Different Kinds Of Sensors Research Articles

Monitoring climatic impacts on the moisture uptake of the first Swiss wildlife bridge made of wood

Wildlife bridges represent a major component of any sustainable strategy to counteract the negative consequences of cutting the natural habitat of wildlife into disconnected parts by motorways and rail. This is especially true for a small and densely populated country such as Switzerland with diverse wildlife scattered through its territory. Up to now all wildlife bridges in Switzerland have been made of concrete with steel reinforcement. The wildlife bridge under investigation here is the first one in Switzerland made of wood-based materials. The idea of building a wildlife bridge by using wood-based materials represents besides a challenging technological endeavor also an ecological progress regarding embodied energy. A further advantage which resulted after realizing the construction, was the short interruption time it needed for installation on a motorway in operation. The most urgent question with respect to the wood-based elements is their moisture uptake when subjected to weather conditions. The present paper reports on a long-term monitoring of this wooden wildlife bridge over a period of approximately 2 years. Different kind of sensors, data from a nearby meteorological station, data regarding hourly number of different kind of vehicles passing beneath the wildlife bridge as well as lab measurements have been used to enable a robust and reliable statement on wooden wildlife bridges subjected to Swiss flatland weather conditions.

Structural health monitoring of a lenticular truss bridge: a comprehensive study

The combined use of finite element modeling and structural health monitoring is becoming increasingly relevant in bridge maintenance. A growing number of new and old structures is instrumented with different kinds of sensors driven by dedicated hardware and software, while numerical models are used to simulate the response of the structures under various scenarios. This paper presents the monitoring of the Smithfield Street Bridge, one of the oldest and most iconic bridges in the city of Pittsburgh (Pennsylvania, USA). The bridge was instrumented by an independent party, not involved with the research presented here, with strain, displacement, and rotation wireless sensors. A detailed finite element model of one of the spans of the bridge was developed to calculate the strains induced by standardized trucks under pristine and simulated damage conditions. In addition, the model enabled to determine the sensitivity necessary to detect relevant structural changes in the bridge. The results of the numerical analyses were then compared with the results of a test in which a truck of known weight crossed the bridge multiple times. Finally, the data relative to 3 years of uninterrupted monitoring were processed and analyzed to identify eventual anomalies.

A Comprehensive Study of IoT Enabled Smart Grid

Systems that are used for monitoring and controlling are known as supervisory control and data acquisition systems. Not the entire control permissions, but rather the supervisory level is the primary focus of this. Several different kinds of sensors are being utilized in order to collect data in real-time. What is the Internet of Things (IoT)? It is a three-dimensional connectivity that can be used for anything, at any time, in any location. The comparison between the SCADA system and the Internet of Things is carried out in this study. In addition, this section of the study focused on the benefits of the Internet of Things (IoT) and offered some suggestions for integrating the IoT with the SCADA system.

Industrial Internet of Things: An Analysis of Emergence, Component and Challenges

Industry 4.0 brought a solution to disrupted industrial manufacturing powered by AI, machine learning, IoT, Cloud Computing, and Wireless Technologies. The factories turn into smart industrial units where the processes are automated, self-reliant, continuously monitored, and scaled as per the observed conditions. The Industrial Internet of Things (IIoT) solves the issues by real- time decision making or even alarming the hazardous situations say breaking down of systems or processes based on predictive analytics. From the raw material to the customer satisfaction the whole production line turns intelligent making the production fast, quality-based, well maintained, monitored, and innovative. The 3D printing, simulation, and augmented reality encourage innovations, designing & testing of customized products in the virtual environment. Big data analytics and accurate prediction not only foster manufacturing quality & quantity but also attracts high-valued staff with increased efficiency. Getting trustworthy data in real-time and eliminating life hazards in monitoring the typical processes are the key attractions of Industry 4.0 for instance, IoT with different kinds of sensors replace human monitoring and even performs well where the humans cannot go like the radioactive, humidity pressure. The product is tested well, safer to use, and promoted to the areas predicted by the advanced computing algorithms in data analytics. However, emerging technology can pose challenges for employers in a country where unemployment exists. So technological advancements, training, and skill developments are urgent areas to look after. This paper presented literature survey focuses on the major components, characteristics and associated challenges of Industrial IoT.

Spiking neural network based heterogeneous federal filter method for sensor failure in multi-actuator systems

A Spiking neural network based heterogeneous federal filter method is proposed in this paper to deal with sensor failure problems in multi actuator systems. Firstly, an accurate dynamic model of multi-motor system is built, which can provide a high precision estimated state information in viral system state simulator. Secondly, a heterogeneous federal filter is proposed to provide a stable and accurate state feedback whether the sensor is normal or not. This filter collects real-time measured state data from different kinds of sensors and the estimated state information, and gives out an filtered state output for each sensor according the spiking neural network based reliability diagnosis module. By this way, the noise and disturbance can be eliminated. When one or more sensors break down, an estimated accurate state feedback can be obtained. Finally, this heterogeneous federal filter is applied into an four-motor servo system. The experiment results indicate that: the spiking neural network based reliability diagnosis module could quickly monitor sensor status accurately and the heterogeneous federal filter could handle sensor failures with control accuracy remained the normal level.

Self-Supervised Learning of Depth and Ego-Motion for 3D Perception in Human Computer Interaction

3D perception of depth and ego-motion is of vital importance in intelligent agent and Human Computer Interaction (HCI) tasks, such as robotics and autonomous driving. There are different kinds of sensors that can directly obtain 3D depth information. However, the commonly used Lidar sensor is expensive, and the effective range of RGB-D cameras is limited. In the field of computer vision, researchers have done a lot of work on 3D perception. While traditional geometric algorithms require a lot of manual features for depth estimation, Deep Learning methods have achieved great success in this field. In this work, we proposed a novel self-supervised method based on Vision Transformer (ViT) with Convolutional Neural Network (CNN) architecture, which is referred to as ViT-Depth . The image reconstruction losses computed by the estimated depth and motion between adjacent frames are treated as supervision signal to establish a self-supervised learning pipeline. This is an effective solution for tasks that need accurate and low-cost 3D perception, such as autonomous driving, robotic navigation, 3D reconstruction, and so on. Our method could leverage both the ability of CNN and Transformer to extract deep features and capture global contextual information. In addition, we propose a cross-frame loss that could constrain photometric error and scale consistency among multi-frames, which lead the training process to be more stable and improve the performance. Extensive experimental results on autonomous driving dataset demonstrate the proposed approach is competitive with the state-of-the-art depth and motion estimation methods.

On the Uncertainty Analysis of the Data-Enabled Physics-Informed Neural Network for Solving Neutron Diffusion Eigenvalue Problem

We performed uncertainty analysis and further numerical studies on the data-enabled physics-informed neural network (DEPINN). The purpose of DEPINN is to accurately and efficiently use a small amount of prior data to solve the neutron diffusion eigenvalue equations based on the physics-informed neural network. However, in practical engineering experiments, these prior data are acquired through different kinds of sensors, which are inevitably polluted by noise. Numerical results of three typical benchmark problems show that the classical DEPINN is not so robust with respect to noise. To improve the noise robustness, we propose an interval loss function to deal with the noisy prior data term; the weight of the noisy prior data term is also set to be noise dependent. Numerical results show that the proposed framework effectively enhances the robustness of DEPINN and improves the efficiency of utilizing the noisy prior data and thus promotes the engineering application of DEPINN.

Overview of the platform for experimentation and awareness-raising on fire risks at wildland urban interfaces (EXPLORII platform)

The aim of this article is to present a new experimental platform built by the University of Corsica in order to study the vulnerability of buildings to fires occurring at wildland urban interfaces. The platform is made up of a slope 10 m long by 6 m wide with an inclination of 20° and a flat surface located at its top hosting an instrumented single-storey house. The platform is equipped with different kinds of sensors: meteorological station, anemometers, load cell, heat flux gauges, thermocouples, cameras and smoke chemicals analysers. They allow obtaining experimental data on the burning of fuels and on the impact of fire on the buildings. Having a full-scale platform for studying fires at the wildland-urban interface (WUI) is important, particularly in terms of minimizing damage. By recreating realistic conditions and complex interactions between wildland fires, ornamental vegetation and residential areas, researchers can analyse and understand the mechanisms of fire spread in these vulnerable environments. In addition, a platform such as this enables testing and assessment of prevention and protection measures, such as fire-resistant building materials. Two experiments have been carried out to commission the platform with Douglas fir trees and test the experimental device. They have highlighted the protective effect of shutters, but also the role of double glazing as a thermal screen. The analysis of these first results has enabled improvements to be proposed to the experimental facility in order to better quantify the aerology and the heat fluxes impacting the building.

Integrity assured multi-functional multi-application secure data aggregation in wireless sensor networks (IAMFMA-SDA)

Industrial revolutions and demand of novel applications drive the development of sensors which offer continuous monitoring of remote hostile areas by collecting accurate measurement of physical phenomena. Data aggregation is considered as one of the significant energy-saving mechanism of resource constraint Wireless Sensor Networks (WSNs) which reduces bandwidth consumption by eliminating redundant data. Novel applications demand WSN to provide information about the monitoring region in multiple aspects in large scale. To meet this requirement, different kinds of sensors of different parameters are deployed in the same region which in turn demands the aggregator node to integrate diverse data in a smooth and secure manner. Novelty in applications also requires Base station (BS) to apply multiple statistical functions. Hence, we propose to develop a novel secure cost-efficient data aggregation scheme based on asymmetric privacy homomorphism to aggregate data of multiple parameters and facilitate the BS to compute multiple functions in one round of data collection by providing elaborated view of monitoring region. To meet the claim of large scale WSN which requires dynamic change in size, vector-based data collection method is adopted in our proposed scheme. The security aspect is strengthened by allowing BS to verify the authenticity of source node and validity of data received. The performance of the system is analyzed in terms of computation and communication overhead using the mathematical model and simulation results.

Comparison of Acoustic Performance of Different Sensors for the Purposes of On-Body Auscultation in Noisy Environments

Lung auscultation is a widely used clinical tool for quick physical assessment of the respiratory system. The ideal sensor preferable for lung auscultation would have high sensitivity to body-conducted sounds with a good resilience to airborne noise. This work envisions to benchmark different sensors starting from air coupled microphones, a bone conduction microphone and PVDF (Polyvinylidene difluoride) sensors on a small chest simulator in a controlled environment. The purpose of this work is to assess the suitability of different sensors, which could be used for lung auscultation both in silent and noisy environments. The proposed work evaluates the performance of different kinds of sensors in terms of their frequency response, noise resilience and a quality metric that expresses sensor’s robustness to airborne noise.

High-Performance Perception: A camera-based approach for smart autonomous electric vehicles in smart cities

Mobility is fundamental for the wealth and health of the world’s population, and it has a significant influence on our daily life. However, with the increasing complexity of traffic, the need to transport goods, and growing urbanization, improving the quality of mobility in terms of time, space, and air becomes more challenging. An autonomous electric vehicle offers technology and potential for new mobility concepts in smart cities. Today, many vehicles have been developed with automated driving capabilities. A safety driver is still required to intervene in most cases if the autonomous electric vehicle is not able to handle a situation in a safe and, at the same time, reliable way. One important aspect to achieve safety and reliability goals is a robust and efficient perception of the vehicle’s environment. The tragic accident involving an Uber self-driving car killing a pedestrian in 2018 highlighted the importance of perception in autonomous driving. In its investigation, the U.S. National Transportation Safety Board found that the Uber self-driving car and its safety driver involved in the accident failed to detect the pedestrian at the same time. Since this accident, the autonomous vehicle industry has been working to improve perception systems through the use of advanced sensors, machine learning algorithms, and other technologies. A variety of sensor technologies are used in vehicles to detect objects and perceive the vehicles’ surroundings. Cameras and radars are among the widely used technologies for sensing systems, as they are cheap and reliable, and as these sensors are operating at different wavelengths, they are not susceptible to common errors. While some companies solely use cameras and radars, others also use lidars in their sensing systems. These sensors are widely used in the industry, and the technology itself is continuously enhanced, leading to new developments, such as 4D lidar, which are capable of measuring not only the distance of an object but also its velocity by evaluating the phase shift of the returned light. Various methods for the perception of the environment exist and rely on different kinds of sensors. Machine learning-based methods have evolved rapidly in recent years and are currently leading the field in perception, particularly in the tasks of object detection and classification.

Near real-time monitoring of tropical forest disturbance by fusion of Landsat, Sentinel-2, and Sentinel-1 data

The ability to detect forest disturbance rapidly relies heavily on the frequency of usable satellite observations. Combining optical and SAR data can provide the most usable observations in the tropics and hence has the potential to achieve more timely detection of forest disturbance than using any individual sensor system. A new algorithm (Fusion Near Real-Time, or FNRT) for near real-time monitoring of tropical forest disturbance by fusion of Landsat, Sentinel-2, and Sentinel-1 data fits a time series model to the data of each individual sensor system and produces a change score for each clear observation during the monitoring period. The change scores from the three different kinds of sensors are then combined for disturbance monitoring. Applying FNRT using all three data sources in eight selected sites in the Amazon Basin detected 69.8 ± 5% and 84.6 ± 4.1% of the forest disturbances within the first 30 and 60 days, respectively. FNRT using all three sensors reached a peak producer's accuracy of 91.6 ± 3.2% at approximately a 100-day lag time, compared to 86.4 ± 3.8% using the two optical sensors combined, and 71.7 ± 4.7% using Landsat only (the best-performing single-sensor). The user's accuracy of FNRT using all three sensors increased to 90.9 ± 3.3% when excluding false detections in non-forest areas. Overall, the model result using all three data sources had the highest detection rate with shorter lag times. Although modest, including Sentinel-1 data in the multi-sensor fusion approach improved the detection speed of some disturbance events. Given the abundance of optical data during the dry season in our study region, Sentinel-1 data would be more valuable during the wet season or in persistently cloudy regions such as Madagascar or coastal West Africa.

IoT Based Animal Health Monitoring System using Raspberry Pi

IoT has bought many changes in the existing technologies, where its applications are numerous in the field of industry automation, bio-medical, agriculture, transport, smart cities etc. The key benefits of IoT enabled livestock management in real time is enabling farmers to quickly treat animals and prevent the spread of illness or disease, track gazing animals to prevent loss and to identify grazing patterns, gather and analyze historical data to identify trends in cattle health or to track the spread of illness, monitor readiness to give birth , preventing the loss of new calves and optimizing breeding practices. In this paper a smart animal health monitoring system is developed for monitoring the parameters such as body temperature, heart rate and rumination pattern has been monitored. Different kind of sensors are mounted on the body of animals and related information is gathered which can be accessed by the authorized person through internet. Raspberry Pi is used in this work, where different information is gathered from the sensors and placed on to the cloud so that the information can be accessed from the mobile using internet.

Parachute shape ultra‐wideband wearable antenna for remote health care monitoring

SummaryWireless body area networks (WBAN) is used to measure patients' health conditions continuously. Different kinds of sensors are required to measure health conditions. When such types of antennas are used on the human body, they are flexible with the movements. The usage of wearable devices is currently increasing in the biomedical field. The presented wearable antenna is suitable for biomedical applications. The presented ultra‐wideband (UWB) flexible parachute shape wearable antenna is fabricated on a jeans textile substrate. The prototype antenna has a −10 dB measured impedance bandwidth of 5800 MHz (7 to 12.8 GHz) with average radiation efficiency of 75.28%. The prototype antenna's size is 40 × 40 mm2 (1.32 × 1.32 at centre frequency 9.9 GHz) and a peak gain of 4.5 dB at 12.33 GHz. The fabricated antenna is suitable for biomedical applications in X‐band frequencies and can be implemented with a low‐cost manufacturing process. The radiating element is made by conductive copper tape. Muscle‐equivalent phantoms are used to analyze the body effect on antenna performance. The radiation effect emitted by the presented antenna on the human body is calculated by the specific absorption rate (SAR) value. The maximum SAR value of the proposed antenna is 1.84 W/kg at 12.33 GHz. This leads to promising results for wearable applications related to remote health care monitoring, such as biotelemetry and mobile health with a sensor‐driven approach.

School Vehicle Safety System

School vehicle safety system (SVSS) is a solution to ensure school transportation safety. Schools must provide safe transportation facilities for students. One of the reasons for vehicle accidents is the negligence of driving the school bus due to drunk driving, improper checking of the conditions of vehicle doors, and negligence while driving the vehicle. The proposed system enables various safety measures to avoid accidents. The system provides the real-time tracking location of the vehicle to ensure the safety of school kids. Different kinds of sensors are used for this purpose such as Alcohol sensors (MQ3), Halleffect sensors (KY024), and PIR motion sensors. If the driver consumed alcohol it senses the alcohol concentration Door open detection uses a halleffect sensor, and PIR motion sensor to detect the school kids at the ventral side of the vehicle. Before moving the vehicle.It alerts the driver and alerts the school administration using the blink server it helps the schools to check and manage the behaviour of the driver.

Bearing Fault Diagnosis Based on Multisensor Information Coupling and Attentional Feature Fusion

The effective fault diagnosis of bearing can guarantee the safety of rotating machinery and is very important for its stable operation. The information fusion of multi-sensor data has been a feasible method to enhance the performance of fault diagnosis. However, how to fuse the joint information from different channels or even different kinds of sensors is still an important challenge. The present study proposes a novel multi-sensor information coupling network (MICN) for bearing fault diagnosis, which handles the signals from the same or different types of sensors, and the deeper features can be extracted from multi-sensors independently and simultaneously fused layer by layer. Especially, during the multi-layer feature fusion process, a novel feature-level information coupling model is developed based on the mutual attention mechanism. Finally, to validate the efficiency of the proposed method, several different experiments are designed, and the results show validity and superiority.

MobiWise: Eco-routing decision support leveraging the Internet of Things

Eco-routing distributes traffic in cities to improve mobility sustainability. The implementation of eco-routing in real-life requires a diverse set of information, including different kinds of sensors. These sensors are often already integrated in city infrastructure, some are technologically outdated, and are often operated by multiple entities. In this work, we provide a use case-oriented system design for an eco-routing service leveraging Internet-of-Things (IoT) technologies. The methodology involves six phases: (1) defining an eco-routing use case for a vehicle fleet; (2) formulating a routing problem as a multi-objective optimisation to divert traffic at a relevant hub facility; (3) identifying data sources and processing required information; (4) proposing a microservice-based architecture leveraging IoT technologies adequate to a multi-stakeholder scenario; (5) applying a microscopic traffic simulator as a digital twin to deal with data sparsity; and (6) visually illustrating eco-routing trade-offs to support decision making. We built a proof-of-concept for a mid-sized European city. Using real data and a calibrated digital twin, we would achieve hourly total emissions reductions up to 2.1%, when applied in a car fleet composed of 5% of eco-routing vehicles. This traffic diversion would allow annual carbon dioxide and nitrogen oxides savings of 400 tons and 1.2 tons, respectively.

On the Impact of Multimodal and Multisensor Biometrics in Smart Factories

Smart factories are fostered by integrating intelligent systems and ICT technologies. The role they play is crucial in the spread of Industry 4.0 and the economic growth of developed countries. Smart factories can be empowered by using several sensors aimed at making them more and more “smart.” Unfortunately, work accidents are still very common resulting in human losses and permanent injuries. This makes it urgent and key to implementing security and safety measures also in the context of a smart factory. In this article, a novel framework for supporting smart devices in a smart factory, using multiple sensors to monitor different biometric features, both physical and behavioral is proposed. Thanks to the fusion of several biometric traits with the support of machine learning technologies working together with different kinds of sensors, it is possible to guarantee three fundamental aspects within the interaction between an operator of a smart device and the device itself: continuous authentication (i.e., continuous face recognition), drowsiness detection, and liveness detection. With the application of the proposed framework, it is possible to significantly improve the safety of operators avoiding fatal accidents for them. Experiments made using COTS-hardware showed that the authors’ idea is easy to implement in a large-scale smart factory and further improves the spread of Industry 4.0.

Multisensor Data Fusion by Means of Voxelization: Application to a Construction Element of Historic Heritage

Point clouds are very common tools used in the work of documenting historic heritage buildings. These clouds usually comprise millions of unrelated points and are not presented in an efficient data structure, making them complicated to use. Furthermore, point clouds do not contain topological or semantic information on the elements they represent. Added to these difficulties is the fact that a variety of different kinds of sensors and measurement methods are used in study and documentation work: photogrammetry, LIDAR, etc. Each point cloud must be fused and integrated so that decisions can be taken based on the total information supplied by all the sensors used. A system must be devised to represent the discrete set of points in order to organise, structure and fuse the point clouds. In this work we propose the concept of multispectral voxels to fuse the point clouds, thus integrating multisensor information in an efficient data structure, and applied it to the real case of a building element in an archaeological context. The use of multispectral voxels for the fusion of point clouds integrates all the multisensor information in their structure. This allows the use of very powerful algorithms such as automatic learning and machine learning to interpret the elements studied.

Mechanical Sensors for Cardiovascular Monitoring: From Battery-Powered to Self-Powered.

Cardiovascular disease is one of the leading causes of death worldwide. Long-term and real-time monitoring of cardiovascular indicators is required to detect abnormalities and conduct early intervention in time. To this end, the development of flexible wearable/implantable sensors for real-time monitoring of various vital signs has aroused extensive interest among researchers. Among the different kinds of sensors, mechanical sensors can reflect the direct information of pressure fluctuations in the cardiovascular system with the advantages of high sensitivity and suitable flexibility. Herein, we first introduce the recent advances of four kinds of mechanical sensors for cardiovascular system monitoring, based on capacitive, piezoresistive, piezoelectric, and triboelectric principles. Then, the physio-mechanical mechanisms in the cardiovascular system and their monitoring are described, including pulse wave, blood pressure, heart rhythm, endocardial pressure, etc. Finally, we emphasize the importance of real-time physiological monitoring in the treatment of cardiovascular disease and discuss its challenges in clinical translation.