Linear Error Correction Research Articles

Leveraging Linear Network Error Correction for steganographic network codes

Steganography is an ingenious method employed to conceal data within an unassuming, non-secret file or message, thereby evading detection. The concealed information is later extracted at its intended destination. Many legitimate applications of steganography exist, ranging from hiding watermarks or trademarks covertly within multimedia or other content files, to embedding sensitive information securely. In this paper, we introduce Steganographic schemes employing Linear Network Error Correction Codes (LNECCs). Initially, we emphasize the enhanced throughput achieved through the integration of steganography into joint channel-network codes, then we demonstrate the feasibility of leveraging LNECCs and their corresponding error patterns to formulate distortion-free steganographic schemes. Specifically, for a given LNECC in relation to a terminal and a resolvable error pattern, with the precondition that the minimum cut between them is positive, we delineate the corresponding distortion-free steganographic protocols. Moreover, we conduct a thorough analysis of our scheme and fortify it against potent steganalysis attacks. Finally, we propose a prospective solution for cases involving erroneous retrievals.

A short-term power load forecasting system based on data decomposition, deep learning and weighted linear error correction with feedback mechanism

Accurate power load forecasting enables Independent System Operators (ISOs) to precisely quantify the demand patterns of users and achieve efficient management of the smart grid. However, with the increasing variety of power consumption patterns, the power load data displays increasingly irregular characteristics, which posing great challenges for accurate load forecasting. In order to solve above problem, a novel power load forecasting system is proposed based on data denoising, customized deep learning and weighted linear error correction. Specifically, we first proposed an improved optimization algorithm IGWO-JAYA which enhanced the Grey Wolf Optimizer (GWO) algorithm by using Halton low-discrepancy sequence and the mechanism of JAYA algorithm. In data denoising, the proposed optimizer was employed to optimize the Variational Mode Decomposition (VMD), enabling data-driven intelligent denoising. The customized deep learning framework contained multi-layer Convolution Neural Network (CNN), Bi-directional Long Short-Term Memory (Bi-LSTM) and Multi-Head Attention mechanism. The effective integration of these layers can significantly improve the capacity for nonlinear fitting of deep learning. In weighted linear error correction, the IGWO-JAYA algorithm was employed to determine the appropriate weight for point forecasting values and residual forecasting values. By weighting them, the forecasting precision has been further enhanced. To verify the forecasting ability of the system, we conducted experiments on power load datasets from four states in Australia and found that it has the best performance compared with all rivals. In the discussion, we demonstrated the convergence efficiency of the IGWO-JAYA algorithm by CEC test function.

Monetary policy, threshold of profitability and dynamics of private investment in the West African Economic and Monetary Union

This study investigates the interactions between optimal monetary policy, investment profitability thresholds, and private sector investment dynamics within the West African Economic and Monetary Union. Analyzing data from 2000 to 2021, it employs advanced econometric models (linear, asymmetric, and threshold effect dynamic panel error correction models) to provide a nuanced understanding of the investment environment in the union. The findings reveal the confluence of low returns and high borrowing costs for private investment. Notably, this study suggests that simply pursuing an expansionary monetary policy does not offer a sufficient solution. Instead, it proposes a more accommodative stance by the Central Bank, featuring lower policy rates, to alleviate the negative impact of high credit costs on private investment and ultimately stimulate economic activity.

A novel wind power forecasting system integrating time series refining, nonlinear multi-objective optimized deep learning and linear error correction

Wind power prediction is crucial for successfully integrating large-scale wind energy with the grid and achieving a carbon-neutral energy mix. However, previous studies encountered challenges to noise reduction, information extraction, error handling, and uncertainty estimation. To address these limitations, this study proposed a novel wind power forecasting system, systematically enhancing forecasting capability by refining the raw time series using horizontal denoising and vertical granulation methods, utilizing a bidirectional deep learning model for capturing nonlinear features and optimizing its core hyperparameters with a newly developed multi-objective enhanced version of metaheuristic algorithm to achieve Pareto optimal solutions, employing the traditional statistical approach for residual linear feature extraction from error series and effective correction, and integrating quantile regression for interval prediction. The predictive performance was evaluated based on wind power, speed, and direction data from two different countries, the system yielded correlation coefficients of 0.9796, 0.9943, 0.9795, and 0.9780 in spring, summer, autumn and winter for China, and 0.9950 for Turkey, demonstrating its pronounced advantages over comparative models. Overall, the proposed system contributes to the advancement of wind-power prediction theory and holds substantial potential for practical applications.

Threshold Cointegration and Price Transmission in Commodity Marketsof India

The purpose of this research work is to examine the relationships and price dynamics between agricultural commodities in India, i.e.maize, wheat, barley and soybean. Our approach is to study the long-term relationship using the method of modelling the price transmission for both linear and threshold autoregressive (AR) models and vector error correction (VEC) models. Results revealed that all the price series are well integrated, and threshold error correction models prove that all price series move to restore the long-run relationship, whereas commodity stock prices respond slightly faster than market prices in the short-run. Conclusions from this study show that understanding the price transmission flow and its impact on pricing might help in making better trading strategies. It also regulates the public policy implications of the active participation of farmers in national-level commodity exchanges.

Forecast accuracy of the linear and nonlinear autoregressive models in macroeconomic modeling

AbstractMost nonlinear vector autoregressive methods in the econometric literature are based on specific functional forms, such as the smooth transition autoregressive model. This study proposes a general form of the nonlinear vector autoregressive model based on global approximators, such as neural networks, Volterra, and Weiner series. The simulation results of 20 linear and nonlinear multivariate time series processes indicate that nonlinear vector autoregressive methods, especially multi‐output neural networks, are more accurate based on the root mean square error and model confidence set criteria. Applying the global approximator approach to a small‐scale macroeconometric model reveals that the new approach can improve forecast accuracy compared to linear and other nonlinear vector error correction models. In addition, forecasting the relevant variables in a typical exchange rate and monetary policy models based on nonlinear specifications gives more successful results than in the linear case.

Local bit line 8T SRAM based in-memory computing architecture for energy-efficient linear error correction codec implementation

Memory reliability is a critical issue in SRAM-based In-Memory Computing (IMC) architecture. The rapid advance in transistor technology makes SRAM more sensitive to soft errors. Various developments have recommended different methods to prevent store data corruption using Hamming Code. In order to encode or decode the information, a piece of Error Correction Code (ECC) hardware is necessary, which results in an area and energy overhead. In this paper, an IMC-based linear Error Correction Codec (IMC-ECC) hardware for parity check bit generation (encoding) and syndrome vector generation (decoding) is implemented by using the proposed Transmission Pass Gate (TPG) Local Bit-line (LB-8T) SRAM structure based IMC architecture. The local bit line structure resolves the half-select issues that occur for bit-interleave architecture. The TPG improves the write stability by delivering both strong VDD and GND. Furthermore, the proposed IMC-ECC scheme, along with bit-interleave architecture, makes the IMC architecture more resilient to multi-bit soft error. The proposed IMC-ECC scheme is validated by implementing a 4 Kb LB-8T SRAM array in 65 nm CMOS technology and Hamming code (7, 4) as an example. The proposed IMC-ECC scheme achieves the average energy consumption of 0.294 pJ/bit and 0.075 pJ/bit for the encoding and decoding process, respectively, at a supply voltage of 1 V.

Spatial Market Integration, Price Transmission and Transaction Costs in Major Onion Markets of India

The price information is one of the prime objectives of marketing strategies, and the farmers are unable to determine the marketing strategies without knowing the price movements of the agricultural commodities. In this context, the study has examined the spatial price integration among four major onion markets using the threshold vector error correction model (TVECM) that takes into account transaction costs in the price adjustment process. Augmented Dickey–Fuller and Phillips–Perron tests for unit root suggest that the time series is I(1). The application of the Johansen cointegration technique supports the presence of long-run price association and equilibrium in all pairs of onion markets. The Granger Causality test unveils that Bengaluru Granger causes all the markets except Kolkata. The Hansen and Seo supreme Lagrange Multiplier ( SupLM) test of linearity suggests that non-linear TVECM with one threshold and two regimes is best fit for the underlying data for three pairs of markets. While the rest of the three pairs, the SupLM test rejects the null of linearity, therefore, linear vector error correction model (VECM) is estimated. Finally, VECM and TVECM results reveal that Mumbai and Bengaluru are dominant markets in price formation in rest of the markets. Against these findings, it is suggested that the prices should be stabilised in the dominant markets so that the price shocks are not transferred to other markets. The threshold parameter, which is analogous to transaction cost, reveals the high transaction costs between the selected markets pairs, especially Mumbai and Delhi. One of the reasons for the high transaction costs may be the inefficiencies in infrastructure and communication. While a more correct explanation for this difference can be attributed to the differences in marketing fees, taxes, commission charges, license fees, etc., across the spatially separated agricultural markets. JEL Codes: Q1, Q13, D4, C22, D23

Price discovery in equity markets: A state-dependent analysis of spot and futures markets

This paper investigates the potentially time-varying importance of spot and futures markets in the price discovery process of financial assets. For this purpose, we generalize the concept of component shares and information shares to allow for state-dependent relevance of different markets over time. Instead of using a linear vector error correction model (VECM) to construct the component shares and information shares as it is common in the literature, we employ a Markov-switching VECM with time-varying adjustment behaviour and state-dependent covariance matrices. Simulation results confirm that state-dependent component shares and information shares can be estimated consistently within this framework. We apply our approach to spot and futures prices for different stock indices such as EuroStoxx50, FTSE100, Nikkei225, and SP500. We find a clear state probability pattern over the trading day that closely resembles the well-studied volatility smile and might be related to the presence of heterogeneous agents in those markets.

Lifted Bilinear Model-based Linear Model Predictive Control with Scalability

We propose a novel linear model predictive control (MPC) using a lifted bilinear model based on Koopman theory, which is computationally scalable against the dimension of the target system and the prediction horizon length. In MPC, the accuracy of the prediction model determines control performance, but it is a challenge to reduce the computational cost especially when considering nonlinearity of the model. To address this, a method has been proposed which represents the nonlinear input affine system as a lifted bilinear model and utilizes linear approximation and prediction error correction regarding the lifted state to achieve a low-computational-cost linear MPC with equivalent performance to nonlinear MPC. However, although the previous studies have shown its effectiveness for relatively low-order systems, it has not been applied to practical systems with higher dimensions. In this study, we extend the conventional method and propose a scalable linear MPC using a lifted bilinear model and apply it to higher-order nonlinear systems. In this paper, a quadrotor system operating in three-dimensional space is considered and its analytical lifted bilinear model is derived. In the formulation of linear MPC using the lifted bilinear model, an error correction method is newly introduced to feedback error for adjustment of the numerical relationships among the elements in the lifted state. The effectiveness of the proposed method is demonstrated through numerical simulations.

A Revisit of Linear Network Error Correction Coding

A Revisit of Linear Network Error Correction Coding

THE ANALYSIS OF ANTIDUMPING DUTIES IMPOSION AGAINST POLYESTER STAPLE FIBER COMMODITIES IMPORT VALUE

Dumping practices can damage the domestic product market because they are imported at lower prices. Indonesia has imposed antidumping measures on Polyester Staple Fiber (PSF) commodities since 2010 until now. This study aims to analyze the impact of long-term and short-term antidumping duties (BMAD) imposition on the import value of PSF commodities in Indonesia. This study was conducted using quantitative analysis methods with multiple linear regression testing model Error Correction Model (ECM). Based on the results of quantitative testing, in the long term the variable partially has a positive and significant effect on the import value of PSF commodities. Meanwhile, in the short term, BMAD variable partially has no effect on the value of PSF commodity imports from the target country. This shows that the imposition of antidumping duties does not directly have a restrictive effect on the import value of PSF commodities.

Analisis Dampak Instrumen Kebijakan Moneter Terhadap Stabilitas Perekonomian di Indonesia

The purpose of this study is to determine how the influence of monetary policy instruments on economic stability in Indonesia. Monetary policy instruments as exogenous variables are policies in the money supply M2 (X1) and Bank Indonesia interest rates (X2) while indicators of economic stability as endogenous variables can be seen from price stability (Y1) and exchange rate stability (Y2). This research is descriptive and associative research. The data used in this study is monthly secondary data from January 2001 to December 2020 which was collected through documentation from the relevant agencies. The data analysis used in this research is descriptive analysis and inductive analysis. In the inductive analysis there are several tests, namely (1) Stationarity Test; (2) Cointegration Test; (3) Multiple Linear Regression Test and Error Correction Model (ECM); (4) Classical Assumption Test; (5) T test and F test. The results of this study is: (1) in the long term and short term the money supply M2 has a negative effect on price stability in Indonesia; (2) Bank Indonesia interest rates in the long term and short term have a positive influence on price stability in Indonesia; (3) in the long term and short term The money supply M2 has a positive influence on the stability of the exchange rate in Indonesia; (4) Bank Indonesia interest rates in the long term and short term have a positive influence on exchange rate stability in Indonesia.

Pengaruh Harga Minyak dan Inflasi Terhadap Pertumbuhan Ekonomi di Indonesia

The purpose of this study is to analyze the effect of world oil prices and inflation on economic growth in Indonesia. The type of data used is time series data from 1987 to 2020 taken from FRED Economic Data, the World Bank and the Central Statistics Agency. This type of research is descriptive and associative research. The data analysis used is descriptive analysis and inductive analysis. Stationarity test is one of the tests used in inductive analysis; (2) Cointegration Test; (3) Multiple Linear Regression Test and Error Correction Model (ECM); (4) Classical Assumption Test; (5) T test and F test. The results of this study reveal that: (1) world oil prices (X1) have an insignificant negative effect on economic growth (Y1) in Indonesia in the long term and in the short term world oil prices (X1) have an insignificant negative effect on economic growth (Y1) in the long term. positive and significant to economic growth (Y1) in Indonesia; (2) inflation (X2) has an insignificant negative effect on economic growth (Y1) in Indonesia in the long term while in the short term inflation (X2) has an insignificant negative effect on economic growth (Y1) in Indonesia.

How Well Do Contemporary Theories Explain Floating Exchange Rate Changes in an Emerging Economy: The Case of EUR/PLN

The purpose of this paper is to investigate how well contemporary exchange rate theories explain fluctuations in exchange rates of emerging economies, before and after the Global Financial Crisis (GFC). As an example, the EUR/PLN exchange rate in 1999–2015 was selected as the currency pair that was the most liquid in the region; it had a stable exchange rate regime in the given period. The whole analysis was performed within the selected linear vector error correction (VEC) model framework. VEC models incorporate such well-known theories as purchasing power parity (PPP), the uncovered interest rate parity (UIP), the Harrod–Balassa–Samuelson (HBS) effect, the terms of trade (TOT), the net financial asset (NFA) theory and risk premium. The results indicate the greater importance of external factors—in particular, the Euro Area (EA) short-term interest rates and EA price shocks after the GFC. The main sources of EUR/PLN variability were found to be exchange rate shocks, terms of trade shocks and foreign and domestic short-term interest rate shocks, as well as foreign price shocks. These results are of particularly high importance for our own exchange rate shocks and indicate that a large part of exchange rate fluctuations in EUR/PLN still remains unexplained.

A double serial concatenated code using CRC-aided error correction for highly reliable communication

Highly Reliable Communication (HRC) is an emerging paradigm for meeting the quality of service requirements of new applications such as intelligent vehicle networking and industrial Internet. HRC brings challenges to channel coding and multiple access. Various channel coding techniques have been proposed to improve reliability, but often suffer from limited reliability and high decoding complexity. This work presents a double serial concatenated code using CRC-aided error correction, abbreviated as DSC 3 EC , to improve reliability and reduce complexity. The encoding scheme is a double serial concatenated code, with the cascade of a set of CRC encoders, a linear block encoder, a series of interleavers , and a set of convolutional encoders . The decoding scheme consists of two modules: (i) decoding of serial concatenated codes, which includes the decoding of single parity check convolutional code (SPC-CC) and the decoding of Reed–Solomon convolutional code (RS-CC) for intra-block error correction at the physical layer to control BER; (ii) decoding of CRC-aided linear error correction, which can effectively identify, locate and correct error bits at the inter-block level to solve the problem of block loss caused by residual errors at the physical layer. Simulation results show that the joint scheme can effectively improve communication reliability while reducing complexity.

Leveraging half-cycle orthogonal signal generation approach for PLL-less tracking of single-phase grid parameters

This article presents a fast and accurate single-phase grid voltage parameter tracking scheme under distorted grid voltage conditions. The input stage of the proposed scheme consists of a half-cycle moving-window discrete Fourier transform (HC-MWDFT) based pre-filter, whose design guidelines are discussed in the viewpoint of its stability and orthogonal signal generation. Combining a positive feed-forward comb filter and a fractional complex resonator helps to attenuate odd-order harmonics and allows fast extraction of fundamental components. Unlike the phase-locked loop (PLL) techniques, the grid frequency is indirectly estimated using a simple open-loop frequency detector. The proposed method is free from tedious controller tuning aspects and can avoid external phase/frequency feedback loops. The amplitude and phase information are adaptive to the grid frequency by employing a simple linear regression-based error correction technique. The overall efficacy of the proposed algorithm is assessed using numerical and experimental methods.

Linear Error Correction Codec Implementation Based on an In-Memory Computing Architecture for Nonvolatile Memories

Nonvolatile memories (NVMs) have been widely studied in the post-Moore era. However, noise/defect in NVMs has brought a challenging reliability problem in write/read access operations. A linear error correction code (ECC) has been widely used in memories to correct errors. In general, a codec hardware is required to encode or decode the information, resulting in area and energy overhead. In this article, we propose a new approach to implement the ECC codec by exploiting the existing write and read peripheral circuitry of NVMs based on an in-memory computing (IMC) architecture. In our approach, the desired code words can be generated through using write-like logic operations, given the original information and generation matrix during the encoding (write) process, while syndrome vectors (or error correction) can also be achieved through using read-like logic operations, given the parity-check matrix during the decoding (read) process, respectively. Therefore, we do not need a specific codec hardware but exploit the write-like and read-like logic operations. With an emerging toggle spin torque magnetic random access memory (TST-MRAM) and Hamming code as an example, we validated the feasibility of our approach and evaluated the performance of the design in the 40-nm technology node. The results show that our approach can significantly reduce energy consumption compared with state-of-the-art designs.

Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method

This paper develops an open-high-low-close (OHLC) data forecasting framework to forecast EUA futures price based on EU ETS data and extended exogenous variables from 2013 to 2020. The challenge of forecasting such an OHLC structure lies in handling its three intrinsic constraints, i.e., the positive constraint, interval constraint, and boundary constraint. This paper proposes a novel unconstrained transformation method for OHLC data and combines it with various forecasting models. Out-of-sample modelings identify the extraordinary performance of the convolutional neural network (CNN) in terms of MAPE (1.371%), MAE (0.274), RMSE (0.370), and AR (0.621), better than that of multiple linear regression (MLR), vector auto-regression (VAR) and vector error correction model (VECM), support vector regression (SVR), and multi-layer perceptron (MLP). The proposed transformation-based forecasting framework demonstrates the considerable potential for OHLC data forecasting in the energy finance field, e.g., crude and natural gas. Practicable and concrete suggestions are provided to ensure the profitability of trading EUA futures.

A Comprehensive Review of Low Density Parity Check Encoder Techniques

This paper presents a survey on various technologies of low density parity check encoder. LDPC codes are capable to handle high speed communication channel, by reducing attenuation, hazards and efficiently rectifying the linear error correction. Various coding technologies used in new generation communication system, such as turbo code, hamming code, low-density parity check (LDPC) code and Bose–Chaudhuri–Hocquenghem (BHC) code, are widely used in recent communication system. The LDPC has technical remarkable advantages and better performance in high speed communication process compared to turbo code. This paper deals with study of LDPC encoding techniques with various methods of detecting error and its correction. Here classification and performance analysis of LDPC encoding techniques on the basis of resources utilization, systematic, non-systematic approaches and consumer data right etc. have been analyzed in this paper. Apart from above mentioned criteria, this study deals with hardware and software architecture of LDPC encoder in rectification of forward error correction, parallel execution of instruction set. This study and analysis could offer scalability, the future scope of improving the performance of LDPC encoder in all aspects of the next generation communication process. This paper gives overview of various LDPC encoder applications, drawbacks and solution to overcome it.