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15 pages, 10317 KiB

Open AccessArticle

Near-Space Wide-Area and High-Resolution Imaging System Design and Implementation

by Zhanchao Wang, Min Huang, Lulu Qian, Yan Sun, Xiangning Lu, Wenhao Zhao, Zixuan Zhang, Guangming Wang and Yixin Zhao

Sensors 2023, 23(14), 6454; https://doi.org/10.3390/s23146454 - 17 Jul 2023

Viewed by 1321

Abstract

The near-space atmosphere is thin, and the atmospheric refraction and scattering on optical observation is very small, making it very suitable for wide-area and high-resolution surveillance using high-altitude balloon platforms. This paper adopts a 9344 × 7000 CMOS sensor to obtain high-resolution images, [...] Read more.

The near-space atmosphere is thin, and the atmospheric refraction and scattering on optical observation is very small, making it very suitable for wide-area and high-resolution surveillance using high-altitude balloon platforms. This paper adopts a 9344 × 7000 CMOS sensor to obtain high-resolution images, generating large-field-of-view imaging through the swing scanning of the photoelectric sphere and image stitching. In addition, a zoom lens is designed to achieve flexible applications for different scenarios, such as large-field-of-view and high-resolution imaging. The optical design results show that the camera system has good imaging quality within the focal length range of 320 mm–106.7 mm, and the relative distortion values at different focal lengths are less than 2%. The flight results indicate that the system can achieve seamless image stitching at a resolution of 0.2 m@20 km and the imaging field of view angle exceeds 33°. This system will perform other near-space flight experiments to verify its ultra-wide (field of view exceeding 100°) high-resolution imaging application. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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12 pages, 2324 KiB

Open AccessArticle

Multi-Task Learning Radar Transformer (MLRT): A Personal Identification and Fall Detection Network Based on IR-UWB Radar

by Xikang Jiang, Lin Zhang and Lei Li

Sensors 2023, 23(12), 5632; https://doi.org/10.3390/s23125632 - 16 Jun 2023

Cited by 4 | Viewed by 1433

Abstract

Radar-based personal identification and fall detection have received considerable attention in smart healthcare scenarios. Deep learning algorithms have been introduced to improve the performance of non-contact radar sensing applications. However, the original Transformer network is not suitable for multi-task radar-based applications to effectively [...] Read more.

Radar-based personal identification and fall detection have received considerable attention in smart healthcare scenarios. Deep learning algorithms have been introduced to improve the performance of non-contact radar sensing applications. However, the original Transformer network is not suitable for multi-task radar-based applications to effectively extract temporal features from time-series radar signals. This article proposes the Multi-task Learning Radar Transformer (MLRT): a personal Identification and fall detection network based on IR-UWB radar. The proposed MLRT utilizes the attention mechanism of Transformer as its core to automatically extract features for personal identification and fall detection from radar time-series signals. Multi-task learning is applied to exploit the correlation between the personal identification task and the fall detection task, enhancing the performance of discrimination for both tasks. In order to suppress the impact of noise and interference, a signal processing approach is employed including DC removal and bandpass filtering, followed by clutter suppression using a RA method and Kalman filter-based trajectory estimation. An indoor radar signal dataset is generated with 11 persons under one IR-UWB radar, and the performance of MLRT is evaluated using this dataset. The measurement results show that the accuracy of MLRT improves by 8.5% and 3.6% for personal identification and fall detection, respectively, compared to state-of-the-art algorithms. The indoor radar signal dataset and the proposed MLRT source code are publicly available. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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15 pages, 4471 KiB

Open AccessArticle

Autonomous Face Classification Online Self-Training System Using Pretrained ResNet50 and Multinomial Naïve Bayes

by Łukasz Maciura, Tomasz Cieplak, Damian Pliszczuk, Michał Maj and Tomasz Rymarczyk

Sensors 2023, 23(12), 5554; https://doi.org/10.3390/s23125554 - 14 Jun 2023

Viewed by 1046

Abstract

This paper presents a novel, autonomous learning system working in real-time for face recognition. Multiple convolutional neural networks for face recognition tasks are available; however, these networks need training data and a relatively long training process as the training speed depends on hardware [...] Read more.

This paper presents a novel, autonomous learning system working in real-time for face recognition. Multiple convolutional neural networks for face recognition tasks are available; however, these networks need training data and a relatively long training process as the training speed depends on hardware characteristics. Pretrained convolutional neural networks could be useful for encoding face images (after classifier layers are removed). This system uses a pretrained ResNet50 model to encode face images from a camera and the Multinomial Naïve Bayes for autonomous training in the real-time classification of persons. Faces of several persons visible in a camera are tracked using special cognitive tracking agents who deal with machine learning models. After a face in a new position of the frame appears (in a place where there was no face in the previous frames), the system checks if it is novel or not using a novelty detection algorithm based on an SVM classifier; if it is unknown, the system automatically starts training. As a result of the conducted experiments, one can conclude that good conditions provide assurance that the system can learn the faces of a new person who appears in the frame correctly. Based on our research, we can conclude that the critical element of this system working is the novelty detection algorithm. If false novelty detection works, the system can assign two or more different identities or classify a new person into one of the existing groups. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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25 pages, 6156 KiB

Open AccessArticle

ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment

by Shiyao Liu, Wei Guo, Yu Hua and Wudian Kou

Sensors 2023, 23(11), 5176; https://doi.org/10.3390/s23115176 - 29 May 2023

Viewed by 915

Abstract

The core of eLoran ground-based timing navigation systems is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for a complex terrestrial propagation environment, and may even lead to microsecond-level [...] Read more.

The core of eLoran ground-based timing navigation systems is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for a complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on a Back-Propagation neural network (BPNN) for a complex meteorological environment, which realizes the function of directly mapping propagation delay fluctuation through meteorological factors. First, the theoretical influence of meteorological factors on each component of propagation delay is analyzed based on calculation parameters. Then, through the correlation analysis of the measured data, the complex relationship between the seven main meteorological factors and the propagation delay, as well as their regional differences, are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with that of the existing linear model and simple neural network model. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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16 pages, 5533 KiB

Open AccessArticle

The Practice of Detecting Potential Cosmic Rays Using CMOS Cameras: Hardware and Algorithms

by Tomasz Hachaj and Marcin Piekarczyk

Sensors 2023, 23(10), 4858; https://doi.org/10.3390/s23104858 - 18 May 2023

Cited by 2 | Viewed by 1783

Abstract

In this paper, we discuss a practice of potential cosmic ray detection using off-the-shelves CMOS cameras. We discuss and presents the limitations of up-to-date hardware and software approaches to this task. We also present a hardware solution that we made for long-term testing [...] Read more.

In this paper, we discuss a practice of potential cosmic ray detection using off-the-shelves CMOS cameras. We discuss and presents the limitations of up-to-date hardware and software approaches to this task. We also present a hardware solution that we made for long-term testing of algorithms for potential cosmic ray detection. We have also proposed, implemented and tested a novel algorithm that enables real-time processing of image frames acquired by CMOS cameras in order to detect tracks of potential particles. We have compared our results with already published results and obtained acceptable results overcoming some limitation of already existing algorithms. Both source codes and data are available to download. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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27 pages, 9961 KiB

Open AccessArticle

The Development of a Cost-Effective Imaging Device Based on Thermographic Technology

by Ivo Stančić, Ana Kuzmanić Skelin, Josip Musić and Mojmil Cecić

Sensors 2023, 23(10), 4582; https://doi.org/10.3390/s23104582 - 9 May 2023

Viewed by 2506

Abstract

Thermal vision-based devices are nowadays used in a number of industries, ranging from the automotive industry, surveillance, navigation, fire detection, and rescue missions to precision agriculture. This work describes the development of a low-cost imaging device based on thermographic technology. The proposed device [...] Read more.

Thermal vision-based devices are nowadays used in a number of industries, ranging from the automotive industry, surveillance, navigation, fire detection, and rescue missions to precision agriculture. This work describes the development of a low-cost imaging device based on thermographic technology. The proposed device uses a miniature microbolometer module, a 32-bit ARM microcontroller, and a high-accuracy ambient temperature sensor. The developed device is capable of enhancing RAW high dynamic thermal readings obtained from the sensor using a computationally efficient image enhancement algorithm and presenting its visual result on the integrated OLED display. The choice of microcontroller, rather than the alternative System on Chip (SoC), offers almost instantaneous power uptime and extremely low power consumption while providing real-time imaging of an environment. The implemented image enhancement algorithm employs the modified histogram equalization, where the ambient temperature sensor helps the algorithm enhance both background objects near ambient temperature and foreground objects (humans, animals, and other heat sources) that actively emit heat. The proposed imaging device was evaluated on a number of environmental scenarios using standard no-reference image quality measures and comparisons against the existing state-of-the-art enhancement algorithms. Qualitative results obtained from the survey of 11 subjects are also provided. The quantitative evaluations show that, on average, images acquired by the developed camera provide better perception quality in 75% of tested cases. According to qualitative evaluations, images acquired by the developed camera provide better perception quality in 69% of tested cases. The obtained results verify the usability of the developed low-cost device for a range of applications where thermal imaging is needed. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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13 pages, 2384 KiB

Open AccessArticle

A Low-Cost Hardware/Software Platform for Lossless Real-Time Data Acquisition from Imaging Spectrometers

by Jesús Fernández-Conde

Sensors 2023, 23(9), 4349; https://doi.org/10.3390/s23094349 - 28 Apr 2023

Viewed by 974

Abstract

In real-time data-intensive applications, achieving real-time data acquisition from sensors and simultaneous storage with the necessary performance is challenging, especially if “no-data-lost” requirements are present. Ad hoc solutions are generally expensive and suffer from a lack of modularity and scalability. In this work, [...] Read more.

In real-time data-intensive applications, achieving real-time data acquisition from sensors and simultaneous storage with the necessary performance is challenging, especially if “no-data-lost” requirements are present. Ad hoc solutions are generally expensive and suffer from a lack of modularity and scalability. In this work, we present a hardware/software platform built using commercial off-the-shelf elements, designed to acquire and store digitized signals captured from imaging spectrometers capable of supporting real-time data acquisition with stringent throughput requirements (sustained rates in the boundaries of 100 MBytes/s) and simultaneous information storage in a lossless fashion. The correct combination of commercial hardware components with a properly configured and optimized multithreaded software application has satisfied the requirements in determinism and capacity for processing and storing large amounts of information in real time, keeping the economic cost of the system low. This real-time data acquisition and storage system has been tested in different conditions and scenarios, being able to successfully capture 100,000 1 Mpx-sized images generated at a nominal speed of 23.5 MHz (input throughput of 94 Mbytes/s, 4 bytes acquired per pixel) and store the corresponding data (300 GBytes of data, 3 bytes stored per pixel) concurrently without any single byte of information lost or altered. The results indicate that, in terms of throughput and storage capacity, the proposed system delivers similar performance to data acquisition systems based on specialized hardware, but at a lower cost, and provides more flexibility and adaptation to changing requirements. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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24 pages, 2223 KiB

Open AccessArticle

Coverage and Lifetime Optimization by Self-Optimizing Sensor Networks

by Franciszek Seredyński, Tomasz Kulpa, Rolf Hoffmann and Dominique Désérable

Sensors 2023, 23(8), 3930; https://doi.org/10.3390/s23083930 - 12 Apr 2023

Cited by 1 | Viewed by 1454

Abstract

We propose an approach to self-optimizing wireless sensor networks (WSNs) which are able to find, in a fully distributed way, a solution to a coverage and lifetime optimization problem. The proposed approach is based on three components: (a) a multi-agent, social-like interpreted system, [...] Read more.

We propose an approach to self-optimizing wireless sensor networks (WSNs) which are able to find, in a fully distributed way, a solution to a coverage and lifetime optimization problem. The proposed approach is based on three components: (a) a multi-agent, social-like interpreted system, where the modeling of agents, discrete space, and time is provided by a 2-dimensional second-order cellular automata, (b) the interaction between agents is described in terms of the spatial prisoner’s dilemma game, and (c) a local evolutionary mechanism of competition between agents exists. Nodes of a WSN graph created for a given deployment of WSN in the monitored area are considered agents of a multi-agent system that collectively make decisions to turn on or turn off their batteries. Agents are controlled by cellular automata (CA)-based players participating in a variant of the spatial prisoner’s dilemma iterated game. We propose for players participating in this game a local payoff function that incorporates issues of area coverage and sensors energy spending. Rewards obtained by agent players depend not only on their personal decisions but also on their neighbor’s decisions. Agents act in such a way to maximize their own rewards, which results in achieving by them a solution corresponding to the Nash equilibrium point. We show that the system is self-optimizing, i.e., can optimize in a distributed way global criteria related to WSN and not known for agents, provide a balance between requested coverage and spending energy, and result in expanding the WSN lifetime. The solutions proposed by the multi-agent system fulfill the Pareto optimality principles, and the desired quality of solutions can be controlled by user-defined parameters. The proposed approach is validated by a number of experimental results. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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12 pages, 12631 KiB

Open AccessArticle

Head-Mounted Display for Clinical Evaluation of Neck Movement Validation with Meta Quest 2

by Manuel Trinidad-Fernández, Benoît Bossavit, Javier Salgado-Fernández, Susana Abbate-Chica, Antonio J. Fernández-Leiva and Antonio I. Cuesta-Vargas

Sensors 2023, 23(6), 3077; https://doi.org/10.3390/s23063077 - 13 Mar 2023

Cited by 1 | Viewed by 2433

Abstract

Neck disorders have a significant impact on people because of their high incidence. The head-mounted display (HMD) systems, such as Meta Quest 2, grant access to immersive virtual reality (iRV) experiences. This study aims to validate the Meta Quest 2 HMD system as [...] Read more.

Neck disorders have a significant impact on people because of their high incidence. The head-mounted display (HMD) systems, such as Meta Quest 2, grant access to immersive virtual reality (iRV) experiences. This study aims to validate the Meta Quest 2 HMD system as an alternative for screening neck movement in healthy people. The device provides data about the position and orientation of the head and, thus, the neck mobility around the three anatomical axes. The authors develop a VR application that solicits participants to perform six neck movements (rotation, flexion, and lateralization on both sides), which allows the collection of corresponding angles. An InertiaCube3 inertial measurement unit (IMU) is also attached to the HMD to compare the criterion to a standard. The mean absolute error (MAE), the percentage of error (%MAE), and the criterion validity and agreement are calculated. The study shows that the average absolute errors do not exceed 1° (average = 0.48 ± 0.09°). The rotational movement’s average %MAE is 1.61 ± 0.82%. The head orientations obtain a correlation between 0.70 and 0.96. The Bland–Altman study reveals good agreement between the HMD and IMU systems. Overall, the study shows that the angles provided by the Meta Quest 2 HMD system are valid to calculate the rotational angles of the neck in each of the three axes. The obtained results demonstrate an acceptable error percentage and a very minimal absolute error when measuring the degrees of neck rotation; therefore, the sensor can be used for screening neck disorders in healthy people. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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27 pages, 8593 KiB

Open AccessArticle

Smart Gateway for Healthcare Networks Based on Beam Steering Technology

by Kazuhiro Honda, Kosuke Takakura and Yuki Otsubo

Sensors 2023, 23(6), 2959; https://doi.org/10.3390/s23062959 - 9 Mar 2023

Viewed by 1498

Abstract

To ensure high-reliability communication in healthcare networks, this paper presents a smart gateway system that includes an angle-of-arrival (AOA) estimation and a beam steering function for a small circular antenna array. To form a beam toward healthcare sensors, the proposed antenna estimates the [...] Read more.

To ensure high-reliability communication in healthcare networks, this paper presents a smart gateway system that includes an angle-of-arrival (AOA) estimation and a beam steering function for a small circular antenna array. To form a beam toward healthcare sensors, the proposed antenna estimates the direction of the sensors utilizing the radio-frequency-based interferometric monopulse technique. The fabricated antenna was assessed based on the measurements of complex directivity and the over-the-air (OTA) testing in Rice propagation environments using a two-dimensional fading emulator. The measurement results reveal that the accuracy of the AOA estimation agrees well with that of the analytical data obtained through the Monte Carlo simulation. This antenna is embedded with a beam steering function employing phased array technology, which can form a beam spaced at 45° intervals. The ability of full-azimuth beam steering with regard to the proposed antenna was evaluated by beam propagation experiments using a human phantom in an indoor environment. The received signal of the proposed antenna with beam steering increases more than that of a conventional dipole antenna, confirming that the developed antenna has great potential of achieving high-reliability communication in a healthcare network. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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23 pages, 6787 KiB

Open AccessArticle

Low-False-Alarm-Rate Timing and Duration Estimation of Noisy Frequency Agile Signal by Image Homogeneous Detection and Morphological Signature Matching Schemes

by Yuan-Pin Cheng, Chia-Hsuan Chang and Jung-Chih Chen

Sensors 2023, 23(4), 2094; https://doi.org/10.3390/s23042094 - 13 Feb 2023

Viewed by 1462

Abstract

Frequency hopping spread spectrum (FHSS) applies widely to communication and radar systems to ensure communication information and channel signal quality by tuning frequency within a wide frequency range in a random sequence. An efficient signal processing scheme to resolve the timing and duration [...] Read more.

Frequency hopping spread spectrum (FHSS) applies widely to communication and radar systems to ensure communication information and channel signal quality by tuning frequency within a wide frequency range in a random sequence. An efficient signal processing scheme to resolve the timing and duration signature from an FHSS signal provides crucial information for signal detection and radio band management purposes. In this research, hopping time was first identified by a two-dimensional temporal correlation function (TCF). The timing information was shown at TCF phase discontinuities. To enhance and resolve the timing signature of TCF in a noisy environment, three stages of signature enhancement and morphological matching processes were applied: first, computing the TCF of the FHSS signal and enhancing discontinuities via wavelet transform; second, a dual-diagonal edge finding scheme to extract the timing pattern signature and eliminate mismatching distortion morphologically; finally, Hough transform resolved the agile frequency timing from purified line segments. A grand-scale Monte Carlo simulation of the FHSS signals with additive white Gaussian noise was carried out in the research. The results demonstrated reliable hopping time estimation obtained in SNR at 0 dB and above, with a minimal false detection rate of 1.79%, while the prior related research had an unattended false detection rate of up to 35.29% in such a noisy environment. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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23 pages, 7633 KiB

Open AccessArticle

Variable Baseline and Flexible Configuration Stereo Vision Using Two Aerial Robots

by Borwonpob Sumetheeprasit, Ricardo Rosales Martinez, Hannibal Paul, Robert Ladig and Kazuhiro Shimonomura

Sensors 2023, 23(3), 1134; https://doi.org/10.3390/s23031134 - 18 Jan 2023

Cited by 5 | Viewed by 2035

Abstract

In this work, a new method for aerial robot remote sensing using stereo vision is proposed. A variable baseline and flexible configuration stereo setup is achieved by separating the left camera and right camera on two separate quadrotor aerial robots. Monocular cameras, one [...] Read more.

In this work, a new method for aerial robot remote sensing using stereo vision is proposed. A variable baseline and flexible configuration stereo setup is achieved by separating the left camera and right camera on two separate quadrotor aerial robots. Monocular cameras, one on each aerial robot, are used as a stereo pair, allowing independent adjustment of the pose of the stereo pair. In contrast to conventional stereo vision where two cameras are fixed, having a flexible configuration system allows a large degree of independence in changing the configuration in accordance with various kinds of applications. Larger baselines can be used for stereo vision of farther away targets while using a vertical stereo configuration in tasks where there would be a loss of horizontal overlap caused by a lack of suitable horizontal configuration. Additionally, a method for the practical use of variable baseline stereo vision is introduced, combining multiple point clouds from multiple stereo baselines. Issues from using an inappropriate baseline, such as estimation error induced by insufficient baseline, and occlusions from using too large a baseline can be avoided with this solution. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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12 pages, 2675 KiB

Open AccessArticle

The Effect of Glycerol-Based Suspensions on the Characteristics of Resonators Excited by a Longitudinal Electric Field

by Alexander Semyonov, Boris Zaitsev, Andrey Teplykh and Irina Borodina

Sensors 2023, 23(2), 608; https://doi.org/10.3390/s23020608 - 5 Jan 2023

Cited by 3 | Viewed by 1051

Abstract

This study examines the effect of suspensions based on pure glycerol and diamond powder with different concentrations on the characteristics of resonators with a longitudinal electric field. We used two disk resonators made of the quartz and langasite plates with round electrodes on [...] Read more.

This study examines the effect of suspensions based on pure glycerol and diamond powder with different concentrations on the characteristics of resonators with a longitudinal electric field. We used two disk resonators made of the quartz and langasite plates with round electrodes on both sides of the plate and resonant frequencies of 4.4 and 4.1 MHz, operating in shear and longitudinal acoustic modes, respectively. Each resonator was mounted on the bottom of a 30 mL liquid container. During the experiments, the container was filled with the suspension under study in such a way that the resonator was completely immersed in the suspension, and the frequency dependences of the real and imaginary parts of the electrical impedance of the resonator were measured. As a result, the shear modulus of the elasticity and shear coefficient of the viscosity of the studied suspensions were determined. The material constants of the suspensions were found by fitting the theoretical frequency dependences of the real and imaginary parts of the electrical impedance of the resonator to the experimentally measured ones, which was calculated using Mason’s equivalent circuit. As a result, the dependencies of the density, shear modulus of elasticity, shear viscosity coefficient, and velocity of the shear acoustic wave on the volume concentration of the diamond particles were constructed. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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Review

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17 pages, 555 KiB

Open AccessReview

Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

by Aristidis G. Vrahatis, Konstantina Skolariki, Marios G. Krokidis, Konstantinos Lazaros, Themis P. Exarchos and Panagiotis Vlamos

Sensors 2023, 23(9), 4184; https://doi.org/10.3390/s23094184 - 22 Apr 2023

Cited by 18 | Viewed by 5957

Abstract

Alzheimer’s disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a [...] Read more.

Alzheimer’s disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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20 pages, 1719 KiB

Open AccessReview

Immunosensors—The Future of Pathogen Real-Time Detection

by Edyta Janik-Karpinska, Michal Ceremuga, Marcin Niemcewicz, Marcin Podogrocki, Maksymilian Stela, Natalia Cichon and Michal Bijak

Sensors 2022, 22(24), 9757; https://doi.org/10.3390/s22249757 - 13 Dec 2022

Cited by 10 | Viewed by 2463

Abstract

Pathogens and their toxins can cause various diseases of different severity. Some of them may be fatal, and therefore early diagnosis and suitable treatment is essential. There are numerous available methods used for their rapid screening. Conventional laboratory-based techniques such as culturing, enzyme-linked [...] Read more.

Pathogens and their toxins can cause various diseases of different severity. Some of them may be fatal, and therefore early diagnosis and suitable treatment is essential. There are numerous available methods used for their rapid screening. Conventional laboratory-based techniques such as culturing, enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) are dominant. However, culturing still remains the “gold standard” for their identification. These methods have many advantages, including high sensitivity and selectivity, but also numerous limitations, such as long experiment-time, costly instrumentation, and the need for well-qualified personnel to operate the equipment. All these existing limitations are the reasons for the continuous search for a new solutions in the field of bacteria identification. For years, research has been focusing on the use of immunosensors in various types of toxin- and pathogen-detection. Compared to the conventional methods, immunosensors do not require well-trained personnel. What is more, immunosensors are quick, highly selective and sensitive, and possess the potential to significantly improve the pathogen and toxin diagnostic-processes. There is a very important potential use for them in various transport systems, where the risk of contamination by bioagents is very high. In this paper, the advances in the field of immunosensor usage in pathogenic microorganism- and toxin-detection, are described. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

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