The Distance and Dynamical History of the Virgo Cluster Ultradiffuse Galaxy VCC 615

We use deep Hubble Space Telescope imaging to derive a distance to the Virgo Cluster ultradiffuse galaxy (UDG) VCC 615 using the tip of the red giant branch (TRGB) distance estimator. We detect 5023 stars within the galaxy, down to a 50% completeness limit of F814W ≈ 28.0, using counts in the surrounding field to correct for contamination due to background sources and Virgo intracluster stars. We derive an extinction-corrected F814W tip magnitude of m tip , 0 = 27.19 − 0.05 + 0.07 , yielding a distance of d = 17.7 − 0.4 + 0.6 Mpc. This places VCC 615 on the far side of the Virgo Cluster (d Virgo = 16.5 Mpc), at a Virgocentric distance of 1.3 Mpc and near the virial radius of the main body of Virgo. Coupling this distance with the galaxy’s observed radial velocity, we find that VCC 615 is on an outbound trajectory, having survived a recent passage through the inner parts of the cluster. Indeed, our orbit modeling gives a 50% chance the galaxy passed inside the Virgo core (r < 620 kpc) within the past gigayear, although very close passages directly through the cluster center (r < 200 kpc) are unlikely. Given VCC 615's undisturbed morphology, we argue that the galaxy has experienced no recent and sudden transformation into a UDG due to the cluster potential, but rather is a long-lived UDG whose relatively wide orbit and large dynamical mass protect it from stripping and destruction by the Virgo cluster tides. Finally, we also describe the serendipitous discovery of a nearby Virgo dwarf galaxy projected 90″ (7.2 kpc) away from VCC 615.

Capital Expenditure and Operating Efficiency from Vertical Integration in the Global Semiconductor Industry

This paper uses a nonparametric production frontier approach to investigate the operating efficiency differences by the impacts of business model and capital expenditure in the global semiconductor industry. Handling the impact of capital expenditure as fixed input by the directional distance estimator, this study compares the operating efficiencies between the integrated device manufacturers and the fabless and foundry firms in the global semiconductor industry over 1999–2018. The estimation results indicate that vertically integrated manufacturers dominate the semiconductor industry, and the capital-intensive companies operate more efficiently than the asset-light fabless firms on average.

Towards human distance estimation using a thermal sensor array

Human distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of $$\pm 0.2$$ ± 0.2 m in continuous-based estimation and $$96.8\%$$ 96.8 % achieved-accuracy in discrete distance estimation.

Parametric Estimation of Long Memory Multivariate Gaussian random fields

The aim of this paper is to make a theoretically study of the minimum Hellinger distance estimator of multivariate, gaussian, stationary, isotropic long-memory random fields The variables are observed on a finite set of points in space. We establish under certain assumptions, the almost sure convergence and the asymptotic distribution of this estimator.

Parametric Estimation of Long Memory Multivariate Gaussian random fields

The aim of this paper is to make a theoretically study of the minimum Hellinger distance estimator of multivariate, gaussian, stationary, isotropic long-memory random fields The variables are observed on a finite set of points in space. We establish under certain assumptions, the almost sure convergence and the asymptotic distribution of this estimator.

Regime‐dependent effects of uncertainty shocks: A structural interpretation

Using a Markov‐switching VAR, we show that the effects of uncertainty shocks on output are four times higher in a regime of economic distress than in a tranquil regime. We then provide a structural interpretation of these facts. To do so, we develop a business cycle model in which agents are aware of the possibility of regime changes when forming expectations. The model is estimated using a Bayesian minimum distance estimator that minimizes, over the set of structural parameters, the distance between the regime‐switching VAR‐based impulse response functions and those implied by the model. Our results point to worsening credit‐market conditions that amplify shocks during distress periods. Finally, we show that the expectation effect of regime switching in financial conditions is an important component of the financial accelerator mechanism. If agents are more pessimistic about future financial conditions, then the output effects of shocks are amplified.

Closed-Form Distance Estimators under Kalman Filtering Framework with Application to Object Tracking

In this paper, the minimum mean square error (MMSE) estimation problem for calculation of distances between two signals via the Kalman filtering framework is considered. The developed algorithm includes two stages: the Kalman estimate of a state vector computed at the first stage is nonlinearly transformed at the second stage based on a distance function and the MMSE criterion. In general, the most challenging aspect of application of the distance estimator is calculation of the multivariate Gaussian integral. However, it can be successfully overcome for the specific metrics between two points in line, between point and line, between point and plane, and others. In these cases, the MMSE estimator is defined by an analytical closed-form expression. We derive the exact closed-form bilinear and quadratic MMSE estimators that can be effectively applied for calculation of an inner product, squared norm, and Euclidean distance. A novel low-complexity suboptimal estimator for special composite functions of linear, bilinear, and quadratic forms is proposed. Radar range-angle responses are described by the functions. The proposed estimators are validated through a series of experiments using real models and metrics. Experimental results show that the MMSE estimators outperform existing estimators that calculate distance and angle in nonoptimal manner.

Distance Estimation Based on Deep Convolutional Neural Network Using Ultra-Wideband Signals

Recently, high accuracy localization technique is required to provide indoor location services. The purpose of this paper is to propose a distance estimation technique based on deep convolutional neural network (DCNN) for indoor environments. Among distance estimation techniques based on wireless communication signals, the use of ultra-wideband (UWB) signals has the advantage of high accuracy in the time domain. The proposed distance estimation method uses UWB signals and proposes a new DCNN-based distance estimator. The superiority of the proposed method is confirmed through computer simulation. Widely used conventional distance estimators are based on the power threshold. The threshold is determined by signal to noise ratio (SNR) of the received signal. The arrival time of the received signal that exceeds the threshold is considered as the time-of-arrival (ToA) and the distance between transmitter and receiver is obtained from the ToA. On the other hand, the proposed distance estimator requires only the received signal without SNR estimation, which make the proposed technique simpler to implement. According to computer simulation, the conventional method is highly sensitive to SNR and distance. In contrast, the proposed method shows less than 2 m root mean square error (RMSE) performance in a wide range of SNR and the RMSE performance is not degraded in long distances. The proposed distance estimator shows excellent distance estimation performance at low SNR and long distance, so it can be applied to indoor localization system of large indoor space and can be used for precise location service.

Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

In this work, a novel distance estimation mechanism using received signal strength indication (RSSI) signals with ZigBee modules is designed, implemented and tested in several scenarios. This estimator was used for a research project focused on a wildlife behavioral classification system deployed in Doñana’s National Park. As a supporting feature for that project, this work was implemented for locating animal’s collars acting as wireless nodes in order to find those who went outside of the coverage area of the network or that were accidentally detached from animals. This work describes the system architecture and the implementation of a mobile assistant capable of recovering devices located beyond the coverage of the network. The analytical model needed for distance estimation and the signal filtering are described, as well as the difficulties that the researchers must deal when building robust location estimators. This theoretical model was applied to three different scenarios and tested with two validation experiments.