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Globally the land subsidence is a significant problem of the rapidly growing urban area. The factor responsible for the land subsidence caused by over-exploitation of the underground fluid such as water, petroleum, and gas respectively. In present study we present the result of detail investigation of active ground subsidance in New Delhi, National Capital Region (NCR). This area indicates a high rate of urban growth during the past decades. To analyze the land subsidence, we used multiple SAR sensor data and exploited the PS-InSAR technique. The data used for this study are Cosmo-skymed acquired between 08/06/2011 to 15/11/2017, Sentinel-1A-B (18-12-2014 to 27-11-2018), and ALOS PALSAR acquired between 19/01/2007 and 20/01/2011. These radar sensors operate in X, C, and L-band, which covers over ten years, from 2007 to 2018. The PSI results of Cosmo-skymed reveals that the Delhi NCR region has undergone an average deformation ± 15 mm/y, a maximum surface deformation observed from ALOS-PALSAR is 10 to 18 mm/y and the ground displacement observed from the SENTINAL-1A data is −2 to 16 mm/y. Groundwater level data also collected for the same period and a ground water level depletion compared with the subsidence. Monitoring land subsidence with ground-based conventional technology is time-consuming and can be carried out in a limited area due to the financial implication. PS-InSAR is an established method to detect the surface movement using the SAR sensor's time-series data. The result shows that a twenty centimeter of land subsidence is visible in some areas, validated with the collected ground evidence. The affected area is also showing resemblance to the groundwater depleting condition in those areas. This study also establish that multiple sensor data can be used to monitor the long term land subsidence. [ABSTRACT FROM AUTHOR]

The LD (Linz Donawitz) steelmaking process is the most used in the steel industry due to its high-volume capacity and low cost per ton of steel produced. However, the basic oxygen steelmaking process in LD converters is subjected to potential steel charge overflows, often called 'slopping'. Besides yield losses, slopping events can damage the environment and expose employees to danger. More than ever, steelmaking plants need to avoid this type of event to keep producing as environmental impacts are no more tolerable by society. Steelmaking plants already use different methods to monitor and detect slopping events, but they are often limited and unreliable. Therefore, this paper proposes a multi-sensor data fusion process to generate a reliable slopping index to warn operators of potential slopping events and detect the triggered ones. The work is based on sound and image data (67 heats with 27 slopping events) collected on previous trials at a 350-ton LD converter. The Kalman filter was applied as a data fusion agent of two indexes, one resulted from computer vision analysis of the LD converter mouth (image data), the other resulted from digital signal analysis of sound captured on the converter's hood (sound data). Fuzzy sets were applied for adaptative tuning of the Kalman filter to improve the data fusion process. Besides the increase of alarm accuracy and heat classification, the data fusion index worked better on different scenarios and produced a more reliable indicator for a slopping prevention system. [ABSTRACT FROM AUTHOR]

The existence of supraglacial lakes influences debris-covered glaciers in two ways. The absorption of solar radiation in the water leads to a higher ice ablation, and water draining through the glacier to its bed leads to a higher velocity. Rising air temperatures and changes in precipitation patterns provoke an increase in the supraglacial lakes in number and total area. However, the seasonal evolution of supraglacial lakes and thus their potential for influencing mass balance and ice dynamics have not yet been sufficiently analyzed. We present a summertime series of supraglacial lake evolution on Baltoro Glacier in the Karakoram from 2016 to 2020. The dense time series is enabled by a multi-sensor and multi-temporal approach based on optical (Sentinel-2 and PlanetScope) and Synthetic Aperture Radar (SAR; Sentinel-1 and TerraSAR-X) remote sensing data. The mapping of the seasonal lake evolution uses a semi-automatic approach, which includes a random forest classifier applied separately to each sensor. A combination of linear regression and the Hausdorff distance is used to harmonize between SAR- and optical-derived lake areas, producing consistent and internally robust time series dynamics. Seasonal variations in the lake area are linked with the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) based on air temperature and precipitation data derived from the climate reanalysis dataset ERA5-Land. The largest aggregated lake area was found in 2018 with 5.783 km2, followed by 2019 with 4.703 km2, and 2020 with 4.606 km2. The years 2016 and 2017 showed the smallest areas with 3.606 and 3.653 km2, respectively. Our data suggest that warmer spring seasons (April–May) with higher precipitation rates lead to increased formation of supraglacial lakes. The time series decomposition shows a linear increase in the lake area of 11.12 ± 9.57% per year. Although the five-year observation period is too short to derive a significant trend, the tendency for a possible increase in the supraglacial lake area is in line with the pronounced positive anomalies of the SPI and STI during the observation period.

As a sensitive indicator of climate change, forest phenology (e.g., the start of season (SOS)) has profound impacts on the global carbon cycle. Traditional phenological observations are based on surface observation networks. Generally, the outcomes of this manner are less representative and hard to implement in wide forested regions. Remote sensing based observation of forest SOS has currently been a popular way. Those sensors with coarse spatial resolution have been widely used to estimate forest SOS, but they create serious estimation errors in areas of high heterogeneity. Medium-resolution sensors, such as Landsat, face significant challenges in SOS monitoring due to the long revisit period. In this study, we aimed to develop a new method to estimate forest SOS from 2013 to 2019. First, we collected all available Landsat and Sentinel-2 images, and then redefined the linear regression coefficients for the bandpass adjustment to weaken the surface reflectance (SR) differences in different sensors. Subsequently, we improved and developed the modified continuous change detection and classification (MCCDC) model to generate daily vegetation index curves. Finally, we adopted the logistic regression model to test the potential of the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI) and land surface water index (LSWI) in evaluating the annual SOS. The reduced root mean square error (RMSE) for all bands after the integration indicated that the adjustment was successful. We visually compared Landsat’s synthetic images with the actual acquired images and found that their respective false-colour composites were highly similar. Assessing the SOS from the EVI, NDVI and LSWI showed different estimated results. By comparing the annual SOS derived from the three indices with the field observations, it was found that the SOS based on the EVI maintained a low consistency with the field observations. The SOS accuracy from LSWI was the highest and most forest SOS from LSWI in the study area were mainly concentrated in 80–150 days. These three indices all showed that the SOS always fluctuated by ± 4 days from 2013 to 2019. Facing the lack of clear remote sensing images with medium spatial resolution in cloudy and rainy areas, this study proposes an improved method to generate clear daily vegetation index images at a spatial resolution of 30 m, making annual SOS monitoring promising and feasible.

Stereotypical Motor Movements (SMMs) may seriously impede learning and social relationships are one of the distinctive and typical postural or motor behaviours linked with autism spectrum disorders (ASDs). A reliable infrastructure for automatic and quick SMM detection is provided by wireless retail sensor technology, which would facilitate targeted intervention and perhaps provide early warning of meltdown occurrences. However, because of significant inter- and intra-subject variability that is challenging for handmade features to handle, the detection and quantification of SMM patterns remain challenging. In this work, we suggest using the Enhanced Convolutional Neural Network (ECNN) to extract distinguishing characteristics directly from multi-sensor accelerometer inputs. Parameters of the ECNN are tuned using whale optimization. Results with Enhanced convolutional neural networks produce accurate and robust SMM detectors.

Based on the time and location information of nodes, multi-sensor complex networks can achieve high-speed and low-delay exchange of control and sensor data between sensor nodes to ensure the accuracy and real-time performance of the entire detection and control system. However, complex multi-sensor networks face limitations in terms of computing, storage, and network resources. After grouping the sensor data of nodes, this paper employs an adaptive weighted data fusion method to complete the data fusion processing in the network. This approach can significantly reduce the data redundancy in the wireless sensor network, save a large amount of storage resources, and lower the network bandwidth occupation, with high efficiency and good scalability. The least square (LS) fitting is performed by two sets of temperature and humidity data obtained by weighted fusion at the cluster head node. Experimental results indicate that the fusion of two-dimensional data transmitted to the base station or control center can further diminish the amount of data transmission. In addition, after the adaptive weighted data fusion of the two-dimensional data at the cluster head node, the energy consumption of the cluster head node without the fusion and the cluster head node after the fusion is compared. The results demonstrate that the energy consumption of the cluster head node is notably abridged, the energy of the cluster head node is saved to a greater extent, and the life cycle of the network is prolonged. [ABSTRACT FROM AUTHOR]

During the driving of the smart car, due to the complexity of the environment, a single sensor or multiple homogeneous sensors cannot fully perceive the traffic environment around the smart car. Therefore, it is necessary to study the information fusion scheme of heterogeneous sensors, use the advantages of heterogeneous sensors to make up for the shortcomings of a single sensor, to realize the function of cooperation and mutual compensation among multiple heterogeneous sensors. Therefore, this paper proposes a data fusion method based on discrete factor multi-sensor target recognition. The output data from multiple sensors acquired over too many periods and multiple regions give the discrete factor of the sensor corresponding to the target characteristic. The current weight of multi-sensor target recognition is given according to the discrete factor, and the relative consistency and the relativeness of multi-sensor target recognition is established. Weighted consistency and other functions; combined with the current weights of multi-sensor target recognition and related consistency functions, a data fusion support calculation model for multi-sensor target recognition is constructed. The test results show that the scheme is more reliable and has a certain anti-interference ability, which can make up for the shortcomings of a single sensor and improve the target recognition rate. [ABSTRACT FROM AUTHOR]

Research on mobile robots began in the late 1960s. Mobile robots are a typical autonomous intelligent system and a hot spot in the high-tech field. They are the intersection of multiple technical disciplines such as computer artificial intelligence, robotics, control theory and electronic technology. The product not only has potentially very attractive application value and commercial value, but the research on it is also a challenge to intelligent technology. The development of mobile robots provides excellent research for various intelligent technologies and solutions. This dissertation aims to study the research of multi-sensor information fusion and intelligent optimization methods and the methods of applying them to mobile robot related technologies, and in-depth study of the construction of mobile robot maps from the perspective of multi-sensor information fusion. And, in order to achieve this function, combined with autonomous exploration and other related theories and algorithms, combined with the Robot Operating System (ROS). This paper proposes the area equalization method, equalization method, fuzzy neural network and other methods to promote the realization of related technologies. At the same time, this paper conducts simulation research based on the SLAM comprehensive experiment of the JNPF-4WD square mobile robot. On this basis, the high precision and high reliability of robot positioning are further realized. The experimental results in this paper show that the maximum error of the X-axis and Y-axis, FastSLAM algorithm is smaller than EKF algorithm, and the improved FASTSALM algorithm error is further reduced compared with the original FastSLAM algorithm, the value is less than 0.1. [ABSTRACT FROM AUTHOR]

Multi-sensor information fusion plays an important role in practical application. Although D-S evidence theory can handle this information fusion task regardless of prior knowledge, counter-intuitive conclusions may arise when dealing with highly conflicting evidence. To address this weakness, an improved algorithm of evidence theory is proposed. First, a new distribution distance measurement method is first proposed to measure the conflict between the evidences, and the credibility degree of the evidences can be obtained. Next, a modified information volume calculation method is also introduced to measure the effect of the evidence itself, and the information volume of the evidences can be generated. Afterwards, the credibility degree of each evidence can be modified based on the information volume to obtain the weight of each evidence. Ultimately, the weights of the evidences will be used to adjust the body of evidence before fusion. A numerical example for engine fault diagnosis exhibits the availability and effectiveness of the proposed method, where the BPA of the true fault is 89.680%. Furthermore, an application for target recognition is given to show the validity of the proposed algorithm, where the BPA of the true target is 98.948%. The experimental results show that the proposed algorithm has the best performance than other methods. [ABSTRACT FROM AUTHOR]

To solve the problem of inaccurate results of vehicle routing prediction caused by a large number of uncertain information collected by different sensors in previous automatic vehicle route prediction algorithms, an automatic vehicle route prediction algorithm based on multi-sensor fusion is studied. The process of fusion of multi-sensor information based on the D-S evidence reasoning fusion algorithm is applied to automatic vehicle route prediction. According to the contribution of a longitudinal acceleration sensor and yaw angular velocity sensor detection information to the corresponding motion model, the basic probability assignment function of each vehicle motion model is obtained; the basic probability assignment function of each motion model is synthesized by using D-S evidence reasoning synthesis formula. The new probability allocation of each motion model is obtained under all evidence and then deduced according to the decision rules. Guided by the current optimal motion model, the optimal motion model at each time is used to accurately predict the vehicle movement route. The simulation results show that the prediction error of the algorithm is less than 4% in the process of 30 minutes of automatic vehicle route prediction. [ABSTRACT FROM AUTHOR]

Bats represent a highly diverse group of mammals and are essential for ecosystem functioning. However, knowledge about their behaviour, ecology and conservation status is limited. Direct observation of marked individuals (commonly applied to birds) is not possible for bats due to their small size, rapid movement and nocturnal lifestyle, while neither popular observation methods such as camera traps nor conventional tracking technologies sufficiently capture the behaviour of individuals. The combination and networking of different sensors in a single system can overcome these limitations, but this potential has been explored only to a limited extent.We present BatRack, a multi‐sensor device that combines ultrasonic audio recordings, automatic radio telemetry and video camera recordings in a single modular unit. BatRack facilitates the individual or combined scheduling of sensors and includes a mutual triggering mode. It consists of off‐the‐shelf hardware and both its hardware blueprints and the required software have been published under an open license to allow scientists and practitioners to replicate the system.We tested the suitability of radio telemetry and audio sensors as camera triggers and evaluated the detection of individuals in video recordings compared to radio telemetry signals. Specifically, BatRack was used to monitor the individual swarming behaviour of six members of a maternity colony of Bechstein's bat. Preliminary anecdotal results indicate that swarming intensity is related to reproductive state and roost switching.BatRack allows researchers to recognize individual bats and monitor their behavioural patterns using an easily deployed and scalable system. BatRack is thus a promising approach to obtaining detailed insights into the behavioural ecology of bats. [ABSTRACT FROM AUTHOR]

As the world population increases and the need for food monoculture farms are using more and more agrochemicals, there is also an increase in the possibility of theft, misuse, environmental damage, piracy of products, and health problems. This article addresses these issues by introducing the agrochemical pervasive traceability model (APTM), which integrates machine learning, sensors, microcontrollers, gamification, and two blockchains. It contributes in two dimensions: (I) the study of the environmental, product piracy and regulatory of agrochemical control; (II) the technological dimension: application of an adequate set of sensors collecting multiple data; modeling and implementation of a system via machine learning for analyzing and predicting the behavior and use of agrochemicals; development of a scoring system via gamification for reverse use of agrochemicals; and presenting a record of transactions in a consortium of two blockchains, simultaneously. Its main advantage is to be a flexible, adaptable, and expansive model. Results indicated that the model has positive aspects, from detecting the agrochemical, its handling, and disposal, recording of transactions, and data visualization along the reverse supply chain. This study obtained a round trip time of 0.510 ms on average; data transfers between layer one and its persistence in the database were between 4 to 5 s. Thus, blockchain nodes consumed only 34 to 38% of CPU and recorded transactions between 2 to 4 s. These results point to a horizon of applicability in real situations within agricultural farms. [ABSTRACT FROM AUTHOR]

The demand for electricity in economic development is increasing, so it is necessary to establish a unified power marketing audit evaluation system, standardize the power marketing audit workflow, and formulate scientific audit evaluation standards to make the power marketing audit more scientific and objective. This paper introduces multi-sensor cooperative sensing from IoT technology and uses multi-source data fusion technology for complete multi-sensor information fusion. The objective is to illustrate how multi-sensor information fusion and Internet of Things (IoT) improvements may improve the power industry's auditing process, especially at substations, resulting in increased efficiency, accuracy, and risk management. We focus on analyzing the environmental characteristics of the power IoT sensing system in the substation scenario and compare the advantages and disadvantages of various types of networking algorithms. Experiments show that our method has a low bandwidth footprint, energy consumption, and high data accuracy. [ABSTRACT FROM AUTHOR]

Recent classifications in Earth Observation (EO) commonly involve a combination of Hyperspectral Image (HSI) and Light Detection and Ranging (LiDAR) signals. However, many current methods fail to consider the HSI-LiDAR information concurrently, especially in terms of both its intra- and inter-modality aspects. Additionally, current methods are generally limited in their ability to fuse the features extracted from different modalities. Hence, this paper proposes a center-bridged framework, called Interaction Fusion (IF), that can leverage diverse information concerning the intra- and inter-modality relationships at the same time. More specifically, intra- and inter-modality information can be enriched by introducing the center patch of HSI (cp-HSI) as an extra input, This introduces additional contextual information within and across modalities that can be leverage for deeper insights. Further, we propose a fusion matrix as a structural feature map designed to integrate nine views generated by a view generator, enabling the adaptive combination of intra- and inter-modality information. Overall, our approach allows potential patterns to be captured, while mitigating any bias resulting from incomplete information. Extensive experiments conducted on three widely recognized datasets – Trento, MUUFL, and Houston – demonstrate that the IF framework achieves state-of-the-art results, surpassing existing methods. [ABSTRACT FROM AUTHOR]

The dynamic of marine environmental parameters affects the distribution of commercial tuna in the sea of the maritime continent. Hence, the objectives of this study are to develop spatial distribution models for the four main tuna species in the Maritime continent's sea with reasonable accuracy, identify their correlation with marine environmental parameters, and investigate areas of interaction between those tuna species. The study develops the distribution models for albacore (Thunnus alalunga), bigeye (Thunnus obesus), yellowfin (Thunnus albacares), and skipjack (Katsuwonus pelamis) tuna species, utilizing multi-sensor satellite remote sensing and maximum entropy. The results show models have good performance, focusing on environmental factors such as sea surface temperature (SST), chlorophyll-a (CHL), and sea surface height anomalies (SSHA), combined with eddy kinetic energy (EKE). Seasonal variations in potential tuna habitats are revealed, emphasizing the influence of those marine environmental conditions. From December to May, the four commercial tuna species were distributed in conditions characterized by SST of 26–31.5 °C, CHL levels of 0–3 mg/l, SSHA of −0.3 to 0.2 m, and EKE of 0–1 m2/s2, while from June to November, they experienced SST of 23–31 °C, CHL levels of 0–4 mg/l, SSHA of −0.5 to 0.3 m, and EKE of 0–1.1 m2/s2. The spatial persistence of the four tuna species emerged mainly around the south sea of Java, with skipjack being the most common species found in the sea of the maritime continent. With sufficient and evenly distributed tuna presence records, the results indicate the potential for extrapolation beyond the training data to estimate habitat suitability for the four commercial tuna distributions. The results also suggest potential competition between tuna species sharing ecological niches and highlight possible overlapping areas where different tuna species interact with the same fishing gear. • The species distribution models for the four tuna species, which performed well, were developed. • Environmental factors affecting tuna distribution in the sea of the Maritime Continent: SST, CHL, SSHA, and EKE. • Seasonal variations for the distribution of the four tuna species were revealed with skipjack being the most common species. • Tuna species distributed mainly persisted in the southern Java Sea. • This study also reveals overlapping areas of tuna habitats and their interactions with the same fishing gear. [ABSTRACT FROM AUTHOR]

This paper is concerned with distributed fusion (DF) estimation problem for nonlinear multi-sensor systems with correlated noises. Based on a recursive linear minimum variance estimation (RLMVE) framework, a novel filter is developed. It is proved that the RLMVE-based filter and the existing de-correlated filter have the functional equivalence. Then, for multi-sensor cases, cross-covariance matrices between any two local filters are derived. Based on the RLMVE-based filter and cross-covariance matrices, a DF filter weighted by matrices is proposed in the sense of linear minimum variance. Finally, based on the existing de-correlated filter, the algorithm of cross-covariance for de-correlated systems and the DF algorithm weighted by matrices, a de-correlated DF filtering algorithm is proposed. An example verifies the effectiveness of the proposed RLMVE-based DF filter.

Sleep recognition refers to detection or identification of sleep posture, state or stage, which can provide critical information for the diagnosis of sleep diseases. Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep data. Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which lead to the decline of the learning ability and over-fitting. Self-supervised learning technologies have shown their capabilities to learn significant feature representations. In this paper, a novel self-supervised learning model is proposed for sleep recognition, which is composed of an upstream self-supervised pre-training task and a downstream recognition task. The upstream task is conducted to increase the data capacity, and the information of frequency domain and the rotation view are used to learn the multi-dimensional sleep feature representations. The downstream task is undertaken to fuse bidirectional long-short term memory and conditional random field as the sequential data recognizer to produce the sleep labels. Our experiments shows that our proposed algorithm provide promising results in sleep identification and can further be applied in clinical and smart home environments as a diagnostic tool. The source code is provided at: “https://github.com/zhaoaite/SSRM”.

Fusion technologies have rapidly evolved. These technologies are normally customized according to the needs of domains. Despite a large number of publications on intelligence fusion applications for various domains, they are scattered. The aim of this review is to present the state of the art for intelligence fusion applications within a specific domain. We identified three major domains for the purpose, namely robotics, military, and healthcare, during the initial process of the systematic review. These three domains are always in need of superior intelligence. Articles were searched mainly in IEEE Xplore. We limit the range of publications to the year 2014 to 2019, to focus on the most recent publications. We adopt the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to screen, filter and evaluate qualities of each retrieved article. As a result, we retrieved 675 articles at the initial stage of the search, we conducted screening and filtering process and reviewed 153 articles potential articles, and finally, we excluded 36 articles as they do not comply with our quality assessment criteria. Only 117 articles are included. The results of this study are a list of classified applications within the domains and a number of relevant techniques or approaches used in each classified application. The finding of this review showed that the most published works for the use of intelligence fusion are mainly applications in the robotics domain, where mostly used techniques are Kalman Filter and its variants. Outcomes of this study can be a guideline or an insight for researchers to further develop and implement in this field.

Gait is a significant factor that affects human health, and monitoring a person's gait with sensing devices during daily life can detect abnormal gait events that affect numerous physical health problems. In particular, flat feet can cause changes in alignment conditions of the foot, ankle, leg, pelvis and spine. The primary problem with previous studies of wearable devices for measuring gait have focused on quantitatively monitoring the degree of gait rather than the limited gait ability. The existing method of feeding back the degree of gait or activity does not consider the severity of the subject and is insufficient for qualitative evaluation or training of gait. The significance of this study is development of convenient detecting and long-term tracking tools that can be used by both patients and clinicians for prescreening flat feet and monitoring the progress of flat feet treatment. For wearable devices for flatfoot detection to be most effective, detection systems and algorithms must be accurate, robust, reliable and computationally-efficient. In this paper, we developed an integrated smart wearable gait-monitoring device comprised of three sensors: front force, rear force, and an ankle flex sensor. We propose a new flat feet detection methodology based on a dynamic sensing window and a deep neural network with scaled principal component analysis (PCA). We tested 24 subjects, including both those with healthy gait and flat-feet-affected gait. Our study shows that the proposed sensing devices could be worn comfortably. The proposed deep neural network (DNN) model outperformed the other five classifier algorithms considered, and the area under the curve (AUC) value of the method was 87.1%. This wearable device can thus be easily and simply used both by patients and doctors to monitor the progress of flat feet and prescreen for possible gait problems in daily life.

What drives growth becomes cancerous when it goes beyond limits. Contrary to this common sense, today, consumerism drives our economies and feeds our appetite for ever-growing wants. As a result, we are damaging our ecosystems and risking our very existence on Earth. Though too late, various efforts are promoted by governments and driven by industries to rapidly decarbonize our energy systems and sustainably consume and recycle raw materials. We have discussed two ongoing projects in the domain of energy transition and circular economy. The first one transforms industrial carbon emissions into green fuels and the second one helps in efficient and sustainable segregation and recycling of plastic waste using multi-sensor-driven AI and blockchain tools. These examples demonstrate how circular economy and energy transitions complement each other in the battle against climate change and pollution.

Wireless sensor network (WSN) is one of the most important components of the Internet of Things (IoT). IoT on the WSN layer, measures different parameters by different sensors embedded in the multi-sensor nodes. The limitation of energy sources in the sensor nodes is one of the most important challenges in exploiting WSNs. Routing and data aggregation are two basic methods to reduce the energy consumption in the WSNs. Compressive sensing (CS) is one of the most effective techniques for data aggregation in WSNs. The most studies related to the use of CS techniques to reduce communication cost in the network are based on the single-sensor node WSNs. So, in this paper, we show that how CS techniques can be applied to the multi-sensor IoT-based WSNs. Given that the sparsity of the environmental data read by multi-sensor nodes is an important parameter for using the CS techniques in WSNs, various different scenarios have been analyzed from the point of view of data sparsity in this study, as well as transmission methods, and data aggregation techniques in a multi-sensor WSN. To evaluate the performance of the CS techniques in a multi-sensor WSN, all investigated scenarios are evaluated for two important techniques of CS named compressive data gathering (CDG) and hybrid compressive sensing (HCS), and in order to show the efficiency of the system in using of the CS, these techniques have been compared to the conventional Non-CS method. We show that the use of HCS technique has a considerable effect on reducing communication costs in a multi-sensor IoT-based WSN.

The cueing model for sensor cueing phased array radar is built in this paper to research the mutual cueing of multi-sensor in multi-platform for target searching. For the purpose of establishing the distribution probability density model of targets in cueing airspace, cueing error of multi-platform is derived with error transfer method. The successful cueing probability and false hand-off probability between platforms are analyzed, meanwhile the requirement of timeliness is defined. An optimized target searching strategy of cueing radar which adopts distance search through using the cueing center as starting point and controlling false hand-off probability of a single wave position is put forward. The simulation result illustrates that the searching strategy can significantly save radar search resources to capture target successfully, simultaneously decrease the false hand-off probability. The strategy can be applied to the design of dynamic programming software of multi-sensor in multi-platform for collaborative search, which requires accuracy and real-time capability.

Forest aboveground biomass (AGB) is a critical measure of ecosystem structure and plays a key role in global carbon cycling. Due to its widespread availability, optical remotely sensed data are key for regional- and global-scale AGB assessment, and with the planned and recent launches of spaceborne imaging spectroscopy missions such as the Environmental Mapping and Analysis Program (EnMAP), understanding the benefit of added spectral information for AGB mapping is important. We used simulated EnMAP imagery derived from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery acquired over Sonoma County, California, USA in combination with Landsat time series to map forest AGB. A Gaussian Process Regression model was implemented to estimate forest AGB from one- and two-date (April and June) EnMAP imagery. A lidar-based reference AGB map was used as base data for training and validation sample extraction. As a comparison, we used corresponding Landsat Best Available Pixel (BAP) composites as well as a year-long 16-day interpolated Landsat time series (TS) for 2013. EnMAP imagery was able to effectively map forest AGB, with the two-date model (RMSE = 97.5 Mg/ha) outperforming the two single date models. All EnMAP models outperformed the corresponding Landsat BAP models, the best of which was the two-date model (RMSE = 108.8 Mg/ha). The added temporal dimension of the Landsat time series resulted in the best Landsat-based AGB map (RMSE = 102.3 Mg/ha). Combining the two datasets further improved AGB mapping efforts, with 2-date EnMAP + 2013 Landsat TS providing the best overall AGB maps (RMSE = 86.0 Mg/ha). This study demonstrates not only the added value of hyperspectral imagery for forest AGB mapping, but also the possible synergies between hyperspectral and multispectral data sources and hence between spectrally and temporally dense information. It can be expected that with the next generation of spaceborne hyperspectral sensors, the combination of dense spectral and temporal data will work to further improve global efforts for mapping forest AGB from optical Earth observation data.

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Architectural structures today are increasingly complex and structural health monitoring plays an important role in guaranteeing their safety. How to improve the reliability of deformation analysis is, thus, one of the key problems. This article combines laser-based measurement technology and the Carrera unified formulation (CUF) method to investigate the deformation of engineering structures. Within this article, we simulate architectural structures using the CUF geometric model, which is consistent with the results of the laser tracker experiment. We aimed at constructing an intelligent and efficient CUF model which can be applied extensively in the monitoring of various constructs, such as tunnels and bridges. The innovation of this article is that high-accuracy laser tracker technology is integrated with an effective CUF model to investigate the load-displacement relationship considering lateral displacement. [ABSTRACT FROM AUTHOR]

Freeform parameterizations to reproduce structure deformation are increasingly important topics in laser-scanner-based deformation analyses. High-accuracy assurance of free-form surface approximation is extremely critical for reliable deformation analysis. One main challenge in this field is the model selection. Improper model complexity could result in under-fitting the real object shape or overfitting data noises, and thus a failure of deformation analysis. A multi-sensor system could integrate advantages of different sensors and improve the quality of mission completed. This paper combines terrestrial laser scanning (TLS) and laser tracker (LT) technologies, to enhance high-accuracy surface modeling in deformation analysis. A surface-based B-spline approximation and a multi-sensor system are investigated, the latter of which focuses mainly on the combination of TLS and LT technologies. The innovation of this paper is that the surface-based B-spline approximation is validated and optimized with LT corner cube reflectors. Hypothesis testing is adopted to select the best parameter setting by judging most consistency of TLS and LT in various epochs. In the B-spline surface modeling, both instrumental and numerical uncertainties are considered. We use the instrumental uncertainty model based on intensity value, as well as numerical uncertainty based on adjustment theories. A sampling strategy is proposed to avoid data gaps and obtain even distributed data points. [ABSTRACT FROM AUTHOR]

Track and field sports are known as the "mother of sports". Whether in the field of athletics, fitness, or education, modern track and field sports have developed rapidly. The field of athletics has reached the point where it challenges the limits of humans. The development of China is inseparable from the support of science and technology, and it is inseparable from human scientific research on track and field sports. In order to improve the scientific level of track and field training methods and develop our country's sports industry, this paper designs a track and field training information collection and feedback system based on multi-sensor information fusion. In the method part, this article briefly introduces the content of track and field sports, the mode of multi-sensor information fusion and the existing sports information collection system, using weight coefficient fusion method, D-S evidence theory algorithm and Kalman filter algorithm. This paper designs an information collection and feedback system based on multi-sensor information fusion, and conducts demand analysis, comparative analysis, and data record analysis on this system. By designing the experimental group and the control group, it can be seen that the average performance of the two groups of athletes in the 50-meter run in 8 weeks has improved, and the data of the experimental group and the control group show significant differences. After the experiment, the average performance of the male athletes in the control group increased from around 8.32 to around 8.12, an increase of 4.7%. The performance of male athletes in the experimental group increased from 8.37 to 7.92, an increase of 5.6%. It can also be known that before the experiment, the average performance of the athletes in the selected control group was due to the experimental group, but after 8 weeks of experiment, the increase in the experimental group was higher than that of the control group. This shows that the data collection and feedback system using multi-sensor information fusion can be more accurately and differentiatedly applied to track and field training, and can find problems in athletes, so as to prescribe the right medicine. [ABSTRACT FROM AUTHOR]

The sensing properties of two chalcogenide materials were investigated. It is shown that membranes based on the PbI2-Ag2S-As2S3 and PbS-Ag2S-As2S3 materials show sensitivity to the presence of Pb2+ species in solution. In both cases they exhibit near-Nernstian response. We mainly focused on the best matrix. The basic physicochemical parameters were then measured. The developed ISE exhibits a near-Nernstian slope of 27 mV/decade versus Pb2+ in the range of 10-5,5 - 10-3 M. Moreover the proposed Pb-SE shows excellent out-put signal in the presence of foreign ions. The analytical properties such as reproducibility, response time, and stability were studied. We report a multi-sensor process that we developed for in-situ real-time monitoring of waste water. The very first results of prototype measurements are presented. [ABSTRACT FROM AUTHOR]

With the increasing complexity of electromagnetic environment, strong interferences and high clutter rate lead to the low detection probability problem, which brings a new challenge for the observation system to accurately estimate the number and states of target in surveillance area. Aiming at the problem of low detection probability, a multi-sensor networking target tracking algorithm based on labelled multi-Bernoulli (LMB) in the framework of random finite sets is proposed. The proposed algorithm incorporates the LMB framework into target tracking scene of multi-sensor networking with different detection ranges for the first time and realizes the target number and corresponding sate stability estimation of multi-target tracking. The simulation results show that the proposed algorithm not only provides stable estimation of trajectories under the condition of low detection probability, including targets born and die, but also gives an effective superposition of different detection ranges, which makes full advantage of multiple sensors. [ABSTRACT FROM AUTHOR]

Aquatic invasive weeds affect hydrological, ecological, and socio-economic activities on freshwater ecosystems. On the Lower Volta River (LVR) of Ghana, invasive aquatic weeds have been known to be nuisance to fishing, navigation, aquaculture, hydropower production and other agricultural practices in the area. While information on the spatial and temporal distribution of aquatic weeds would be beneficial in improving weed management and control measures on the river, such information is very scanty. Also, these aquatic weeds are also biomass resources, that can be transformed to bioenergy. Thus, this study evaluated the spatial and temporal variations of aquatic weeds on the Lower Volta River, and assessed their potential biomass for bioenergy production.Random Forest (RF) algorithm and Landsat images were used to map the distribution of the weeds in 1975, 2003, and 2020, respectively. Accuracy assessment results showed mean Overall Accuracy (OA) of 83.44% and mean User Accuracy (UA) of 79.24%. The results indicated that as of 1975, aquatic weeds covered only 1495 ha and appeared in some specific locations such as Kpong and Ada. However, by 2003, the weeds had spread to most parts of the river covering 5600 ha, which was an increase of approximately 4-fold within a period of 28 years. The area covered by the weeds, however declined by 1505 ha between 2003 and 2020. Thus, in 2020, water hyacinth covered about 36% of the aquatic weeds relative to 28% in 2003. The results showed that, the quantity of the water hyacinth biomass per unit area was 21.5 kg/m2. This result can also be used as the basis for resource assessment as well as determination of its viability for bioenergy production and strategies for its modern utilisation. The conversion of water hyacinth into bioenergy remains one of the best aquatic weed management strategies that must be adopted in LVR.

In order to solve the difficulty for intelligent vehicles in detecting near-field targets, this paper proposes a near-field object detection method based on multi-sensor information fusion. Firstly, the F-CenterFusion method is proposed to fuse the information from LiDAR, millimeter wave (mmWave) radar, and camera to fully obtain target state information in the near-field area. Secondly, multi-attention modules are constructed in the image and point cloud feature extraction networks, respectively, to locate the targets’ class-dependent features and suppress the expression of useless information. Then, the dynamic connection mechanism is used to fuse image and point cloud information to enhance feature expression capabilities. The fusion results are input into the predictive inference head network to obtain target attributes, locations, and other data. This method is verified by the nuScenes dataset. Compared with the CenterFusion method using mmWave radar and camera fusion information, the NDS and mAP values of our method are improved by 5.1% and 10.9%, respectively, and the average accuracy score of multi-class detection is improved by 22.7%. The experimental results show that the proposed method can enable intelligent vehicles to realize near-field target detection with high accuracy and strong robustness.

In this study, we propose a facile fabrication process for all-solid-state ion-selective electrodes by laminating and drop-casting; these electrodes are suitable for applications in the environmental, agricultural, and medical fields, such as oral condition monitoring. To date, research has focused only on developing ion-selective electrodes for specific applications. However, an ion-selective electrode with compatibility for wide use has not been realized because specialized processing techniques and equipment are required. Our fabrication process achieved an ion-selective multi-sensor with a wireless system, which is crucial for overcoming the aforementioned challenge. The developed sensors exhibited sufficient sensitivity, repeatability, response time, and selectivity for medical, environmental, and agricultural applications.

In the field of automatic detection of ship exhaust behavior, a deep learning-based multi-sensor hierarchical detection method for tracking inland river ship chimneys is proposed to locate the ship exhaust behavior detection area quickly and accurately. Firstly, the primary detection uses a target detector based on a convolutional neural network to extract the shipping area in the visible image, and the secondary detection applies the Ostu binarization algorithm and image morphology operation, based on the infrared image and the primary detection results to obtain the chimney target by combining the location and area features; further, the improved DeepSORT algorithm is applied to achieve the ship chimney tracking. The results show that the multi-sensor-based hierarchical detection and tracking method can achieve real-time detection and tracking of ship chimneys, and can provide technical reference for the automatic detection of ship exhaust behavior.

Multisensor Earth observation has significantly accelerated the development of multisensor collaborative remote sensing applications such as all-weather mapping using synthetic aperture radar (SAR) images and optical images. However, in the real-world application scenarios, not all data sources may be available, namely, the missing-modality problem, e.g., the poor imaging conditions obstruct the optical sensors, and only SAR images are available for the mapping. This real-world scenario raises the challenge of how to leverage historical multisensor data to improve the representation ability of the available model. The knowledge transfer based and knowledge distillation based approaches, as feasible solutions, can be used to transfer knowledge from other sensor models to the available model. However, these approaches suffer from the problem of forgotten knowledge and the multi-modality co-registration problem, which means that the leveraging of historical multisensor data is inefficient. The essential problem lies in the fact that these approaches are designed following the single-sensor data-driven approach. In this paper, a registration-free multisensor data-driven learning method, namely, deep multisensor learning, in a new perspective of knowledge retention, is proposed to overcome the above problems by learning a meta-sensory representation. To explore the existence of the meta-sensory representation, the meta-sensory representation hypothesis is first proposed, which reveals that the essential difference of the deep models trained on data from different sensors lies in the parameter distribution of the sensor-invariant and sensor-specific operations. Based on this hypothesis, a prototype network is proposed to learn the meta-sensory representation by modeling the knowledge retention mechanism, using the proposed difference alignment operation (DiffAlignOp). DiffAlignOp enables the prototype network to dynamically generate sensor-specific networks to gather supervised signals from registration-free multisensor data. This dynamic network generation is differentiable. Therefore, multisensor gradients can be obtained to learn the meta-sensory representation. To demonstrate the flexibility and practicality of deep multisensor learning, the application of all-weather mapping in a missing-modality scenario was performed. The experiments were conducted on a large public multisensor all-weather mapping dataset, which consists of high-resolution optical and SAR imagery with a spatial resolution of 0.5 m. The experimental results suggest that deep multisensor learning is superior to the other learning approaches in performance and stability, and reveals the importance of meta-sensory representation in multisensor remote sensing applications. [ABSTRACT FROM AUTHOR]

The accuracy of attitude and heading measurement, as well as the system real-time performance are basic indicators used to evaluate an attitude and heading reference system (AHRS). In order to improve the attitude and heading measurement accuracy under dynamic complex environment, the AHRS system should also have numerical stability and calculation robustness. The AHRS system based on MEMS multi-sensor fusion can realize fusion processing of data measured by multiple sensors, so as to calculate and obtain the optimal carrier attitude and heading information, conduct real-time output, and improve the accuracy and reliability of attitude and heading measurement. For the AHRS system consisting of MEMS gyroscope, accelerometer and triaxial magnetometer, attitude and heading detection principle and algorithm based on MEMS multi-sensor fusion were proposed in this study: The information of the system itself was firstly used to discriminate motion state of the carrier within the filtering cycle, and then Kalman filtering was conducted using different measured information according to motion state to correct the attitude error angle caused by gyroscopic drift. On this basis, an attitude fusion algorithm based on extended Kalman filtering technology was designed for time update process of Kalman filtering, output information of accelerometer was taken as observed quantity under certain conditions to realize measurement updating process of Kalman filtering, and then attitude angle was calculated. In an optical fiber attitude and heading system project in practical engineering, a vehicle field test analysis was carried out simultaneously with the system using ordinary attitude algorithm, and the results showed that the extended Kalman filtering algorithm designed according to the simulation results could realize multi-sensor information fusion, improve measurement accuracy and realize accurate attitude positioning, so as to provide simpler and more flexible criteria for carrier motion status. The results have verified the accuracy and reliability of the algorithm, so it is feasible in practical engineering. [ABSTRACT FROM AUTHOR]

Sports training generally focuses on speed of response and variety of strategies aimed at encouraging sustainable physical activity and improving learning skills for players, thus, enhance their performance and skills through matches. In this paper, we are presenting a methodology for multi-sensor fusion method of IR depth camera and smart band sensors using curve fitting to recognize and group the characteristics of strokes performed by players and adapt the training accordingly. The main aim of the methodology is to classify various techniques played in table tennis and enhance the strokes based on various body joints. Moreover, the main contribution of this paper is to experiment different sensors to get the most optimal tools in classifying players strokes. Also, to test various classification algorithms to get the optimal and best result possible. Overall, based on the experiments we have concluded that sensor fusion between internal sensors and IR depth camera has increased the classification results and robustness of the solution. The system's results indicate an average accuracy of 95% - 100%. [ABSTRACT FROM AUTHOR]

In recent years, offshore wind farms have frequently encountered engineering geological disasters such as seabed liquefaction and scouring. Consequently, in situ monitoring has become essential for the safe siting, construction, and operation of these installations. Current technologies are hampered by limitations in single-parameter monitoring and insufficient probe-penetration depth, hindering comprehensive multi-parameter dynamic monitoring of seabed sediments. To address these challenges, we propose a foldable multi-sensor probe and establish an underwater adaptive continuous penetration system capable of concurrently measuring seabed elevation changes and sediment pore water pressure profiles. The reliability of the equipment design is confirmed through static analysis of the frame structure and sealed cabin. Furthermore, laboratory tests validate the stability and accuracy of the electrical and mechanical sensor measurements. Preliminary tests conducted in a harbor environment demonstrate the system's effectiveness.

With the continuous development of new sensor features and tracking algorithms for object tracking, researchers have opportunities to experiment using different combinations. However, there is no standard or agreed method for selecting an appropriate architecture for autonomous vehicle (AV) crash reconstruction using multi-sensor-based sensor fusion. This study proposes a novel simulation method for tracking performance evaluation (SMTPE) to solve this problem. The SMTPE helps select the best tracking architecture for AV crash reconstruction. This study reveals that a radar-camera-based centralized tracking architecture of multi-sensor fusion performed the best among three different architectures tested with varying sensor setups, sampling rates, and vehicle crash scenarios. We provide a brief guideline for the best practices in selecting appropriate sensor fusion and tracking architecture arrangements, which can be helpful for future vehicle crash reconstruction and other AV improvement research.

For mobile robots, the high-precision integrated calibration and structural robustness of multi-sensor systems are important prerequisites for ensuring healthy operations in the later stage. Currently, there is no well-established validation method for the calibration accuracy and structural robustness of multi-sensor systems, especially for dynamic traveling situations. This paper presents a novel validation method for the calibration accuracy and structural robustness of a multi-sensor mobile robot. The method employs a ground-object-air cooperation mechanism, termed the "ground surface simulation field (GSSF)-mobile robot -photoelectric transmitter station (PTS)". Firstly, a static high-precision GSSF is established with the true north datum as a unified reference. Secondly, a rotatable synchronous tracking system (PTS) is assembled to conduct real-time pose measurements for a mobile vehicle. The relationship between each sensor and the vehicle body is utilized to measure the dynamic pose of each sensor. Finally, the calibration accuracy and structural robustness of the sensors are dynamically evaluated. In this context, epipolar line alignment is employed to assess the accuracy of the evaluation of relative orientation calibration of binocular cameras. Point cloud projection and superposition are utilized to realize the evaluation of absolute calibration accuracy and structural robustness of individual sensors, including the navigation camera (Navcam), hazard avoidance camera (Hazcam), multispectral camera, time-of-flight depth camera (TOF), and light detection and ranging (LiDAR), with respect to the vehicle body. The experimental results demonstrate that the proposed method offers a reliable means of dynamic validation for the testing phase of a mobile robot.

Data labeling has become a major problem in industries aiming to create and use ground truth labels from massive multi-sensor archives to feed into Artificial Intelligence (AI) applications. Annotation of multi-sensor set-ups with multiple cameras and LIDAR is now particularly relevant for the automotive industry aiming to build Autonomous Driving (AD) functions. In this paper, we present the Video Content Description (VCD), as the first open source metadata structure and set of tools, able to structure annotations for such complex scenes, including unprecedented flexibility to label 2D and 3D objects, pixel-wise labels, actions, events, contexts, semantic relations, odometry, and calibration. Several example cases are reported to demonstrate the flexibility of the VCD.

To deal with the increasingly severe land degradation in the downstream of the Heihe River Basin (HRB) in northwest China, an Ecological Water Diversion Project (EWDP) was implemented since 2000. A comprehensive analysis of the effects associated with the project on land degradation is necessary. According to the concept of Land Degradation Neutrality (LDN), multi-sensor data has been used to extract information on Land Use/Land Cover (LULC), Aeolian desertification, and vegetation dynamics, which were chosen as monitoring indices to reveal the process of land degradation in the HRB. Then, these results were combined with meteorological data, socio-economic statistics, and hydrological data to discuss the main driving factors influencing the land degradation process to evaluate the effects of the EWDP on land degradation in the HRB. The results showed that the process of land degradation in the HRB could be divided into two stages, in which the degradation trend was dominant from 1990 to 2000, and the rehabilitation trend was dominant from 2000 to 2015. Although both climate variation and human activities have been favorable to land degradation development in the HRB, climate factors have no significant influence on land degradation in the midstream and downstream in the HRB. The decrease of available water resources is the dominant driving factor of a series of ecological environment problems in the downstream of the HRB, and the land degradation process of the HRB has been stopped and reversed, mainly attributed to the EWDP. The EWDP facilitated the recovery of the deteriorated ecosystem by improving the efficiency of surface water resources reallocation in the downstream. Still, the EWDP indirectly led to the sink of the groundwater table in the midstream, resulting in local vegetation degradation.

Abstract Mobile robot is a very important branch of robotics. In practice, the performance of mobile robot is required to be higher and higher. It is required that mobile robot can adapt to different complex environments through its own intelligent system to achieve the established functional goals. Compared with traditional path planning with single sensor information, Kalman filter is used to fuse multi-sensor information, and path planning method based on improved dynamic artificial potential field method is studied. Using the improved dynamic artificial potential field method, the robot can also achieve optimal path planning and obstacle avoidance in complex dynamic environment. The simulation results show that the proposed algorithm is feasible.

As a result of the dependence worldwide on satellite technology, it is now necessary to use advanced multi-target tracking algorithms for space debris tracking systems to maintain custody of space objects around the earth. One principal challenge is the correct association of observations with objects. This paper presents a multi-sensor, space-debris tracking algorithm using δ-generalized labeled multi-Bernoulli (δ-GLMB) filtering. The algorithm provides a solution to the key challenges (e.g., detection uncertainty, data association uncertainty, and clutter) in multiple object tracking. An efficient implementation of the multi-sensor δ-GLMB filter is proposed. In order to avoid exhaustively computing all the terms, we propose to use the ranked assignment algorithm with an extended assignment matrix for multiple sensors to determine the most significant terms. A measurement-based birth model is used to identify the previously unknown space objects. Sensors can have the same or different observation volumes. The expectation-maximization (EM) algorithm is used to approximate densities across observation volumes. The performance is demonstrated using the simulation results.

This paper presents an effective multi-sensor approach for improving the accuracy of laser scanner by using a tactile probe to perform rapid and accurate reverse engineering of complex objects. The proposed system is unique in that it includes not only the physical integration of the two digitizers but also their combination at the measurement information level. With the coordinate data acquired using the optical scanner, intelligent data segmentation and feature recognition algorithm are proposed to divide the original point set into geometric elements and free-form surfaces. The tactile probe is guided to re-measure each feature with a small number of sampling points. Then different data fusion algorithms are proposed to compensate the scanned data patches of geometric features and free-form features with the accurate tactile probing data. Finally, the compensated point data can be exploited for accurate reverse engineering of a CAD model. Through the fusion of different sensor's information, both sensors complement each other with their advantages. Experimental results show that for the measurement of geometric surface, the proposed method can improve the accuracy of optical measurement to the accuracy of contact measurement. For free-form surface measurement, the proposed method reduces the optical measurement error from +0.032/-0.019 mm to ±0.016 mm, and the standard deviation from 0.012 mm to 0.004 mm. The measurement efficiency of proposed method is 4.5 times higher than that of contact measurement alone.

Multi-sensor observation is very important for monitoring landslide disasters. Since various surveying techniques are currently available for detecting variational slope activities from different perspectives, studies have focused on integration of multi-source information for the analysing landslide displacements. In this study, a general multi-source data fusion scheme for landslide monitoring based on three-dimensional variation (3DVar) data assimilation was developed. The scheme was used to fuse different observations of Xishancun Landslide in Li County, Sichuan Province, China. First, the displacement observations obtained by a Global Positioning System (GPS) and Borehole Inclinometers (BIs) were assimilated for accurate evaluation of slope activities. Then, slope Stability Index (SI) was introduced to validate the assimilation results within a time interval. SIAssi values calculated using the integration model developed in the present study were compared with SIFS simulated by a physically based landslide model. The correlation coefficient between them ss 0.75, which is larger than those with SIGPS (0.45) or SIBIs (0.41) values determined by the GPS and BIs respectively. The assimilation results are thus confirmed to be more credible for slope stability simulation.

Implicit authentication is a new research direction to enhance the privacy protection of smartphones. However, implicit authentication has low robustness due to its vulnerability to environment. Motivated by this, this paper proposes a context-aware implicit authentication, which is a scheme to improve the robustness of authentication by introducing context awareness module. In the scheme, multi-sensor data (including accelerometer, gyroscope, magnetometer, timestamp, pressure, touch size) is first captured in a fine-grained manner to characterize one's touch action. Then, based on various sensors data, gesture features and touch features are extracted by employing both statistical method and distance measurement method. Particularly, one's body posture when touch action happens can be used as context. In each context, we present a weighted sum fusion rule to fuse the results of different features obtained by One-Class SVM (OC-SVM). We have collected 10000+ sensor data from 87 participants for experimental evaluation. The results show that the authentication in an unrestricted environment can achieve a best equal error rate (EER) of 0.0071%, which is more than one percent lower than the non-context-aware authentication. The proposed method can effectively improve the reliability and practicability of implicit authentication.

Internet of Moving Things are connected to a variety of different types of sensors to form a world of moving things, including people, animals, vehicles, drones, and boats, etc. As the data of collectible moving things continue to increase, anomaly detection of moving things has become an increasingly popular data mining task. Traditional trajectory outlier detection algorithms can detect common anomalies effectively, but it is hard to detect generalized anomalies, such as viewable direction anomalies, gravity anomalies, and magnetic field anomalies which can be collected by the accelerometer, gyroscope, magnetometer, and RPM sensor, etc. For this, we proposed a generalized approach for anomaly detection from the Internet of Moving Things, called the moving things outlier detection algorithm (MTOD). We propose the distance of moving things, which is equal to the weighted sum of the location distance and the multi-sensor distance, and then use the multi-sensor data generalization and moving things partitioning and anomaly detection three-step framework to detect the generalized anomaly. The experimental results show that our MTOD algorithm can detect moving things anomaly efficiency and accurately.