Sensors

Open AccessEditor’s ChoiceReview

Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review

by

Mohammad Abrar Shakil Sejan

,

,

,

,

and

Sensors 2022, 22(14), 5405; https://doi.org/10.3390/s22145405 - 20 Jul 2022

Cited by 13 | Viewed by 3474

Abstract

An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. [...] Read more.

An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication. Full article

(This article belongs to the Special Issue Intelligent Reflecting Surfaces for 5G Communication and Beyond)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

An Aptasensor Based on a Flexible Screen-Printed Silver Electrode for the Rapid Detection of Chlorpyrifos

by

A. K. M. Sarwar Inam

,

Martina Aurora Costa Angeli

,

,

,

and

Sensors 2022, 22(7), 2754; https://doi.org/10.3390/s22072754 - 02 Apr 2022

Cited by 11 | Viewed by 2216

Abstract

In this work, we propose a novel disposable flexible and screen-printed electrochemical aptamer-based sensor (aptasensor) for the rapid detection of chlorpyrifos (CPF). To optimize the process, various characterization procedures were employed, including Fourier transform infrared spectroscopy (FT-IR), electrochemical impedance spectroscopy (EIS), and cyclic [...] Read more.

In this work, we propose a novel disposable flexible and screen-printed electrochemical aptamer-based sensor (aptasensor) for the rapid detection of chlorpyrifos (CPF). To optimize the process, various characterization procedures were employed, including Fourier transform infrared spectroscopy (FT-IR), electrochemical impedance spectroscopy (EIS), and cyclic voltammetry (CV). Initially, the aptasensor was optimized in terms of electrolyte pH, aptamer concentration, and incubation time for chlorpyrifos. Under optimal conditions, the aptasensor showed a wide linear range from 1 to 10⁵ ng/mL with a calculated limit of detection as low as 0.097 ng/mL and sensitivity of 600.9 µA/ng. Additionally, the selectivity of the aptasensor was assessed by identifying any interference from other pesticides, which were found to be negligible (with a maximum standard deviation of 0.31 mA). Further, the stability of the sample was assessed over time, where the reported device showed high stability over a period of two weeks at 4 °C. As the last step, the ability of the aptasensor to detect chlorpyrifos in actual samples was evaluated by testing it on banana and grape extracts. As a result, the device demonstrated sufficient recovery rates, which indicate that it can find application in the food industry. Full article

(This article belongs to the Special Issue Electrochemical Sensors in the Food Industry)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

by

Marco Civera

and

Cecilia Surace

Sensors 2022, 22(4), 1627; https://doi.org/10.3390/s22041627 - 18 Feb 2022

Cited by 29 | Viewed by 5531

Abstract

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several [...] Read more.

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well. Full article

(This article belongs to the Special Issue Artificial Intelligence for Fault Diagnostics and Prognostics)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants—A Review

by

,

,

and

Sensors 2022, 22(4), 1510; https://doi.org/10.3390/s22041510 - 15 Feb 2022

Cited by 13 | Viewed by 3332

Abstract

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of [...] Read more.

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography–mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities. Full article

(This article belongs to the Special Issue Gas Sensors and Gas Chromatography for Analytical Applications)

► Show Figures

Graphical abstract

Open AccessEditor’s ChoiceReview

A Systematic Review of Wearable Sensors for Monitoring Physical Activity

by

Annica Kristoffersson

and

Maria Lindén

Sensors 2022, 22(2), 573; https://doi.org/10.3390/s22020573 - 12 Jan 2022

Cited by 14 | Viewed by 4054

Abstract

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological [...] Read more.

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing. Full article

(This article belongs to the Special Issue Embedded Sensor Systems for Health)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Human Activity Recognition via Hybrid Deep Learning Based Model

by

Imran Ullah Khan

,

Sitara Afzal

and

Jong Weon Lee

Sensors 2022, 22(1), 323; https://doi.org/10.3390/s22010323 - 01 Jan 2022

Cited by 38 | Viewed by 4938

Abstract

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features [...] Read more.

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications. Full article

(This article belongs to the Special Issue Human Activity Recognition Using Deep Learning)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Reversible Room Temperature H₂ Gas Sensing Based on Self-Assembled Cobalt Oxysulfide

by

,

,

,

,

,

,

,

,

,

and

Sensors 2022, 22(1), 303; https://doi.org/10.3390/s22010303 - 31 Dec 2021

Cited by 12 | Viewed by 1564

Abstract

Reversible H₂ gas sensing at room temperature has been highly desirable given the booming of the Internet of Things (IoT), zero-emission vehicles, and fuel cell technologies. Conventional metal oxide-based semiconducting gas sensors have been considered as suitable candidates given their low-cost, high [...] Read more.

Reversible H₂ gas sensing at room temperature has been highly desirable given the booming of the Internet of Things (IoT), zero-emission vehicles, and fuel cell technologies. Conventional metal oxide-based semiconducting gas sensors have been considered as suitable candidates given their low-cost, high sensitivity, and long stability. However, the dominant sensing mechanism is based on the chemisorption of gas molecules which requires elevated temperatures to activate the catalytic reaction of target gas molecules with chemisorbed O, leaving the drawbacks of high-power consumption and poor selectivity. In this work, we introduce an alternative candidate of cobalt oxysulfide derived from the calcination of self-assembled cobalt sulfide micro-cages. It is found that the majority of S atoms are replaced by O in cobalt oxysulfide, transforming the crystal structure to tetragonal coordination and slightly expanding the optical bandgap energy. The H₂ gas sensing performances of cobalt oxysulfide are fully reversible at room temperature, demonstrating peculiar p-type gas responses with a magnitude of 15% for 1% H₂ and a high degree of selectivity over CH₄, NO₂, and CO₂. Such excellent performances are possibly ascribed to the physisorption dominating the gas–matter interaction. This work demonstrates the great potentials of transition metal oxysulfide compounds for room-temperature fully reversible gas sensing. Full article

(This article belongs to the Special Issue Chemiresistive Sensors: Materials and Applications)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

Electrical and Electrochemical Sensors Based on Carbon Nanotubes for the Monitoring of Chemicals in Water—A Review

by

,

,

and

Sensors 2022, 22(1), 218; https://doi.org/10.3390/s22010218 - 29 Dec 2021

Cited by 15 | Viewed by 2957

Abstract

Carbon nanotubes (CNTs) combine high electrical conductivity with high surface area and chemical stability, which makes them very promising for chemical sensing. While water quality monitoring has particularly strong societal and environmental impacts, a lot of critical sensing needs remain unmet by commercial [...] Read more.

Carbon nanotubes (CNTs) combine high electrical conductivity with high surface area and chemical stability, which makes them very promising for chemical sensing. While water quality monitoring has particularly strong societal and environmental impacts, a lot of critical sensing needs remain unmet by commercial technologies. In the present review, we show across 20 water monitoring analytes and 90 references that carbon nanotube-based electrochemical sensors, chemistors and field-effect transistors (chemFET) can meet these needs. A set of 126 additional references provide context and supporting information. After introducing water quality monitoring challenges, the general operation and fabrication principles of CNT water quality sensors are summarized. They are sorted by target analytes (pH, micronutrients and metal ions, nitrogen, hardness, dissolved oxygen, disinfectants, sulfur and miscellaneous) and compared in terms of performances (limit of detection, sensitivity and detection range) and functionalization strategies. For each analyte, the references with best performances are discussed. Overall, the most frequently investigated analytes are H⁺ (pH) and lead (with 18% of references each), then cadmium (14%) and nitrite (11%). Micronutrients and toxic metals cover 40% of all references. Electrochemical sensors (73%) have been more investigated than chemistors (14%) or FETs (12%). Limits of detection in the ppt range have been reached, for instance Cu(II) detection with a liquid-gated chemFET using SWCNT functionalized with peptide-enhanced polyaniline or Pb(II) detection with stripping voltammetry using MWCNT functionalized with ionic liquid-dithizone based bucky-gel. The large majority of reports address functionalized CNTs (82%) instead of pristine or carboxyl-functionalized CNTs. For analytes where comparison is possible, FET-based and electrochemical transduction yield better performances than chemistors (Cu(II), Hg(II), Ca(II), H₂O₂); non-functionalized CNTs may yield better performances than functionalized ones (Zn(II), pH and chlorine). Full article

(This article belongs to the Special Issue Micro- and Nanostructures for Sensing Applications)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Toward Modular and Flexible Open RAN Implementations in 6G Networks: Traffic Steering Use Case and O-RAN xApps

by

Marcin Dryjański

,

Łukasz Kułacz

and

Adrian Kliks

Sensors 2021, 21(24), 8173; https://doi.org/10.3390/s21248173 - 07 Dec 2021

Cited by 21 | Viewed by 4807

Abstract

The development of cellular wireless systems has entered the phase when 5G networks are being deployed and the foundations of 6G solutions are being identified. However, in parallel to this, another technological breakthrough is observed, as the concept of open radio access networks [...] Read more.

The development of cellular wireless systems has entered the phase when 5G networks are being deployed and the foundations of 6G solutions are being identified. However, in parallel to this, another technological breakthrough is observed, as the concept of open radio access networks is coming into play. Together with advancing network virtualization and programmability, this may reshape the way the functionalities and services related to radio access are designed, leading to modular and flexible implementations. This paper overviews the idea of open radio access networks and presents ongoing O-RAN Alliance standardization activities in this context. The whole analysis is supported by a study of the traffic steering use case implemented in a modular way, following the open networking approach. Full article

(This article belongs to the Special Issue Next Generation Radio Communication Technologies)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

by

,

,

,

,

and

Sensors 2021, 21(23), 7996; https://doi.org/10.3390/s21237996 - 30 Nov 2021

Cited by 20 | Viewed by 1840

Abstract

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional [...] Read more.

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians. Full article

(This article belongs to the Collection Medical Applications of Sensor Systems and Devices)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

Research Progress of Small Molecule Fluorescent Probes for Detecting Hypochlorite

by

,

,

and

Sensors 2021, 21(19), 6326; https://doi.org/10.3390/s21196326 - 22 Sep 2021

Cited by 13 | Viewed by 2686

Abstract

Hypochlorous acid (HOCl) generates from the reaction between hydrogen peroxide and chloride ions via myeloperoxidase (MPO)-mediated in vivo. As very important reactive oxygen species (ROS), hypochlorous acid (HOCl)/hypochlorite (OCl⁻) play a crucial role in a variety of physiological and pathological processes. [...] Read more.

Hypochlorous acid (HOCl) generates from the reaction between hydrogen peroxide and chloride ions via myeloperoxidase (MPO)-mediated in vivo. As very important reactive oxygen species (ROS), hypochlorous acid (HOCl)/hypochlorite (OCl⁻) play a crucial role in a variety of physiological and pathological processes. However, excessive or misplaced production of HOCl/OCl⁻ can cause variety of tissue damage and human diseases. Therefore, rapid, sensitive, and selective detection of OCl⁻ is very important. In recent years, the fluorescent probe method for detecting hypochlorous acid has been developed rapidly due to its simple operation, low toxicity, high sensitivity, and high selectivity. In this review, the progress of recently discovered fluorescent probes for the detection of hypochlorous acid was summarized with the aim to provide useful information for further design of better fluorescent probes. Full article

(This article belongs to the Section Chemical Sensors)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

by

,

,

,

and

Sensors 2021, 21(18), 6296; https://doi.org/10.3390/s21186296 - 20 Sep 2021

Cited by 18 | Viewed by 5656

Abstract

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin [...] Read more.

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

by

Alfonso Maria Ponsiglione

,

,

,

and

Sensors 2021, 21(18), 6136; https://doi.org/10.3390/s21186136 - 13 Sep 2021

Cited by 47 | Viewed by 2649

Abstract

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty [...] Read more.

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors. Full article

(This article belongs to the Special Issue Sensors for Heart Rate Monitoring)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning

by

,

,

,

and

Sensors 2021, 21(18), 6037; https://doi.org/10.3390/s21186037 - 09 Sep 2021

Cited by 39 | Viewed by 4497

Abstract

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy, and health [...] Read more.

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy, and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. However, new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges, and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, as well as missing and needed contributions. However, we also propose directions, research opportunities, and solutions to accelerate advances in this field. Full article

(This article belongs to the Special Issue Multi-Sensor for Human Activity Recognition)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson’s Disease

by

,

,

,

,

,

,

,

,

,

,

,

,

and

Sensors 2021, 21(16), 5437; https://doi.org/10.3390/s21165437 - 12 Aug 2021

Cited by 16 | Viewed by 3932

Abstract

Gait is a core motor function and is impaired in numerous neurological diseases, including Parkinson’s disease (PD). Treatment changes in PD are frequently driven by gait assessments in the clinic, commonly rated as part of the Movement Disorder Society (MDS) Unified PD Rating [...] Read more.

Gait is a core motor function and is impaired in numerous neurological diseases, including Parkinson’s disease (PD). Treatment changes in PD are frequently driven by gait assessments in the clinic, commonly rated as part of the Movement Disorder Society (MDS) Unified PD Rating Scale (UPDRS) assessment (item 3.10). We proposed and evaluated a novel approach for estimating severity of gait impairment in Parkinson’s disease using a computer vision-based methodology. The system we developed can be used to obtain an estimate for a rating to catch potential errors, or to gain an initial rating in the absence of a trained clinician—for example, during remote home assessments. Videos (

n = 729

) were collected as part of routine MDS-UPDRS gait assessments of Parkinson’s patients, and a deep learning library was used to extract body key-point coordinates for each frame. Data were recorded at five clinical sites using commercially available mobile phones or tablets, and had an associated severity rating from a trained clinician. Six features were calculated from time-series signals of the extracted key-points. These features characterized key aspects of the movement including speed (step frequency, estimated using a novel Gamma-Poisson Bayesian model), arm swing, postural control and smoothness (or roughness) of movement. An ordinal random forest classification model (with one class for each of the possible ratings) was trained and evaluated using 10-fold cross validation. Step frequency point estimates from the Bayesian model were highly correlated with manually labelled step frequencies of 606 video clips showing patients walking towards or away from the camera (Pearson’s

r = 0.80

,

p < 0.001

). Our classifier achieved a balanced accuracy of 50% (chance = 25%). Estimated UPDRS ratings were within one of the clinicians’ ratings in 95% of cases. There was a significant correlation between clinician labels and model estimates (Spearman’s

ρ = 0.52

,

p < 0.001

). We show how the interpretability of the feature values could be used by clinicians to support their decision-making and provide insight into the model’s objective UPDRS rating estimation. The severity of gait impairment in Parkinson’s disease can be estimated using a single patient video, recorded using a consumer mobile device and within standard clinical settings; i.e., videos were recorded in various hospital hallways and offices rather than gait laboratories. This approach can support clinicians during routine assessments by providing an objective rating (or second opinion), and has the potential to be used for remote home assessments, which would allow for more frequent monitoring. Full article

(This article belongs to the Collection Explainable and Augmented Machine Learning for Biosignals and Biomedical Images)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Non-Invasive Detection and Staging of Colorectal Cancer Using a Portable Electronic Nose

by

Heena Tyagi

,

Emma Daulton

,

Ayman S. Bannaga

,

Ramesh P. Arasaradnam

and

James A. Covington

Sensors 2021, 21(16), 5440; https://doi.org/10.3390/s21165440 - 12 Aug 2021

Cited by 13 | Viewed by 3041

Abstract

Electronic noses (e-nose) offer potential for the detection of cancer in its early stages. The ability to analyse samples in real time, at a low cost, applying easy–to-use and portable equipment, gives e-noses advantages over other technologies, such as Gas Chromatography-Mass Spectrometry (GC-MS). [...] Read more.

Electronic noses (e-nose) offer potential for the detection of cancer in its early stages. The ability to analyse samples in real time, at a low cost, applying easy–to-use and portable equipment, gives e-noses advantages over other technologies, such as Gas Chromatography-Mass Spectrometry (GC-MS). For diseases such as cancer with a high mortality, a technology that can provide fast results for use in routine clinical applications is important. Colorectal cancer (CRC) is among the highest occurring cancers and has high mortality rates, if diagnosed late. In our study, we investigated the use of portable electronic nose (PEN3), with further analysis using GC-TOF-MS, for the analysis of gases and volatile organic compounds (VOCs) to profile the urinary metabolome of colorectal cancer. We also compared the different cancer stages with non-cancers using the PEN3 and GC-TOF-MS. Results obtained from PEN3, and GC-TOF-MS demonstrated high accuracy for the separation of CRC and non-cancer. PEN3 separated CRC from non-cancerous group with 0.81 AUC (Area Under the Curve). We used data from GC-TOF-MS to obtain a VOC profile for CRC, which identified 23 potential biomarker VOCs for CRC. Thus, the PEN3 and GC-TOF-MS were found to successfully separate the cancer group from the non-cancer group. Full article

(This article belongs to the Special Issue Portable Electronic-Nose Devices for Noninvasive Early Disease Detection)

► Show Figures

Graphical abstract

Open AccessEditor’s ChoiceArticle

The Algorithm of Determining an Anti-Collision Manoeuvre Trajectory Based on the Interpolation of Ship’s State Vector

by

Piotr Borkowski

,

Zbigniew Pietrzykowski

and

Janusz Magaj

Sensors 2021, 21(16), 5332; https://doi.org/10.3390/s21165332 - 06 Aug 2021

Cited by 11 | Viewed by 2165

Abstract

The determination of a ship’s safe trajectory in collision situations at sea is one of the basic functions in autonomous navigation of ships. While planning a collision avoiding manoeuvre in open waters, the navigator has to take into account the ships manoeuvrability and [...] Read more.

The determination of a ship’s safe trajectory in collision situations at sea is one of the basic functions in autonomous navigation of ships. While planning a collision avoiding manoeuvre in open waters, the navigator has to take into account the ships manoeuvrability and hydrometeorological conditions. To this end, the ship’s state vector is predicted—position coordinates, speed, heading, and other movement parameters—at fixed time intervals for different steering scenarios. One possible way to solve this problem is a method using the interpolation of the ship’s state vector based on the data from measurements conducted during the sea trials of the ship. This article presents the interpolating function within any convex quadrilateral with the nodes being its vertices. The proposed function interpolates the parameters of the ship’s state vector for the specified point of a plane, where the values in the interpolation nodes are data obtained from measurements performed during a series of turning circle tests, conducted for different starting conditions and various rudder settings. The proposed method of interpolation was used in the process of determining the anti-collision manoeuvre trajectory. The mechanism is based on the principles of a modified Dijkstra algorithm, in which the graph takes the form of a regular network of points. The transition between the graph vertices depends on the safe passing level of other objects and the degree of departure from the planned route. The determined shortest path between the starting vertex and the target vertex is the optimal solution for the discrete space of solutions. The algorithm for determining the trajectory of the anti-collision manoeuvre was implemented in autonomous sea-going vessel technology. This article presents the results of laboratory tests and tests conducted under quasi-real conditions using physical ship models. The experiments confirmed the effective operation of the developed algorithm of the determination of the anti-collision manoeuvre trajectory in the technological framework of autonomous ship navigation. Full article

(This article belongs to the Special Issue Sensors and Sensor's Fusion in Autonomous Vehicles)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

Surface Plasmonic Sensors: Sensing Mechanism and Recent Applications

by

,

,

,

and

Sensors 2021, 21(16), 5262; https://doi.org/10.3390/s21165262 - 04 Aug 2021

Cited by 27 | Viewed by 7574

Abstract

Surface plasmonic sensors have been widely used in biology, chemistry, and environment monitoring. These sensors exhibit extraordinary sensitivity based on surface plasmon resonance (SPR) or localized surface plasmon resonance (LSPR) effects, and they have found commercial applications. In this review, we present recent [...] Read more.

Surface plasmonic sensors have been widely used in biology, chemistry, and environment monitoring. These sensors exhibit extraordinary sensitivity based on surface plasmon resonance (SPR) or localized surface plasmon resonance (LSPR) effects, and they have found commercial applications. In this review, we present recent progress in the field of surface plasmonic sensors, mainly in the configurations of planar metastructures and optical-fiber waveguides. In the metastructure platform, the optical sensors based on LSPR, hyperbolic dispersion, Fano resonance, and two-dimensional (2D) materials integration are introduced. The optical-fiber sensors integrated with LSPR/SPR structures and 2D materials are summarized. We also introduce the recent advances in quantum plasmonic sensing beyond the classical shot noise limit. The challenges and opportunities in this field are discussed. Full article

(This article belongs to the Special Issue Surface Plasmon Sensors)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

by

Elham Eslami

and

Hae-Bum Yun

Sensors 2021, 21(15), 5137; https://doi.org/10.3390/s21155137 - 29 Jul 2021

Cited by 21 | Viewed by 3521

Abstract

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in [...] Read more.

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder

by

,

,

and

Sensors 2021, 21(15), 4968; https://doi.org/10.3390/s21154968 - 21 Jul 2021

Cited by 40 | Viewed by 7731

Abstract

As technology evolves, more components are integrated into printed circuit boards (PCBs) and the PCB layout increases. Because small defects on signal trace can cause significant damage to the system, PCB surface inspection is one of the most important quality control processes. Owing [...] Read more.

As technology evolves, more components are integrated into printed circuit boards (PCBs) and the PCB layout increases. Because small defects on signal trace can cause significant damage to the system, PCB surface inspection is one of the most important quality control processes. Owing to the limitations of manual inspection, significant efforts have been made to automate the inspection by utilizing high resolution CCD or CMOS sensors. Despite the advanced sensor technology, setting the pass/fail criteria based on small failure samples has always been challenging in traditional machine vision approaches. To overcome these problems, we propose an advanced PCB inspection system based on a skip-connected convolutional autoencoder. The deep autoencoder model was trained to decode the original non-defect images from the defect images. The decoded images were then compared with the input image to identify the defect location. To overcome the small and imbalanced dataset in the early manufacturing stage, we applied appropriate image augmentation to improve the model training performance. The experimental results reveal that a simple unsupervised autoencoder model delivers promising performance, with a detection rate of up to 98% and a false pass rate below 1.7% for the test data, containing 3900 defect and non-defect images. Full article

(This article belongs to the Special Issue Artificial Intelligence for Smart Manufacturing: Methods and Applications)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Regulatory, Legal, and Market Aspects of Smart Wearables for Cardiac Monitoring

by

Jan Benedikt Brönneke

,

Jennifer Müller

,

Konstantinos Mouratis

,

Julia Hagen

and

Ariel Dora Stern

Sensors 2021, 21(14), 4937; https://doi.org/10.3390/s21144937 - 20 Jul 2021

Cited by 13 | Viewed by 5240

Abstract

In the area of cardiac monitoring, the use of digitally driven technologies is on the rise. While the development of medical products is advancing rapidly, allowing for new use-cases in cardiac monitoring and other areas, regulatory and legal requirements that govern market access [...] Read more.

In the area of cardiac monitoring, the use of digitally driven technologies is on the rise. While the development of medical products is advancing rapidly, allowing for new use-cases in cardiac monitoring and other areas, regulatory and legal requirements that govern market access are often evolving slowly, sometimes creating market barriers. This article gives a brief overview of the existing clinical studies regarding the use of smart wearables in cardiac monitoring and provides insight into the main regulatory and legal aspects that need to be considered when such products are intended to be used in a health care setting. Based on this brief overview, the article elaborates on the specific requirements in the main areas of authorization/certification and reimbursement/compensation, as well as data protection and data security. Three case studies are presented as examples of specific market access procedures: the USA, Germany, and Belgium. This article concludes that, despite the differences in specific requirements, market access pathways in most countries are characterized by a number of similarities, which should be considered early on in product development. The article also elaborates on how regulatory and legal requirements are currently being adapted for digitally driven wearables and proposes an ongoing evolution of these requirements to facilitate market access for beneficial medical technology in the future. Full article

(This article belongs to the Special Issue Smart Wearables for Cardiac Monitoring)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT

by

Addie Ira Borja Parico

and

Tofael Ahamed

Sensors 2021, 21(14), 4803; https://doi.org/10.3390/s21144803 - 14 Jul 2021

Cited by 49 | Viewed by 10563

Abstract

This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT. This study also provided a systematic and pragmatic methodology [...] Read more.

This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT. This study also provided a systematic and pragmatic methodology for choosing the most suitable model for a desired application in agricultural sciences. In terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an AP@0.50 of 98%. In terms of speed and computational cost, YOLOv4-tiny was found to be the ideal model, with a speed of more than 50 FPS and FLOPS of 6.8–14.5. If considering the balance in terms of accuracy, speed and computational cost, YOLOv4 was found to be most suitable and had the highest accuracy metrics while satisfying a real time speed of greater than or equal to 24 FPS. Between the two methods of counting with Deep SORT, the unique ID method was found to be more reliable, with an F1count of 87.85%. This was because YOLOv4 had a very low false negative in detecting pear fruits. The ROI line is more reliable because of its more restrictive nature, but due to flickering in detection it was not able to count some pears despite their being detected. Full article

(This article belongs to the Section Remote Sensors)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

by

Francesco Salamone

,

Massimiliano Masullo

and

Sergio Sibilio

Sensors 2021, 21(14), 4727; https://doi.org/10.3390/s21144727 - 10 Jul 2021

Cited by 23 | Viewed by 4121

Abstract

The so-called Internet of Things (IoT), which is rapidly increasing the number of network-connected and interconnected objects, could have a far-reaching impact in identifying the link between human health, well-being, and environmental concerns. In line with the IoT concept, many commercial wearables have [...] Read more.

The so-called Internet of Things (IoT), which is rapidly increasing the number of network-connected and interconnected objects, could have a far-reaching impact in identifying the link between human health, well-being, and environmental concerns. In line with the IoT concept, many commercial wearables have been introduced in recent years, which differ from the usual devices in that they use the term “smart” alongside the terms “watches”, “glasses”, and “jewellery”. Commercially available wearables aim to enhance smartphone functionality by enabling payment for commercial items or monitoring physical activity. However, what is the trend of scientific production about the concept of wearables regarding environmental monitoring issues? What are the main areas of interest covered by scientific production? What are the main findings and limitations of the developed solution in this field? The methodology used to answer the above questions is based on a systematic review. The data were acquired following a reproducible methodology. The main result is that, among the thermal, visual, acoustic, and air quality environmental factors, the last one is the most considered when using wearables even though in combination with some others. Another relevant finding is that of the acquired studies; in only one, the authors shared their wearables as an open-source device, and it will probably be necessary to encourage researchers to consider open-source as a means to promote scalability and proliferation of new wearables customized to cover different domains. Full article

(This article belongs to the Section Wearables)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance

by

,

,

,

,

,

,

and

Sensors 2021, 21(14), 4676; https://doi.org/10.3390/s21144676 - 08 Jul 2021

Cited by 21 | Viewed by 3835

Abstract

Industry 4.0, allied with the growth and democratization of Artificial Intelligence (AI) and the advent of IoT, is paving the way for the complete digitization and automation of industrial processes. Maintenance is one of these processes, where the introduction of a predictive approach, [...] Read more.

Industry 4.0, allied with the growth and democratization of Artificial Intelligence (AI) and the advent of IoT, is paving the way for the complete digitization and automation of industrial processes. Maintenance is one of these processes, where the introduction of a predictive approach, as opposed to the traditional techniques, is expected to considerably improve the industry maintenance strategies with gains such as reduced downtime, improved equipment effectiveness, lower maintenance costs, increased return on assets, risk mitigation, and, ultimately, profitable growth. With predictive maintenance, dedicated sensors monitor the critical points of assets. The sensor data then feed into machine learning algorithms that can infer the asset health status and inform operators and decision-makers. With this in mind, in this paper, we present TIP4.0, a platform for predictive maintenance based on a modular software solution for edge computing gateways. TIP4.0 is built around Yocto, which makes it readily available and compliant with Commercial Off-the-Shelf (COTS) or proprietary hardware. TIP4.0 was conceived with an industry mindset with communication interfaces that allow it to serve sensor networks in the shop floor and modular software architecture that allows it to be easily adjusted to new deployment scenarios. To showcase its potential, the TIP4.0 platform was validated over COTS hardware, and we considered a public data-set for the simulation of predictive maintenance scenarios. We used a Convolution Neural Network (CNN) architecture, which provided competitive performance over the state-of-the-art approaches, while being approximately four-times and two-times faster than the uncompressed model inference on the Central Processing Unit (CPU) and Graphical Processing Unit, respectively. These results highlight the capabilities of distributed large-scale edge computing over industrial scenarios. Full article

(This article belongs to the Section Internet of Things)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

Recent Advances in Enzymatic and Non-Enzymatic Electrochemical Glucose Sensing

by

,

,

and

Sensors 2021, 21(14), 4672; https://doi.org/10.3390/s21144672 - 08 Jul 2021

Cited by 85 | Viewed by 7008

Abstract

The detection of glucose is crucial in the management of diabetes and other medical conditions but also crucial in a wide range of industries such as food and beverages. The development of glucose sensors in the past century has allowed diabetic patients to [...] Read more.

The detection of glucose is crucial in the management of diabetes and other medical conditions but also crucial in a wide range of industries such as food and beverages. The development of glucose sensors in the past century has allowed diabetic patients to effectively manage their disease and has saved lives. First-generation glucose sensors have considerable limitations in sensitivity and selectivity which has spurred the development of more advanced approaches for both the medical and industrial sectors. The wide range of application areas has resulted in a range of materials and fabrication techniques to produce novel glucose sensors that have higher sensitivity and selectivity, lower cost, and are simpler to use. A major focus has been on the development of enzymatic electrochemical sensors, typically using glucose oxidase. However, non-enzymatic approaches using direct electrochemistry of glucose on noble metals are now a viable approach in glucose biosensor design. This review discusses the mechanisms of electrochemical glucose sensing with a focus on the different generations of enzymatic-based sensors, their recent advances, and provides an overview of the next generation of non-enzymatic sensors. Advancements in manufacturing techniques and materials are key in propelling the field of glucose sensing, however, significant limitations remain which are highlighted in this review and requires addressing to obtain a more stable, sensitive, selective, cost efficient, and real-time glucose sensor. Full article

(This article belongs to the Special Issue Electrochemical (Bio)sensors for Biomedical Applications)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

A Vision-Based Social Distancing and Critical Density Detection System for COVID-19

by

,

,

,

and

Sensors 2021, 21(13), 4608; https://doi.org/10.3390/s21134608 - 05 Jul 2021

Cited by 58 | Viewed by 8220

Abstract

Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow [...] Read more.

Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value. The proposed system is also ethically fair: it does not record data nor target individuals, and no human supervisor is present during the operation. The proposed system was evaluated across real-world datasets. Full article

(This article belongs to the Special Issue Machine Learning in Wireless Sensor Networks and Internet of Things)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

Chiroptical Metasurfaces: Principles, Classification, and Applications

by

,

,

,

,

,

,

Muhammad Qasim Mehmood

and

Junsuk Rho

Sensors 2021, 21(13), 4381; https://doi.org/10.3390/s21134381 - 26 Jun 2021

Cited by 33 | Viewed by 5123

Abstract

Chiral materials, which show different optical behaviors when illuminated by left or right circularly polarized light due to broken mirror symmetry, have greatly impacted the field of optical sensing over the past decade. To improve the sensitivity of chiral sensing platforms, enhancing the [...] Read more.

Chiral materials, which show different optical behaviors when illuminated by left or right circularly polarized light due to broken mirror symmetry, have greatly impacted the field of optical sensing over the past decade. To improve the sensitivity of chiral sensing platforms, enhancing the chiroptical response is necessary. Metasurfaces, which are two-dimensional metamaterials consisting of periodic subwavelength artificial structures, have recently attracted significant attention because of their ability to enhance the chiroptical response by manipulating amplitude, phase, and polarization of electromagnetic fields. Here, we reviewed the fundamentals of chiroptical metasurfaces as well as categorized types of chiroptical metasurfaces by their intrinsic or extrinsic chirality. Finally, we introduced applications of chiral metasurfaces such as multiplexing metaholograms, metalenses, and sensors. Full article

(This article belongs to the Special Issue Metasurfaces in Depth Sensing and 3D Display)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

by

Marco Altini

and

Hannu Kinnunen

Sensors 2021, 21(13), 4302; https://doi.org/10.3390/s21134302 - 23 Jun 2021

Cited by 33 | Viewed by 31750

Abstract

Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous [...] Read more.

Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

Towards Supply Chain Visibility Using Internet of Things: A Dyadic Analysis Review

by

,

,

,

,

,

and

Sensors 2021, 21(12), 4158; https://doi.org/10.3390/s21124158 - 17 Jun 2021

Cited by 24 | Viewed by 6007

Abstract

The Internet of Things (IoT) and its benefits and challenges are the most emergent research topics among academics and practitioners. With supply chains (SCs) gaining rapid complexity, having high supply chain visibility (SCV) would help companies ease the processes and reduce complexity by [...] Read more.

The Internet of Things (IoT) and its benefits and challenges are the most emergent research topics among academics and practitioners. With supply chains (SCs) gaining rapid complexity, having high supply chain visibility (SCV) would help companies ease the processes and reduce complexity by improving inaccuracies. Extant literature has given attention to the organisation’s capability to collect and evaluate information to balance between strategy and goals. The majority of studies focus on investigating IoT’s impact on different areas such as sustainability, organisational structure, lean manufacturing, product development, and strategic management. However, research investigating the relationships and impact of IoT on SCV is minimal. This study closes this gap using a structured literature review to critically analyse existing literature to synthesise the use of IoT applications in SCs to gain visibility, and the SC. We found key IoT technologies that help SCs gain visibility, and seven benefits and three key challenges of these technologies. We also found the concept of Supply 4.0 that grasps the element of Industry 4.0 within the SC context. This paper contributes by combining IoT application synthesis, enablers, and challenges in SCV by highlighting key IoT technologies used in the SCs to gain visibility. Finally, the authors propose an empirical research agenda to address the identified gaps. Full article

(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

A Review of Nanocomposite-Modified Electrochemical Sensors for Water Quality Monitoring

by

Olfa Kanoun

,

Tamara Lazarević-Pašti

,

,

,

,

,

,

,

,

and

Sensors 2021, 21(12), 4131; https://doi.org/10.3390/s21124131 - 16 Jun 2021

Cited by 38 | Viewed by 7338

Abstract

Electrochemical sensors play a significant role in detecting chemical ions, molecules, and pathogens in water and other applications. These sensors are sensitive, portable, fast, inexpensive, and suitable for online and in-situ measurements compared to other methods. They can provide the detection for any [...] Read more.

Electrochemical sensors play a significant role in detecting chemical ions, molecules, and pathogens in water and other applications. These sensors are sensitive, portable, fast, inexpensive, and suitable for online and in-situ measurements compared to other methods. They can provide the detection for any compound that can undergo certain transformations within a potential window. It enables applications in multiple ion detection, mainly since these sensors are primarily non-specific. In this paper, we provide a survey of electrochemical sensors for the detection of water contaminants, i.e., pesticides, nitrate, nitrite, phosphorus, water hardeners, disinfectant, and other emergent contaminants (phenol, estrogen, gallic acid etc.). We focus on the influence of surface modification of the working electrodes by carbon nanomaterials, metallic nanostructures, imprinted polymers and evaluate the corresponding sensing performance. Especially for pesticides, which are challenging and need special care, we highlight biosensors, such as enzymatic sensors, immunobiosensor, aptasensors, and biomimetic sensors. We discuss the sensors’ overall performance, especially concerning real-sample performance and the capability for actual field application. Full article

(This article belongs to the Special Issue Sensors for Environmental and Life Science Applications)

► Show Figures

Graphical abstract

Open AccessEditor’s ChoiceReview

Roadmap of Terahertz Imaging 2021

by

,

,

,

and

Sensors 2021, 21(12), 4092; https://doi.org/10.3390/s21124092 - 14 Jun 2021

Cited by 80 | Viewed by 8297

Abstract

In this roadmap article, we have focused on the most recent advances in terahertz (THz) imaging with particular attention paid to the optimization and miniaturization of the THz imaging systems. Such systems entail enhanced functionality, reduced power consumption, and increased convenience, thus being [...] Read more.

In this roadmap article, we have focused on the most recent advances in terahertz (THz) imaging with particular attention paid to the optimization and miniaturization of the THz imaging systems. Such systems entail enhanced functionality, reduced power consumption, and increased convenience, thus being geared toward the implementation of THz imaging systems in real operational conditions. The article will touch upon the advanced solid-state-based THz imaging systems, including room temperature THz sensors and arrays, as well as their on-chip integration with diffractive THz optical components. We will cover the current-state of compact room temperature THz emission sources, both optolectronic and electrically driven; particular emphasis is attributed to the beam-forming role in THz imaging, THz holography and spatial filtering, THz nano-imaging, and computational imaging. A number of advanced THz techniques, such as light-field THz imaging, homodyne spectroscopy, and phase sensitive spectrometry, THz modulated continuous wave imaging, room temperature THz frequency combs, and passive THz imaging, as well as the use of artificial intelligence in THz data processing and optics development, will be reviewed. This roadmap presents a structured snapshot of current advances in THz imaging as of 2021 and provides an opinion on contemporary scientific and technological challenges in this field, as well as extrapolations of possible further evolution in THz imaging. Full article

(This article belongs to the Special Issue Terahertz Imaging and Sensors)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

by

Yannis Spyridis

,

Thomas Lagkas

,

Panagiotis Sarigiannidis

,

Vasileios Argyriou

,

Antonios Sarigiannidis

,

George Eleftherakis

and

Jie Zhang

Sensors 2021, 21(11), 3936; https://doi.org/10.3390/s21113936 - 07 Jun 2021

Cited by 18 | Viewed by 3246

Abstract

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of [...] Read more.

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs. Full article

(This article belongs to the Special Issue 6G Wireless Communication Systems)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients

by

Sarmad Maqsood

,

Robertas Damaševičius

and

Rytis Maskeliūnas

Sensors 2021, 21(11), 3865; https://doi.org/10.3390/s21113865 - 03 Jun 2021

Cited by 33 | Viewed by 3096

Abstract

Diabetic retinopathy (DR) is the main cause of blindness in diabetic patients. Early and accurate diagnosis can improve the analysis and prognosis of the disease. One of the earliest symptoms of DR are the hemorrhages in the retina. Therefore, we propose a new [...] Read more.

Diabetic retinopathy (DR) is the main cause of blindness in diabetic patients. Early and accurate diagnosis can improve the analysis and prognosis of the disease. One of the earliest symptoms of DR are the hemorrhages in the retina. Therefore, we propose a new method for accurate hemorrhage detection from the retinal fundus images. First, the proposed method uses the modified contrast enhancement method to improve the edge details from the input retinal fundus images. In the second stage, a new convolutional neural network (CNN) architecture is proposed to detect hemorrhages. A modified pre-trained CNN model is used to extract features from the detected hemorrhages. In the third stage, all extracted feature vectors are fused using the convolutional sparse image decomposition method, and finally, the best features are selected by using the multi-logistic regression controlled entropy variance approach. The proposed method is evaluated on 1509 images from HRF, DRIVE, STARE, MESSIDOR, DIARETDB0, and DIARETDB1 databases and achieves the average accuracy of 97.71%, which is superior to the previous works. Moreover, the proposed hemorrhage detection system attains better performance, in terms of visual quality and quantitative analysis with high accuracy, in comparison with the state-of-the-art methods. Full article

(This article belongs to the Collection Medical Image Classification)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

by

Igor Stancin

,

Mario Cifrek

and

Alan Jovic

Sensors 2021, 21(11), 3786; https://doi.org/10.3390/s21113786 - 30 May 2021

Cited by 47 | Viewed by 6867

Abstract

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. [...] Read more.

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection. Full article

(This article belongs to the Special Issue Intelligent Biosignal Analysis Methods)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Magnetic Lateral Flow Immunoassay for Small Extracellular Vesicles Quantification: Application to Colorectal Cancer Biomarker Detection

by

Amanda Moyano

,

Esther Serrano-Pertierra

,

José María Duque

,

Virginia Ramos

,

Estefanía Teruel-Barandiarán

,

María Teresa Fernández-Sánchez

,

María Salvador

,

José Carlos Martínez-García

,

Luis Sánchez

,

Luis García-Flórez

,

Montserrat Rivas

and

María del Carmen Blanco-López

Sensors 2021, 21(11), 3756; https://doi.org/10.3390/s21113756 - 28 May 2021

Cited by 11 | Viewed by 3386

Abstract

Colorectal cancer (CRC) is the third leading cause of cancer death and the fourth most common cancer in the world. Colonoscopy is the most sensitive test used for detection of CRC; however, their procedure is invasive and expensive for population mass screening. Currently, [...] Read more.

Colorectal cancer (CRC) is the third leading cause of cancer death and the fourth most common cancer in the world. Colonoscopy is the most sensitive test used for detection of CRC; however, their procedure is invasive and expensive for population mass screening. Currently, the fecal occult blood test has been widely used as a screening tool for CRC but displays low specificity. The lack of rapid and simple methods for mass screening makes the early diagnosis and therapy monitoring difficult. Extracellular vesicles (EVs) have emerged as a novel source of biomarkers due to their contents in proteins and miRNAs. Their detection would not require invasive techniques and could be considered as a liquid biopsy. Specifically, it has been demonstrated that the amount of CD147 expressed in circulating EVs is significant higher for CRC cell lines than for normal colon fibroblast cell lines. Moreover, CD147-containing EVs have been used as a biomarker to monitor response to therapy in patients with CRC. Therefore, this antigen could be used as a non-invasive biomarker for the detection and monitoring of CRC in combination with a Point-of-Care platform as, for example, Lateral Flow Immunoassays (LFIAs). Here, we propose the development of a quantitative lateral flow immunoassay test based on the use of magnetic nanoparticles as labels coupled to inductive sensor for the non-invasive detection of CRC by CD147-positive EVs. The results obtained for quantification of CD147 antigen embedded in EVs isolated from plasma sample have demonstrated that this device could be used as a Point-of-Care tool for CRC screening or therapy monitoring thanks to its rapid response and easy operation. Full article

(This article belongs to the Special Issue Electrochemical Sensors and (Bio)assays for Health Applications)

► Show Figures

Graphical abstract

Open AccessEditor’s ChoiceArticle

A High-Resolution Reflective Microwave Planar Sensor for Sensing of Vanadium Electrolyte

by

Nazli Kazemi

,

Kalvin Schofield

and

Petr Musilek

Sensors 2021, 21(11), 3759; https://doi.org/10.3390/s21113759 - 28 May 2021

Cited by 28 | Viewed by 2684

Abstract

Microwave planar sensors employ conventional passive complementary split ring resonators (CSRR) as their sensitive region. In this work, a novel planar reflective sensor is introduced that deploys CSRRs as the front-end sensing element at

f_{res} = 6

GHz with an extra loss-compensating [...] Read more.

Microwave planar sensors employ conventional passive complementary split ring resonators (CSRR) as their sensitive region. In this work, a novel planar reflective sensor is introduced that deploys CSRRs as the front-end sensing element at

f_{res} = 6

GHz with an extra loss-compensating negative resistance that restores the dissipated power in the sensor that is used in dielectric material characterization. It is shown that the

S_{11}

notch of −15 dB can be improved down to −40 dB without loss of sensitivity. An application of this design is shown in discriminating different states of vanadium redox solutions with highly lossy conditions of fully charged

V^{5 +}

and fully discharged

V^{4 +}

electrolytes. Full article

(This article belongs to the Special Issue State-of-the-Art Technologies in Microwave Sensors)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

by

Aoxin Ni

,

Arian Azarang

and

Nasser Kehtarnavaz

Sensors 2021, 21(11), 3719; https://doi.org/10.3390/s21113719 - 27 May 2021

Cited by 34 | Viewed by 6116

Abstract

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of [...] Read more.

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

by

Wejdan L. Alyoubi

,

Maysoon F. Abulkhair

and

Wafaa M. Shalash

Sensors 2021, 21(11), 3704; https://doi.org/10.3390/s21113704 - 26 May 2021

Cited by 68 | Viewed by 8356

Abstract

Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stopping or delaying the deterioration of sight, [...] Read more.

Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stopping or delaying the deterioration of sight, highlighting the importance of regular scanning using high-efficiency computer-based systems to diagnose cases early. The current work presented fully automatic diagnosis systems that exceed manual techniques to avoid misdiagnosis, reducing time, effort and cost. The proposed system classifies DR images into five stages—no-DR, mild, moderate, severe and proliferative DR—as well as localizing the affected lesions on retain surface. The system comprises two deep learning-based models. The first model (CNN512) used the whole image as an input to the CNN model to classify it into one of the five DR stages. It achieved an accuracy of 88.6% and 84.1% on the DDR and the APTOS Kaggle 2019 public datasets, respectively, compared to the state-of-the-art results. Simultaneously, the second model used an adopted YOLOv3 model to detect and localize the DR lesions, achieving a 0.216 mAP in lesion localization on the DDR dataset, which improves the current state-of-the-art results. Finally, both of the proposed structures, CNN512 and YOLOv3, were fused to classify DR images and localize DR lesions, obtaining an accuracy of 89% with 89% sensitivity, 97.3 specificity and that exceeds the current state-of-the-art results. Full article

(This article belongs to the Special Issue Computer Aided Diagnosis Sensors)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Evaluation of Misalignment Effect in Vehicle-to-Vehicle Visible Light Communications: Experimental Demonstration of a 75 Meters Link

by

Sebastian-Andrei Avătămăniței

,

,

,

and

Sensors 2021, 21(11), 3577; https://doi.org/10.3390/s21113577 - 21 May 2021

Cited by 19 | Viewed by 2345

Abstract

The use of visible light communications technology in communication-based vehicle applications is gaining more and more interest as the research community is constantly overcoming challenge after challenge. In this context, this article addresses the issues associated with the use of Visible Light Communications [...] Read more.

The use of visible light communications technology in communication-based vehicle applications is gaining more and more interest as the research community is constantly overcoming challenge after challenge. In this context, this article addresses the issues associated with the use of Visible Light Communications (VLC) technology in Vehicle-to-Vehicle (V2V) communications, while focusing on two crucial issues. On the one hand, it aims to investigate the achievable communication distance in V2V applications while addressing the least favorable case, namely the one when a standard vehicle rear lighting system is used as a VLC emitter. On the other hand, this article investigates another highly unfavorable use case scenario, i.e., the case when two vehicles are located on adjacent lanes, rather than on the same lane. In order to evaluate the compatibility of the VLC technology with the usage in inter-vehicle communication, a VLC prototype is intensively evaluated in outdoor conditions. The experimental results show a record V2V VLC distance of 75 m, while providing a Bit Error Ratio (BER) of 10⁻⁷–10⁻⁶. The results also show that the VLC technology is able to provide V2V connectivity even in a situation where the vehicles are located on adjacent lanes, without a major impact on the link performances. Nevertheless, this situation generates an initial no-coverage zone, which is determined by the VLC receiver reception angle, whereas in some cases, vehicle misalignment can generate a BER increase that can go up to two orders of magnitude. Full article

(This article belongs to the Special Issue Automotive Visible Light Communications (AutoVLC))

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Digital Twin-Based Safety Risk Coupling of Prefabricated Building Hoisting

by

,

,

and

Sensors 2021, 21(11), 3583; https://doi.org/10.3390/s21113583 - 21 May 2021

Cited by 31 | Viewed by 3253

Abstract

Safety management in hoisting is the key issue to determine the development of prefabricated building construction. However, the security management in the hoisting stage lacks a truly effective method of information physical fusion, and the safety risk analysis of hoisting does not consider [...] Read more.

Safety management in hoisting is the key issue to determine the development of prefabricated building construction. However, the security management in the hoisting stage lacks a truly effective method of information physical fusion, and the safety risk analysis of hoisting does not consider the interaction of risk factors. In this paper, a hoisting safety risk management framework based on digital twin (DT) is presented. The digital twin hoisting safety risk coupling model is built. The proposed model integrates the Internet of Things (IoT), Building Information Modeling (BIM), and a security risk analysis method combining the Apriori algorithm and complex network. The real-time perception and virtual–real interaction of multi-source information in the hoisting process are realized, the association rules and coupling relationship among hoisting safety risk factors are mined, and the time-varying data information is visualized. Demonstration in the construction of a large-scale prefabricated building shows that with the proposed framework, it is possible to complete the information fusion between the hoisting site and the virtual model and realize the visual management. The correlative relationship among hoisting construction safety risk factors is analyzed, and the key control factors are found. Moreover, the efficiency of information integration and sharing is improved, the gap of coupling analysis of security risk factors is filled, and effective security management and decision-making are achieved with the proposed approach. Full article

(This article belongs to the Special Issue Smart Sensing in Building and Construction)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceArticle

Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse

by

Sandro Augusto Magalhães

,

Luís Castro

,

Germano Moreira

,

Filipe Neves dos Santos

,

Mário Cunha

,

Jorge Dias

and

António Paulo Moreira

Sensors 2021, 21(10), 3569; https://doi.org/10.3390/s21103569 - 20 May 2021

Cited by 47 | Viewed by 7181

Abstract

The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage [...] Read more.

The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of

66.15

%, an mAP of

51.46

% and an inference time of

16.44

m

s

with the NVIDIA Turing Architecture platform, an NVIDIA Tesla T4, with 12 GB. YOLOv4 Tiny also had impressive results, mainly concerning inferring times of about 5

m

s

. Full article

(This article belongs to the Section Remote Sensors)

► Show Figures

Figure 1

Open AccessEditor’s ChoiceReview

Recent Advances in Transducers for Intravascular Ultrasound (IVUS) Imaging

by

,

,

,

and

Sensors 2021, 21(10), 3540; https://doi.org/10.3390/s21103540 - 19 May 2021

Cited by 33 | Viewed by 7986

Abstract

As a well-known medical imaging methodology, intravascular ultrasound (IVUS) imaging plays a critical role in diagnosis, treatment guidance and post-treatment assessment of coronary artery diseases. By cannulating a miniature ultrasound transducer mounted catheter into an artery, the vessel lumen opening, vessel wall morphology [...] Read more.

As a well-known medical imaging methodology, intravascular ultrasound (IVUS) imaging plays a critical role in diagnosis, treatment guidance and post-treatment assessment of coronary artery diseases. By cannulating a miniature ultrasound transducer mounted catheter into an artery, the vessel lumen opening, vessel wall morphology and other associated blood and vessel properties can be precisely assessed in IVUS imaging. Ultrasound transducer, as the key component of an IVUS system, is critical in determining the IVUS imaging performance. In recent years, a wide range of achievements in ultrasound transducers have been reported for IVUS imaging applications. Herein, a comprehensive review is given on recent advances in ultrasound transducers for IVUS imaging. Firstly, a fundamental understanding of IVUS imaging principle, evaluation parameters and IVUS catheter are summarized. Secondly, three different types of ultrasound transducers (piezoelectric ultrasound transducer, piezoelectric micromachined ultrasound transducer and capacitive micromachined ultrasound transducer) for IVUS imaging are presented. Particularly, the recent advances in piezoelectric ultrasound transducer for IVUS imaging are extensively examined according to their different working mechanisms, configurations and materials adopted. Thirdly, IVUS-based multimodality intravascular imaging of atherosclerotic plaque is discussed. Finally, summary and perspectives on the future studies are highlighted for IVUS imaging applications. Full article

(This article belongs to the Special Issue Feature Papers in Physical Sensors Section 2020)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Editor’s Choice Articles