Search

Quantitative analysis is a solid, well established mathematical technique that can be used to analyze the result of survey(s) in a specific field. Survey analysis is usually based on the study of the effect of independent variables on a dependent variable. Although quantitative analysis can use an R-Squared value as a method to measure the strength of the relationship between the independent and dependent variables, it does not capture the effect of participants’ ambiguity when answering questionnaires. The source of such ambiguity stems from the process of completing the survey, whereby the respondent may have answered most of the independent questions with ease, but has difficulty in responding to the overall dependent question (or vice versa). The objective of this paper is to demonstrate the use of fuzzy logic as a mechanism to measure the uncertainty faced by participants when filling a questionnaire. Based on the participants’ responses to the independent variables, the proposed technique uses fuzzy logic inference to measure the subjectivity (qualitative aspect) of the participants’ response to the dependent variable. Beyond quantitative analysis, augmentation with such a fuzzy module can provide clearer picture to analysts when analyzing the survey results. In this paper, Cloud acceptance survey will be used as a vehicle to provide step-by-step explanation of the proposed augmentation technique to unleash the hidden knowledge in similar cases to cloud computing long survey questionnaire where participants may change their mind at the end of the survey causing uncertainty represented in discrepancy of the collected data. The proposed technique would only be valid for long surveys like the presented Cloud computing acceptability where uncertainty in the data is inevitable. [ABSTRACT FROM AUTHOR]

This paper addresses the evaluation of object detection performance for non-contact safety-related sensors in low-visibility environments due to adverse weather conditions. An evaluation metric, MOT (Minimum Object-detectable Transmittance), is proposed to quantitatively evaluate the influence of the low-visibility environments on the object detection performance. Traditionally, the Meteorological Optical Range (MOR) has been used for this purpose. However, MOR is not appropriate for the sensor evaluation because it is a meteorological metric which is optimized for human eyes to estimate the distance at which a person can see clearly through an atmosphere. On the other hand, MOT is a physical and optical measure that quantifies the relationship between the spatial transmittance and the distance at which a sensor can detect a specified object. It is specialized for the sensor evaluation and can express the sensor characteristics in the low-visibility environments at each measurement distance. The usefulness of MOT will be demonstrated by presenting the experimental results of MOT measurements for various sensor devices in a fog space reproduced with environmental simulator equipment. [ABSTRACT FROM AUTHOR]

Location-aware networks enable new services and applications in fields such as autonomous driving, smart cities, and the Internet-of-Things. Network localization and navigation (NLN) is a recently proposed paradigm for accurate ubiquitous localization without the need for extensive infrastructure. In NLN, devices form an interconnected network for the purpose of cooperatively localizing one another. This paper introduces Peregrine, a system that combines real-time distributed NLN algorithms with ultra-wideband (UWB) sensing and communication. The Peregrine software integrates three NLN algorithms to jointly perform 3-D localization and network operation in a technology agnostic manner, leveraging both spatial and temporal cooperation. Peregrine hardware is composed of compact low-cost devices that comprise a microprocessor and a UWB radio. The contribution of each algorithmic component is characterized through indoor network experimentation. Results show that Peregrine is robust, scalable, and capable of sub-meter accuracy in challenging wireless environments. [ABSTRACT FROM AUTHOR]

The availability of novel materials is one of the bases of the development of science and technology. During the last decades, the research on artificial materials exhibiting novel electrical and magnetic properties received significant attention from the scientific and industrial communities. In particular, the so-called metamaterials represent an interdisciplinary field of investigation nowadays and offer promising applications in many fields, such as electromagnetism, optics, thermal, and acoustics. Metamaterials are engineered composite structures which can exhibit physical properties not readily observed in common materials available in nature. Their crucial aspect is to reproduce structured materials, whose repeated elementary cell represents the “metamolecule.” The linear dimensions of the cells are sub-wavelength. As a consequence, the material behaves uniformly. Special attention was received by metamaterials showing negative values of the effective permittivity and permeability in a given frequency range. In this paper, we revisit the concept of such macroscopic parameters, in particular, from the measurement point of view on a macroscopic scale. Indeed, an accurate characterization of such material properties is a key issue for paving new technological applications. Thus, we start from the very definition of the quantities of interest at a macroscopic level. We focus on the meaning of measurement of magnetism in composite magnetic materials, deriving the relevant measurement models. [ABSTRACT FROM AUTHOR]

We evaluated whether a vibrotactile breathing pacer would influence two measures of affect during a cognitive stressor. In particular, we examined whether changes in breathing would be evident, and if so, whether these would mediate the effects of breathing pacer on self-report anxiety and skin conductance. Our results were surprising: although we observed the expected effects on breathing, we were unable to demonstrate that changes in breathing parameters were responsible for the observed changes in self-report anxiety. In addition, we failed to show that the pacer had an impact on skin conductance. In this paper, we investigate why we did not observe the expected effects. We believe our negative results have implications for evaluating technological interventions for affect regulation. [ABSTRACT FROM AUTHOR]

Stellar atmospheric refraction imaging error, as the optical imaging variation of starlight before and after passing through Earth’s atmosphere, is one of the key issues for terrestrial star trackers to obtain accurate attitude measurement results. To resolve the problem, a refraction surface-based stellar atmospheric refraction correction method is presented in this paper. Different from existing correction methods, a new atmospheric refraction feature, i.e., stellar atmospheric refraction surface, is proposed to represent the random irregular optical phenomena of starlight, and avoids the limitations of theoretical atmospheric models and empirical formulas. On the basis of the principles of “collinear refraction surfaces”, “invariable refraction surface”, the proposed model successfully realizes the accuracy atmospheric refraction correction by the sensor itself, without external atmospheric parameters. Then, by analyzing the error factors parallel and perpendicular to the atmospheric refraction surface, the atmospheric refraction correction error estimation model is established to forecast the attitude accuracy after correction. Numerical simulations and night sky experiments validate the reliability of our atmospheric refraction correction method and error estimation model. The average angular distance error of the test star tracker is reduced from $31.1310''$ into $1.7705''$ , which is significantly decreased by 94.31%. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to analyse the human musculoskeletal and energetic adaptation mechanisms when physically interacting with a unilateral knee orthosis during treadmill walking. Methods: Test subjects participated in two walking trials, whereby the orthosis was controlled to deliver five predefined torque profiles of different duration (as % of a gait cycle). The adaptations to assistive torques of different duration were analysed in terms of gait parameters, metabolic effort, and muscle activity. Results: Orthotic assistance’s kinematic effects remain local to the assisted leg and joint, unlike the muscles spanning the knee joint, which engage in a balancing-out action to retain stability. Duration of assistive torque significantly affects only the timing of the knee joint’s peak flexion angle in the stance phase, while the observed joint kinematics and muscle activity demonstrate different recovery times upon changing robotic support (washout effects). Conclusion: Human body adaptations to external robotic knee joint assistance during walking take place on multiple levels and to a different extent in a joint effort to keep the gait stable. Significance: This paper provides important insights into the human body’s multiple adaptation mechanisms in the presence of external robotic assistance. [ABSTRACT FROM AUTHOR]

Measurement of total ionizing dose in a radiation field is efficiently carried out by ionisation chambers. The paper details the design of a mixed-signal ASIC for the front-end electronics of ionisation chambers. A single chip solution for ultra-low current measurement is designed by combining the current processing analog section realized using low leakage thick gate transistors and the data handling digital section implemented using fast thin gate transistors. The design succeeds in limiting the cross coupling between the two circuit domains using deep n-wells and guard rings. The ASIC fabricated in 130 nm technology attains a wide dynamic range of −7 fA to $- 20\,\,\mu \text{A}$ with maximum error in measurement less than ±4 %. The ASIC occupies an area of 3.52 mm2 and has a total power consumption of 17.4 mW. The femtoampere range input leakage current of the ASIC contributed primarily by the ESD diodes was found to be varying exponentially with temperature. Dose rate measurements from $5 ~\mu $ Sv/h to 7.4 Sv/h is demonstrated by interfacing the ASIC to an ionisation chamber. [ABSTRACT FROM AUTHOR]

The position is one of the foremost imperative properties of an object. The Inertial Measurement Unit (IMU) development has enabled the system to continuously decrease in size, reduce power consumption, and become more and more universal. Although IMU meets the real-time needs of the localization system, it faces accumulative error and drifting problems. This paper presents an error constraint enhanced particle filter algorithm using quantum particle swarm optimization (EC-QPF), which achieves high-precision position estimation of target tracking with IMU. First, we proposed a quantum particle swarm optimization-based resampling method taking the place of the traditional weight-based resampling method in the particle filter, which avoids the particle impoverishment problem and keeps the particles’ diversity. The theoretical foundation of error constraint was raised applied to enhance the performance of the proposed particle filter. The error constraint is established utilizing the known estimated center and confidence scale to achieve particle screening based on the geometrical position of particles. Numerical experimental results show that the proposed EC-QPF has a 67% improvement instead of other modified particle filters and more efficiently eliminates the cumulative error. Furthermore, higher accuracy and stability are obtained under the exact condition, demonstrating better performance than traditional particle filter methods. [ABSTRACT FROM AUTHOR]

City-wide package delivery becomes popular due to the dramatic rise of online shopping. In order to speed up the package delivery process without increasing the delivery cost, a promising system has been proposed, which leverages the crowdsourced taxis. Many efforts have been done on this novel system in recent literature. However, a fundamental problem still remains open, i.e., measuring the maximum capacity of taxi-based logistics at the urban scale. In this paper, we first propose an accurate and efficient measurement mechanism to tackle this problem in the Non-stop package delivery method. The basic idea is to construct a spatial-temporal graph according to the passenger demands and calculate the maximum urban capacity by combining the results of several carefully designed max-flow problems. Then, we expand our measurement mechanism to be used in other taxi-based package delivery methods after a few adaptations, including the One-hop method and the Stop-and-wait method. At last, we evaluate our measurement mechanism and compare the maximum urban capacity of various package delivery methods with a real-world dataset from an online taxi-taking platform. [ABSTRACT FROM AUTHOR]

As password distribution lays the foundation for various password research, accurately characterizing it receives considerable attention. At IEEE TIFS’17, Wang et al. proposed the CDF-Zipf distribution model with the golden-section-search (GSS) fitting method to find the optimal parameters. Their model has been adopted by over 120 password-related studies. In this paper, we address their remaining, fundamental goodness-of-fit issue of password distribution in a principled approach. First, we prove that the confidence level of the state-of-the-art Monte Carlo approach (MCA, for the goodness-of-fit test) converges asymptotically to 0. By experimenting on 228.92 million real-world passwords, we confirm Wang et al.’s conjecture on the effect of sample size that minor deviations would lead to statistical significance for large-scale datasets. We propose both absolute and relative deviation metrics, and find that 1% random deviations in both metrics suffice to reject CDF-Zipf. Second, we attempt to reduce the non-negligible gap between the empirical and fitted distributions (with the maximum deviation of cumulative distribution function (CDF) being 1.91% on average). We explore eight alternative distribution models in two coordinate systems, and find that three models are more accurate than CDF-Zipf, but none can pass MCA. Particularly, we reveal that stretched-exponential, a variant of CDF-Zipf, can on average reduce the maximum CDF deviation from 1.91% to 1.25%. Third, to replace MCA, we introduce a new goodness-of-fit measure based on log-likelihoods. We find that stretched-exponential constantly has a larger log-likelihood than its counterparts. In all, stretched-exponential fits passwords better and further supports Zipf’s law in passwords. [ABSTRACT FROM AUTHOR]

Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located together in proximate areas. Previous studies of SCPM solely concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose the task of spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations embedded in a co-location. Specifically, InterPCI measure is designed to capture the inter-features coupling by considering the comprehensive interaction of objects for the features in a pattern, and luckily it possesses the anti-monotone property. Another measure, IntraCAI, is proposed to capture the congregating behavior of intra-feature objects under the restriction of a co-location. A general framework is designed for SCPM-CR task and experiments show that a large fraction of computation time is devoted to identifying the participating objects of a candidate pattern. To tackle this calculation bottleneck, a novel candidate-and-search algorithm is suggested, CS-HBS, equipped with heuristic backtracking search. Extensive experiments are conducted on real and synthetic datasets to demonstrate the superiority of SCPM-CR compared with traditional SCPM methods, and also to validate the efficiency and scalability of CS-HBS. Experimental results show that CS-HBS outperforms the baselines by several times or even orders of magnitude. [ABSTRACT FROM AUTHOR]

Prior studies suggest that emotional empathy is one of the components of intercultural sensitivity - the affective dimension under the concept of intercultural communication competence. Based on existing theories and findings, this paper reports a randomised parallel longitudinal study investigating the use of virtual reality (VR) exposure to enhance intercultural sensitivity. A total of 80 participants (36 females and 44 males) joined the study and were included in the data analysis. The participants were randomly assigned to the VR group, the video group, and the control group. Their intercultural sensitivity was measured three times: one week before the exposure ($T_{1}$), right after the exposure ($T_{2}$), and three weeks after the exposure ($T_{3}$). The results suggested that (1) the intercultural sensitivity of the VR group was significantly enhanced in both within-subject comparisons and between-subject comparisons, (2) there were no significant differences in intercultural sensitivity between the VR group and the video group at $T_{2}$, but the VR group retained the enhancement better at $T_{3}$, and (3) the sense of presence and emotional empathy well predicted the change in intercultural sensitivity of the VR group. The results, together with the participants' feedback and comments, provide new insights into the practice of using VR for intercultural sensitivity training and encourage future research on exploring the contributing factors of the results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing data-driven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements. [ABSTRACT FROM AUTHOR]

Data have significant economic or social value in many application fields including science, business, governance, etc. This naturally leads to the emergence of many data markets such as GBDEx and YoueData. As a result, the data trade through data markets has started to receive attentions from both industry and academia. During the data buying and selling, how to price the data is an indispensable problem. However, pricing incomplete data is more challenging, even though incomplete data exist pervasively in a vast lot of real-life scenarios. In this paper, we attempt to explore the pricing problem for queries over incomplete data. We propose a sophisticated pricing mechanism, termed as ${\sf iDBPricer}$ iDBPricer , which takes a series of essential factors into consideration, including the data contribution/usage, data completeness, and query quality. We present two novel price functions, namely, the usage, and completeness-aware price function (UCA price for short) and the quality, usage, and completeness-aware price function (QUCA price for short). Moreover, we develop efficient algorithms for deriving the query prices. Extensive experiments using both real and benchmark datasets demonstrate ${\sf iDBPricer}$ iDBPricer is of excellent performance in terms of effectiveness and scalability, compared with the state-of-the-art price functions. [ABSTRACT FROM AUTHOR]

Code-based masking is a recent line of research on masking schemes aiming at provably counteracting side-channel attacks. It generalizes and unifies many masking schemes within a coding-theoretic formalization. In code-based masking schemes, the tuning parameters are the underlying linear codes, whose choice significantly affects the side-channel resilience. In this paper, we investigate the exploitability of the information leakage in code-based masking and present attack-based evaluation results of higher-order optimal distinguisher (HOOD). Particularly, we consider two representative instances of code-based masking, namely inner product masking (IPM) and Shamir’s secret sharing (SSS) based masking. Our results do confirm the state-of-the-art theoretical derivatives in an empirical manner with numerically simulated measurements. Specifically, theoretical results are based on quantifying information leakage; we further complete the panorama with attack-based evaluations by investigating the exploitability of the leakage. Moreover, we classify all possible candidates of linear codes in IPM with 2 and 3 shares and (3, 1)-SSS based masking, and highlight both optimal and worst codes for them. Relying on our empirical evaluations, we therefore recommend investigating the coding-theoretic properties to find the best linear codes in strengthening instances of code-based masking. As for applications, our attack-based evaluation directly empowers designers, by employing optimal linear codes, to enhance the protection of code-based masking. Our framework leverages simulated leakage traces, hence allowing for source code validation or patching in case it is found to be attackable. [ABSTRACT FROM AUTHOR]

Existing global navigation satellite systems (GNSSs) provide insufficient accuracy in terms of GNSS elevation and are limited by two-dimensional maps. Therefore, when traveling along a road that branches off to an overpass, it is impossible to immediately determine whether the vehicle is on the overpass. Inaccurate navigation not only causes anxiety for some drivers but also can lead to car accidents. This problem has existed since the invention of GNSSs. To the best of our knowledge, we are the first to conduct a large number of on-road experiments at different locations using off-the-shelf smartphones equipped with barometers. Based on the preliminary results of the wind tunnel and on-road experiments, we propose barometer-assisted road-layer detection (BARLD), which uses the changes in atmospheric pressure to determine the relative altitude. Comprehensive experiments in many different scenarios, including on overpasses, underpasses, and in parking lots, are discussed in this paper. The results confirm that the BARLD can effectively assist consumer-grade GNSSs to determine road layers. The source code of BARLD is publicly available, which would benefit the research community. [ABSTRACT FROM AUTHOR]

Most of siamese network and correlation filter (CF) based trackers usually employ the context regression scheme to achieve appealing performance in both accuracy and efficiency. However, they are prone to drifting in challenging situations exhibiting occlusion, out-of-view and large-scale variations due to the lack of failure correction ability in these regressors. Particle filter based trackers can help to recover from tracking failures since several particles of high confidence about the target can be remained for the probability estimation of next frames, but need the large numbers of particles for each frame. In this paper, we propose a generic dynamic particle filter framework, which can reasonably control the number of particles in different scenarios, to improve the robustness of siamese and CF trackers by jointing the target classifier to relieve drifting. Our fundamental insight is that general scenarios are processed efficiently by the context regressor with few particles, while special scenarios are handled effectively by the target classifier with many particles. We propose a novel measure to determine whether to adopt the regressor and few particles to estimate target states with high measure scores, or increase the number of particles to prevent drifting with the proposed multi-template matching strategy in the classifier. In extensive experiments on eight large-scale benchmarks including OTB, UAV, TC128, VOT2017, VOT2019, LaSOT, TrackingNet and Got-10k, the proposed framework enables many basic siamese and CF trackers to operate at least over 24 frames per second and achieve superior tracking performance than themself, as well as the comparable accuracy with the state-of-the-art trackers. Furthermore, our framework is flexible and still has great potential for improvement and generalization. [ABSTRACT FROM AUTHOR]

Autonomous systems are nowadays having an undisputed pervasiveness in modern society. Autonomous driving cars as well as applications of service robots (e.g., cleaning robots, companion robots, intelligent healthcare solutions, tour guided systems) are becoming more and more popular, and a general acceptance is now developing around such systems. Nonetheless, one of the major hurdles in building such applications relies on the capability of autonomous systems to understand their surroundings and then plan proper actions. The most popular solutions, which are gaining more and more attention, rely on artificial intelligence and deep learning as a means to perceive the structured and complex natural environment. Nonetheless, classical concepts of metrology, such as standard uncertainty, accuracy and precision, are still necessary for a clear and effective understanding of modern autonomous systems applications. In this paper, some fundamental measurement concepts will be reviewed in light of the autonomous systems domain, with an emphasis on localization and positioning problems for mobile robots. In particular, we will discuss and present the main issues and concepts that build around the statistical approach to measurements and the main role of uncertainties. [ABSTRACT FROM AUTHOR]

Adopting speech as their mode of interaction, speech-enabled conversational agent (CA) systems hold the potential to enable more natural and engaging self-report experiences than traditional media (e.g., pen and paper, web, or smartphone systems). Our recent research concerns the potential design and use of CAs to support mental health and wellbeing. In this article, we present findings from a study during which 22 individuals with affective disorders (e.g., depression, bipolar disorder) used either a speech-enabled CA or web app to self-report their emotional wellbeing. Analysis of users' experiences and engagement with the system for daily self-report suggests that despite many technical limitations, users rated self-reporting via CA as a significantly novel and attractive experience yielding similar rates of engagement to a traditional web-based method. [ABSTRACT FROM AUTHOR]

Bionic navigation is an essential technology in GPS-denied environment for vehicle navigation. This paper combined strapdown inertial navigation system, polarized skylight sensors and odometer to design a new navigation model. In particular, a novel measurement model is developed based on polarized skylight. This model primarily utilizes the cross product to compute the error between the measured polarization vector and the theoretical polarization vector. It differs from the error computed by strapdown inertial navigation system in design vector measurement model. It deals with the problem of the directional ambiguity of polarization vector difference directly. Considering the measured polarization vector error due to computation and multiple scattering from atmospheric molecules, the states are augmented with the measured vector error to form a partially nonlinear, bionic integrated navigation model. To reduce the computation burden, marginalised unscented Kalman filter is presented to estimate the unknown states. The observability analysis method of piece-wise constant systems is applied to compute the observability matrix and singular value. Simulation results show that linear maneuver and angular maneuver can improve the degree of the observability of attitude and measured polarized vector error. Finally, experiments further verify the effectiveness of proposed models and method. [ABSTRACT FROM AUTHOR]

As one of the important parameters of the satellite transponder, the group delay of the transponder not only determines the magnitude of the signal transmission delay that is generated by the system or network but is also closely related to the signal transmission distortion, which has a substantial impact on the signal transmission quality. When the satellite is in orbit, it is difficult to measure this index due to the particularity of its position. To overcome this problem, this paper proposes a method for measuring the group delay of an on-orbit satellite transponder. By using two sets of ground station antenna systems that are installed in parallel to conduct the observation of the transponders to be measured simultaneously in the mode of “receiving its own station-disseminated signal,” the measurement results of the group delay of the transponders can be obtained after the calculation. Based on the orbit determination by the transfer tracking system of the National Time Service Center, a group time delay measurement experiment of the C5B transponder of the APSTAR-7 satellite in orbit was conducted. The results of an analysis of the data demonstrate that this method can effectively measure the group delay of a satellite transponder and that the root mean square of the two measurement results is less than 0.2 ns, which provides a useful reference for further optimization and improvement of the orbit determination accuracy of the system and provides a performance evaluation service for satellite companies. [ABSTRACT FROM AUTHOR]

This paper proposes a two-stage methodology to discover malfunctioning sensors in an air quality sensor network. The two-stage methodology consists of a supervised learner to predict the future PM2.5 values of each sensor and a detector that leverages the result of the previous stage to detect the malfunctioning sensors. Consequently, even if each sensor’s health status (i.e., normal or malfunctioning) is unavailable, we can still apply powerful supervised learners to this task. We conduct experiments on a nationwide air quality sensor network that includes 10,000+ sensors and utilize periodic maintenance records on some of these sensors as the ground truth of their health status. Experimental results show that this two-stage methodology can effectively discover problematic sensors. As maintaining a large-scale sensor network is laborious, the methodology can dramatically reduce the human resource required for regular inspection. [ABSTRACT FROM AUTHOR]

Traffic prediction is one of the most important use cases for smart cities. Accurate traffic information is key to managing traffic issues. Many approaches that use traffic time series data to predict traffic flow have been proposed. In addition to traffic- specific parameters, some other features (called signatures) may be associated with road traffic, i.e., air and noise pollution. In this paper, we show how noise pollution and traffic time-series data were used to train Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNNs), which led to better traffic prediction on major roads in Madrid. This approach has already been used with pollution signatures. This work addresses a new potential investigation path closely related to the use of signature profiles and Artificial Intelligent techniques as a way to reduce the specialization of sensing infrastructure. [ABSTRACT FROM AUTHOR]

In this paper, based on experimental results from sensors implemented in two SOI (Silicon On Insulator) processes offered by MEMSCAP ($10~\mu \text{m}$ and $25~\mu \text{m}$), a comparative study of the implementation of a Pirani-based multifunction sensor has been conducted. The purpose of this study is to choose the most adequate process for the design and implementation of Pirani-based multifunction sensor for both temperature and pressure measurement. Particularly unlike the conventional use of Pirani Vacuum sensor, the Pirani-based sensor is exploited for near and even above atmospheric pressure measurement. The comparison is made in terms of sensor implementation robustness against technological dispersions, its impact on temperature measurement and on pressure measurement under atmospheric condition (1Bar). The optimized design of the multifunction sensor is also investigated through the choice of sensor resistance by way of power consumption, electro-thermal conversion as well as measurement resolution. A design guideline is provided. The experimental results have put into evidence that a better performance is achieved with the $25~\mu \text{m}$ process. [ABSTRACT FROM AUTHOR]

Pollution by particulate matter (PM) poses a serious and growing risk to human health. Film Bulk Acoustic Resonators (FBARs) have been proposed as a low-cost way to monitor particle concentration. This paper presents particle detection by a 1.1 GHz aluminium nitride based FBAR designed and fabricated by SilTerra Malaysia. The FBARs are used in a differential configuration with one sensing and one reference device in a system comprising a microchannel with an electric microfan and a thermophoretic precipitator microhotplate array for improved sampling. A custom-built test chamber was designed to characterise the device at different levels of particulate matter concentration in air. The particle feed rate into the test chamber was varied between 9.5, 19, 43, 66 and $94~\mu \text{g}/\text{m}^{3}\text{s}$. The FBAR frequency was found to decrease with increasing particulate matter concentration in the test chamber air. The experiments were conducted with and without the microchannel and it was found that the sampling channel increased sensitivity of FBAR resonant frequency to particle concentration per cubic meter of air from 5 Hz/ $\mu $ gm−3 to 20 Hz/ $\mu $ gm−3. The detection limit in this test was estimated at ca. $50~\mu \text{g}/\text{m}^{3}$ , which is around current European limits for PM10 pollution. In addition, the use of the sampling microfan to aid cleaning of FBARs after particle measurement was also investigated and found to be feasible especially for lower particle concentration. [ABSTRACT FROM AUTHOR]

Space exploration is evolving with the recent increase in interest and investment. For the success of planned long-duration crewed missions, good interpersonal interactions between crew members are crucial. In this study, we evaluate the use of wearables for detection and estimation of the quality of each social interaction participants have throughout a long mission rather than aggregate measures of interactions. Our proposed method utilizes Temporal Convolutional Networks(TCNs) for extracting individual representations from acceleration and audio streams and learnable pooling layers(NetVLAD) to aggregate these representations into fixed-size representations. Use of NetVLAD layers provides an intelligent alternative to simple aggregation for handling variable-sized interactions and interactions with missing data. We evaluate our method on a 4-month simulated space mission where 5 participants wore Sociometric Badges and provided reports on their interactions in terms of effectiveness, frustration, and satisfaction. Our method provides an average ROC-AUC score of 0.64. Since we are not aware of any comparable baselines, we compare our method to hand-crafted features formerly utilized for cohesion estimation in similar scenarios and show it significantly outperforms them. We also present ablation studies where we replace the components in our approach with well-known alternatives and show that they provide better performance than their respective counterparts. [ABSTRACT FROM AUTHOR]

This research investigates how experiencing virtual embodiment in a wheelchair affects implicit bias towards people who use wheelchairs. We also investigate how receiving information from a virtual instructor who uses a wheelchair affects implicit bias towards people who use wheelchairs. Implicit biases are actions or judgments of people towards various concepts or stereotypes (e.g., races). We hypothesized that experiencing a Disability Simulation (DS) through an avatar in a wheelchair and receiving information from an instructor with a disability will have a significant effect on participants’ ability to recall disability-related information and will reduce implicit biases towards people who use wheelchairs. To investigate this hypothesis, a 2x2 between-subjects user study was conducted where participants experienced an immersive VR DS that presents information about Multiple Sclerosis (MS) with factors of instructor (i.e., instructor with a disability versus instructor without a disability) and locomotion interface (i.e., without a disability – locomotion through in-place-walking, with a disability – locomotion in a wheelchair). Participants took a disability-focused Implicit Association Test two times, once before and once after experiencing the DS. They also took a test of knowledge retention about MS. The primary result is: experiencing the DS through locomotion in a wheelchair was better for both the disability-related information recall task and reducing implicit bias towards people who use wheelchairs. [ABSTRACT FROM AUTHOR]

The problem of the optimal allocation (in the expected mean square error sense) of a measurement budget for particle filtering is addressed. We propose three different optimal intermittent filters, whose optimality criteria depend on the information available at the time of decision making. For the first, the stochastic program filter, the measurement times are given by a policy that determines whether a measurement should be taken based on the measurements already acquired. The second, called the offline filter, determines all measurement times at once by solving a combinatorial optimization program before any measurement acquisition. For the third one, which we call online filter, each time a new measurement is received, the next measurement time is recomputed to take all the information that is then available into account. We prove that in terms of expected mean square error, the stochastic program filter outperforms the online filter, which itself outperforms the offline filter. However, these filters are generally intractable. For this reason, the filter estimate is approximated by a particle filter. Moreover, the mean square error is approximated using a Monte-Carlo approach, and different optimization algorithms are compared to approximately solve the combinatorial programs (a random trial algorithm, greedy forward and backward algorithms, a simulated annealing algorithm, and a genetic algorithm). Finally, the performance of the proposed methods is illustrated on two examples: a tumor motion model and a common benchmark for particle filtering. [ABSTRACT FROM AUTHOR]

We present exploratory research of virtual reality techniques and mnemonic devices to assist in retrieving knowledge from scholarly articles. We used abstracts of scientific publications to represent knowledge in scholarly articles; participants were asked to read, remember, and retrieve knowledge from a set of abstracts. We conducted an experiment to compare participants’ recall and recognition performance in three different conditions: a control condition without a pre-specified strategy to test baseline individual memory ability, a condition using an image-based variant of a mnemonic called a “memory palace,” and a condition using a virtual reality-based variant of a memory palace. Our analyses show that using a virtual reality-based memory palace variant greatly increased the amount of knowledge retrieved and retained over the baseline, and it shows a moderate improvement over the other image-based memory palace variant. Anecdotal feedback from participants suggested that personalizing a memory palace variant would be appreciated. Our results support the value of virtual reality for some high-level cognitive tasks and help improve future applications of virtual reality and visualization. [ABSTRACT FROM AUTHOR]

Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans by the means of language are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the role of emotion in the prediction of two important subjective goals in a negotiation – outcome satisfaction and partner perception. We devise ways to measure and compare different degrees of emotion expression in negotiation dialogues, consisting of emoticon, lexical, and contextual variables. Through an extensive analysis of a large-scale dataset in chat-based negotiations, we find that incorporating emotion expression explains significantly more variance, above and beyond the demographics and personality traits of the participants. Further, our temporal analysis reveals that emotive information from both early and later stages of the negotiation contributes to this prediction, indicating the need for a continual learning model of capturing emotion for automated agents. Finally, we extend our analysis to another dataset, showing promise that our findings generalize to more complex scenarios. We conclude by discussing our insights, which will be helpful for designing adaptive negotiation agents that interact through realistic communication interfaces. [ABSTRACT FROM AUTHOR]

This is article provides an overview of the IEEE Instrumentation and Measurement Society Technical Committee TC-22 on Intelligent Measurement Systems (https://ieee-ims.org/technical-committee/tc-22). The scope of the TC-22 is related to fostering development and use of artificial intelligence, computational intelligence, and soft computing in measurement systems and related applications [1]–[3]. [ABSTRACT FROM AUTHOR]

The atmospheric electric current, "air–earth current", flows between the low ionosphere and Earth's surface. The source of this current is the potential difference between the global equalizing layer called the ionosphere and the ground surface. According to Wilson's concept of the Earth's Global Electric Circuit, in the areas of so-called fair weather, based on current measurements at the Earth's surface, it is possible to conclude the global electrical processes in the ionosphere and higher layers. The theoretical basis for this inference is the law of continuity of electric current or the principle of conservation of electric charge. We present the results of simulations of the distribution of electric field lines for sensors with different geometries placed in a uniform electric field, representing the atmospheric electric field. The sensors are metal surfaces on which electric charges are induced or deposited. In the external measuring circuit to which the sensor is connected, an electric current [A] will flow, related to the air–earth current density [A/m2], but their relationship may be challenging to interpret. We analyze the impact of sensor geometry on the possibility of interpreting the atmospheric electric conduction and atmospheric displacement current based on the current measured in the external circuit. This present method can be used for the geometric construction of new sensors at the stage of determining the electrical characteristics of the sensor (e.g., effective collecting area). It can support the comprehensive design of a measurement system at the interface between an atmosphere, sensor, and electronic equipment. [ABSTRACT FROM AUTHOR]

Two people's physiological responses become more similar as those people talk or cooperate, a phenomenon called physiological synchrony. The degree of synchrony correlates with conversation engagement and cooperation quality, and could thus be used to characterize interpersonal interaction. In this study, we used a combination of physiological synchrony metrics and pattern recognition algorithms to automatically classify four different dyadic conversation scenarios: two-sided positive conversation, two-sided negative conversation, and two one-sided scenarios. Heart rate, skin conductance, respiration and peripheral skin temperature were measured from 16 dyads in all four scenarios, and individual as well as synchrony features were extracted from them. A two-stage classifier based on stepwise feature selection and linear discriminant analysis achieved a four-class classification accuracy of 75.0% in leave-dyad-out crossvalidation. Removing synchrony features reduced accuracy to 65.6%, indicating that synchrony is informative. In the future, such classification algorithms may be used to, e.g., provide real-time feedback about conversation mood to participants, with applications in areas such as mental health counseling and education. The approach may also generalize to group scenarios and adjacent areas such as cooperation and competition. [ABSTRACT FROM AUTHOR]

In the above paper , there are errors in the following two : [ABSTRACT FROM AUTHOR]

Temperature and humidity profiles in the atmospheric boundary layer are essential for climate studies. The ground-based infrared hyperspectral spectrometer has the advantage of measuring radiances emitted from the atmosphere at a high temporal and moderate vertical resolution. In this article, the retrieval of temperature and humidity profiles from ground-based infrared hyperspectral observations is exploited. Although existing inversion algorithms based on physical models or statistical learning have made some progress, they still suffer from high computational complexity or poor performance. Motivated by the strength of the deep learning, we present a deep retrieval architecture (DReA) by skillfully designing a light-weight one-dimensional convolution neural network (CNN) to retrieve the temperature and humidity profiles. Experiments were conducted using atmospheric emitted radiance interferometer (AERI) and radiosonde data to demonstrate the superiority of the proposed DReA. The validation of the DReA with the radiosonde, using 802 profiles with 37 layers below 3 km, presents an excellent retrieval ability with a root mean square error (RMSE) of 0.87 K for the temperature and 1.06 g/kg for the water vapor mixing ratio. Furthermore, a thorough comparison with commonly used inversion methods such as the traditional back propagation (BP) and the eigenvector (EV) regression method, shows that our proposed DReA method obtains a leading solution in retrieving temperature and humidity profiles. [ABSTRACT FROM AUTHOR]

In this article we introduce Mementos: the first multimodal corpus for computational modeling of affect and memory processing in response to video content. It was collected online via crowdsourcing and captures 1995 individual responses collected from 297 unique viewers responding to 42 different segments of music videos. Apart from webcam recordings of their upper-body behavior (totaling 2012 minutes) and self-reports of their emotional experience, it contains detailed descriptions of the occurrence and content of 989 personal memories triggered by the video content. Finally, the dataset includes self-report measures related to individual differences in participants’ background and situation (Demographics, Personality, and Mood), thereby facilitating the exploration of important contextual factors in research using the dataset. We describe 1) the construction and contents of the corpus itself, 2) analyse the validity of its content by investigating biases and consistency with existing research on affect and memory processing, 3) review previously published work that demonstrates the usefulness of the multimodal data in the corpus for research on automated detection and prediction tasks, and 4) provide suggestions for how the dataset can be used in future research on modeling Video-Induced Emotions, Memory-Associated Affect, and Memory Evocation. [ABSTRACT FROM AUTHOR]

Changes in emotions affect our physiological responses, and perhaps vice versa. We investigate a new game interaction system that uses false heart rate (fHR) feedback to improve the player experience (PX). The fHR feedback presents false HR information to players so that they perceive changes in the presented HR as being a result of alteration in PX. We introduced auditory fHR feedback into game interaction and investigated its effects through an experiment. Participants repeated gameplay of an action game while hearing heartbeat-like sounds and answered questionnaires regarding PX. Some participants heard the heartbeat-like sounds synchronized with their actual HR, whereas others heard the heartbeat-like sounds whose tempo became gradually faster or slower than their actual HR. The results indicated that an accelerating fHR feedback pattern with +5 bpm/min was appropriate for improving PX; participants were able to maintain their motivation to continue the game. The experiment also indicated that it is necessary for participants to perceive the presented heartbeat-like sounds as reflecting their actual HR. Participants did not maintain their motivation when they were told that the presented sounds were not correlated with their actual HR. The present work provides new principles for video game interaction design based on physiological measurements. [ABSTRACT FROM AUTHOR]

The prevalence and impact of balance impairments and falls in older adults have motivated several studies on the characterization of human balance. This study aimed to determine the ability of recurrence quantification analysis (RQA) measures to characterize balance control during quiet standing in young and older adults and to discriminate between different fall risk groups. We analyze center pressure trajectories in the medial-lateral and anterior-posterior directions from a publicly available static posturography dataset that contains tests acquired under four vision-surface testing conditions. Participants were retrospectively classified as young adults (age< 60, n=85), non-fallers (age≥60, falls=0, n=56), and fallers (age≥60, falls≥1, n=18). Mixed ANOVA and post hoc analyzes were performed to test for differences between groups. For CoP fluctuations in the anterior-posterior direction, all RQA measures showed significantly higher values for young than older adults when standing on a compliant surface, indicating less predictable and stable balance control among seniors under testing conditions where sensory information is restricted or altered. However, no significant differences between non-fallers and fallers were observed. These results support the use of RQA to characterize balance control in young and old adults, but not to discriminate between different fall risk groups. [ABSTRACT FROM AUTHOR]

Cyber challenges faced by organizations today involve malicious inside actors, often labeled insider threats (ITs). These present a difficult challenge in that the most well-designed cybersecurity apparatus is vulnerable to those inside the organization who have privileged access to information systems. Innovative methods must be developed to help security analysts narrow the large pool of potential ITs in large organizations to a more manageable number. The purpose of this article is to develop and validate eye-tracking metrics that are diagnostic of IT behavior. Key stimuli, or called active indicator probes, were embedded into a simulated workflow to elicit diagnostic eye-tracking responses. Two environments were simulated: financial and intelligence analysis. We evaluated participants performing as regular workers relative to ITs to identify metrics that distinguished between the two groups. Detection of illicit eye gaze behavior while using chat programs was possible when conversations with accomplices occurred in a separate chat window from normal permissible chat conversations. Validation of results in real work environments is necessary for practical application. However, if the approach proves to translate successfully, automated monitoring of eye-tracking responses may augment existing insider detection methods, within frameworks for best practices in organizational security and cyber defense. [ABSTRACT FROM AUTHOR]

Prior research in depth perception and perceptuo-motor calibration have primarily focused on participants completing experiments in single sessions and therefore do not empirically evaluate changes over time. Further, these studies do not typically take into account the amount of experience that the participants have in virtual reality (VR) prior to participation, the role of experience during participation, or calibration that may occur throughout the experiment session. In this contribution, we conducted a novel empirical evaluation of how calibration affects perception-action coordination over time. We recruited novice VR users and they completed eight sessions of a depth perception reaching experiment over the course of 12 weeks. During these experiments, we examined how participants' ability to estimate depth in a virtual environment changed as they gradually gained experience. While previous literature has shown that participants tend to underestimate distances, we found that this underestimation diminished over time as they gained experience in the virtual environment. Our study highlights the need for carrying out VR studies over time and the influence that longitudinal calibration can have on spatial perception in long-term VR experiences. [ABSTRACT FROM AUTHOR]

Physiological sensing often complements studies of human behavior in virtual reality (VR) to detect users' affective and cognitive states. Some psychological states, such as fear and frustration, can be particularly hard to differentiate from a physiological perspective as they are close in the arousal and valence emotional space. Moreover, it is largely unclear how users' physiological reactions are expressed in response to transient psychological states such as fear, frustration, and insight—especially since these are rich indicators for characterizing users' responses to dynamic systems but are hard to capture in highly interactive settings. We conducted a study ($N=24$) to analyze participants' pulmonary, electrodermal, cardiac, and pupillary responses to moments of fear, frustration, and insight in immersive settings. Participants interacted in five VR environments, throughout which we measured their physiological reactions and analyzed the patterns we observed. We also measured subjective fear and frustration using questionnaires. We found differences between fear and frustration pupillary, respiratory, and electrodermal responses, as well as between the pupillary changes that followed fear in a horror game and those that followed fear in a vertigo experiment. We present the relationships between fear levels, frustration levels, and their physiological responses. To detect these affective events and states, we introduce user-independent binary classification models that achieved an average micro $F_{1}$ score of 71% for detecting fear in a horror game, 75% for fear of vertigo, 76% for frustration, and 75% for insight, showing the promise for detecting these states from passive and objective signals. [ABSTRACT FROM AUTHOR]

Vector quantization is a viable solution to the problem as to full utilization of randomness provided by statistically dependent channel measurements in secret key extraction from randomly fluctuating channels. By aiming at atmospheric optical wireless channels, new vector quantization schemes based on sample grouping (SG) are proposed by using different ways, specifically, the $K\text {-dimensional}$ tree (KDT) partition and random placement, to separate samples of the dimension-reduced channel coefficient vector into a given number of groups, with each containing equal number of samples. Use of the KDT partition leads to both the basic KDT-partition-based quantization (B-KDTPQ) and improved KDT-partition-based quantization (I-KDTPQ) schemes. On the other hand, utilization of the random placement brings forth the random equisized grouping based quantization (REGQ) scheme. Performance evaluation shows that, due to use of quantization symbol modification, the I-KDTPQ scheme always has lower quantization symbol disagreement rate (QSDR) than the B-KDTPQ scheme; compared with the I-KDTPQ scheme, the REGQ scheme has more flexibility in accommodating itself to various conditions of channel coefficient fluctuations and noise levels. [ABSTRACT FROM AUTHOR]

Functional corticomuscular coupling (FCMC) between the cerebral motor cortex and muscle activity reflects multi-layer and nonlinear interactions in the sensorimotor system. Considering the inherent multiscale characteristics of physiological signals, we proposed multiscale transfer spectral entropy (MSTSE) and introduced the unidirectionally coupled Hénon maps model to verify the effectiveness of MSTSE. We recorded electroencephalogram (EEG) and surface electromyography (sEMG) in steady-state grip tasks of 29 healthy participants and 27 patients. Then, we used MSTSE to analyze the FCMC base on EEG of the bilateral motor areas and the sEMG of the flexor digitorum superficialis (FDS). The results show that MSTSE is superior to transfer spectral entropy (TSE) method in restraining the spurious coupling and detecting the coupling more accurately. The coupling strength was higher in the ${\boldsymbol{\beta}}$ 1, ${\boldsymbol{\beta}}$ 2, and $\ {\boldsymbol{\gamma}}$ 2 bands, among which, it was highest in the ${\boldsymbol{\beta}}$ 1 band, and reached its maximum at the 22–30 scale. On the directional characteristics of FCMC, the coupling strength of EEG→sEMG is superior to the opposite direction in most cases. In addition, the coupling strength of the stroke-affected side was lower than that of healthy controls’ right hand in the ${\boldsymbol{\beta}}$ 1 and ${\boldsymbol{\beta}}$ 2 bands and the stroke-unaffected side in the ${\boldsymbol{\beta}}$ 1 band. The coupling strength of the stroke-affected side was higher than that of the stroke-unaffected side and the right hand of healthy controls in the sEMG→EEG direction of ${\boldsymbol{\gamma}}$ 2 band. This study provides a new perspective and lays a foundation for analyzing FCMC and motor dysfunction. [ABSTRACT FROM AUTHOR]

Infrared thermography is a widely used noncontact temperature measurement method. However, the infrared temperature measurement result is susceptible to interference factors. To overcome the infrared temperature measurement errors caused by the simultaneous influence of multiple interference factors, this article takes the measuring distance and dust as specific interference factors and proposes an intelligent compensation method for the infrared temperature measurement errors caused by the measuring distance and dust. First, the influence of the measuring distance and dust is analyzed from the perspective of the infrared temperature measurement principle. Second, a weighted ensemble stacked denoising autoencoder (WE-SDAE) is proposed and used to construct an intelligent compensation model. Then, the measuring distance, dust transmittance, and measured temperature are used as the input of the compensation model to estimate the errors caused by the measuring distance and dust. Finally, the estimated error is used to compensate for the original infrared temperature measurement results under multifactor interference. Experimental results in two cases show that the proposed method is effective in compensating for the infrared temperature measurement errors caused by the measuring distance and dust, which is beneficial to ensure the accuracy of the infrared thermal imager in complex temperature measurement scenarios. [ABSTRACT FROM AUTHOR]

Exploration of the Red Planet is essential on the way through both human colonization and establishing a habitat on the planet. Due to the high costs of space missions, the use of distributed sensor networks has been investigated to make in situ explorations affordable. Along with this, the devices with ultralow-power receivers, which are called zero-energy (ZE) devices, can pave the way to further discoveries for the environment of Mars. This article focuses on wireless power transmission to provide the power required by ZE devices on the Martian surface. The main motivation of this study is to investigate whether conventional harvesters and communication units can supply the required power for a long distance. The numerical results show that it is possible to deliver power to ZE devices without utilizing any sophisticated hardware. In addition, the effects of pointing error and dust storms on harvesting performance are investigated. Comprehensive simulation results reveal that harvester selection and design should be done by considering propagation channel and transmitter characteristics. [ABSTRACT FROM AUTHOR]

We extend upper bounds on the quantum independence number and the quantum Shannon capacity of graphs to their counterparts in the commuting operator model. We introduce a von Neumann algebraic generalization of the fractional Haemers bound (over $\mathbb {C}$) and prove that the generalization upper bounds the commuting quantum independence number. We call our bound the tracial Haemers bound, and we prove that it is multiplicative with respect to the strong product. In particular, this makes it an upper bound on the Shannon capacity. The tracial Haemers bound is incomparable with the Lovász theta function, another well-known upper bound on the Shannon capacity. We show that separating the tracial and fractional Haemers bounds would refute Connes’ embedding conjecture. Along the way, we prove that the tracial rank and tracial Haemers bound are elements of the (commuting quantum) asymptotic spectrum of graphs (Zuiddam, Combinatorica, 2019). We also show that the inertia bound (an upper bound on the quantum independence number) upper bounds the commuting quantum independence number. [ABSTRACT FROM AUTHOR]

High-frequency aerosol observation from a new-generation geostationary meteorological satellite is capable to capture and monitor the spatiotemporal dynamic variation of aerosols, which is of vital significance to environmental research and climate studies. Due to the diversity and complexity of land cover, it is a challenge to retrieve aerosol properties with high accuracy over land, especially over heterogeneous land surfaces. In this study, a geometry-discrete minimum reflectance aerosol retrieval algorithm (GeoMRA) has been proposed to retrieve 10-min high temporal resolution aerosol optical depth (AOD) datasets for geostationary Himawari-8 Advanced Himawari Imager (AHI) sensor, aiming at providing universal bidirectional reflectance distribution function (BRDF) descriptions for different land surfaces with different heterogeneous extent. The AOD retrievals from GeoMRA demonstrate good consistency against the ground-based AERONET measurements in East Asia from 2015 to 2020, with a correlation coefficient (${R}$) of 0.883 and approximately 65.6% of matchups falling within the expected error envelope of ±(0.05% +15%). Intercomparison between the GeoMRA retrieved AOD and other operational AOD products shows that the GeoMRA AOD retrievals, which generally possess similar spatial distribution and accuracy as Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products, have better performances than the Japan Aerospace Exploration Agency (JAXA) AOD products by providing more accurate AOD retrievals with higher spatial coverage. Moreover, the AOD bias analyses further demonstrate the robustness of GeoMRA algorithm, and an extreme haze event shows that the continuous GeoMRA AOD images illustrate smoother temporal variations than JAXA AOD products, demonstrating its efficacy and reliability in capturing the process of haze transport and monitoring the continuous spatiotemporal variation of aerosol. The above results suggest the considerable accuracy of GeoMRA algorithm for scientific application requirement and demonstrate the robustness of the proposed BRDF scheme in describing heterogeneous surfaces with diverse reflectance distribution. [ABSTRACT FROM AUTHOR]

In the city, it is still a challenge to implement a mobile measurement device to accurately monitor its complex pollution state. Current research proposes new technological developments and many studies implement an Unmanned Aerial Vehicle (UAV)-based platforms. However, many questions arise on the representativeness of such measurements and on the UAV impact on data quality. Here, UAV is used to link between sparsely located ground monitoring sites and lidar station measuring pollution, the latter of which are at heights of typically >200 m. Information at intermediate heights are missing but needed for health studies and also for lidar validation algorithms. In this regard, we propose and demonstrate a UAV-based platform aimed at measuring meteorological and air quality parameters, namely, air temperature (T air), relative humidity (RH) and PM2.5 (Particulate matter pollution with a diameter of less than 2.5 μ m) on both compact and large UAV integrations. Each integration was separately investigated both numerically and experimentally in terms of the possible set of instruments used in the setup. In particular, custom-made meteorological shelters were constructed and tested in the laboratory under different propeller conditions. The best placement locations on the UAVs were verified using numerical airflow simulations employing real UAV geometries. Next, we compared between different sensors for T air on a diurnal cycle and deduced the best work time corresponding to minimal impact of the outside conditions (mainly due to direct sun exposure). The influence of propeller rotation on measured parameters has been studied in detail. In addition, a pilot field experiment was conducted in the urban domain using our platform and these measurements were compared to all available data sources. Our field experiment indicated a mixed local pollution layer up to 300 m. Finally, we suggest a practical guideline for carrying out field experiments employing both UAVs. We have also considered the fact that wide use of UAV technologies in crowded cities in the context presented in this paper is currently restricted by regulations and privacy that vary among different countries. [Display omitted] • We propose an Unmanned Aerial Vehicles (UAV)-based platform aimed at measuring meteorological and air quality parameters. • The impact of different sensor configurations on a UAV-based platform performance was studied. • Pre-flight inspection using numerical simulations and laboratory experiments was conducted. • We characterize the city pollution state using UAVs, surface observations and lidar traces. • We suggest practical guidelines for carrying out field experiments in the city employing UAVs. [ABSTRACT FROM AUTHOR]

We present the design and results of an experiment investigating the occurrence of self-illusion and its contribution to realistic behavior consistent with a virtual role in virtual environments. Self-illusion is a generalized illusion about one's self in cognition, eliciting a sense of being associated with a role in a virtual world, despite sure knowledge that this role is not the actual self in the real world. We validate and measure self-illusion through an experiment where each participant occupies a non-human perspective and plays a non-human role using this role's behavior patterns. 77 participants were enrolled for the user study according to the priori power analysis. In the mixed-design experiment with different levels of manipulations, we asked the participants to play a cat (a non-human role) within an immersive VE and captured their different kinds of responses, finding that the participants with higher self-illusion can connect themselves to the virtual role more easily. Based on statistical analysis of questionnaires and behavior data, there is some evidence that self-illusion can be considered a novel psychological component of presence because it is dissociated from sense of embodiment (SoE), plausibility illusion (Psi), and place illusion (PI). Moreover, self-illusion has the potential to be an effective evaluation metric for user experience in a virtual reality system for certain applications. [ABSTRACT FROM AUTHOR]