Finite Impulse Response Filter-Based Track Formation for Preceding Vehicle Tracking Using Automotive Radars

Automotive radars, which are used for preceding vehicle tracking, have attracted significant attention in recent years. However, the false measurements that occur in cluttered roadways hinders the tracking process in vehicles; thus, it is essential to develop automotive radar systems that are robust against false measurements. This study proposed a novel track formation algorithm to initialize the preceding vehicle tracking in automotive radar systems. The proposed algorithm is based on finite impulse response filtering, and exhibited significantly higher accuracy in highly cluttered environments than a conventional track formation algorithm. The excellent performance of the proposed algorithm was demonstrated using extensive simulations under real conditions.

Distributed Periodic Steady State Kalman Filter

In this paper a distributed implementation for the periodic steady state Kalman filter is proposed. The distributed algorithm has parallel structure and can be implemented using processors in parallel without idle time. The number of processors is equal to the model period. The resulting speedup is also derived. The Finite Impulse Response (FIR) form of the periodic steady state Kalman filter is derived.

Denoising of ECG Signal using UFIR Smoothing with Notch Filter

The electrical activity of the heart is test with an electrocardiogram (ECG). The fundamental information for the taking decision about various types of heart diseases identified by electrocardiogram. There have been numerous attempts over decades to extract the characteristics of the heartbeat through ECG records with high accuracy and efficiency using a variety of strategies and techniques. In this paper a novel scheme is acquainted, the problem is solved by isolated time space using q-lag unbiased finite impulse response (UFIR), then the received time changing of optimal average horizon for the shape of the ECG signal. A complete statistical analysis is furnished by normalized histogram and statistical classifiers, P wave features extraction based on the detected fiducial points is deliberated. In this concept by utilizing QRS detection, morphological top-bottom hat transformation and notch filters is ameliorated PSNR and latency constraints, furnishes high accuracy and reduced elapsed time. Keywords: Electrocardiogram (ECG) denoising, unbiased finite impulse response (UFIR) filtering, P wave feature extraction, normalized histogram, QRS complex detection.

Stock Market Reactions during Different Phases of the COVID-19 Pandemic: Cases of Italy and Spain

The COVID-19 pandemic and pandemic-induced lockdowns and quarantine establishments have inevitably affected individuals, businesses, and governments. At the same time, the spread of the COVID-19 pandemic had a dramatic impact on financial markets all over the world and caused an increased level of uncertainty; the stock markets were no exception either. Most of the studies on the impact of the COVID-19 pandemic on stock markets are based either on the analysis of a relatively short period (the beginning of pandemic) or a longer period, which, in turn, is very heterogeneous in terms of both the information available on the COVID-19 virus and the measures taken to contain the virus and address the consequences of the pandemic. However, it is very important to assess the impact not only at the beginning of the pandemic but also in the subsequent periods and to compare the nature of this impact; the studies of this type are still fragmentary. Therefore, this research aims to investigate the impact of the COVID-19 pandemic on stock markets of two of the most severely affected European countries—Italy and Spain. To reach the aim of the research OLS regression models, heteroscedasticity-corrected models, GARCH (1,1) models, and VAR-based impulse response functions are employed. The results reveal that the stock market reaction to the spread of the COVID-19 pandemic differs depending on the country and period analyzed: OLS regression and heteroscedasticity-corrected models have not revealed the statistically significant impact of the spread of the COVID-19 pandemic, while impulse response functions demonstrated the non-zero primary response of analyzed markets to the COVID-19 shock, and GARCH models (in the case of Spain) confirmed that the COVID-19 pandemic increased the volatility of stock market return. This research contributes to the literature by providing a comprehensive impact assessment both during the whole pre-vaccination period of the pandemic and during different stages of this period.

Estimating Impulse-Response Functions for Macroeconomic Models using Directional Quantiles

A multivariate vector autoregressive model is used to construct the distribution of the impulse-response functions of macroeconomics shocks. In particular, the paper studies the distribution of the short-, medium-, and long-term effects after a shock. Structural and reduced form quantile vector autoregressive models are developed where heterogeneity in conditional effects can be evaluated through multivariate quantile processes. The distribution of the responses can then be obtained by using uniformly distributed random vectors. An empirical example of exchange rate pass-through in Argentina is presented.

An Analysis of the Dynamic Relationship between the Global Macroeconomy and Shipping and Shipbuilding Industries

Using time-series data from January 2006 to February 2021, this study analyzed the effect of macroeconomic shocks on the shipping and shipbuilding industries. The Granger causality test, recursive structural vector autoregressive models, impulse response analysis, historical decomposition, and local projections model were used to identify the dynamic relationships between the variables and their dynamic effects, based on the results of the theoretical model and previous research. First, the Granger causality test demonstrated that the macroeconomic variables have causal relations with the shipping and shipbuilding industries. Second, the recursive structural vector autoregressive estimation demonstrated that the direction of the shocks from macroeconomic variables is statistically significantly, consistent with the theoretical model. The same results were found in the recursive structural vector autoregressive model and local projection impulse response analysis. Finally, the historical decomposition identified the main causal variables affecting the shipping and shipbuilding industries by period. These findings can help policymakers, operators of shipping and shipbuilding companies, and investors evaluate and make policy-supporting decisions on industry conditions.