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Advanced Techniques for Acquisition and Sensing

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Intelligent Sensors".

Viewed by 33858

Editors

Department of Information Engineering, Marche Polytechnic University, 60131 Ancona, Italy
Interests: medical signal processing; signal reconstruction; compressed sensing; patient monitoring; Internet of Things; skin; wearable computers; doppler radar; bioelectric potentials; body sensor networks; data acquisition; data compression

Topical Collection Information

Dear Colleagues,

Recent advances in electronics and sensing technologies have enabled the development of innovative methods and instruments which integrate acquisition, processing, storage, and communication capabilities. New acquisition systems and intelligent sensing devices can perform onboard computations, but they require proper techniques to reduce the data rate while retaining the information content, to optimize the use of available power resources and channel capacity. This Topical Collection aims to promote research that explores new frontiers and challenges in techniques for data acquisition and sensing, as well as the related measurement issues. Papers presenting innovative works, case studies, and reviews of the state-of-the-art are invited.

The topics of interest for this Collection include but are not limited to:

  • New methods of data acquisition and sensing principles;
  • Advanced techniques for signal processing;
  • intelligent measurement devices;
  • Innovative systems or instruments for characterization and testing;
  • Measurement methodologies for Internet-of-Things and remote monitoring;
  • Artificial intelligence in measurement and sensing techniques;
  • Data analysis and uncertainty evaluation from smart sensors and wearables;
  • Measurement uncertainty in big data analytics.

Dr. Grazia Iadarola
Dr. Susanna Spinsante
Collection Editors

Manuscript Submission Information

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Keywords

  • measurement
  • acquisition and sensing techniques
  • data acquisition systems
  • smart sensors
  • wearable devices
  • remote monitoring
  • Internet-of-Things
  • Artificial Intelligence
  • measurement uncertainty
  • characterization
  • testing
  • signal processing
  • image processing

Published Papers (12 papers)

2023

Jump to: 2022

23 pages, 5455 KiB  
Article
Surface Defect Detection of Bearing Rings Based on an Improved YOLOv5 Network
by Haitao Xu, Haipeng Pan and Junfeng Li
Sensors 2023, 23(17), 7443; https://doi.org/10.3390/s23177443 - 26 Aug 2023
Cited by 2 | Viewed by 948
Abstract
Considering the characteristics of complex texture backgrounds, uneven brightness, varying defect sizes, and multiple defect types of the bearing surface images, a surface defect detection method for bearing rings is proposed based on improved YOLOv5. First, replacing the C3 module in the backbone [...] Read more.
Considering the characteristics of complex texture backgrounds, uneven brightness, varying defect sizes, and multiple defect types of the bearing surface images, a surface defect detection method for bearing rings is proposed based on improved YOLOv5. First, replacing the C3 module in the backbone network with a C2f module can effectively reduce the number of network parameters and computational complexity, thereby improving the speed and accuracy of the backbone network. Second, adding the SPD module into the backbone and neck networks enhances their ability to process low-resolution and small-object images. Next, replacing the nearest-neighbor upsampling with the lightweight and universal CARAFE operator fully utilizes feature semantic information, enriches contextual information, and reduces information loss during transmission, thereby effectively improving the model’s diversity and robustness. Finally, we constructed a dataset of bearing ring surface images collected from industrial sites and conducted numerous experiments based on this dataset. Experimental results show that the mean average precision (mAP) of the network is 97.3%, especially for dents and black spot defects, improved by 2.2% and 3.9%, respectively, and that the detection speed can reach 100 frames per second (FPS). Compared with mainstream surface defect detection algorithms, the proposed method shows significant improvements in both accuracy and detection time and can meet the requirements of industrial defect detection. Full article
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22 pages, 7158 KiB  
Article
Measurement of Thermal Conductivity and Thermal Diffusivity of Porcine and Bovine Kidney Tissues at Supraphysiological Temperatures up to 93 °C
by Leonardo Bianchi, Silvia Fiorentini, Sara Gianella, Sofia Gianotti, Carolina Iadanza, Somayeh Asadi and Paola Saccomandi
Sensors 2023, 23(15), 6865; https://doi.org/10.3390/s23156865 - 02 Aug 2023
Viewed by 1109
Abstract
This experimental study aimed to characterize the thermal properties of ex vivo porcine and bovine kidney tissues in steady-state heat transfer conditions in a wider thermal interval (23.2–92.8 °C) compared to previous investigations limited to 45 °C. Thermal properties, namely thermal conductivity ( [...] Read more.
This experimental study aimed to characterize the thermal properties of ex vivo porcine and bovine kidney tissues in steady-state heat transfer conditions in a wider thermal interval (23.2–92.8 °C) compared to previous investigations limited to 45 °C. Thermal properties, namely thermal conductivity (k) and thermal diffusivity (α), were measured in a temperature-controlled environment using a dual-needle probe connected to a commercial thermal property analyzer, using the transient hot-wire technique. The estimation of measurement uncertainty was performed along with the assessment of regression models describing the trend of measured quantities as a function of temperature to be used in simulations involving heat transfer in kidney tissue. A direct comparison of the thermal properties of the same tissue from two different species, i.e., porcine and bovine kidney tissues, with the same experimental transient hot-wire technique, was conducted to provide indications on the possible inter-species variabilities of k and α at different selected temperatures. Exponential fitting curves were selected to interpolate the measured values for both porcine and bovine kidney tissues, for both k and α. The results show that the k and α values of the tissues remained rather constant from room temperature up to the onset of water evaporation, and a more marked increase was observed afterward. Indeed, at the highest investigated temperatures, i.e., 90.0–92.8 °C, the average k values were subject to 1.2- and 1.3-fold increases, compared to their nominal values at room temperature, in porcine and bovine kidney tissue, respectively. Moreover, at 90.0–92.8 °C, 1.4- and 1.2-fold increases in the average values of α, compared to baseline values, were observed for porcine and bovine kidney tissue, respectively. No statistically significant differences were found between the thermal properties of porcine and bovine kidney tissues at the same selected tissue temperatures despite their anatomical and structural differences. The provided quantitative values and best-fit regression models can be used to enhance the accuracy of the prediction capability of numerical models of thermal therapies. Furthermore, this study may provide insights into the refinement of protocols for the realization of tissue-mimicking phantoms and the choice of tissue models for bioheat transfer studies in experimental laboratories. Full article
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18 pages, 11603 KiB  
Article
Comparative Analysis of HRTFs Measurement Using In-Ear Microphones
by Valeria Bruschi, Alessandro Terenzi, Nefeli A. Dourou, Susanna Spinsante and Stefania Cecchi
Sensors 2023, 23(13), 6016; https://doi.org/10.3390/s23136016 - 29 Jun 2023
Viewed by 1050
Abstract
The head-related transfer functions (HRTFs) describe the acoustic path transfer functions between sound sources in the free-field and the listener’s ear canal. They enable the evaluation of the sound perception of a human being and the creation of immersive virtual acoustic environments that [...] Read more.
The head-related transfer functions (HRTFs) describe the acoustic path transfer functions between sound sources in the free-field and the listener’s ear canal. They enable the evaluation of the sound perception of a human being and the creation of immersive virtual acoustic environments that can be reproduced over headphones or loudspeakers. HRTFs are strongly individual and they can be measured by in-ear microphones worn by real subjects. However, standardized HRTFs can also be measured using artificial head simulators which standardize the body dimensions. In this paper, a comparative analysis of HRTF measurement using in-ear microphones is presented. The results obtained with in-ear microphones are compared with the HRTFs measured with a standard head and torso simulator, investigating different positions of the microphones and of the sound source and employing two different types of microphones. Finally, the HRTFs of five real subjects are measured and compared with the ones measured by the microphones in the ear of a standard mannequin. Full article
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26 pages, 2811 KiB  
Article
YOLOX-Ray: An Efficient Attention-Based Single-Staged Object Detector Tailored for Industrial Inspections
by António Raimundo, João Pedro Pavia, Pedro Sebastião and Octavian Postolache
Sensors 2023, 23(10), 4681; https://doi.org/10.3390/s23104681 - 11 May 2023
Cited by 2 | Viewed by 1830
Abstract
Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You [...] Read more.
Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You Only Look Once (YOLO) object detection algorithms and integrates the SimAM attention mechanism for improved feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Moreover, it also employs the Alpha-IoU cost function for enhanced small-scale object detection. YOLOX-Ray’s performance was assessed in three case studies: hotspot detection, infrastructure crack detection and corrosion detection. The architecture outperforms all other configurations, achieving mAP50 values of 89%, 99.6% and 87.7%, respectively. For the most challenging metric, mAP50:95, the achieved values were 44.7%, 66.1% and 51.8%, respectively. A comparative analysis demonstrated the importance of combining the SimAM attention mechanism with Alpha-IoU loss function for optimal performance. In conclusion, YOLOX-Ray’s ability to detect and to locate multi-scale objects in industrial environments presents new opportunities for effective, efficient and sustainable inspection processes across various industries, revolutionizing the field of industrial inspections. Full article
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18 pages, 6584 KiB  
Article
SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements
by Gregorio Andria, Marco Scarpetta, Maurizio Spadavecchia, Paolo Affuso and Nicola Giaquinto
Sensors 2023, 23(9), 4491; https://doi.org/10.3390/s23094491 - 05 May 2023
Cited by 4 | Viewed by 1981
Abstract
Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from [...] Read more.
Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea–land segmentation. Full article
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27 pages, 2725 KiB  
Article
A Data-Driven System Identification Method for Random Eigenvalue Problem Using Synchrosqueezed Energy and Phase Portrait Analysis
by Swarup Mahato, Arunasis Chakraborty and Paulius Griškevičius
Sensors 2023, 23(7), 3421; https://doi.org/10.3390/s23073421 - 24 Mar 2023
Viewed by 1343
Abstract
The primary purpose of this research is to evaluate the uncertainty associated with modal parameter estimation for an inverse dynamic problem in which the structural parameters are random. The random nature of the structure’s parameters will be reflected in the modal features of [...] Read more.
The primary purpose of this research is to evaluate the uncertainty associated with modal parameter estimation for an inverse dynamic problem in which the structural parameters are random. The random nature of the structure’s parameters will be reflected in the modal features of the respected system. However, this may result in additive/subtractive errors in modal parameter identification, affecting the identification technique’s efficiency. With this in mind, the present study aims to develop an automated modal identification algorithm for a random eigenvalue problem. This is achieved by a recently developed advanced version of the wavelet transform (i.e., synchrosqueezing), which offers better resolution. Using this technique, the measured responses are transformed into a time-frequency plane, which is further processed by unsupervised learning using K-means clustering for quantification of the modal parameters. This automated identification is repeated for an ensemble of measurements to quantify the random eigenvalues in a statistical sense. The proposed methodology is first tested using simulated time histories of a two degree-of-freedom (dof) system. It is followed by an experimental validation using a beam whose mass matrix is random. The numerical results presented in this work clearly demonstrate the performance (i.e., in terms of efficiency and accuracy) of the proposed output-only automated data-driven identification scheme for random eigenvalue problems. Full article
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26 pages, 1589 KiB  
Review
Internet of Nano-Things (IoNT): A Comprehensive Review from Architecture to Security and Privacy Challenges
by Abdullah Alabdulatif, Navod Neranjan Thilakarathne, Zaharaddeen Karami Lawal, Khairul Eahsun Fahim and Rufai Yusuf Zakari
Sensors 2023, 23(5), 2807; https://doi.org/10.3390/s23052807 - 03 Mar 2023
Cited by 10 | Viewed by 6776
Abstract
Throughout the course of human history, owing to innovations that shape the future of mankind, many technologies have been innovated and used towards making people’s lives easier. Such technologies have made us who we are today and are involved with every domain that [...] Read more.
Throughout the course of human history, owing to innovations that shape the future of mankind, many technologies have been innovated and used towards making people’s lives easier. Such technologies have made us who we are today and are involved with every domain that is vital for human survival such as agriculture, healthcare, and transportation. The Internet of Things (IoT) is one such technology that revolutionizes almost every aspect of our lives, found early in the 21st century with the advancement of Internet and Information Communication (ICT) Technologies. As of now, the IoT is served in almost every domain, as we mentioned above, allowing the connectivity of digital objects around us to the Internet, thus allowing the remote monitoring, control, and execution of actions based on underlying conditions, making such objects smarter. Over time, the IoT has progressively evolved and paved the way towards the Internet of Nano-Things (IoNT) which is the use of nano-size miniature IoT devices. The IoNT is a relatively new technology that has lately begun to establish a name for itself, and many are not aware of it, even in academia or research. The use of the IoT always comes at a cost, owing to the connectivity to the Internet and the inherently vulnerable nature of IoT, wherein it paves the way for hackers to compromise security and privacy. This is also applicable to the IoNT, which is the advanced and miniature version of IoT, and brings disastrous consequences if such security and privacy violations were to occur as no one can notice such issues pertaining to the IoNT, due to their miniaturized nature and novelty in the field. The lack of research in the IoNT domain has motivated us to synthesize this research, highlighting architectural elements in the IoNT ecosystem and security and privacy challenges pertaining to the IoNT. In this regard, in the study, we provide a comprehensive overview of the IoNT ecosystem and security and privacy pertaining to the IoNT as a reference to future research. Full article
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20 pages, 7716 KiB  
Article
Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms
by Gilles Burel, Emanuel Radoi, Roland Gautier and Denis Le Jeune
Sensors 2023, 23(4), 2263; https://doi.org/10.3390/s23042263 - 17 Feb 2023
Cited by 1 | Viewed by 1171
Abstract
Wideband spectrum sensing is a challenging problem in the framework of cognitive radio and spectrum surveillance, mainly because of the high sampling rates required by standard approaches. In this paper, a compressed sensing approach was considered to solve this problem, relying on a [...] Read more.
Wideband spectrum sensing is a challenging problem in the framework of cognitive radio and spectrum surveillance, mainly because of the high sampling rates required by standard approaches. In this paper, a compressed sensing approach was considered to solve this problem, relying on a sub-Nyquist or Xsampling scheme, known as a modulated wideband converter. First, the data reduction at its output is performed in order to enable a highly effective processing scheme for spectrum reconstruction. The impact of this data transformation on the behavior of the most popular sparse reconstruction algorithms is then analyzed. A new mathematical approach is proposed to demonstrate that greedy reconstruction algorithms, such as Orthogonal Matching Pursuit, are invariant with respect to the proposed data reduction. Relying on the same formalism, a data reduction invariant version of the LASSO (least absolute shrinkage and selection operator) reconstruction algorithm was also introduced. It is finally demonstrated that the proposed algorithm provides good reconstruction results in a wideband spectrum sensing scenario, using both synthetic and measured data. Full article
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13 pages, 491 KiB  
Perspective
Over the Limits of Traditional Sampling: Advantages and Issues of AICs for Measurement Instrumentation
by Grazia Iadarola, Pasquale Daponte, Luca De Vito and Sergio Rapuano
Sensors 2023, 23(2), 861; https://doi.org/10.3390/s23020861 - 11 Jan 2023
Cited by 4 | Viewed by 1526
Abstract
Data acquisition systems have shown the need of wideband spectrum monitoring for many years. This paper describes and discusses a recently proposed architecture aimed at acquiring efficiently wideband signals, named the Analog-to-Information Converter (AIC). AIC framework and working principle implementing the sub-Nyquist sampling [...] Read more.
Data acquisition systems have shown the need of wideband spectrum monitoring for many years. This paper describes and discusses a recently proposed architecture aimed at acquiring efficiently wideband signals, named the Analog-to-Information Converter (AIC). AIC framework and working principle implementing the sub-Nyquist sampling are analyzed in general terms. Attention is specifically focused on the idea of exploiting the condition of the signals that, despite their large bandwidth, have a small information content in the frequency domain. However, as clarified in the paper, employing AICs in measurement instrumentation necessarily entails their characterization, through the analysis of their building blocks and the corresponding non-idealities, in order to improve the signal reconstruction. Full article
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19 pages, 4262 KiB  
Article
Design and Test of a New Dielectric-Loaded Resonator for the Accurate Characterization of Conductive and Dielectric Materials
by Andrea Alimenti, Kostiantyn Torokhtii, Pablo Vidal García, Nicola Pompeo and Enrico Silva
Sensors 2023, 23(1), 518; https://doi.org/10.3390/s23010518 - 03 Jan 2023
Cited by 3 | Viewed by 1896
Abstract
The spread of additive manufacturing techniques in the prototyping and realization of high-frequency applications renewed the interest in the characterization of the electromagnetic properties of both dielectric and conductive materials, as well as the design of new versatile measurement techniques. In this framework, [...] Read more.
The spread of additive manufacturing techniques in the prototyping and realization of high-frequency applications renewed the interest in the characterization of the electromagnetic properties of both dielectric and conductive materials, as well as the design of new versatile measurement techniques. In this framework, a new configuration of a dielectric-loaded resonator is presented. Its optimization, realization, and use are presented. A measurement repeatability of about one order of magnitude lower than the commonly found values (103 on the Q-factor and 15×106 on the resonance frequency, given in terms of the relative standard deviations of repeated measurements) was reached thanks to the design of a closed resonator in which the samples can be loaded without disassembling the whole measurement fixture. The uncertainty levels, the ease of use, and the versatility of the realized system make its use of potential interest in numerous scenarios. Full article
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2022

Jump to: 2023

20 pages, 2882 KiB  
Review
Unlocking All-Solid Ion Selective Electrodes: Prospects in Crop Detection
by Jiawei Zhai, Bin Luo, Aixue Li, Hongtu Dong, Xiaotong Jin and Xiaodong Wang
Sensors 2022, 22(15), 5541; https://doi.org/10.3390/s22155541 - 25 Jul 2022
Cited by 2 | Viewed by 4982
Abstract
This paper reviews the development of all-solid-state ion-selective electrodes (ASSISEs) for agricultural crop detection. Both nutrient ions and heavy metal ions inside and outside the plant have a significant influence on crop growth. This review begins with the detection principle of ASSISEs. The [...] Read more.
This paper reviews the development of all-solid-state ion-selective electrodes (ASSISEs) for agricultural crop detection. Both nutrient ions and heavy metal ions inside and outside the plant have a significant influence on crop growth. This review begins with the detection principle of ASSISEs. The second section introduces the key characteristics of ASSISE and demonstrates its feasibility in crop detection based on previous research. The third section considers the development of ASSISEs in the detection of corps internally and externally (e.g., crop nutrition, heavy metal pollution, soil salinization, N enrichment, and sensor miniaturization, etc.) and discusses the interference of the test environment. The suggestions and conclusions discussed in this paper may provide the foundation for additional research into ion detection for crops. Full article
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25 pages, 13274 KiB  
Article
9-DOF IMU-Based Attitude and Heading Estimation Using an Extended Kalman Filter with Bias Consideration
by Sajjad Boorghan Farahan, José J. M. Machado, Fernando Gomes de Almeida and João Manuel R. S. Tavares
Sensors 2022, 22(9), 3416; https://doi.org/10.3390/s22093416 - 29 Apr 2022
Cited by 10 | Viewed by 7830
Abstract
The attitude and heading reference system (AHRS) is an important concept in the area of navigation, image stabilization, and object detection and tracking. Many studies and works have been conducted in this regard to estimate the accurate orientation of rigid bodies. In most [...] Read more.
The attitude and heading reference system (AHRS) is an important concept in the area of navigation, image stabilization, and object detection and tracking. Many studies and works have been conducted in this regard to estimate the accurate orientation of rigid bodies. In most research in this area, low-cost MEMS sensors are employed, but since the system’s response will diverge over time due to integration drift, it is necessary to apply proper estimation algorithms. A two-step extended Kalman Filter (EKF) algorithm is used in this study to estimate the orientation of an IMU. A 9-DOF device is used for this purpose, including a 6-DOF IMU with a three-axis gyroscope and a three-axis accelerometer, and a three-axis magnetometer. In addition, to have an accurate algorithm, both IMU and magnetometer biases and disturbances are modeled and considered in the real-time filter. After applying the algorithm to the sensor’s output, an accurate orientation as well as unbiased angular velocity, linear acceleration, and magnetic field were achieved. In order to demonstrate the reduction of noise power, fast Fourier transform (FFT) diagrams are used. The effect of the initial condition on the response of the system is also investigated. Full article
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