Multi-sensor Technique Research Articles

Algorithms for Optimizing Energy Consumption for Fermentation Processes in Biogas Production

Problems related to reducing energy consumption constitute an important basis for scientific research worldwide. A proposal to use various renewable energy sources, including creating a biogas plant, is emphasized in the introduction of this article. However, the indicated solutions require continuous monitoring and control to maximise the installations’ effectiveness. The authors took up the challenge of developing a computer solution to reduce the costs of maintaining technological process monitoring systems. Concept diagrams of a metrological system using multi-sensor techniques containing humidity, temperature and pressure sensors coupled with Electrical Impedance Tomography (EIT) sensors were presented. This approach allows for effective monitoring of the anaerobic fermentation process. The possibility of reducing the energy consumed during installation operation was proposed, which resulted in the development of algorithms for determining alarm states, which are the basis for controlling the frequency of technological process measurements. Implementing the idea required the preparation of measurement infrastructure and an analytical engine based on AI techniques, including an expert system and developed algorithms. Numerous time-consuming studies and experiments have confirmed reduced energy consumption, which can be successfully used in biogas production.

Performance analysis of video data transmission for telemedicine applications with 5G enabled Internet of Things

Fifth Generation (5G) communication access technology has been implemented to provide highly reliable and efficient video data streaming in telemedicine applications. The Internet of Things (IoT) advancement enhances the 5G network for smart healthcare services and applications. The existing research work focused only on Lagrangian Encoder (LE) based video compression technique with H.265 Protocol for video data transmission in 5G networks for telemedicine applications. This paper proposes a novel KNN classifier-based H.265 protocol with a single buffer model incorporated with multiple sensors for telemedicine applications. The proposed multiple sensors are placed at the transmitter and receiver base stations to exchange data efficiently and accurately between transmitter and receiver devices. The data transmission performance is measured using collision error, propagation error, sensing error, and visual security with encryption for the proposed and existing methods. The performance of the proposed model is compared with the existing LE-based single buffer and identifies the proposed KNN classifier-based single buffer with a multi-sensor technique that performs better.

Multi-sensor techniques to study fatigue damage progression in structural materials

Fatigue failure is catastrophic once the material reaches the critical damage, but the damage accumulation is, in general, gradual. Several methods are used to study the fatigue damage progression, such as: microstructural investigation, stiffness degradation measurements, and loss of ductility etc. to predict the remaining life of dynamically loaded components. Non-destructive test methods such as ultrasonic, eddy current and infrared thermography are the other techniques that are used to sense the change in material response due to fatigue loading. This paper presents a glimpse of results to understand the fatigue damage progression in structural materials; infrared thermography is used to understand fatigue damage in copper, while X-ray computed tomography is used to study damage progression in an impact damaged carbon fiber reinforced plastic laminate. The damage volume is measured based on CT images and correlated with stiffness drop. Based on the study, it was observed that the use of multiple sensors and measurement techniques in tandem with other conventional experimental methods provides an insight into damage progression.

MULTI-SENSOR TECHNIQUES TO FLOOD MAPPING

The aim of this article is to map the flooding area in the Tarauacá city of Acre State (Amazon basin, Brazil) using multi-sensor techniques. Floods are the most common disaster in the Amazon, even though flood and risk mapping has only recently been delimited by governmental issues. The methods to flood mapping included Synthetic Aperture Radar (SAR) (Sentinel-1/S1) and optical sensor (Sentinel-2/S2) data, separately, and in a fusion approach. The flood extend was measured on Sentinel-1 VV, on NDWI Sentinel-2, and on unsupervised classification from S1 and S2 fusion and classes: soil exposed, urban, vegetation, shadow, water and clouds. The resulting total areas vary by 8.23 km² (S1), 7.86 km² (NDWI-S2), and 11.87 km² (multi-sensor fusion). The fusion S1S2 results were validated from the calculation of global accuracy (70%), errors of omission (soil exposed 0; urban 31,25; vegetation 87,5; shadow, water and clouds 0), commission errors (soil exposed 50; urban 0; vegetation 75; shadow 6,25, water 6,25 and clouds 87,5), and the kappa index (0.59). Using multi-sensors is an alternative to calculate flood extension and can aid in mapping hydrological hazards in Amazon cities.

Multi-sensor measurement of O2, CO2 and reheating in triticale silage: An extended approach from aerobic stability to aerobic microbial respiration

The biochemical reactions of aerobic microbial respiration (AMR) suggest that silage temperature (Tsi) rise, oxygen (O2) consumption and carbon dioxide (CO2) emission may be equally useful as indicators of silage deterioration during feed-out, but only temperature has been used extensively to assess aerobic stability. Here we extend the study of aerobic stability to incorporate AMR of silage by developing a novel experimental cell integrated with multiple sensors. Silage samples, ensiled from a triticale crop, were made in twelve air-tight barrels (60 L), packed to bulk densities of 190 or 250 kg m−3 dry matter (DM). Tsi and O2 measurements were co-located at 15- and 30-cm behind the working face. CO2 was measured as flux across the working face. The experimental period of aerobic exposure was 7 days. We provide the first reports of: (i) distinct aerobic responses of these parameters, showing that Tsi varied with CO2 in phase but with O2 out-of phase; (ii) CO2 flux was dominated initially by anaerobic discharge and subsequently by aerobic products; (iii) linear relationships between aerobic reheating and both O2 consumption (0.994 ≥ R2 ≥ 0.815, P < 0.01) and CO2 flux (0.981 ≥ R2 ≥ 0.464, P < 0.01); and (iv) variable magnitude of daily aerobic production of CO2 per kg DM from 2.3 to 133.4 mmol kg d−1. These results demonstrate that the novel multi-sensor technique has powerful capacity to provide insight into AMR of silage and thus provide more detailed information to guide silage management than previous measurements of aerobic stability.

Evaluating the Ability of Multi-Sensor Techniques to Capture Topographic Complexity.

This study provides an evaluation of multiple sensors by examining their precision and ability to capture topographic complexity. Five different small unmanned aerial systems (sUAS) were evaluated, each with a different camera, Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU). A lidar was also used on the largest sUAS and as a mobile scanning system. The quality of each of the seven platforms were compared to actual surface measurements gathered with real-time kinematic (RTK)-GNSS and terrestrial laser scanning. Rigorous field and photogrammetric assessment workflows were designed around a combination of structure-from-motion to align images, Monte Carlo simulations to calculate spatially variable error, object-based image analysis to create objects, and MC32-PM algorithm to calculate vertical differences between two dense point clouds. The precision of the sensors ranged 0.115 m (minimum of 0.11 m for MaRS with Sony A7iii camera and maximum of 0.225 m for Mavic2 Pro). In a heterogenous test location with varying slope and high terrain roughness, only three of the seven mobile platforms performed well (MaRS, Inspire 2, and Phantom 4 Pro). All mobile sensors performed better for the homogenous test location, but the sUAS lidar and mobile lidar contained the most noise. The findings presented herein provide insights into cost–benefit of purchasing various sUAS and sensors and their ability to capture high-definition topography.

Ionospheric Response Over Brazil to the August 2018 Geomagnetic Storm as Probed by CSES‐01 and Swarm Satellites and by Local Ground‐Based Observations

AbstractThe geomagnetic storm that occurred on 25 August 25 2018, that is, during the minimum of solar cycle 24, is currently the strongest ever probed by the first China Seismo‐Electromagnetic Satellite (CSES‐01). By integrating the in situ measurements provided by CSES‐01 (orbiting at altitude of 507 km) and by Swarm A satellite (orbiting at ca., 460 km) with ground‐based observations (ionosondes, magnetometers, and Global Navigation Satellite System receivers), we investigate the ionospheric response at lower‐ and mid‐latitudes over Brazil. Specifically, we investigate the electrodynamic disturbances driven by solar wind changes, by focusing on the disturbances driving modifications of the equatorial electrojet (EEJ). Our proposed multisensor technique analysis mainly highlights the variations in the topside and bottomside ionosphere, and the interplay between prompt penetrating electric fields and disturbance dynamo electric fields resulting in EEJ variations. Thanks to this approach and leveraging on the newly available CSES‐01 data, we complement and extend what recently investigated in the Western South American sector, by highlighting the significant longitudinal differences, which mainly come from the occurrence of a daytime counter‐EEJ during both 25 and 26 August at Braziliian longitudes and during part of 26 August only in the Peruvian sector. In addition, the increased thermospheric circulation driven by the storm has an impact on the EEJ during the recovery phase of the storm. The observations at the CSES‐01/Swarm altitudes integrated with the ground‐based observation recorded signatures of equatorial ionospheric anomaly crests formation and modification during daytime coupled with the positive ionospheric storm effects at midlatitude.

Investigating ground-based radar and spaceborne infrared radiometer synergy for lightning areal prediction in complex orography

A new multi-sensor approach, named PoLCast (Probability of Lightning foreCast), for predicting the lightning activity in a complex orography geographical area is proposed and discussed. The PoLCast input information are the ground-based weather radar horizontally polarized reflectivity factor and the atmospheric instability indexes, derived from the Spin Enhanced Visible Infrared Imager (SEVIRI) radiometer onboard the Meteosat Second Generation (MSG) satellite. The weather radar data are used to calculate the probability of lightning following a direct relation between the maximum values of the reflectivity factor and the lightning occurrence, whereas the atmospheric instability indexes from SEVIRI are used to constrain the probability of lighting, derived from weather radar data, and enhance such probability in cases of more unstable troposphere. To test the PoLCast methodology, the output of the Blitzortung and the “Sistema Italiano Rilevamento Fulmini” (SIRF) ground-based lightning sensor networks are used together with the C-band Mt. Midia weather radar within its 180 km diameter coverage over the Central Italy area. Both satellite and radar data are pre-processed into PoLCast to obtain a single time series in terms of the areal probability of lightning (PoL). PoLCast performances are evaluated in terms of statistical scores using 12 heterogeneous case studies over Central Italy. Even though the number of available cases is relatively limited, quantitative results show high areal PoL (from 0 to 100%) with a case-by-case variability of false alarm rate from 3 to 72%. The advantage of a multi-sensor technique, such as PoLCast, with respect to an approach using weather radar data only, becomes more evident when lightning activity is not present and the leading time of lightning forecast exceeds 2.5 h.

Design of an Instrument for Liquid Level Measurement and Concentration Analysis Using Multisensor Data Fusion

This paper presents the design of an instrument for measuring the liquid level. The objective of this proposed work is to measure the level of liquid accurately even with variations in liquid concentration. The designed instrument should also be able to compute the concentration of additives in the liquid. For this purpose, a multisensor model comprising a capacitive level sensor (CLS), ultrasonic level sensor (ULS), and capacitance pressure sensor is used to acquire information of the liquid. The data acquired from all these sensors are processed using Pau’s multisensor data fusion framework to compute the level of liquid along with the concentration of additives added to the solution. Pau’s framework consists of alignment, association function, analysis, and representation functions. The designed multisensor technique is tested with real-life data for varying liquid levels and additives. The results obtained show that the successful implementation of the proposed objective producing a root mean square of percentage error is 1.1% over the full scale is possible.

Soil Property Analysis Process by Multi Sensor Technique

Soil Property Analysis Process by Multi Sensor Technique

&amp;lt;i&amp;gt;A Tale of Two Dust Storms&amp;lt;/i&amp;gt;: analysis of a complex dust event in the Middle East

Abstract. Lofted mineral dust over data-sparse regions presents considerable challenges to satellite-based remote sensing methods and numerical weather prediction alike. The southwest Asia domain is replete with such examples, with its diverse array of dust sources, dust mineralogy, and meteorologically driven lofting mechanisms on multiple spatial and temporal scales. A microcosm of these challenges occurred over 3–4 August 2016 when two dust plumes, one lofted within an inland dry air mass and another embedded within a moist air mass, met over the southern Arabian Peninsula. Whereas conventional infrared-based techniques readily detected the dry air mass dust plume, they experienced marked difficulties in detecting the moist air mass dust plume, becoming apparent when visible reflectance revealed the plume crossing over an adjacent dark water background. In combining information from numerical modeling, multi-satellite and multi-sensor observations of lofted dust and moisture profiles, and idealized radiative transfer simulations, we develop a better understanding of the environmental controls of this event, characterizing the sensitivity of infrared-based dust detection to column water vapor, dust vertical extent, and dust optical properties. Differences in assumptions of dust complex refractive index translate to variations in the sign and magnitude of the split-window brightness temperature difference commonly used for detecting mineral dust. A multi-sensor technique for mitigating the radiative masking effects of water vapor via modulation of the split-window dust-detection threshold, predicated on idealized simulations tied to these driving factors, is proposed and demonstrated. The new technique, indexed to an independent description of the surface-to-500 hPa atmospheric column moisture, reveals parts of the missing dust plume embedded in the moist air mass, with the best performance realized over land surfaces.

Characterization of the atmospheric boundary layer in a narrow tropical valley using remote‐sensing and radiosonde observations and the WRF model: the Aburrá Valley case‐study

The spatiotemporal evolution of the atmospheric boundary layer (ABL), in a narrow, highly complex terrain located in the Colombian Andes, is studied using radiosondes and remote‐sensing equipment. Different techniques are implemented to automatically estimate the ABL height using ceilometer backscattering profiles and a combination of a radar wind profiler and microwave radiometer retrievals. The large aerosol load from anthropogenic emissions within the valley allows the use of ceilometer‐based ABL height detection methods, especially under stable atmospheric conditions. However, convective atmospheres favour aerosol dispersion, increasing the uncertainty associated with the estimation of the convective boundary layer using ceilometers. In contrast, the multisensor technique is more robust, performing better in stable and unstable conditions. All ceilometer‐based methods and the multisensor scheme capture the observed ABL height diurnal cycle. The main difference among ABL height retrievals occurs in the afternoon and during the night when Richardson number estimates tend to detect the top‐down contraction of the residual layer, while ceilometer‐based estimates detect the sudden bottom‐up onset of the nocturnal stable layer. The results also show that intra‐annual and annual variations of cloudiness strongly condition the ABL expansion, leading to a modulation of the ABL height diurnal cycle. The amount of aerosol particles near the surface is influenced by the evolution of the ABL, modifying the available control volume for the pollutants to interact and disperse. The evolution of ABL over the slopes and at the valley floor differs as a result of the local thickening associated with upslope winds. Weather Research and Forecasting model simulations, from a climatological point of view, skilfully simulate the observed ABL height for both the diurnal and annual cycles; the model skill is higher over the valley floor than over the slopes.

Experimental test and numerical analysis for curvature ratios effect on the heat transfer and flow characteristics of a multi-layer winding hose

The effects of curvature ratios on heat transfer and flow characteristics based on multi-sensor technique for the multi-layer winding hoses are studied. A multi-sensor–based experimental platform i...

Sea level trend over Malaysian seas from multi-mission satellite altimetry and vertical land motion corrected tidal data

Rise in sea levels is one of the disastrous effects of climate change. A relatively small increase in sea level could affect natural coastal systems. In a study of long-term changes in sea level and measurements of postglacial rebound, monitoring vertical land motion (VLM) is of crucial interest. This study presents an approach to estimate precise sea level trends based on a combination of multi-sensor techniques in the Malaysian region over 19 years. In this study, satellite altimeters (SALT) were used to derive absolute sea levels (ASLs). Tide gauge (TG) stations along the coast of Malaysia were utilised to derive the rate of relative sea levels using sea level changes and VLMs. To obtain ASL at TGs, VLM at these stations were computed using Global Positioning System (GPS), Persistent Scatterer Interferometric Synthetic Aperture Radar (PS InSAR), and SALT minus TG. The computed VLMs mostly show similarities in signs rather than magnitude. The findings from the multi-sensor techniques showed that regional sea level trends ranged from 2.65 ± 0.86 mm/yr to 6.03 ± 0.79 mm/yr for chosen sub-areas, with an overall mean of 4.47 ± 0.71 mm/yr and overall subsidence. This information is expected to be valuable for a wide variety of climatic applications and for studying environmental issues related to flooding and global warming in Malaysia.

Multi-Scale and Multi-Sensor 3D Documentation of Heritage Complexes in Urban Areas

The 3D documentation of heritage complexes or quarters often requires more than one scale due to its extended area. While the documentation of individual buildings requires a technique with finer resolution, that of the complex itself may not need the same degree of detail. This has led to the use of a multi-scale approach in such situations, which in itself implies the integration of multi-sensor techniques. The challenges and constraints of the multi-sensor approach are further added when working in urban areas, as some sensors may be suitable only for certain conditions. This paper describes the integration of heterogeneous sensors as a logical solution in addressing this problem. The royal palace complex of Kasepuhan Cirebon, Indonesia, was taken as a case study. The site dates to the 13th Century and has survived to this day as a cultural heritage site, preserving within itself a prime example of vernacular Cirebonese architecture. This type of architecture is influenced by the tropical climate, with distinct features designed to adapt to the hot and humid year-long weather. In terms of 3D documentation, this presents specific challenges that need to be addressed both during the acquisition and processing stages. Terrestrial laser scanners, DSLR cameras, as well as UAVs were utilized to record the site. The implemented workflow, some geometrical analysis of the results, as well as some derivative products will be discussed in this paper. Results have shown that although the proposed multi-scale and multi-sensor workflow has been successfully employed, it needs to be adapted and the related challenges addressed in a particular manner.

Data Driven Spatial Filtering Can Enhance Abstract Myoelectric Control in Amputees.

Myoelectric control based on multi-sensor techniques can provide an enhanced signal to noise ratio but increases hardware cost and complexity. Sensor arrays are also attractive in a prosthetics context when exact muscle positions are unknown, such as may be the case after limb loss. We present preliminary data obtained while four amputee participants engaged in an abstract myoelectric decoding task. The decoder was controlled by muscles of the forearm or upper arm depending on the level of limb loss. We compare performance using a pair of surface electromyography sensors and while using a data driven weighting of eight sensors. Performance rates demonstrate that amputee participants are able to learn the myoelectric task. Results trend strongly toward enhanced performance when using multiple spatially weighted sensors. Further studies are required to test whether the use of additional myoelectric sensing hardware in abstract decoding would lead to effective prosthesis control.

Vertical land motion quantification using space-based geodetic methods: a review

Vertical land motion is fundamental in understanding sea level behaviour as the ground subsidence and uplift specifically in coastal area can aggravate the impact of sea level rise. Quantifying the vertical movement of land is a key to forecast the necessities of communities, ecosystem thus implementing appropriate monitoring, and prevention measures for geohazards management. Therefore, this paper provides a review of the space-based geodetic methods used to measure the vertical land motion. An overview of vertical land motion and the basic principles of the four techniques encompassing the measurements from Global Navigation Satellite Systems (GNSS), Persistent Scatterer Interferometry Synthetic Aperture Radar (PS InSAR), Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry minus tide gauge data were summarized to perceive the future outlook. This study also emphasizes the effectiveness and restrictions of each space-based geodetic technique and how they complement each other as multi-sensor technique. The findings show that GNSS technique can measure vertical deformation at millimetre level with the aid of other techniques namely GRACE, PS InSAR and satellite altimeter minus tide gauge. Lastly, the future research direction is also discussed in this review paper.

Multisensor inverse synthetic aperture radar imaging and phase adjustment based on combination of sparsity and total variation

Compared with conventional single-sensor inverse synthetic aperture radar (ISAR), multisensor ISAR technique can reduce the integration time and improve the resolution of the image effectively. One solution is investigated for multisensor ISAR imaging and phase adjustment with sparse measurements via the compressed sensing (CS) framework. In most previous research of CS-based radar imaging, the target is modeled as a few strong scatterers that randomly distributed in the imaging plane and only the image domain sparsity assumption is used in image reconstruction, which will result in the degradation of image quality, especially when the measurements are limited. In practical ISAR imaging, some strong scatterers facing the radar always form flat regions in high-resolution radar imaging. Therefore, the dependence and redundancy of these scatterers can also be exploited in target reconstruction. We utilize this information and propose a multiplatform ISAR imaging method that combines the image domain sparsity and edge-preserving total variation to improve the image quality with sparse measurements. Meanwhile, an iterative minimization approach is also used to process the phase adjustment with the discontinuous echo data. Experimental results show the effectiveness of the proposed method.

Air temperature sensors: dependence of radiative errors on sensor diameter in precision metrology and meteorology

In both meteorological and metrological applications, it is well known that air temperature sensors are susceptible to radiative errors. However, it is not widely known that the radiative error measured by an air temperature sensor in flowing air depends upon the sensor diameter, with smaller sensors reporting values closer to true air temperature. This is not a transient effect related to sensor heat capacity, but a fluid-dynamical effect arising from heat and mass flow in cylindrical geometries. This result has been known historically and is in meteorology text books. However, its significance does not appear to be widely appreciated and, as a consequence, air temperature can be—and probably is being—widely mis-estimated.In this paper, we first review prior descriptions of the ‘sensor size’ effect from the metrological and meteorological literature. We develop a heat transfer model to describe the process for cylindrical sensors, and evaluate the predicted temperature error for a range of sensor sizes and air speeds. We compare these predictions with published predictions and measurements. We report measurements demonstrating this effect in two laboratories at NPL in which the air flow and temperature are exceptionally closely controlled. The results are consistent with the heat-transfer model, and show that the air temperature error is proportional to the square root of the sensor diameter and that, even under good laboratory conditions, it can exceed 0.1 °C for a 6 mm diameter sensor.We then consider the implications of this result. In metrological applications, errors of the order of 0.1 °C are significant, representing limiting uncertainties in dimensional and mass measurements. In meteorological applications, radiative errors can easily be much larger. But in both cases, an understanding of the diameter dependence allows assessment and correction of the radiative error using a multi-sensor technique.

A Smart Meter and Smart House Integrated to an IdM and Key-based Scheme for Providing Integral Security for a Smart Grid ICT

The literature does not present integral solutions to allow using the same credential to access the smart meter and smart house from an electric utility and vice-versa. The main reason being the technology gap in the communication between the Advanced Metering Infrastructure (AMI) and the Internet. The technology used in the Internet domain to communicate with Data Concentrators (DC) and the electric utility is more powerful than the technology used in the communication between smart meters and the DC, which is bandwidth limited and better suited to the Internet of Things (IoT) domain. Therefore, we are proposing the use of Identity Management (IdM) and a key-based scheme to enable the integration of IoT and the Internet using the same credentials, without creating a security bottleneck in the communication. An additional security mechanism is provided in the smart house context to isolate the house from direct accesses from the Internet, though allowing the utility to reconfigure the electric power consumption profile to avoid a potential blackout, for instance. Our proposal includes multi-sensor anti-tampering techniques to provide physical protection to a smart meter, in conjunction with a multilevel integrity mechanism to provide logical protection to its resource-constrained microcontroller, given the smart meter is a key component to mitigate electricity consumption fraud. The prototype has shown that our proposal is feasible for protecting the smart house, smart meter and the end-to-end communication between smart meter or house and the utility.