Ambiguous Measurements Research Articles

Attitude determination in integrated GNSS-inertial navigation systems in information and measurement insufficiency conditions

The article presents the attitude determination algorithm for multi-antenna global navigation satellite and inertial integrated navigation system operating under information and measurement insufficiency conditions. The information and measurement insufficiency conditions are understood as the impossibility of the inertial system alignment on a fixed base and the absence of a redundant number of satellites to resolve the carrier phase integer ambiguity. The ability of the navigation system to operate in such conditions is achieved by combining several existing attitude determination methods and using the new method of attitude determination using ambiguous satellite phase measurements and absolute angular rate measurements. The hardware-in-the-loop simulation results using a 2-axis motion simulator and microelectromechanical systems inertial sensors are presented. The results confirm the efficiency of the proposed approach to the design of the navigation system algorithm.

Fiscal and regulatory effects of the tobacco excise duty reform in Moldova: lessons for Ukraine

The article analyses the experience of reforming the excise duty on tobacco products in Moldova in order to build an information base of how innovations in taxation have been integrated abroad and how this experience is taken into account when making administrative decisions in Ukraine. Moldova has implemented a somewhat radical fiscally oriented excise policy, and its excise duty reform for certain types of tobacco products has often been ambiguous in terms of ensuring compliance with EU standards and attaining the claimed objectives. Up to 2020, the country had diverging excise duty rates for filter and non-filter cigarettes and minimum retail prices for these products, a practice that was contrary to the requirements of European harmonized excise duty accrual. In Moldova, the excise duty on heated tobacco products, which are potentially less harmful to human health, is set at the minimum excise duty for cigarettes, but e-cigarette liquids are subject to no excise taxation at all. The Moldovan excise duty on fine-cut tobacco is greater than 160% of the excise duty on cigarettes, but the steep rise in excise taxes has left cigars, cigarillos and other smoking tobacco unaffected. The country still has a room to increase the sales of certain types of tobacco products that will be in demand among smokers for their low cost, while paying rather modest excise duties. In conclusion, Moldova's experience in tobacco excise duty reform is controversial. None of the “taxation know-how” initiatives in Moldova can be called a success and cannot be recommended as an example for Ukraine to follow. Rather, Moldova's excise policy should be considered as an example of introducing ambiguous measures in order to hedge itself from making any glaring mistakes.

Significance of deep learning for Covid-19: state-of-the-art review

PurposeThe appearance of the 2019 novel coronavirus (Covid-19), for which there is no treatment or a vaccine, formed a sense of necessity for new drug discovery advances. The pandemic of NCOV-19 (novel coronavirus-19) has been engaged as a public health disaster of overall distress by the World Health Organization. Different pandemic models for NCOV-19 are being exploited by researchers all over the world to acquire experienced assessments and impose major control measures. Among the standard techniques for NCOV-19 global outbreak prediction, epidemiological and simple statistical techniques have attained more concern by researchers. Insufficiency and deficiency of health tests for identifying a solution became a major difficulty in controlling the spread of NCOV-19. To solve this problem, deep learning has emerged as a novel solution over a dozen of machine learning techniques. Deep learning has attained advanced performance in medical applications. Deep learning has the capacity of recognizing patterns in large complex datasets. They are identified as an appropriate method for analyzing affected patients of NCOV-19. Conversely, these techniques for disease recognition focus entirely on enhancing the accurateness of forecasts or classifications without the ambiguity measure in a decision. Knowing how much assurance present in a computer-based health analysis is necessary for gaining clinicians’ expectations in the technology and progress treatment consequently. Today, NCOV-19 diseases are the main healthcare confront throughout the world. Detecting NCOV-19 in X-ray images is vital for diagnosis, treatment, and evaluation. Still, analytical ambiguity in a report is a difficult yet predictable task for radiologists.MethodIn this paper, an in-depth analysis has been performed on the significance of deep learning for Covid-19 and as per the standard search database, this is the first review research work ever made concentrating particularly on Deep Learning for NCOV-19.ConclusionThe main aim behind this research work is to inspire the research community and to innovate novel research using deep learning. Moreover, the outcome of this detailed structured review on the impact of deep learning in covid-19 analysis will be helpful for further investigations on various modalities of diseases detection, prevention and finding novel solutions.

Network and Application Performance Measurement Challenges on Android Devices

Modern mobile systems are optimized for energy-efficient computation and communications, and these optimizations affect the way they use the network, and thus the performance of the applications. Therefore, understanding network and application performance are essential for debugging, improving user experience, and performance comparison. In recent years, several tools have emerged that analyze network performance of mobile applications in situ with the help of the VPN service. However, there is a limited understanding of how these measurement tools and system optimizations affect the network and application performance. This paper first demonstrates that mobile systems employ energy-aware system hardware tuning, affecting network latency and throughput. We next show that the VPN-based tools, such as Lumen, PrivacyGuard, and Video Optimizer, aid in ambiguous network performance measurements and degrade the application performance. Our findings suggest that sound Internet traffic measurement on Android devices requires a good understanding of the device, networks, measurement tools, and applications.

Application of Generalized Measure of ‘Useful’ R-norm Inaccuracy and Total Ambiguity

In the present paper, we introduce generalized measure of ‘useful’ R-norm inaccuracy having two parameters and its analogue ‘useful’ R-norm total ambiguity measure by merging together the concepts of probability, fuzziness, Rnorm, ‘useful’ information and inaccuracy. Along with the basic properties, some other important properties of these two proposed measures are stated. These measures are generalizations of some well-known inaccuracy measures. Further, the monotonic behaviour of the proposed ‘useful’ R-norm inaccuracy measures is studied and the graphical overview is given. The measure of information improvement for both the measures is also obtained. Lastly, the application of ‘useful’ R-norm total ambiguity measure is presented in terms of multi-criteria decision making. For all the numerical calculations R software is used.

A Markov regime switching model for asset pricing and ambiguity measurement of stock market

Based on the theoretical framework of expected utility with uncertain probabilities, this paper uses actual prices of CSI300 and Hang Seng index to empirically measure ambiguity degrees in the Chinese mainland and Hong Kong stock markets. A Markov regime-switching model is proposed to divide the stock market into bear and bull states, and then test whether there exist significant differences in the ambiguity degrees under different states. An ambiguity factor and a risk factor are then proposed to analyze the time-varying relationship among risk, ambiguity, and return under different states. In addition to the mean and variance, the high-order moments, including skewness and kurtosis, are used to test whether they affect the relationship among them. The results show that the ambiguity degrees in the Chinese mainland stock market are significantly higher than those in the Hong Kong stock market, and there are significant differences between bear and bull states for the two markets. Moreover, the regression results among risk, ambiguity, and return indicate that with ambiguity, the effects of risk factors on excess returns is significantly negative under bear state, and significantly positive under bull state, while it is significantly negative under the two states without ambiguity.

A measure of ambiguity (Knightian uncertainty)

Uncertain or ambiguous events cannot be objectively measured by probabilities, i.e. different decision-makers may disagree about their likelihood of occurrence. This paper proposes a new decision-theoretical approach on how to measure ambiguity (Knightian uncertainty) that is analogous to axiomatic risk measurement in finance. A decision-theoretical measure of ambiguity is a function from choice alternatives (acts) to non-negative real numbers. Our proposed measure of ambiguity is derived from a novel assumption that ambiguity of any choice alternative can be decomposed into a left-tail ambiguity (uncertainty in the realization of relatively undesirable outcomes) and a right-tail ambiguity (uncertainty in the realization of relatively desirable outcomes). This decomposability assumption is combined with two standard assumptions: ambiguity sources (events) are independent (separable) from outcomes (consequences) and any elementary increase in uncertainty (increasing a more desirable outcome in a binary act) necessarily increases ambiguity.

The Effect of Ambiguity on Price Formation and Trading Behavior in Financial Markets

This paper sets up an experimental asset market in the laboratory to investigate the effects of ambiguity on price formation and trading behavior in financial markets. The obtained trading data is used to analyze the effect of ambiguity on various market outcomes (the price level, volatility, trading activity, market liquidity, and the degree of speculative trading) and to test the quality of popular empirical market-based measures for the degree of ambiguity. We find that ambiguity decreases market prices and trading activity; ambiguity leads to lower market liquidity through wider bid-ask spreads; and ambiguity leads to less speculative trading. We also find that popular market-based measures of ambiguity used in the empirical literature do not seem to correctly capture the true degree of ambiguity.

Eliminating the azimuth ambiguity in reflected S-wave imaging logging based on the azimuthal receiver mode

Due to the symmetrical directivity of a dipole transducer, dipole reflected S-wave imaging logging results in ambiguous azimuth measurements of reflectors. Based on an orthogonal dipole source and an eight-element azimuthal receiving station, we proposed an improved method for measuring the azimuth of a reflector and applied it to a borehole azimuthal acoustic reflection tool in our laboratory. Based on the dipole transmitting and receiving mode, to determine the strike of a reflector, this method superposes the echo amplitude and azimuth distribution curves of X and Y dipole transmitting and azimuthal receiving and compares the amplitude-superposed value of the echo received by two receiving elements close to the reflector azimuth and a virtual image azimuth. It accurately obtained the true azimuth of the reflector. The feasibility of this method was verified by numerical simulation, a physical experiment, and a field test. The results show that the amplitude of the P–P, P–S/S–P, and S–S echoes received by the different receiving elements were different when there was a reflector in place. For any type of echo, the amplitude superposition value of the echo received by a receiving element close to the reflector azimuth was significantly greater than that of the echo received by a receiving element close to a false azimuth. Therefore, based on the azimuthal receiving mode, the azimuth ambiguity of dipole-reflection S-wave logging can be eliminated.

Distributionally robust profit opportunities

This paper expands the notion of robust profit opportunities in financial markets to incorporate distributional ambiguity using Wasserstein distance as the ambiguity measure. Financial markets with risky and risk-free assets are considered. The infinite dimensional primal problems are formulated, leading to their simpler finite dimensional dual problems. A principal motivating question is how distributional ambiguity helps or hurts the robustness of the profit opportunity. Towards answering this question, some theory is developed and computational experiments are conducted. Finally some open questions and suggestions for future research are discussed.

Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk

This paper investigates calculations of robust X-Value adjustment (XVA), in particular, credit valuation adjustment (CVA) and funding valuation adjustment (FVA), for over-the-counter derivatives under distributional ambiguity using Wasserstein distance as the ambiguity measure. Wrong way counterparty credit risk and funding risk can be characterized (and indeed quantified) via the robust XVA formulations. The simpler dual formulations are derived using recent Lagrangian duality results. Next, some computational experiments are conducted to measure the additional XVA charges due to distributional ambiguity under a variety of portfolio and market configurations. Finally some suggestions for further work are discussed.

An algorithm for automatic grasping an UHF RFID passive tag

This paper proposes an automatic system which allows a mobile robot to reach a tagged object in an unknown environment, exploiting the Radio Frequency IDentification (RFID) technology. It is assumed that the robot has an odometry system (e.g., wheel encoders) and an RFID reader which provides it with phase-shift measurements of the RFID signal backscattered by the tagged object. Since phases are ambiguous measurements of the tag-reader distance, a Multi-Hypothesis Extended Kalman Filter is proposed to on-line extract, by fusing phase measurements with odometry readings, the range and the bearing of the unknown tag. Once the relative position of the tag is known, a control algorithm is applied to drive the robot toward the desired destination. Numerical results are reported to illustrate the approach.

Work-Related Resilience and its Various Faces: Integration Through Cross-Level Investigations

Resilience generally indicates how well a system resists threats and how quickly it returns to its initial state after a disturbance (Bhamra, Dani, & Burnard, 2011). Due to its broad appeal, the term resilience has a long history and has been applied to various fields. Though its scientific roots are in the area of material science (Snowdon, 1958), the concept diffused to other disciplines such as developmental psychology (Werner & Smith, 1982), ecology (Holling, 1973), and engineering (Woods, 2003). As organizations and their environments have become increasingly complex and challenging (Whittington, Pettigrew, Peck, Fenton, & Conyon, 1999), resilience has also gained extensive attention in work-related research. Arising in this area at the individual level of analysis in the mid-1980s, work-related resilience research today encompasses several levels of analysis and disciplines. More precisely, research now focuses on individuals, dyads, teams, and whole organizations operating in various setting such as business, healthcare, social work, high reliability organizing, and emergency management (e.g., Chapman et al., 2018; Hart, Brannan, & De Chesnay, 2014; Kossek & Perrigino, 2016; Linnenluecke, 2017; Williams, Gruber, Sutcliffe, Shepherd, & Zhao, 2017; Van Breda, 2018). Given broad practical and scientific interest for research on resilience at multiple levels, numerous theoretical and empirical findings have amassed, which have enhanced our understanding of resilience in work- related settings. Yet while progress has been made, several problems still exist that hinders further progress in this area, including varied and sometimes ambiguous conceptualizations, measurement approaches, and empirical results on antecedents and outcomes. As such, there are increasing calls for more conceptual clarity and integration across different levels of analysis (e.g., Britt, Shen, Sinclair, Grossman, & Klieger, 2016; King, Newman, & Luthans, 2016; Linnenluecke, 2017; Zellars, Justice, & Beck, 2011). To address these calls and offer promising pathways for future research, the symposium’s aim is to bring together and provide an opportunity for scholars to discuss current research focusing on team and cross-level phenomena in the work-related resilience space. The Use of the Resilience Construct in Work-related Research: A Bibliographic and Content Analysis Presenter: Sebastian Raetze; Technical U. of Dresden Presenter: Stephanie Duchek; BTU Cottbus-Senftenberg Presenter: M. Travis Maynard; Colorado State U. We’ve Got Your Back: The Role of Team Support for Member Resilience Presenter: Allison Traylor; Rice U. Presenter: Chelsea LeNoble; Embry Riddle Aeronautical U. Presenter: Thomas W. Britt; Clemson U. Presenter: Marissa Shuffler; Clemson U. Resilient to What? Unpacking Team Responses to Different Resilience Triggers Presenter: Christopher Wiese; Georgia Institute of Technology Presenter: Shawn Burke; U. of Central Florida Presenter: Krisztina Szabo; U. of Central Florida Presenter: Justine Moavero; U. of Central Florida Presenter: Tristin Halfman; U. of Central Florida Leaders’ Behavior for the Development of Resilient Organizations: An Empirical Investigation Presenter: Wolfgang H. Guettel; Johannes Kepler U. Linz Presenter: Stephanie Duchek; BTU Cottbus-Senftenberg Presenter: Charlotte Foerster; Evangelical U. of Applied Sciences Dresden Presenter: Caroline Paparella; -

Information Frictions and Stock Returns

The purpose of this paper is to assess the impact of ambiguity on financial analyst forecast incentives and the associated abnormal stock returns. I present a model incorporating ambiguity aversion into a two-period Lucas tree model. The resulting model confirms the role of ambiguity in the determination of asset returns. In particular, the model with ambiguity aversion generates a lower price and a higher required rate of returns compared to the classical model without ambiguity concern. I construct a measure of ambiguity and provide empirical evidence showing that the incentive of analysts to misrepresent information is a function of ambiguity. Analysts are more likely to bias their forecasts when it is more difficult for investors to detect their misrepresentation. Under ambiguity, analysts’ optimistic forecasts for good/bad news tend to deteriorate. Moreover, stock returns are positively related with ambiguity. Under ambiguity neither good nor bad news is credible. Investors systematically underreact to good news forecast and overreact to bad news forecast when ambiguity exists.

Dark matter axion detection in the radio/mm waveband

We discuss axion dark matter detection via two mechanisms: spontaneous decays and resonant conversion in neutron star magnetospheres. For decays, we show that the brightness temperature signal, rather than flux, is a less ambiguous measure for selecting candidate objects. This is owing principally to the finite beam width of telescopes which prevents one from being sensitive to the total flux from the object. With this in mind, we argue that the large surface-mass-density of the galactic centre or the Virgo cluster centre offers the best chance of improving current constraints on the axion-photon coupling via spontaneous decays. For the neutron star case, we first carry out a detailed study of mixing in magnetised plasmas. We derive transport equations for the axion-photon system via a controlled gradient expansion, allowing us to address inhomogeneous mass-shell constraints for arbitrary momenta. We then derive a non-perturbative Landau-Zener formula for the conversion probability valid across the range of relativistic and non-relativistic axions and show that the standard perturbative resonant conversion amplitude is a truncation of this result in the non-adiabatic limit. Our treatment reveals that that infalling dark matter axions typically convert non-adiabatically in magnetospheres. We describe the limitations of one-dimensional mixing equations and explain how three-dimensional effects activate new photon polarisations, including longitudinal modes and illustrate these arguments with numerical simulations in higher dimensions. We find that the bandwidth of the radio signal is dominated by Doppler broadening from the relative motion of the neutron star with respect to the observer. Therefore, we conclude that the radio signal from the resonant decay is weaker than previously thought, which means one relies on local density peaks to probe weaker axion-photon couplings.

Detecting emotional ambiguity in text

An approach for determining emotional ambiguity in text data is described in this paper. The prediction confidences output from a text classifier are used to measure amount of ambiguity found in target entries. This measure can be used as a filtering mechanism to identify entries that require human feedback. This feedback loop can be implemented in a workflow which retrains a classifier model including newly disambiguated entries and resulting in a boost to classifier accuracy. This emotion ambiguity measure can be utilized to discover concrete emotional content in text data as well as reveal topics which do not have a concrete emotional consensus.

An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory

Abstractmode and effects analysis (FMEA) is an effective tool to assess the risk of a system or process under uncertain environment. However, how to handle the uncertainty in the subjective assessment is an open issue. In this paper, a novel method to deal with the uncertainty coming from subjective assessments of FMEA experts is proposed in the framework of Dempster–Shafer evidence theory. First, the uncertain degree of the assessment is measured by the ambiguity measure. Then, the uncertainty is transformed to the reliability of each FMEA expert and the relative importance of each risk factor. After that, the assessments from FMEA team will be fused with a discounting‐based combination rule to address the potential conflict. Moreover, to avoid the situation that different risk priorities of failure modes may have the same ranking based on classical risk priority number method, the gray relational projection method (GRPM) is adopted for ranking risk priorities of failure modes. Finally, an application of the improved FMEA model in sheet steel production process verifies the reliability and validity of the proposed method.

Multi-PRF and multi-frame track-before-detect algorithm in multiple PRF radar system

This paper considers the detection and tracking of dim targets for multiple pulse repetition frequency (PRF) pulsed Doppler radar systems through the use of multi-frame track-before-detect (TBD) methods. The main challenge is that each measurement frame is a mixture with measurements recorded, periodically, at different PRF settings, thus with different range and Doppler ambiguities. To achieve this goal, we propose variable step size multi-PRF and multi-frame (or scan) TBD (MP-MF-TBD) algorithm. Specifically, by incorporating the ambiguity factors related to both the range and velocity ambiguities, MP-MF-TBD first accomplishes the target SNR integration directly using multiple ambiguous measurement frames. After analyzing the properties of the over-threshold tracks, we further propose an efficient detection procedure, which can effectively eliminate the false tracks caused by the measurement ambiguities and accomplish the target kinematic state estimation and multi-PRF ambiguity resolution simultaneously. The proposed MP-MF-TBD does not require certain limitations on the PRF type (i.e., low, medium and high PRF) or the number of PRFs in pulsed Doppler radar systems, and appears to be robust to the target loss when target fall into a blind frequency band of certain PRFs. Extensive numerical simulations demonstrated the efficacy of the proposed MP-MF-TBD by comparing with classical techniques.

An Analysis of the Asymmetric Volatility under Asymmetric Information and Ambiguity

The asymmetric volatility phenomenon (‘the phenomenon’, henceforth), documented first by Black (1976), refers to the fact that the stock return and its conditional volatility are negatively correlated. To explain ‘the phenomenon’, this paper presents an asymmetric information model under ambiguity, and provides an empirical test of its result as well. We assume that in the Grossman and Stiglitz (1980), uninformed liquidity traders face ambiguity about the distribution of asset payoffs, and that their attitudes toward ambiguity vary depending on the state of the economy. In model I, their utility functions exhibit ambiguity aversion in the bad state and ambiguity neutrality in the good state. In model II, liquidity traders are still ambiguity-averse in the bad state but ambiguity-seeking in the good state. We find that ‘the phenomenon’ appears in model II when the degree of ambiguity is not large. Furthermore, we show that the possibility of ‘the phenomenon’ is higher as the proportion of liquidity traders increases. To perform an empirical analysis, we measure the degree of ambiguity by the Kolmogorov-Smirnov statistic and show that this measure has a positive relationship with the difference between the volatilities in the good and bad states. In addition, we find that the risk factor constructed by the ambiguity measure has explanatory power about returns on 25 portfolios of the Fama-French type in the Korean market.

Estimating Location Accuracy of Stationary Emitter in Presence of Biasing in Receiver Position and Velocity by Exploiting Cross Ambiguity Function

This paper proposes CAF algorithm to estimate localisation accuracy of a stationary emitter which is being monitored by a pair of sensors mounted on high altitudes. It computes joint Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) using Cross Ambiguity Function (CAF) and measures geolocation accuracy in presence of biasing in sensor position and velocity. Previous work in this area utilizes TDOA and FDOA measurements with known sensor kinematics which is fed to Maximum Likelihood or Least Squares algorithm for post processing. However it is computation demanding. In the present work, surface peaks of TDOA and FDOA values are directly mapped to geographic coordinates. This method is computationally efficient. As sensor and emitter geometry keeps changing over time due to moving sensors, multiple CAF snapshots are taken for emitter geolocation. Simulations are carried out using MATLAB. It is observed that at 30 dB SNR, location accuracy of stationary emitter is 100 m at known sensor kinematics and by introducing bias in the receiver position and velocity, it is 200 meters. These measurements are well within and in accordance with theoretical developments.