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Showing papers in "Sensors in 2018"


Journal ArticleDOI
06 Oct 2018-Sensors
TL;DR: An improved sparse convolution method for Voxel-based 3D convolutional networks is investigated, which significantly increases the speed of both training and inference and introduces a new form of angle loss regression to improve the orientation estimation performance.
Abstract: LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference speed and low orientation estimation performance. We therefore investigate an improved sparse convolution method for such networks, which significantly increases the speed of both training and inference. We also introduce a new form of angle loss regression to improve the orientation estimation performance and a new data augmentation approach that can enhance the convergence speed and performance. The proposed network produces state-of-the-art results on the KITTI 3D object detection benchmarks while maintaining a fast inference speed.

1,624 citations


Journal ArticleDOI
14 Aug 2018-Sensors
TL;DR: A comprehensive review of research dedicated to applications of machine learning in agricultural production systems is presented, demonstrating how agriculture will benefit from machine learning technologies.
Abstract: Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

1,262 citations


Journal ArticleDOI
06 Aug 2018-Sensors
TL;DR: The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context, and point out the main open issues and future research directions.
Abstract: The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes IoT vulnerable to privacy and security threats. With its "security by design," Blockchain (BC) can help in addressing major security requirements in IoT. BC capabilities like immutability, transparency, auditability, data encryption and operational resilience can help solve most architectural shortcomings of IoT. This article presents a comprehensive survey on BC and IoT integration. The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context. This paper presents the following novelties, with respect to related work: (i) it covers different application domains, organizing the available literature according to this categorization, (ii) it introduces two usage patterns, i.e., device manipulation and data management (open marketplace solution), and (iii) it reports on the development level of some of the presented solutions. We also analyze the main challenges faced by the research community in the smooth integration of BC and IoT, and point out the main open issues and future research directions. Last but not least, we also present a survey about novel uses of BC in the machine economy.

541 citations


Journal ArticleDOI
25 Jul 2018-Sensors
TL;DR: This paper reviews important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications plus a resumed evolution of these devices based on the prototypes developed along the years.
Abstract: Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.

531 citations


Journal ArticleDOI
28 Jun 2018-Sensors
TL;DR: A comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals is presented.
Abstract: Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

484 citations


Journal ArticleDOI
09 Jan 2018-Sensors
TL;DR: The results show that the blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.
Abstract: In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.

472 citations


Journal ArticleDOI
20 Dec 2018-Sensors
TL;DR: A detailed and complex case-study has been presented to validate the solution in the context of a system that dynamically reverse the traveling direction of a road segment, with all the safety conditions in place.
Abstract: The new Internet of Things/Everything (IoT/IoE) paradigm and architecture allows one to rethink the way Smart City infrastructures are designed and managed, but on the other hand, a number of problems have to be solved. In terms of mobility the cities that embrace the sensoring era can take advantage of this disruptive technology to improve the quality of life of their citizens, also thanks to the rationalization in the use of their resources. In Sii-Mobility, a national smart city project on mobility and transportation, a flexible platform has been designed and here, in this paper, is presented. It permits one to set up heterogeneous and complex scenarios that integrate sensors/actuators as IoT/IoE in an overall Big Data, Machine Learning and Data Analytics scenario. A detailed and complex case-study has been presented to validate the solution in the context of a system that dynamically reverse the traveling direction of a road segment, with all the safety conditions in place. This case study composes several building blocks of the IoT platform, which demonstrate that a flexible and dynamic set-up is possible, supporting security, safety, local, cloud and mixed solutions.

449 citations


Journal ArticleDOI
30 Jan 2018-Sensors
TL;DR: A brief review of researches in the field of FER conducted over the past decades, focusing on an up-to-date hybrid deep-learning approach combining a convolutional neural network for the spatial features of an individual frame and long short-term memory for temporal features of consecutive frames.
Abstract: Facial emotion recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. Although FER can be conducted using multiple sensors, this review focuses on studies that exclusively use facial images, because visual expressions are one of the main information channels in interpersonal communication. This paper provides a brief review of researches in the field of FER conducted over the past decades. First, conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms. Deep-learning-based FER approaches using deep networks enabling “end-to-end” learning are then presented. This review also focuses on an up-to-date hybrid deep-learning approach combining a convolutional neural network (CNN) for the spatial features of an individual frame and long short-term memory (LSTM) for temporal features of consecutive frames. In the later part of this paper, a brief review of publicly available evaluation metrics is given, and a comparison with benchmark results, which are a standard for a quantitative comparison of FER researches, is described. This review can serve as a brief guidebook to newcomers in the field of FER, providing basic knowledge and a general understanding of the latest state-of-the-art studies, as well as to experienced researchers looking for productive directions for future work.

437 citations


Journal ArticleDOI
10 Jul 2018-Sensors
TL;DR: A deep neural network model that integrates the CNN and LSTM architectures is developed, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration, the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper.
Abstract: In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

426 citations


Journal ArticleDOI
11 Feb 2018-Sensors
TL;DR: Wang et al. as discussed by the authors proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation, dermoscopic feature extraction, and lesion classification.
Abstract: Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

411 citations


Journal ArticleDOI
Hao Huang1
27 Sep 2018-Sensors
TL;DR: In this review, some recent advances in exploring MMP-9 as a biomarker in different cancers are summarized, and recent discoveries of novel M MP-9 biosensors are also presented.
Abstract: As one of the most widely investigated matrix metalloproteinases (MMPs), MMP-9 is a significant protease which plays vital roles in many biological processes. MMP-9 can cleave many extracellular matrix (ECM) proteins to regulate ECM remodeling. It can also cleave many plasma surface proteins to release them from the cell surface. MMP-9 has been widely found to relate to the pathology of cancers, including but not limited to invasion, metastasis and angiogenesis. Some recent research evaluated the value of MMP-9 as biomarkers to various specific cancers. Besides, recent research of MMP-9 biosensors discovered various novel MMP-9 biosensors to detect this enzyme. In this review, some recent advances in exploring MMP-9 as a biomarker in different cancers are summarized, and recent discoveries of novel MMP-9 biosensors are also presented.

Journal ArticleDOI
21 Nov 2018-Sensors
TL;DR: An overview of five Co-CPS use cases, as introduced in the SafeCOP EU project, and a comprehensive analysis of the main existing wireless communication technologies giving details about the protocols developed within particular standardization bodies are provided.
Abstract: Cooperative Cyber-Physical Systems (Co-CPSs) can be enabled using wireless communication technologies, which in principle should address reliability and safety challenges. Safety for Co-CPS enabled by wireless communication technologies is a crucial aspect and requires new dedicated design approaches. In this paper, we provide an overview of five Co-CPS use cases, as introduced in our SafeCOP EU project, and analyze their safety design requirements. Next, we provide a comprehensive analysis of the main existing wireless communication technologies giving details about the protocols developed within particular standardization bodies. We also investigate to what extent they address the non-functional requirements in terms of safety, security and real time, in the different application domains of each use case. Finally, we discuss general recommendations about the use of different wireless communication technologies showing their potentials in the selected real-world use cases. The discussion is provided under consideration in the 5G standardization process within 3GPP, whose current efforts are inline to current gaps in wireless communications protocols for Co-CPSs including many future use cases.

Journal ArticleDOI
16 Apr 2018-Sensors
TL;DR: This work discusses how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS.
Abstract: Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.

Journal ArticleDOI
16 Nov 2018-Sensors
TL;DR: A detailed description of the technology is given, including existing security and reliability mechanisms, and a strengths, weaknesses, opportunities and threats (SWOT) analysis is presented along with the challenges that LoRa and LoRaWAN still face.
Abstract: LoRaWAN is one of the low power wide area network (LPWAN) technologies that have received significant attention by the research community in the recent years. It offers low-power, low-data rate communication over a wide range of covered area. In the past years, the number of publications regarding LoRa and LoRaWAN has grown tremendously. This paper provides an overview of research work that has been published from 2015 to September 2018 and that is accessible via Google Scholar and IEEE Explore databases. First, a detailed description of the technology is given, including existing security and reliability mechanisms. This literature overview is structured by categorizing papers according to the following topics: (i) physical layer aspects; (ii) network layer aspects; (iii) possible improvements; and (iv) extensions to the standard. Finally, a strengths, weaknesses, opportunities and threats (SWOT) analysis is presented along with the challenges that LoRa and LoRaWAN still face.

Journal ArticleDOI
20 Apr 2018-Sensors
TL;DR: This review compares the fundamental concepts behind the quantification of nucleic acids by dPCR and quantitative real-time PCR and examines how isothermal amplification could be an alternative to PCR in digital assays.
Abstract: Digital Polymerase Chain Reaction (dPCR) is a novel method for the absolute quantification of target nucleic acids. Quantification by dPCR hinges on the fact that the random distribution of molecules in many partitions follows a Poisson distribution. Each partition acts as an individual PCR microreactor and partitions containing amplified target sequences are detected by fluorescence. The proportion of PCR-positive partitions suffices to determine the concentration of the target sequence without a need for calibration. Advances in microfluidics enabled the current revolution of digital quantification by providing efficient partitioning methods. In this review, we compare the fundamental concepts behind the quantification of nucleic acids by dPCR and quantitative real-time PCR (qPCR). We detail the underlying statistics of dPCR and explain how it defines its precision and performance metrics. We review the different microfluidic digital PCR formats, present their underlying physical principles, and analyze the technological evolution of dPCR platforms. We present the novel multiplexing strategies enabled by dPCR and examine how isothermal amplification could be an alternative to PCR in digital assays. Finally, we determine whether the theoretical advantages of dPCR over qPCR hold true by perusing studies that directly compare assays implemented with both methods.

Journal ArticleDOI
26 Oct 2018-Sensors
TL;DR: A comprehensive review of 2D materials-based gas sensor is reported, mainly focused on the recent developments of graphene oxide, exfoliated MoS2 and WS2 and phosphorene, for gas detection applications.
Abstract: After the synthesis of graphene, in the first year of this century, a wide research field on two-dimensional materials opens. 2D materials are characterized by an intrinsic high surface to volume ratio, due to their heights of few atoms, and, differently from graphene, which is a semimetal with zero or near zero bandgap, they usually have a semiconductive nature. These two characteristics make them promising candidate for a new generation of gas sensing devices. Graphene oxide, being an intermediate product of graphene fabrication, has been the first graphene-like material studied and used to detect target gases, followed by MoS2, in the first years of 2010s. Along with MoS2, which is now experiencing a new birth, after its use as a lubricant, other sulfides and selenides (like WS2, WSe2, MoSe2, etc.) have been used for the fabrication of nanoelectronic devices and for gas sensing applications. All these materials show a bandgap, tunable with the number of layers. On the other hand, 2D materials constituted by one atomic species have been synthetized, like phosphorene (one layer of black phosphorous), germanene (one atom thick layer of germanium) and silicone (one atom thick layer of silicon). In this paper, a comprehensive review of 2D materials-based gas sensor is reported, mainly focused on the recent developments of graphene oxide, exfoliated MoS2 and WS2 and phosphorene, for gas detection applications. We will report on their use as sensitive materials for conductometric, capacitive and optical gas sensors, the state of the art and future perspectives.

Journal ArticleDOI
15 Mar 2018-Sensors
TL;DR: A systematic review of studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes and indications on the reliability of sensor-based performance indicators are provided.
Abstract: Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.

Journal ArticleDOI
16 Feb 2018-Sensors
TL;DR: This review provides an exhaustive summary of most recent active thermographic methods used for aerospace applications according to their physical principle and thermal excitation sources.
Abstract: Active infrared thermography is a fast and accurate non-destructive evaluation technique that is of particular relevance to the aerospace industry for the inspection of aircraft and helicopters’ primary and secondary structures, aero-engine parts, spacecraft components and its subsystems. This review provides an exhaustive summary of most recent active thermographic methods used for aerospace applications according to their physical principle and thermal excitation sources. Besides traditional optically stimulated thermography, which uses external optical radiation such as flashes, heaters and laser systems, novel hybrid thermographic techniques are also investigated. These include ultrasonic stimulated thermography, which uses ultrasonic waves and the local damage resonance effect to enhance the reliability and sensitivity to micro-cracks, eddy current stimulated thermography, which uses cost-effective eddy current excitation to generate induction heating, and microwave thermography, which uses electromagnetic radiation at the microwave frequency bands to provide rapid detection of cracks and delamination. All these techniques are here analysed and numerous examples are provided for different damage scenarios and aerospace components in order to identify the strength and limitations of each thermographic technique. Moreover, alternative strategies to current external thermal excitation sources, here named as material-based thermography methods, are examined in this paper. These novel thermographic techniques rely on thermoresistive internal heating and offer a fast, low power, accurate and reliable assessment of damage in aerospace composites.

Journal ArticleDOI
24 Aug 2018-Sensors
TL;DR: This paper is presenting an overview about different layered architectures of IoT and attacks regarding security from the perspective of layers, and suggested a new secure layered architecture of IoT to overcome these issues.
Abstract: The use of the Internet is growing in this day and age, so another area has developed to use the Internet, called Internet of Things (IoT). It facilitates the machines and objects to communicate, compute and coordinate with each other. It is an enabler for the intelligence affixed to several essential features of the modern world, such as homes, hospitals, buildings, transports and cities. The security and privacy are some of the critical issues related to the wide application of IoT. Therefore, these issues prevent the wide adoption of the IoT. In this paper, we are presenting an overview about different layered architectures of IoT and attacks regarding security from the perspective of layers. In addition, a review of mechanisms that provide solutions to these issues is presented with their limitations. Furthermore, we have suggested a new secure layered architecture of IoT to overcome these issues.

Journal ArticleDOI
15 Sep 2018-Sensors
TL;DR: Some advanced applications and key sectors of the global fibre-optic strain sensors market are envisaged, as well as the main market players acting in this field.
Abstract: Fibre Bragg grating (FBG) strain sensors are not only a very well-established research field, but they are also acquiring a bigger market share due to their sensitivity and low costs In this paper we review FBG strain sensors with high focus on the underlying physical principles, the interrogation, and the read-out techniques Particular emphasis is given to recent advances in highly-performing, single head FBG, a category FBG strain sensors belong to Different sensing schemes are described, including FBG strain sensors based on mode splitting Their operation principle and performance are reported and compared with the conventional architectures In conclusion, some advanced applications and key sectors the global fibre-optic strain sensors market are envisaged, as well as the main market players acting in this field

Journal ArticleDOI
Yan Liu1, Hai Wang1, Wei Zhao1, Min Zhang1, Qin Hongbo1, Xie Yongqiang1 
22 Feb 2018-Sensors
TL;DR: This review attempts to summarize the recent progress in flexible and stretchable sensors, concerning the detected health indicators, sensing mechanisms, functional materials, fabrication strategies, basic and desired features.
Abstract: Wearable health monitoring systems have gained considerable interest in recent years owing to their tremendous promise for personal portable health watching and remote medical practices. The sensors with excellent flexibility and stretchability are crucial components that can provide health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous uncomfortableness and invasiveness. The signals acquired by these sensors, such as body motion, heart rate, breath, skin temperature and metabolism parameter, are closely associated with personal health conditions. This review attempts to summarize the recent progress in flexible and stretchable sensors, concerning the detected health indicators, sensing mechanisms, functional materials, fabrication strategies, basic and desired features. The potential challenges and future perspectives of wearable health monitoring system are also briefly discussed.

Journal ArticleDOI
18 Oct 2018-Sensors
TL;DR: An overview of the state-of-the-art in evanescent field biosensing technologies including interferometer, microcavity, photonic crystal, and Bragg grating waveguide-based sensors, as well as real biomarkers for label-free detection are exhibited and compared.
Abstract: Thanks to advanced semiconductor microfabrication technology, chip-scale integration and miniaturization of lab-on-a-chip components, silicon-based optical biosensors have made significant progress for the purpose of point-of-care diagnosis In this review, we provide an overview of the state-of-the-art in evanescent field biosensing technologies including interferometer, microcavity, photonic crystal, and Bragg grating waveguide-based sensors Their sensing mechanisms and sensor performances, as well as real biomarkers for label-free detection, are exhibited and compared We also review the development of chip-level integration for lab-on-a-chip photonic sensing platforms, which consist of the optical sensing device, flow delivery system, optical input and readout equipment At last, some advanced system-level complementary metal-oxide semiconductor (CMOS) chip packaging examples are presented, indicating the commercialization potential for the low cost, high yield, portable biosensing platform leveraging CMOS processes

Journal ArticleDOI
08 Mar 2018-Sensors
TL;DR: The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks.
Abstract: The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or "things" to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.

Journal ArticleDOI
30 Jun 2018-Sensors
TL;DR: This paper describes an energy consumption model based on LoRa and LoRaWAN, which allows estimating the consumed power of each sensor node element and can be used to compare different Lo RaWAN modes to find the best sensor node design to achieve its energy autonomy.
Abstract: Energy efficiency is the key requirement to maximize sensor node lifetime. Sensor nodes are typically powered by a battery source that has finite lifetime. Most Internet of Thing (IoT) applications require sensor nodes to operate reliably for an extended period of time. To design an autonomous sensor node, it is important to model its energy consumption for different tasks. Each task consumes a power consumption amount for a period of time. To optimize the consumed energy of the sensor node and have long communication range, Low Power Wide Area Network technology is considered. This paper describes an energy consumption model based on LoRa and LoRaWAN, which allows estimating the consumed power of each sensor node element. The definition of the different node units is first introduced. Then, a full energy model for communicating sensors is proposed. This model can be used to compare different LoRaWAN modes to find the best sensor node design to achieve its energy autonomy.

Journal ArticleDOI
12 Jan 2018-Sensors
TL;DR: A region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity, which outperforms the state of the art.
Abstract: Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.

Journal ArticleDOI
24 Feb 2018-Sensors
TL;DR: This paper proposes an evaluation framework allowing a rigorous comparison of features extracted by different methods, and uses it to carry out extensive experiments with state-of-the-art feature learning approaches and provides all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of the framework easier for other researchers.
Abstract: Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.

Journal ArticleDOI
04 Sep 2018-Sensors
TL;DR: The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process and that the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input.
Abstract: With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process

Journal ArticleDOI
18 May 2018-Sensors
TL;DR: From these results, two new sets of recommended EMG features (along with a novel feature, L-scale) are identified that provide better performance for these emerging low-sampling rate systems.
Abstract: Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly ( p.

Journal ArticleDOI
03 Mar 2018-Sensors
TL;DR: It can be concluded that it is necessary to evaluate the propagation conditions of the deployment scenario prior to the system implantation in order to reach a compromise between the robustness of the network and the transmission data-rate.
Abstract: New verticals within the Internet of Things (IoT) paradigm such as smart cities, smart farming, or goods monitoring, among many others, are demanding strong requirements to the Radio Access Network (RAN) in terms of coverage, end-node’s power consumption, and scalability. The technologies employed so far to provide IoT scenarios with connectivity, e.g., wireless sensor network and cellular technologies, are not able to simultaneously cope with these three requirements. Thus, a novel solution known as Low Power - Wide Area Network (LP-WAN) has emerged as a promising alternative to provide with low-cost and low-power-consumption connectivity to end-nodes spread in a wide area. Concretely, the Long-Range Wide Area Network (LoRaWAN) technology is one of the LP-WAN platforms that is receiving greater attention from both the industry and the academia. For that reason, in this work, a comprehensive performance evaluation of LoRaWAN under different environmental conditions is presented. The results are obtained from three real scenarios, namely, urban, suburban, and rural, considering both dynamic and static conditions, hence a discussion about the most proper LoRaWAN physical-layer configuration for each scenario is provided. Besides, a theoretical coverage study is also conducted by the use of a radio planning tool considering topographic maps and a precise propagation model. From the attained results, it can be concluded that it is necessary to evaluate the propagation conditions of the deployment scenario prior to the system implantation in order to reach a compromise between the robustness of the network and the transmission data-rate.

Journal ArticleDOI
02 Apr 2018-Sensors
TL;DR: This paper proposes an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention, used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels.
Abstract: Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.