol, and finally relies on the host to collect the sensor information at each location. The Message Queuing Telemetry Transport (MQTT) technology and fire sensors are utilized and integrated to extract the conditions of the environment. In the system, the subscriber server collects the data on a personal computer. Also, the website and database server monitor the wards in a hospital. Integrating and designing the systems help to understand the emergent situation.
Title: Segmentation of Articulated Vehicles based on Region Growing
Authors: Chien-Chou Lin1,*, Yu-Jyun Huang1, Chuan-Yu Chang1, Wei-Lung Mao2, and Teng-Wen Chang3
Affiliation: 1 Dept. of Computer Science and Information Engineering, National Yunlin University of Science & Technology, Taiwan; [email protected] 2 Dept. of Electrical Engineering, National Yunlin University of Science & Technology, Taiwan; [email protected] 3 Dept. of Digital Media Design, National Yunlin University of Science & Technology, Taiwan;[email protected]
Abstract: Since an articulated vehicle has diverse shapes with revolute joints, articulating vehicles’ recognition is challenging. However, if an articulated vehicle is segmented into several parts according to its joints, the recognition of the articulated vehicle can be simplified as rigid objects. Furthermore, other possible poses of the articulated vehicle can be predicted to make the recognition easier. This paper proposes a joint detection approach for the point cloud of an articulated vehicle. The proposed method uses two 3D point clouds of the same target vehicle with different poses as inputs. Two point clouds are converted into 2D bearing images. Then the corresponding pixels between two bearing images obtained by the SURF algorithm are used to derive the optimal transformation matrix. After two point clouds are transformed to the same coordinate system, the region growing method segments two point clouds with the surface norms. Experimental results show that the proposed method has a high detection rate of 85.7%. Furthermore, since the proposed method uses 2D images for alignment, the proposed algorithm is very efficient.
Title: Segmentation of Articulated Vehicles based on Region Growing
Authors: Chin-Yi Cheng*, Ilham Saputra, Cheng-En Shi
Affiliation: Department of Mechanical Engineering, National Yunlin University of Science and Technology, Douliu 64002, 5 Taiwan; [email protected] (I.S); [email protected] (C.E.S) * Correspondence: [email protected]; +886-5-5342601
Abstract: Soft pneumatic robotic grippers have been studied for more than 30 years but are still an area of challenge. Today, most of the soft robotic grippers are created through the injection molding process. It is limited in production consistency and difficult in multiple transformations. To solve this problem, a study was performed with a pneumatic soft robotic gripper which has been introduced by applying the Fused deposition modelling method of 3D printing technology in the fabrication process. The introduced pneumatic soft robotic gripper will utilize an actuation system using neumatic compression as an activator, because of the characteristics of the pneumatic system which has fast speed, easy control and easy maintenance. Making soft robotics easy to apply in areas such as industrial robot arm grippers, objects that need to be protected on surfaces, and more. In an effort to create intelligent robots, computer vision was applied to create gripper responsiveness which was created by utilizing real-time image processing and pressure regulation applied by PID control. The combination of soft robots with sensing capabilities from sensors applied to the gripper is a new approach to enable soft robots to be applied in gripping tasks quickly and precisely.
Title: An Insight into Entropy-Based Concentration and Instantaneous Frequency of Time-Frequency Distributions from Cohen's, Affine and Reassigned Classes
Authors: prof. dr. sc. Jonatan Lerga
Affiliation: Head of the Center for Artificial Intelligence and Cybersecurity, University of Rijeka Head of the Department of Computer Engineering, Faculty of Engineering, University of Rijeka Head of the Information Processing Laboratory, Faculty of Engineering, University of Rijeka Vukovarska 58, HR-51000 Rijeka, Croatia
Abstract: This paper explores three groups of time-frequency distributions: Cohen's, Affine, and Reassigned class of time-frequency representations (TFRs). The study provides detailed insight into the theory behind the selected TFRs belonging to these classes. Next, extensive numerical simulations were performed with examples that illustrate the behavior of analyzed TFR classes in the joint time-frequency domain. The methods were applied both on synthetic and real-life non-stationary signals. The obtained results were assessed with respect to time-frequency concentration (measured by the Rényi entropy), instantaneous frequency (IF) estimation accuracy, and cross-terms presence in the TFRs. The study gives valuable insight into the advantages and limitations of the analyzed TFRs helping select the proper distribution when analyzing given non-stationary signals in the time-frequency domain.