Multi-sensor Platform Research Articles

Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation.

The growth of location-based services (LBS) has increased rapidly in last years, mainly due to the possibility to exploit low-cost sensors installed in portable devices, such as smartphones and tablets. This work aims to show a low-cost multi-sensor platform developed by the authors in which an ultra-wideband (UWB) indoor positioning system is added to a classical global navigation satellite systems–inertial navigation system (GNSS-INS) integration, in order to acquire different synchronized data for further data fusion analysis in order to exploit seamless positioning. The data fusion is based on an extended Kalman filter (EKF) and on a geo-fencing approach which allows the navigation solution to be provided continuously. In particular, the proposed algorithm aims to solve a navigation task of a pedestrian user moving from an outdoor space to an indoor environment. The methodology and the system setup is presented with more details in the paper. The data acquired and the real-time positioning estimation are analysed in depth and compared with ground truth measurements. Particular attention is given to the UWB positioning system and its behaviour with respect to the environment. The proposed data fusion algorithm provides an overall horizontal and 3D accuracy of 35 cm and 45 cm, respectively, obtained considering 5 different measurement campaigns.

Protocol for PD SENSORS: Parkinson’s Disease Symptom Evaluation in a Naturalistic Setting producing Outcome measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson’s disease

IntroductionThe impact of disease-modifying agents on disease progression in Parkinson’s disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability...

Textile sensors platform for the selective and simultaneous detection of chloride ion and pH in sweat

The development of wearable sensors, in particular fully-textile ones, is one of the most interesting open challenges in bioelectronics. Several and significant steps forward have been taken in the last decade in order to achieve a compact, lightweight, cost-effective, and easy to wear platform for healthcare and sport activities real-time monitoring. We have developed a fully textile, multi-thread biosensing platform that can detect different bioanalytes simultaneously without interference, and, as an example, we propose it for testing chloride ions (Cl−) concentration and pH level. The textile sensors are simple threads, based on natural and synthetic fibers, coated with the conducting polymer poly(3,4-ethylenedioxythiophene):poly(styrene-sulfonate) (PEDOT:PSS) and properly functionalized with either a nano-composite material or a chemical sensitive dye to obtain Cl− and pH selective sensing functionality, respectively. The single-thread sensors show excellent sensitivity, reproducibility, selectivity, long term stability and the ability to work with small volumes of solution. The performance of the developed textile devices is demonstrated both in buffer solution and in artificial human perspiration to perform on-demand and point-of-care epidermal fluids analysis. The possibility to easily knit or sew the thread sensors into fabrics opens up a new vision for a textile wearable multi-sensing platform achievable in the near future.

Data fusion approach for map-based variable-rate nitrogen fertilization in barley and wheat

Nitrogen (N) is a main parameter affecting crop growth and yield. However, when variable rate N fertilization (VRNF) is adopted, a main question arises of whether to add more on the highly fertile zone or on the less fertile zone. This study aimed to compare two map-based VRNF treatments, e.g., variable-rate 1 (VRNF1) with the application of recommended rate (RR) of N fertilizer plus 40-50% to the high fertility zones, RR to the medium-fertility zones and RR minus 40-50% to the least fertile zones, and VRNF2 following the opposite scenario to that of VRNF1. Recommendations for both VRNF treatments were derived from the fusion of soil data and crop data, e.g., normalized difference vegetation index (NDVI) and historical yield and were compared with traditional uniform rate (UR) application. Online soil measurement was carried out in two fields in Belgium, using a multi-sensor platform that collects high-resolution data on key soil properties using a fiber type visible and near-infrared (vis-NIR) spectrophotometer. The vis-NIR sensor was calibrated using a partial least squares regression (PLSR) to predict pH, organic carbon (OC), moisture content (MC), calcium (Ca), sodium (Na), magnesium (Mg), phosphorous (P) and potassium (K). The predicted soil properties were fused with NDVI and historical yield data to delineate management zone (MZ) maps with different fertility classes. A cost-benefit analysis was carried out using the cost of fertilizer applied as input cost and the price of barley and wheat in 2019. Results showed that the VRNF2 treatment was more profitable compared to both VRNF1 (11.55 and 19.52 EUR ha-1) and UR (16.40 and 148.78 EUR ha-1). The VRNF2 treatment maintained or increased crop yield by up to 10.4% and reduced the amount of N fertilizer applied by up to 19.4%, compared to the traditional UR. Because of the lower amount of N fertilizer used compared to the other two treatments, the VRNF2 would reduce the negative environmental impact by reducing the amount of N fertilizer applied. Therefore, we recommend the VRNF2 based-on data fusion of soil and crop parameters as the best approach to manage N fertilization site specifically.

Process‐Level Evaluation of a Hyper‐Resolution Forest Snow Model Using Distributed Multisensor Observations

AbstractThe complex dynamics of snow accumulation and melt processes under forest canopies entail major observational and modeling challenges, as they vary strongly in space and time. In this study, we present novel data sets acquired with mobile multisensor platforms in subalpine and boreal forest stands. These data sets include spatially and temporally resolved measurements of shortwave and longwave irradiance, air and snow surface temperatures, wind speed, and snow depth, all coregistered to canopy structure information. We then apply the energy balance snow model FSM2 to obtain concurrent, distributed simulations of the forest snowpack at very high (“hyper”) resolution (2 m). Our data sets allow us to assess the performance of alternative canopy representation strategies within FSM2 at the level of individual snow energy balance components and in a spatially explicit manner. We demonstrate the benefit of accounting for detailed spatial patterns of shortwave and longwave radiation transfer through the canopy and show the importance of describing wind attenuation by the canopy using stand‐scale metrics. With the proposed canopy representation, snowmelt dynamics in discontinuous forest stands were successfully reproduced. Hyper‐resolution simulations resolving these effects provide an optimal basis for assessing the snow‐hydrological impacts of forest disturbances and for validating and improving the representation of forest snow processes in land surface models intended for coarser‐scale applications.

COMBINATION OF THE PHOTOGRAMMETRIC AND MICROWAVE REMOTE SENSING FOR CULTURAL HERITAGE DOCUMENTATION AND PRESERVATION – PRELIMINARY RESULTS

Abstract. Nowadays, cultural heritage is an integral part of modern societies and it is necessary to preserve the tangible and intangible evidences of the past. Cultural heritage objects and sites are being devastated by natural causes and human acts. There are many existing techniques for structural health monitoring, but in the cultural heritage area, there is a need for a non-destructive measurements. The main advantages and drawbacks of some of these technologies have been presented for both laser scanner and photogrammetry techniques. The aim of this article is to present the integration of existing methods for spatial documentation (classical surveying, laser scanner, photogrammetry) and structures health monitoring using electromagnetic spectroscopy. The multi-sensor platform was developed to characterise and analyse various building materials such as marble, sandstone and bricks. The spatial techniques were used for generation of architectural documentation and geolocalisation of the multi-sensor platform. The preliminary results demonstrated that the proposed technology enables commonly used image and rage-based surface measurements as well as measurements through the entire structure for more detailed analysis.

SEMANTIC SCENE UNDERSTANDING FOR THE AUTONOMOUS PLATFORM

Abstract. In this paper we describe a new multi-sensor platform for data collection and algorithm testing. We propose a couple of methods for solution of semantic scene understanding problem for land autonomous vehicles. We describe our approaches for automatic camera and LiDAR calibration; three-dimensional scene reconstruction and odometry calculation; semantic segmentation that provides obstacle recognition and underlying surface classification; object detection; point cloud segmentation. Also, we describe our virtual simulation complex based on Unreal Engine, that can be used for both data collection and algorithm testing. We collected a large database of field and virtual data: more than 1,000,000 real images with corresponding LiDAR data and more than 3,500,000 simulated images with corresponding LiDAR data. All proposed methods were implemented and tested on our autonomous platform; accuracy estimates were obtained on the collected database.

CHARACTERIZATION OF A MOBILE MAPPING SYSTEM FOR SEAMLESS NAVIGATION

Abstract. Mobile Mapping Systems (MMS) are multi-sensor technologies based on SLAM procedure, which provides accurate 3D measurement and mapping of the environment as also trajectory estimation for autonomous navigation. The major limits of these algorithms are the navigation and mapping inconsistence over the time and the georeferencing of the products. These issues are particularly relevant for pose estimation regardless the environment like in seamless navigation. This paper is a preliminary analysis on a proposed multi-sensor platform integrated for indoor/outdoor seamless positioning system. In particular the work is devoted to analyze the performances of the MMS in term of positioning accuracy and to evaluate its improvement with the integration of GNSS and UWB technology. The results show that, if the GNSS and UWB signal are not degraded, using the correct weight to their observations in the Stencil estimation algorithm, is possible to obtain an improvement in the accuracy of the MMS navigation solution as also in the global consistency of the final point cloud. This improvement is measured in about 7 cm for planimetric coordinate and 34 cm along the elevation with respect to the use of the Stencil system alone.

Allocation of Radiation Shielding Boards to Protect the Urban Search and Rescue Robots from Malfunctioning in the Radioactive Environments Arising from Decommissioning of the Nuclear Facility

A group of Urban Search and Rescue (USAR) Unmanned Ground Vehicles (UGVs) works in the city’s dangerous places that are caused by natural disasters or the decommissioning of nuclear facilities such as the nuclear power plant. Consider the multi sensor platform that the USAR UGV is equipped with, protecting the sensors from the danger that dwells in the working environments is highly related to the success of the USAR mission. The radioactive working environments due to the diffusion of the radioactive materials during the decommissioning of the nuclear facilities are proposed in this work. Radiation shielding boards are used to protect the sensors by installing them on the UGV. Generally, the more shielding boards installed on the UGV, the lower the probability of the sensor malfunctioning. With a large number of UGVs that are used in missions, a large number of shielding boards are needed. Considering the cost of the boards, a conflict between the finite budget and the best protection for the UGV swarm occurs. Due to differences in environments and working times, an asymmetric allocation of shielding boards might protect the UGV swarm better than that by allocating evenly. To minimize the absorption radiation doses, two methods including competitive equilibrium analysis and integer linear programming are proposed. The performances that result from the allocation of the shielding boards by two methods with different budgets are studied.

Multiple Synchronized Inertial Measurement Unit Sensor Boards Platform for Activity Monitoring

The synchronization between wireless sensor nodes is a big issue in all those low-cost systems in which each node performs measurements using its own sensors and stores the measured values locally. In the absence of synchronization, it is impossible to relate all the measurements coming from the different sub-systems on a single time scale for the extraction of complex features. The importance of this issue is even bigger when the sensors' sampling rate is high as for the Inertial Measurement Unit platforms. The paper addresses the issue of managing a large multi-sensor platform for off-line activities with a reduced sensor node complexity and guaranteeing the perfect synchronization for each acquired sample. The new simple and light algorithm proposed overcomes the limits of the commonly used protocols and it is suitable for this kind of applications. The entire system consists of an Android smartphone, a master unit and several slave units. Each board has a 9 Degrees of Freedom (DoF) Inertial Measurement Unit, a micro-SD for data storage and an ARM® Cortex ™-M4 CPU SoC supporting the protocols for both Bluetooth Low Energy (BLE) 5 and 2.4 GHz custom communications.

Smart Multi-Sensor Platform for Analytics and Social Decision Support in Agriculture.

Smart agriculture based on new types of sensors, data analytics and automation, is an important enabler for optimizing yields and maximizing efficiency to feed the world’s growing population while limiting environmental pollution. The aim of this paper is to describe a multi-sensor Internet of Things (IoT) system for agriculture consisting of a soil probe, an air probe and a smart data logger. The implementation details will focus of the integration element and the innovative Artificial Intelligence based gas identification sensor. Furthermore, the paper focuses on the analytics and decision support system implementation that provides farming recommendations and is enhanced with a feedback loop from farmers and a social trust index that will increase the reliability of the system.

Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor.

Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70 g/m) and increases the correlation (e.g., R: 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network.

Mean Shift versus Variance Inflation Approach for Outlier Detection—A Comparative Study

Outlier detection is one of the most important tasks in the analysis of measured quantities to ensure reliable results. In recent years, a variety of multi-sensor platforms has become available, which allow autonomous and continuous acquisition of large quantities of heterogeneous observations. Because the probability that such data sets contain outliers increases with the quantity of measured values, powerful methods are required to identify contaminated observations. In geodesy, the mean shift model (MS) is one of the most commonly used approaches for outlier detection. In addition to the MS model, there is an alternative approach with the model of variance inflation (VI). In this investigation the VI approach is derived in detail, truly maximizing the likelihood functions and examined for outlier detection of one or multiple outliers. In general, the variance inflation approach is non-linear, even if the null model is linear. Thus, an analytical solution does usually not exist, except in the case of repeated measurements. The test statistic is derived from the likelihood ratio (LR) of the models. The VI approach is compared with the MS model in terms of statistical power, identifiability of actual outliers, and numerical effort. The main purpose of this paper is to examine the performance of both approaches in order to derive recommendations for the practical application of outlier detection.

Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles.

For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed.

A Smart Sensor-Based Measurement System for Advanced Bee Hive Monitoring.

The widespread decline of honey bee (Apis mellifera L.) colonies registered in recent years has raised great attention to the need of gathering deeper knowledge about this phenomenon, by observing the colonies’ activity to identify possible causes, and design corresponding countermeasures. In fact, honey bees have well-known positive effects on both the environment and human life, and their preservation becomes critical not only for ecological reasons, but also for the social and economic development of rural communities. Smart sensor systems are being developed for real-time and long-term measurement of relevant parameters related to beehive conditions, such as the hive weight, sounds emitted by the bees, temperature, humidity, and CO inside the beehive, as well as weather conditions outside. This paper presents a multisensor platform designed to measure the aforementioned parameters from beehives deployed in the field, and shows how the fusion of different sensor measurements may provide insights on the status of the colony, its interaction with the surrounding environment, and the influence of climatic conditions.

Materials, Mechanics Designs, and Bioresorbable Multisensor Platforms for Pressure Monitoring in the Intracranial Space

AbstractPressures in the intracranial, intraocular, and intravascular spaces are important parameters in assessing patients with a range of conditions, of particular relevance to those recovering from injuries or from surgical procedures. Compared with conventional devices, sensors that disappear by natural processes of bioresorption offer advantages in this context, by eliminating the costs and risks associated with retrieval. A class of bioresorbable pressure sensor that is capable of operational lifetimes as long as several weeks and physical lifetimes as short as several months, as combined metrics that represent improvements over recently reported alternatives, is presented. Key advances include the use of 1) membranes of monocrystalline silicon and blends of natural wax materials to encapsulate the devices across their top surfaces and perimeter edge regions, respectively, 2) mechanical architectures to yield stable operation as the encapsulation materials dissolve and disappear, and 3) additional sensors to detect the onset of penetration of biofluids into the active sensing areas. Studies that involve monitoring of intracranial pressures in rat models over periods of up to 3 weeks demonstrate levels of performance that match those of nonresorbable clinical standards. Many of the concepts reported here have broad applicability to other classes of bioresorbable technologies.

Design of an Integrated Wearable Multi-Sensor Platform Based on Flexible Materials for Neonatal Monitoring

For infants admitted at neonatal intensive care unit, the continuous monitoring of health parameters is critical for their optimal treatment and outcomes. So it’s crucial to provide proper treatment, accurate and comfortable monitoring conditions for newborn infants. In this paper, we propose wearable sensor systems integrated with flexible material based non-invasive sensors for neonatal monitoring. The system aims at providing reliable vital signs monitoring as well as comfortable clinical environments for neonatal care. The system consists of a smart vest and a cloud platform. In the smart vest, a novel stretching sensor based on Polydimethylsiloxane-Graphene (PDMS-Graphene) compound is created to detect newborns’ respiration signal; textile-based dry electrodes are developed to measure Electrocardiograph (ECG) signals; inertial measurement units (IMUs) are embedded to obtain movement information including accelerated speed and angular velocity of newborn wrists. Experiments were conducted to systematically test the sensing related characteristics of the aforementioned flexible materials and the performance of the proposed multi-sensor platform. The results show that the proposed system can achieve high quality signals. The wearable sensor platform is promising for continuous long term monitoring of neonates. The multi-modal physiological and behavioral signals measured by the platform can be further processed for clinical decision support on the neonatal health status.

Ultra low power wireless multi-sensor platform dedicated to machine tool condition monitoring

The H2020 PreCoM project aims at developing and deploying a predictive cognitive maintenance decision-support system for production equipment such as machine tools. The PreCoM system intends to identify and localize damage, predict its evolution, assess remaining lifetime, and increase in-service efficiency of machines by conducting preventive maintenance actions. In this PreCoM system, additional sensors are needed to gather measurements close to critical parts/tools in order to improve failure detection.Commercial wireless sensor nodes are too restrictive regarding requirements for condition monitoring on industrial machine tools (50 g acceleration, 50 m long wireless range, several month lifetime, presence of cooling fluid and hot metal chips projection). Therefore, a dedicated wireless multi-sensor platform has been designed and developed in the frame of this project in order to provide additional measurements on highly moving parts, such as spindle head.The PreCoM wireless sensor nodes are integrating a 50 g/10 kHz - 3 axes accelerometer and a temperature sensor. They have been designed to be standalone devices with lifetime of around 1 year, and to fit all the machine tool environment requirements. The wireless sensor nodes are based on ultra-low power electronic devices and communication protocols to minimize their power consumption during all phases: sensing, communication and sleeping mode.Those sensor nodes and their associated wireless platform have been successfully developed and tested in representative environment in laboratory, and then deployed on pilot machine tools within the PreCoM maintenance system.

IEEE Access Special Section Editorial: 5G and Beyond Mobile Wireless Communications Enabling Intelligent Mobility

Increasing urbanization is one major trend that shapes tomorrow’s society; by 2050, more than 85% of the developed world’s population will live in a comparatively small number of ever-growing cities. Within such cities and their commuter belts, reliable high-rate wireless communication will not only be required for (quasi-) static users but also for hosts of people moving in public and private transportation networks. Yet, wireless connectivity is not restricted to people; frictionless functioning of such a society in motion is supported by Intelligent Mobility, where each connected transportation vehicle (car, train, bus, ship, aircraft, motorcycle, and bicycle) is expected to be a smart object equipped with a powerful multi-sensor platform, communication capability, computing units, and Internet protocol (IP)-based connectivity, such as to be highly efficient in various vehicular and transportation applications. This vision requires a more pervasive and ubiquitous communications and networking core, which will not be only driven by the existing research on 5G but also enabled by future mobile wireless communications that employ new concepts, such as data analytics, artificial intelligence, machine learning, and cloud computing. Therefore, this IEEE Access Special Section focuses on various theoretical and experimental views on researching and developing the required technological enhancements of 5G and beyond mobile wireless communications, to efficiently support the vision of intelligent mobility, providing mobility as a service, and enabling dependable Internet services. Twenty-three high-quality contributions to this Special Section have been selected in a strict peer-review process supported by reputed international experts.

An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders.

Underwater gliders are energy-efficient vehicles that rely on changes in buoyancy in order to convert up and down movement into forward displacement. These vehicles are conceived as multi-sensor platforms, and can be used to collect ocean data for long periods in wide range areas. This endurance is achieved at the cost of low speed, which requires extensive planning to ensure vehicle safety and mission success, particularly when dealing with strong ocean currents. As gliders are often involved on missions that pursue multiple objectives (track events, reach a target point, avoid obstacles, sample specified areas, save energy), path planning requires a way to deal with several constraints at the same time; this makes glider path planning a multi-objective (MO) optimization problem. In this work, we analyse the usage of the non-dominated sorting genetic algorithm II (NSGA-II) to tackle a MO glider path planning application on a complex environment integrating 3D and time varying ocean currents. Multiple experiments using a glider kinematic simulator coupled with NSGA-II, combining different control parameters were carried out, to find the best parameter configuration that provided suitable paths for the desired mission. Ultimately, the system described in this work was able to optimize multi-objective trajectories, providing non dominated solutions. Such a planning tool could be of great interest in real mission planning, to assist glider pilots in selecting the most convenient paths for the vehicle, taking into account ocean forecasts and particular characteristics of the deployment location.