Error Correction Approach Research Articles

Government Expenditure and Economic Growth in Nepal

This study aims to investigate the relationship between the government expenditure on education, health, transportation, agriculture, and economic growth in Nepal from 1975 to 2019. The ARDL approach to co-integration and error correction approach is used to detect the long-run and the short-run relationship between RGDP and exogenous variables in the model. The study revealed the significant and positive impact of educational expenditure on economic growth in the long run. Similarly, health expenditure has a significant but negative impact on GDP in the long-run as well as in the short-run. Moreover, agriculture expenditure of the government is found to have a significant but negative impact on GDP in the long-run. The transportation expenditure is found to be insignificant. The study concludes that government expenditure along with spending pattern, channel of spending and effectiveness of spending are equally important to examine the true effect of government expenditures on economic growth.

OCR error correction using correction patterns and self-organizing migrating algorithm

Optical character recognition (OCR) systems help to digitize paper-based historical achieves. However, poor quality of scanned documents and limitations of text recognition techniques result in different kinds of errors in OCR outputs. Post-processing is an essential step in improving the output quality of OCR systems by detecting and cleaning the errors. In this paper, we present an automatic model consisting of both error detection and error correction phases for OCR post-processing. We propose a novel approach of OCR post-processing error correction using correction pattern edits and evolutionary algorithm which has been mainly used for solving optimization problems. Our model adopts a variant of the self-organizing migrating algorithm along with a fitness function based on modifications of important linguistic features. We illustrate how to construct the table of correction pattern edits involving all types of edit operations and being directly learned from the training dataset. Through efficient settings of the algorithm parameters, our model can be performed with high-quality candidate generation and error correction. The experimental results show that our proposed approach outperforms various baseline approaches as evaluated on the benchmark dataset of ICDAR 2017 Post-OCR text correction competition.

Measurement Approaches and Implementation of a Fading Characterization Tester for Evaluating Optical Turbulent Channels

Optical wireless communication (OWC) systems can exhibit high percentage availability, yet suffering from frequent intensity fades caused by turbulence effects and pointing errors. The intuitive method to character the link is using commercial bit error rate testers (BERT) by measuring the output sequence of the receiver, which provides limited information. Thus we propose the algorithms for measuring the outage and synchronization performance, bit error rate (BER), throughput, and fading statistics. The inputs consist of decoded data sequence and received optical power. Both the bit level and packet level measurements are presented. Then a fading characterization tester (FCT) having these functions is implemented based on a field programmable gate array (FPGA). Our indoor experiment reveals the measurement accuracy of the FCT by comparing the measured results with the derived theoretical ones. Another 1.9 km OWC experiment is also carried out. These measured results by FCT are presented, which can not only measure the OWC link, but also help the researchers to improve the system performance, such as the design of error correction approaches like forward error correction, the design of packet based network protocols.

A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS.

To improve the standard point positioning (SPP) accuracy of integrated BDS (BeiDou Navigation Satellite System)/GPS (Global Positioning System) at the receiver end, a novel approach based on Long Short-Term Memory (LSTM) error correction recurrent neural network is proposed and implemented to reduce the error caused by multiple sources. On the basis of the weighted least square (WLS) method and Kalman filter, the proposed LSTM-based algorithms, named WLS–LSTM and Kalman–LSTM error correction methods, are used to predict the positioning error of the next epoch, and the prediction result is used to correct the next epoch error. Based on the measured data, the results of the weighted least square method, the Kalman filter method and the LSTM error correction method were compared and analyzed. The dynamic test was also conducted, and the experimental results in dynamic scenarios were analyzed. From the experimental results, the three-dimensional point positioning error of Kalman–LSTM error correction method is 1.038 m, while the error of weighted least square method, Kalman filter and WLS–LSTM error correction method are 3.498, 3.406 and 1.782 m, respectively. The positioning error is 3.7399 m and the corrected positioning error is 0.7493 m in a dynamic scene. The results show that the LSTM-based error correction method can improve the standard point positioning accuracy of integrated BDS/GPS significantly.

Determinants of gold price movements: An empirical investigation in the presence of multiple structural breaks

This study investigates the short- and long-run determinants of gold price movements in financial markets by taking into account multiple structural breakpoints using an ARDL-based error correction approach. The study used daily time series data from December 19, 2018 to May 15, 2020. The key variables used include international stocks and bond funds that are frequently traded on stock exchanges around the world. The results, based on the fourth breakpoint regime, reveal a significant positive relationship between gold price movements and LSE, Nikkei stocks, T.Rowe global multi-sector bond funds, and CBOE volatility index; and a significant negative association with Gmo emerging country debt and Pimco emerging markets local currency bond funds both in the short- and long-run. Other stocks, like NASDAQ, DJI, S&P500, only revealed negative short-run relationships; except for NYSE that was found to have a positive short-run association with gold price movements. Conversely, Goldman Sachs bonds revealed a significant positive long-run relationship with gold price movements. These results have significant policy implications for gold producers and investors, as both stocks and bonds are an important source of information in the determination of gold price movements both in the short- and long-run.

Revisiting environmental degradation and economic growth nexus using autoregressive distributed lag approach

PurposeThe paper attempts to revisit the nexus between economic growth, carbon emissions, trade openness, financial effectiveness and FDI for a sample of seven developed and developing countries using curvilinear relationship as per environmental Kuznets curve (EKC) hypothesis over long term.Design/methodology/approachThe authors determine the unit root properties of variables (using Clemente–Montañés–Reyes unit root test with double mean shifts and AO model and augmented Dickey–Fuller test) for structural breaks at different levels. Autoregressive distributed lag (ARDL) and error correction model (ECM) methodology was used to estimate long- and short-run parameters among the selected variables in sample countries from 1965 to 2016. Vector error correction (VEC) and Granger causality approach was used to determine the direction of causality.FindingsThe authors confirmed long-run relationship among the variables and highlighted high economic growth and energy consumption as the main causes of environmental degradation. While in India financial development and FDI inflows depict a negative association with environmental sustainability, however, such relationship was positive in the United Kingdom (UK), which is often considered as a benchmark for policymakers. The authors’ findings were in agreement with existing research insights in reporting FDI and financial development as the major contributors towards (unsustainable) sustainable environment through emissions in case of (developing country like India) developed country like UK. For other sample countries (China, Brazil, Japan, South Africa, United States of America (USA)), the authors’ model failed to capture financial development and FDI as significant contributors of carbon emissions. However, unidirectional causality running from energy to carbon emission was observed leading to the policy adoption of incentivizing alternative energy-based resources to increase energy efficiency across the energy value chain.Research limitations/implicationsManufacturing with renewable energy, in collaboration with private and foreign players, under an institutional framework is desirable. Policy instruments including mandatory administrative controls, economic incentives and voluntary schemes that promote energy efficiency building blocks need to be established. A sound legal system for implementing technological innovation, financial subsidy incentives, interest-free loan programmes and development of financial sector supports creation and thriving of energy efficient units, often a perquisite for accelerated development.Originality/valueBy undertaking a comparative analysis, the authors address the research gap through revisiting EKC hypothesis with different set of trade policy and financial development framework. To the best of the authors’ knowledge, earlier studies were limited to one-country data analysis and did not consider the comparative data set of developed and developing countries with reference to financial development and FDI components.

Fiscal Deficit and Economic Growth Nexus in India: A Simultaneous Error Correction Approach

This paper examines the impact of fiscal deficit on economic growth in India, during the period from 1970–71 to 2018–19. Using a combination of Autoregressive Distributed Lag and Simultaneous Error Correction Approach, this study shows that fiscal deficit and revenue deficit have an adverse effect on economic growth both in the long run and in the short run. The empirical analysis confirms that fiscal deficit influences economic growth both directly, and indirectly through the routes of investment, interest rate, current account deficit and composition of government expenditure. Further, gross investment has a positive and inflation rate has a negative impact on economic growth. For a policy perspective, the government should control fiscal deficit and revenue deficit as suggested by the FRBM Act. The composition of government expenditure should be altered to devote more resources for the formation of productive capital in India.

JGBs’ chronically low nominal yields: a VEC approach

ABSTRACT Low short-term interest rates, induced by the Bank of Japan’s (BoJ) accommodative monetary policy, is mainly responsible for keeping long-term Japanese government bonds’ (JGBs) nominal yields exceptionally low for a protracted period. Elevated government debt and deficit ratios do not exert upward pressure on JGBs’ nominal yields. This paper provides an empirical investigation of chronically low nominal yields of JGBs from a Keynesian perspective. It deploys a Vector Error Correction (VEC) approach to model long-term government bond yields.

Determinants of Rice Import Demand in Nigeria

Primarily, the study examined the determinants of rice import demand in Nigeria by assessing the short run and long run dynamic model relationships among the determinants, trends and extent of causality among per capita income, population, exchange rate and price of rice imports were equally examined, using data obtained from the Central Bank of Nigeria (CBN) and National Bureau of statistics (NBS) over the period 1961 to 2013. Data obtained showed the perceived determinants of imports demand for rice in Nigeria were local rice production, rice import price, rice consumption, per capita income, and exchange rate, price of local rice, domestic stock variation, maize price, meat price and demographic development. The short run dynamic model result showed that rice consumption, price of meat, price of maize, local rice quantity, demography development and stock variance are statistically significant at 5%. The significance of the coefficient of the error correction term confirmed theappropriateness of the error correction approach which also showed that ignoring the long run relationship is detrimental. The result however, revealed that rice import demand increases significantly with increasing rice consumption, increasing price of meat, increasing price of maize (keeping that for imported rice unchanged) and increasing demography development. Rice import price, per capita income, price of local rice and exchange rate had no significant effects on rice import demand. The study therefore recommends that locally-produced rice should be intensively improved.
 Keywords: demography, determinants, Error correction mechanism, rice import demand

The Twinning of Inflation and Unemployment Phenomena in Saudi Arabia: Phillips Curve Perspective

The global economy has witnessed many economic fluctuations and waves of inflation and recession. With the consideration of achieving price stability as a primary goal of the economic policies, any attempt to eliminate inflation means accepting higher rates of unemployment, and vice versa. This conflict relationship was explained by the Economist “Phillips”, who developed the inflation/unemployment curve. After the emergence of the stagflation phenomenon, this relationship became an object of argument and skepticism. Therefore, the key point of this study is to investigate the tradeoff relationship between inflation and unemployment in the Kingdom of Saudi Arabia for the period 1988-2017. The co-integration and error correction approaches have been utilized, to determine the equilibrium relations in the long-run and short-run, and the causality direction between the two phenomena. Johansen test indicated that a long-run co-integration relationship was existed. Based on Vector Error Correction Model (VECM), results provide evidence in favor of the long-runs negative causation running from unemployment to inflation. Contrary to expectation, there was no significant evidence of short-run tradeoff between unemployment and inflation in the Saudi economy.

Resource Reduction for Distributed Quantum Information Processing Using Quantum Multiplexed Photons.

Distributed quantum information processing is based on the transmission of quantum data over lossy channels between quantum processing nodes. These nodes may be separated by a few microns or on planetary scale distances, but transmission losses due to absorption and/or scattering in the channel are the major source of error for most distributed quantum information tasks. Of course, quantum error correction (QEC) and detection techniques can be used to mitigate such effects, but error detection approaches have severe performance limitations due to the signaling constraints between nodes, and so error correction approaches are preferable-assuming one has sufficient high quality local operations. Typically, performance comparisons between loss-mitigating codes assume one encoded qubit per photon. However, single photons can carry more than one qubit of information and so our focus in this Letter is to explore whether loss-based QEC codes utilizing quantum multiplexed photons are viable and advantageous, especially as photon loss results in more than one qubit of information being lost. We show that quantum multiplexing enables significant resource reduction, in terms of the number of single-photon sources, while at the same time maintaining (or even lowering) the number of 2-qubit gates required. Further, our multiplexing approach requires only conventional optical gates already necessary for the implementation of these codes.

Improving Energy Efficiency and Reliability in WuR-Based IoT Systems: An Error Correction Approach

Intelligent connected objects, the building blocks of IoT, represent battery supplied electronic devices. These devices are expected to be deployed in very large numbers, and manual replacement of their batteries will severely restrict their large-scale or wide area deployments. Therefore, energy efficiency is of the utmost importance in the design of the IoT devices. The wireless communication between the distributed sensor devices and the host stations can consume significant energy, even more when larger coverage is required. Ultra-low-power wake up radio (WuR) represent one of the most prominent solutions for energy efficiency in IoT. However, the WuR devices have several limitations that bound their practical applicability and usage, such as short range capabilities and low signal sensitivity. As a result, the WuR devices commonly misinterpret their wake up address and inevitably lead to overall performance degradation of the system. This work, introduces the concept of error correction codes in the wake up address. It is envisioned that the error correction codes can increase the overall robustness and sensitivity of the WuR devices. The work also analyses the potential energy efficiency gains and the energy-latency tradeoff degradation of the WuR based IoT system when utilizing the error correction codes.

Investigation of cereal price transmission in Fars province

How to transfer price of agricultural products and its changes are considered as one of the most important indices of market performance and the main means of resource allocation in the economy. Assessing how price changes are transferred among different market levels is very important for the factors involved in production management and marketing and policymakers, and this information can be used in management decisions. Whenever occurs a disruption in the price transfer mechanism, the market system will not function properly and asymmetric price transfer can disrupt price signals and force the government to intervene to redistribute resources. Materials and methods In this research, the transfer of prices between wholesale and retail levels of beans in Fars province during the period 1999:03 to 2017:02 was studied. Due to the nature of the data studied and the occurrence of structural break in the variables, were used the HEGY unit root test and unit root test with structural break. Also, due to the spillover effects in the market of alternative products, the retail price of alternative products was also included in the models. To investigate price transmission patterns, the error correction approach with structural failure was used. Results and Discussion The results showed that in the short term for tiny pea and large lentil the price transmission is asymmetric and for large pea and tiny lentil is symmetric. In the long term, price transmission is asymmetric for all products, which seems to be due to the continuation of negative effects of economic sanctions and high

How do clean energy sources and financial development affect unemployment? Empirical evidence from Egypt.

Reducing unemployment rate to reach 5% is one of the most important priorities in Egypt's Strategy for Sustainable Development. So, enhancing the finance of medium and small enterprises and attracting investors to clean energy resources sectors are considered the most important government's priorities to achieve this strategic goal (UN 2018). Depending on this strategy, the study investigates the long-run and causal relationships between unemployment, financial development, population, and clean energy sources in Egypt from 1971 to 2014. Johansen and Juselius, auto-regressive distributed lag (ARDL), and vector error correction (VECM) approaches are employed to achieve this purpose. The results indicate that while clean energy resources have an adverse effect on unemployment, financial development and population have positive effect. The main findings of VECM reveal the existence of short run one-way causal relation from unemployment to financial development and from population to financial development and alternative energy sources. The main results imply that devoting domestic credit to renewable energy sector can help in providing job opportunities.

A blockchain-based data storage framework: A rotating multiple random masters and error-correcting approach

A blockchain is resistant to modification based on the consensus of the network majority, which requires a large amount of communication among distributed nodes. Existing data dissemination protocol solves the wrong block problem also at a high communication cost. This paper investigates the error-correcting tamper-proofing data storage problem and proposes a three-layer framework to store the data in the blockchain to achieve data integrity. The data are stored as blocks, and we design a two-dimension chain data structure consisting of horizontal and vertical chains. This paper proposes a Rotating multiple random Masters and Error-Correcting data storage framework based on blockchain (RMEC) to strike a trade-off between system decentralization and the amount of communication. The proposed Rotating Multiple Random Sampling consensus mechanism (RMRS) randomly selects multiple temporary master nodes to handle each data access request so as to reduce the communication cost. We also propose two error-correcting mechanisms to validate and correct the wrong data blocks. Finally, we implement a prototype and conduct analyses on the system performance. The experiments demonstrate that the framework can achieve data tamper-proof and effectively reduce the communication cost.

Factors Affecting Sesame Seed Exports in Burkina Faso: The Vector Error Correction Approach

The research is financed by Academia Sinica, Taiwan. Grant No. AS-SS-107-02. Abstract Burkina Faso's (BFA) dependence on a single export crop, namely cotton, has so far threatened its economy since the fluctuating and uncontrollable drops in world cotton prices. The diversification of cash crops has become a national priority that is appropriated day by day by actors of rural development, and more particularly by farmers seeking to improve and secure their income. Thus, export crops, such as sesame, appear obvious and essential, not only for farmers’ income security but also for the country by its ability to attract currencies. In the last decade, BFA’s the sesame sector has recorded a tremendous growth both in quantity and value of exports to being the eighth world largest exporter of sesame seed. Considering its unequaled performance in the export of sesame seed, we deemed relevant to identify the key factors affecting that performance and to study their effects on sesame seed export earnings in order to draft appropriated policies aimed to enhance the country’s earnings. This study uses the cointegration approach, and has purposely designed exports value as the independent variable to analyze the country exports performance using time series data for the period of 47 years (1970-2016). One cointegration vector is found between the variables employed; thereby the Vector Error Correction Model has been performed. The empirical results reveal that nominal exchange rate, producer price, world export volume of sesame seed as a proxy of world demand, and world price are key factors affecting significantly the country’s exports performance in the short-run. Moreover, in the long-run nominal exchange rate, production, producer price, and world export price are factors determining significantly export earnings of sesame seed in Burkina Faso. For Burkina Faso to increase its market shares and export earnings, especially in the long-run, it is principally recommended to boost the production by increasing significantly yields and ensure an appropriate transmission of international prices increments to farmers. Keywords: Agricultural Export, Determinants of Agricultural Exports, Sesame Seed, Cointegration, Burkina Faso. DOI: 10.7176/DCS/10-4-03 Publication date: April 30 th 2020

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning.

We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

The Determinate of Agricultural Export in Ethiopia: An Error Correction Modle and Cointegration Approach

This paper identified some of the main determinants of agricultural export in Ethiopia for The period 1993-2013. To test empirically the relationship between agricultural export performance and its major selected determinants such as real gross domestic product, real effective exchange rate, fertilizer input, trade openness, institutional quality and export diversification; cointegration and error correction approaches in the regression analysis were used. The results from the cointegration and error correction models revealed that real GDP, fertilizer input and export diversification are significantly affected agricultural export performance in the long run except real effective exchange rate, trade openness and institutional quality. In the short run, real gross domestic product (RGDP), real effective exchange rate, and export diversification became significant. On the other hand, out of the variables significantly affected agricultural export both in the long run and short run; real GDP and export diversification affected agricultural export positively as expected. DOI: 10.7176/JESD/11-3-05 Publication date: February 29 th 2020

Seamless GPS/Inertial Navigation System Based on Self-Learning Square-Root Cubature Kalman Filter

To improve the seamless navigation ability of an integrated Global Positioning System (GPS)/inertial navigation system in GPS-denied environments, a hybrid navigation strategy called the self-learning square-root- cubature Kalman filter (SL-SRCKF) is proposed in this article. The SL-SRCKF process contains two innovative steps: 1) it provides the traditional SRCKF with a self-learning ability, which means that navigation system observations can be provided continuously, even during long-term GPS outages; and 2) the relationship between the current Kalman filter gains and the optimal estimation error is established, which means that the optimal estimation accuracy can be improved by error compensation. The superiority of the proposed SL-SRCKF strategy is verified via experimental results and prominent advantages of this approach include: 1) the SL-SRCKF comprises two cycle filtering systems that work in a tightly coupled mode, and this allows more accurate error correction results to be obtained during GPS outages; 2) the system's error prediction ability is effectively improved by introducing a long short-term memory, which provides much better performance than other neural networks, such as random forest regression or the recursive neural network; and 3) under different (30, 60, and 100 s) GPS outage conditions, the long-term stability of SL-SRCKF is much better than that of other error correction approaches.

Edge Inference for UWB Ranging Error Correction Using Autoencoders

Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU.