Static Sensor Research Articles

PREFACE: TECHNICAL COMMISSION II

Abstract. ISPRS Technical Commission II focuses, at various scales, on geometric, radiometric and multi-temporal aspects of image- and range-based 3D surveying and modeling. Specifically, Commission II deals with image orientation, point cloud generation and processing, 3D feature extraction, dynamic and static scene analysis, sensor and data fusion, and machine learning for geospatial data analysis and big data techniques for massive data processing. Applications in the fields of mapping, infrastructure monitoring, heritage studies, space exploration, underwater photogrammetry and environmental engineering are also considered.The Volume related to Commission II contains 108 papers published in the ISPRS Archives and 20 published in the ISPRS Annals. The papers in Archives were accepted from among 111 abstract submissions and 59 full papers. Of the 108 Archives papers, 32 were full paper submissions and 76 were abstract submissions. The 20 papers in the Annals were selected from among 59 full paper submissions after going through a peer review process.In the aforementioned areas of research, the papers in this volume discuss the challenges and needs, and introduce novel photogrammetric solutions that depict the latest developments in the field.There has been a wide range of coverage of these topics and point cloud generation and processing has been the most active coverage with close to 38% of the accepted papers. This is followed by machine/deep learning methods with 31.8% that provide solutions to the semantic enrichment of images and 3D data. Research in heritage and underwater studies is represented with 9% and 6%, respectively, of the accepted papers.We believe these volumes nicely recap the state-of-the-art, current trends and possible applications of photogrammetry on a wide range of topics with a nice overview of the future research directions.On behalf of Technical Commission II, we would like to thank the local organizers of the 2021 ISPRS Congress, the members of the international program committee, all Working Group officers and all reviewers for their hard organizational work and efforts in the paper reviewing process.

A new predictive filter for nonlinear alignment model of stationary MEMS inertial sensors

A new predictive filter for nonlinear alignment model of stationary MEMS inertial sensors

Pseudolinear kalman filters for target tracking using hybrid measurements

This paper presents new variants of the pseudolinear Kalman filter (PLKF) for target tracking in 2D-plane using angle-of-arrival, time-difference-of-arrival and frequency-difference-of-arrival measurements collected by stationary sensors. Using hybrid measurements can yield performance advantage over the traditional bearings-only estimators, but may involve complex noise vector and correlation between the measurement matrix and the noise vector. A closed-form PLKF is developed by rearranging measurement equations to compensate the non-zero mean of the noise vector. To tackle the bias issue of PLKF, the bias is derived and compensated instantaneously, leading to the proposed BCPLKF estimator. Then a new variant of instrumental variable-based Kalman filter (IVKF) was presented, which alleviates the bias by utilizing BCPLKF estimates instead of noisy measurements to compute the measurement matrix. In addition, the posterior Cramér-Rao lower bound (PCRLB) is derived for the nonlinear filtering problem. Simulation results demonstrate that the proposed estimators outperform the bearings-only estimator significantly and have the mean squared error fairly close to the PCRLB.

PREFACE: TECHNICAL COMMISSION II

Abstract. ISPRS Technical Commission II focuses, at various scales, on geometric, radiometric and multi-temporal aspects of image- and range-based 3D surveying and modeling. Specifically, Commission II deals with image orientation, point cloud generation and processing, 3D feature extraction, dynamic and static scene analysis, sensor and data fusion, and machine learning for geospatial data analysis and big data techniques for massive data processing. Applications in the fields of mapping, infrastructure monitoring, heritage studies, space exploration, underwater photogrammetry and environmental engineering are also considered.The Volume related to Commission II contains 108 papers published in the ISPRS Archives and 20 published in the ISPRS Annals. The papers in Archives were accepted from among 111 abstract submissions and 59 full papers. Of the 108 Archives papers, 32 were full paper submissions and 76 were abstract submissions. The 20 papers in the Annals were selected from among 59 full paper submissions after going through a peer review process.In the aforementioned areas of research, the papers in this volume discuss the challenges and needs, and introduce novel photogrammetric solutions that depict the latest developments in the field.There has been a wide range of coverage of these topics and point cloud generation and processing has been the most active coverage with close to 38% of the accepted papers. This is followed by machine/deep learning methods with 31.8% that provide solutions to the semantic enrichment of images and 3D data. Research in heritage and underwater studies is represented with 9% and 6%, respectively, of the accepted papers.We believe these volumes nicely recap the state-of-the-art, current trends and possible applications of photogrammetry on a wide range of topics with a nice overview of the future research directions.On behalf of Technical Commission II, we would like to thank the local organizers of the 2021 ISPRS Congress, the members of the international program committee, all Working Group officers and all reviewers for their hard organizational work and efforts in the paper reviewing process.

Mobile Wireless Sensor Networks: A Framework Based Solution for Physical Security Attacks

Mobile wireless sensor networks are made up of resource-constrained sensor nodes.The nodes in Mobile wireless sensor networks tend to move wirelessly from one location to another. In physically unattended and insecure areas, these networks are installed where they are vulnerable to numerous security threats, including physical security threats.Clone node attack is a physical attack in which the adversary physically compromises legitimate nodes and copies their data onto fake nodes to create clones of captured nodes. In static wireless sensor networks, comprehensive work isalready done to mitigate and detect the underlying attack. Mitigating and detecting this assault on mobile wireless sensor networks, however, remains a critical issue. To our knowledge, there is no framework-based method for bolstering multiple attacks onmobile wireless sensor networks.Consequently, this article proposes a framework-based approach that mitigates multiple attacks on mobile WSNs. To provide proof-of-concept for the framework, along with clone node attacks solution to jamming attacks is alsoincorporated in the framework. The implementation of the framework is done with the COOJA simulator and Contiki OS.

Strain prediction in Francis runners by means of stationary sensors

The assessment of the remaining useful life due to fatigue in Francis turbine runners implies complex measurements with strain gauges that have to be installed in a submerged and rotating structure, which is excited with high pressure pulsations and strong turbulent flows. Furthermore, the conditioning, storage and transmission of these signals to the stationary frame involves complicated technical solutions. In order to avoid such complex and expensive measurements, in this paper we explore the feasibility of obtaining the strain on the runner with stationary sensors, which can be easily installed and used for a continuous monitoring of the machine. Based on the experimental strain tests performed in a Francis turbine unit, strain on the runner blade is correlated with relevant indicators obtained with stationary sensors. The correlation within indicators is obtained considering linear regression models and improved with artificial intelligence techniques.

Numerical and experimental analysis of pressure fluctuations in the draft-tube of a Francis turbine using the swirl number

Pressure fluctuations in the draft-tubes of hydraulic turbines are the most dominant issues that may cause damages to a hydraulic power plant. When the frequency of the fluctuations coincides with one natural frequencies of the plant, resonance with high amplitude vibrations and pressure pulsations occurs, dramatically impacting the integrity of the plant. This research focuses on the part-load behaviour of a medium specific-speed Francis turbine (with specific speed 184 m.kW), designed by the authors in the framework of an industrial project. This paper combines the results of model tests with CFD simulation to investigate the part-load behaviour of the turbine. To do so, performance of the turbine is numerically simulated at one operating condition by solving the RANS equations via the commercial code ANSYS CFX 19.0. The k-ω SST turbulence model is used to predict the turbine performance. In addition, model tests are conducted in the range of 20% to 110% guide vane opening. To capture pressure fluctuations in the draft-tube, 12 static pressure sensors are installed in 4 different sections of the draft-tube. The no-swirl and other iso-swirl lines of the draft tube flow are first determined by using CFD results. Furthermore, pressure signals from model tests are analysed by performing cross spectral density analysis. Two transitions in the behaviour of the precessing vortex under part-load conditions occurring at given values of swirl number are observed. Finally, it is observed that linear correlations between the Strouhal number of the vortex rope frequency and the swirl number can be established within the 2nd and 3rd part-load regimes, independently of the values of the speed coefficient.

Drivers of Macrophyte and Diatom Diversity in a Shallow Hypertrophic Lake

We studied macrophyte and diatom assemblages and a range of environmental factors in the large hypertrophic Dehtář fishpond (Southern Bohemia, Czech Republic) over the course of several growing seasons. The spatial diversity of the environment was considered when collecting diatoms and water samples in three distinct parts of the fishpond, where automatic sensor stations continually measuring basic factors were established. Macrophytes were mapped in 30 segments of the fishpond littoral altogether. High species richness and spatiotemporal variability were found in assemblages of these groups of autotrophs. Water level fluctuations, caused by the interaction of fish farming management and climatic extremes, were identified as one of the most important factors shaping the structure and species composition of diatom and macrophyte assemblages. The distance of the sampling sites from large inflows reflected well the spatial variability within the fishpond, with important differences in duration of bottom drainage and exposure to disturbances in different parts of the fishpond. Disturbances caused by intensive wave action are most probably a crucial factor allowing the coexistence of species with different nutrient requirements under the hypertrophic conditions of the Dehtář fishpond. Due to a range of variables tested and climatic extremes encountered, our study may be considered as a basis for predictive model constructions in similar hypertrophic water bodies under a progressing climate change.

Extending the dimensions of personal exposure assessment: A methodological discussion on perceived and measured noise and air pollution in traffic

BackgroundThe exposure to air pollution and noise is severely impacting people's health and is especially high alongside urban road- and rail-traffic. In traditional exposure research, air pollution and noise are monitored with stationary measurement devices or based on models. During the last years, mobile measurement techniques with GPS-tracking have increased. Moreover, studies have investigated another dimension of personal exposure: the perceived exposure. AimMost of these studies make use of quantitative methods such as surveys, complemented by stationary or wearable sensors. Little research exist that applies qualitative methods to examine how people experience and perceive exposure on-the-move, contrasting it to actual measurements. The aim of this paper is to discuss the potential of a novel method, which extends the dimensions of personal exposure by including the situational context of exposure perception. Methods/case studyFirstly, different methods for exposure research are presented. Secondly, we introduce a novel mixed-method approach, exploring cyclists and pedestrians perceived and measured exposure on-the-move by combining mobile interviews (Go-/Ride-Alongs) and wearable sensors. We will present the methodological findings using a case study and have a quantitative method (smartphone questionnaire, wearable sensors) as a reference. Results and discussionThe differences of perceived and measured exposure, proven through the reference method, are a result of different situational contexts as shown by the mobile interviews (knowledge, embodied experience, life situation, activities). The methodological findings show, that mobile methods complemented by wearables introduce new dimensions of personal exposure: they shed light on the situational contexts that affect exposure perception during commute. ConclusionWe argue that both, perceived and measured exposure to air pollution and noise, need to be considered simultaneously. Complementing mobile interviews or surveys with wearable sensor data improves the understanding of urban dwellers requirements for healthier mobility. Potentials of these methods should be investigated further, both in research and for supporting urban transport planning decisions adapted to people's needs.

On problem of optimal observers’ placement on plane

A plane problem for optimization of stationary sensors placement in a threat environment to counteract an evading object is considered. For this purpose, an auxiliary task of constructing locally optimal paths of the evading object for a given configuration of sensors is solved. Integral risk functional is minimized for the evading object. The minimum of this functional is required to be maximized in the task of sensors placement. The paper describes the methods of solving both problems and the results of numerical modeling for the case of 2 sensors: examples of local optimal path maps, and the best found configurations of sensors placement.

SINK MOBILITY MODEL FOR WIRELESS SENSOR NETWORKS USING KOHONEN SELF-ORGANIZING MAP

Wireless sensor networks are expected to operate in an unattended manner for long periods of time. As a result, different mobility models were proposed by many research papers in order to improve the performance and extend the lifetime of the network. In this paper a new sink mobility model that is based on kohonen self-organizing maps is proposed in order to provide a mobile sink node with the ability to collect data from static sensor nodes. Moreover, the performance of the proposed mobility model was studied using NS-2 simulator under different network sizes and movement speeds of the mobile sink. Finally, the performance of the proposed model was evaluated based on different performance metrics namely, end-to-end delay, packet delivery ratio and throughput.

Graphene foam – polymer based electronic skin for flexible tactile sensor

Graphene foam (GF) infiltrated with polymer attracts researchers toward practical pressure sensing applications, especially tactile sensors. However, interface between polymer and graphene plays a vital role under a specific type and amount of pressure. Herein, we have investigated the impact of static and dynamic pressure upon GF-polymer. A recovery time of ∼ 0.1 s is detected during static pressure sensing. Unlike static pressure, GF-polymer samples are highly sensitive to dynamic load, and a quick recovery time ∼ 0.06 s is detected. The sensitivity of the dynamic pressure sensor ∼ 1.17 ± 0.1 Pa−1 is almost double compares to the sensitivity of the static pressure sensor ∼ 2.1 ± 0.3 Pa−1. Technologically, tactile sensors exert pressure in different forms and GF-polymer is an excellent choice for forming a pressure sensor. The present study will help develop efficient tactile sensors.

Implementing MRCRLB technique on modulation schemes in wireless rechargeable sensor networks

As battery technology has not advanced as quickly as other technologies like semiconductor technology, energy efficiency is still one of the primary concerns in Wireless Sensor Networks (WSNs). One of the finest Wireless Energy Transfer (WET) technique is Magnetic Resonant Coupling (MRC) used to re-energize the nodes in WSNs. Recent advancements in MRC show that more than one node can be re-charged at the same time. This paper aims to exploit multiple nodes WET to overcome the issues pertaining to energy in Wireless Sensor Network (WSN). For transferring energy to various static sensor nodes deployed in a network, Unmanned Aerial Vehicle (UAV) is considered which travels inside a network and performs a process of re-charging. Further, WSNs being most acceptable face issues like: battery constraint, and congestion. Therefore, in order to address such issues, we have proposed MRCRLB (Magnetic Resonant Coupling Based Recharging and Load Balancing) scheme. Then, finally the proposed model is tested on various modulation schemes at the physical layer of WSNs to find out most appropriate modulation scheme type for lifetime extension model (MRCRLB).

Advanced hybrid secure multipath optimized routing in Internet of Things (IoT)‐based WSN

SummaryExploration has been done on Internet of Things (IoT) with accentuation on the security like remote system security, correspondence security, sensor information security, respectability of physical signals, and impelling gadgets. The procedure for current security is not reasonable for IoT applications as gadgets that are included at the ground level have some of the restricted assets, low multifaceted nature, and vitality requirements. IoT being a system of systems permits billions of people and machines to connect with each other. IoT offers us a chance to build compelling organizations, applications for assembling, lifesaving arrangements, appropriate development, and the sky is the limit from there. The manuscript proposes a broad review of the IoT innovation and its differed applications in life sparing, brilliant urban communities, rural, mechanical by looking into the ongoing examination work. Existing strategies manage static remote sensor web to reason the interruptions in which the sensor hubs are conveyed in a uniform way to hold the consistency. Since the sensor hubs are referred to through various transmission locales having a determination of sensor checking hubs has become a difficult activity in ongoing examination. The enemies are additionally moving starting with one area then onto the next to investigate its particular tasks around the system. Hence, to given adaptable security, a protected routing along with the observing convention with multivariation tuples utilizing two‐fish symmetric key (TFSK) way to deal with the foes in the worldwide sensor organize. The proposed approach is the advanced hybrid secure multipath optimized routing in Internet of Things (IoT)‐based WSN. The two methodologies that are involved making a better routing protocol is the enhanced‐multipath optimized link state routing (E‐MP‐OLSR) and secure and efficient ad hoc on‐demand multipath distance vector (SE‐AOMDV) protocols. Besides, the proposed directing system is tough to different versatile foes; and henceforth, it guarantees multipath conveyance.

Fusion protocol for improving coverage and connectivity WSNs

The authors proposed a fusion protocol using multilevel clustering (FPMC), which is completely dependent on the increased coverage and wireless sensor network (WSN) reliability-clustering framework to eliminate duplication. Therefore, a static sensor for a point of interest is allocated where possible so that the particular area can be protected. Furthermore, FPMC ensures that only one sensor node is allocated to cover a specific point of interest. The network coverage, energy usage, mean movement of WSNs nodes and sensor nodes are active in each loop due to which efficiency of the proposed protocol was assessed under various densities, and its performance was also analysed. Evaluations have shown that with restricted active nodes and sensor nodes, the FPMC can increase the networking coverage. The FPMC should be stressed as reducing the overlap level. The proposed protocol is based on the fusion protocol using multi-level clustering for improving coverage and connectivity WSNs.

Pressure measurements in detonation engines

The influence of waveguide tubes on the signals received by remote pressure sensors measuring pressure histories in pulsed detonation engines (PDEs) and rotating detonation engines (RDEs) is studied computationally. Two types of pressure sensors are considered: low-frequency static pressure sensors and high-frequency sensors of pressure pulsations. Three approaches to solving the problem are used: based on Euler (inviscid flow), Navier–Stokes (laminar flow), and unsteady Reynolds-averaged Navier–Stokes (turbulent flow) equations. The approaches based on the inviscid and laminar flow models are shown to provide the best predictive capability. The laminar flow model is applied to analyzing the readings of pressure sensors installed remotely in the waveguide tubes attached to the hydrogen-fueled PDE, RDE, and detonation ramjet (DR). It is shown that the measurements of static pressure and pressure pulsations by remote pressure sensors do not correspond to the time-averaged mean and local instantaneous values of pressure in the combustors. The pressure time histories can be recovered based on the measurements and computational fluid dynamics calculations of the operation process. The latter is demonstrated by analyzing the results of test fires with a dual-duct DR model.

Benefits of Spectral Property Engineering in Distributed Brillouin Fiber Sensing.

As one of the most consolidated distributed fiber sensors based on stimulated Brillouin scattering, the Brillouin optical time-domain analyzer (BOTDA) has been developed for decades. Despite the commercial availability and outstanding progresses which has been achieved, the intrinsic Lorentzian gain spectrum restricts the sensing performance from possible further enhancements and hence limits the field of validity of the technique. In this paper, the novel method of engineering the gain spectral properties of the Brillouin scattering and its application on static and dynamic BOTDA sensors will be reviewed. Such a spectral property engineering has not only provided improvements to BOTDA, but also might open a new way to enhance the performance of all kinds of distributed Brillouin fiber sensors.

Indirect/Direct Learning Coverage Control for Wireless Sensor and Mobile Robot Networks

This article proposes indirect/direct learning control schemes for wireless sensor and mobile robot networks to cover an environment according to the density function, which is the distribution of an important quantity within the environment. When stationary sensors cooperate with mobile robots, the density estimation can be enhanced by using nonstationary basis functions to relax the assumption of matching conditions in the previous approach. To improve the density function estimation, this study employs an expectation–maximization algorithm and log-likelihood, which maximizes the similarity between the proposed normalized density and normalized coverage function. Subsequently, the adaptive weighting algorithm is combined with the proposed indirect coverage control for tunable basis centers and the weighting of the basis functions. For direct coverage control, mobile robots are driven to cover the regions of higher importance while simultaneously estimating the density function utilizing a sensory model function. We prove that the Lloyd algorithm is a special case of the direct method when the density function and Voronoi partitions are available. The efficiency of the proposed methods is confirmed in numerical examples and semiexperiments.

Challenges and opportunities of multimodal data in human learning: The computer science students' perspective

AbstractMultimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non‐technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student‐facing MLA innovations.

Optimizing temperature-dependent molar volume fraction of biodiesel fuel in a pseudo-binary mixture through Bragg fiber waveguide sensor having defect layer

In the present communication, the temperature-dependent molar volume fraction of various kinds of renewable biodiesel fuel resources in a pseudo-binary mixture is theoretically optimized through the Bragg fiber waveguide (BFW) sensor with a geometrical defect in periodic cylindrical Bragg reflectors. BFW structure is modelled using a transfer matrix method (TMM) and Henkel formalism in a cylindrical coordinate system. The temperature dependence molar volume fraction is linked to the change in the refractive index of the pseudo-binary mixture, which is further predicted using the capacity of various models; Lorentz-Lorenz, Dale-Gladstone, Eykman, Newton, and Kay. In the presence of a geometric defect layer, a sharp transmission peak of 0.1nmfull width at half maxima (FWHM) is obtained in the considered photonic band gap (PBG), which is sensitive to the change in core refractive index. The temperature-dependent (due to different weather conditions) maximum sensitivity of the proposed sensor is found to be 1280 nm/RIU, which is further compared with the phoxonic crystal-based static temperature sensor having sensitivity 142 nm/RIU only. Along with improved sensitivity in the proposed sensor, other sensing performance parameters detection accuracy and quality parameter (which are inversely proportional to the FWHM) are also improved due to least FWHM of resonant transmission peak about 0.1 nm comparatively.