Error Correction Techniques Research Articles

A two-stage interval-valued carbon price forecasting model based on bivariate empirical mode decomposition and error correction.

Economic development has brought about global greenhouse gas emissions and, thus, global climate change, a common challenge worldwide and urgently needs to be addressed. Accurate carbon price forecasting plays a pivotal role in providing a reasonable basis for carbon pricing and ensuring the healthy development of carbon markets. Therefore, this paper proposes a two-stage interval-valued carbon price combination forecasting model based on bivariate empirical mode decomposition (BEMD) and error correction. In Stage I, the raw carbon price and multiple influencing factors are decomposed into several interval sub-modes by BEMD. Then, we select artificial intelligence-based multiple neural network methods such as IMLP, LSTM, GRU, and CNN to conduct combination forecasting for interval sub-modes. In Stage II, the error generated in Stage I is calculated, and LSTM is used to predict the error; then, the error forecasting result is added to the first stage result to obtain the error-corrected forecasting result. Taking the carbon trading prices of Hubei, Guangdong, and the national carbon market, China, as the research object, the empirical analysis proves that the combination forecasting of interval sub-modes of Stage I outperforms the single forecasting method. In addition, the error correction technique in Stage II can further improve the forecasting accuracy and stability, which is an effective model for interval-valued carbon price forecasting. This study can help policymakers formulate regulatory policies to reduce carbon emissions and help investors avoid risks.

Anaysis of Interest Rate Spread, Financial Development and Foreign Capital Inflow Nexus in Nigeria

ABSTRACT: The world has gone through various economic crises in the past century, and their effects have had varying implications on the economies of countries all over the world. In a bid to help stabilise economies all over the world, various measures have been put in place. Some of these measures include regulations and policies by the Bretton Woods institutions (World Bank and International Monetary Fund) and their agencies. The idea behind these measures is to ensure sustainable growth and development among member states and other countries of the world. Some of these policies include the privatization and liberalization policies of the Bretton Woods institutions as well as other policies bordering on such economic and social benefits for people all over the world. The Nigerian economy in our contemporary time can be described as passing through a rather 'volatile' phase with respect to interest rate, financial development and foreign capital inflow. Thus, this study investigated the impact of interest rate spread and financial development on foreign capital inflow in Nigeria. Several studies have been carried out using different methodologies, such as; co-integration equation, multivariate vector auto regressive (VAR) model and vector error correction technique. Each methodology used was in line with the objective of the research in question. However, to achieve its objective, this paper adopted the Classical Linear Regression Model. The results of the study showed that financial development has a positive impact on foreign capital inflow, while interest rate spread on the other hand, was also found to have a positive impact on foreign capital inflow. Other control variables such as market capitalization and treasury bills rate were also found to positively affect foreign capital inflow, while the all share index had a negative impact on foreign capital inflow. Some economists have opined that the Nigerian economy is currently going through a phase of recession. In order for large scale investments to be made within the country, there is a need for foreign participants to be attracted to the Nigerian economy and pull their resources, both financial and non-financial into the country (but, not without regulation). However, for these investors to have confidence to make investments in the Nigerian economy, there has to be a high degree of economic stability in the country. This can be supported by enacting and implementing policies that can enhance the quality of interest rates spread and financial development within the country. The policy implication here involves enhancing financial development through sound policies and adopting competitive interest rates, could increase the rate of foreign inflow into the economy. In view of this, it is also recommended that government policies should target interest rate competitiveness, financial and economic development as well as sustainability.

Wind Speed Prediction Based on VMD-BLS and Error Compensation

As one of the fastest-growing new energy sources, wind power technology has attracted widespread attention from all over the world. In order to improve the quality of wind power generation, wind speed prediction is an indispensable task. In this paper, an error correction-based Variational Mode Decomposition and Broad Learning System (VMD-BLS) hybrid model is proposed for wind speed prediction. First, the wind speed is decomposed into multiple components by the VMD algorithm, and then an ARMA model is established for each component to find the optimal number of sequence divisions. Second, the BLS model is used to predict each component, and the prediction results are summed to obtain the wind speed forecast value. However, in some traditional methods, there is always time lag, which will reduce the forecast accuracy. To deal with this, a novel error correction technique is developed by utilizing BLS. Through verification experiment with actual data, it proves that the proposed method can reduce the phenomenon of prediction lag, and can achieve higher prediction accuracy than traditional approaches, which shows our method’s effectiveness in practice.

Wind Speed Prediction Based on Error Compensation

Wind speed prediction is very important in the field of wind power generation technology. It is helpful for increasing the quantity and quality of generated wind power from wind farms. By using univariate wind speed time series, this paper proposes a hybrid wind speed prediction model based on Autoregressive Moving Average-Support Vector Regression (ARMA-SVR) and error compensation. First, to explore the balance between the computation cost and the sufficiency of the input features, the characteristics of ARMA are employed to determine the number of historical wind speeds for the prediction model. According to the selected number of input features, the original data are divided into multiple groups that can be used to train the SVR-based wind speed prediction model. Furthermore, in order to compensate for the time lag introduced by the frequent and sharp fluctuations in natural wind speed, a novel Extreme Learning Machine (ELM)-based error correction technique is developed to decrease the deviations between the predicted wind speed and its real values. By this means, more accurate wind speed prediction results can be obtained. Finally, verification studies are conducted by using real data collected from actual wind farms. Comparison results demonstrate that the proposed method can achieve better prediction results than traditional approaches.

Channel Emulator Framework for Underwater Acoustic Communications

In this paper, we develop a tractable mathematical model and an emulation framework for communicating information through water using acoustic signals. Water is considered one of the most complex media to model due to its vastness and variety of characteristics, which depend on the scenario, the type of water body (lakes, rivers, tanks, sea, etc.), and the geographical location of the water body being considered. Our proposed mathematical model involves the concept of damped harmonic oscillators to represent the medium (water); Milne’s oscillator technique is used to map the interaction between the acoustic signal and water. Wave equations formulated for acoustic pressure and acoustic wave velocity are employed to characterise the travelling acoustic signal. The signal strength, phase shift, and time delay generated from the mathematical model are then inputted into a Simulink-based emulator framework to generate channel samples and channel impulse responses. The emulator utilises the wide sense stationary uncorrelated scattering (WSSUS) assumption and a finite sum-of-sinusoids (SOS) approach with a uniformly distributed phase to generate the channel samples. By utilising this emulator platform, it becomes feasible to generate profiles for amplitude variation, the Doppler shift, and spread experienced by any travelling signal in various underwater communication scenarios. Such a platform can be employed to simulate different communication scenarios, underwater network topologies, and data for training various learning models. Additionally, it can predict the performance of different modulation, multiplexing, error correction, and multi-access techniques for underwater acoustic communication (UWAC) systems.

An empirical investigation of the relationship between government revenue, expenditure, and economic growth in selected EMEs

Purpose ─ This article explores the relationship between government revenue, government expenditure, and economic growth for nine emerging market economies using annual data from 1991-92 to 2019-20. Method ─ This paper distinguishes itself from the existing literature through the application of co-integration tests, vector error correction, DOLS and FMOLS for an empirical investigation of a unique panel data set of select emerging economies across Asia, Africa, Europe and Latin America. A bi-directional causal long-run relationship between economic growth and government expenditure, as well as between government expenditure and government revenue, was found using standard panel co-integration tests. Findings ─ The long-run elasticities computed using VECM were confirmed from DOLS as well as FMOLS estimates. A one per cent increase in expenditure and revenue, in the long run, would result in an increase in GDP by 0.94 and 0.90 per cent, respectively. Similarly, an increase in GDP by one per cent would lead to an increase in government expenditure by 1.1 per cent. On the other hand, an increase in government revenue by one per cent would cause a corresponding increase in government expenditure by nearly one per cent. The findings of this research point to a positive association between government revenue, expenditure, and economic growth, which will be valuable to policymakers. Contribution ─ Our combination of country selection covering economies from different continents is a first of its kind to the best of our knowledge. Another contribution is the application of panel cointegration and panel error correction techniques to fully use the panel data set, while most previous studies utilised the typical time series modelling with individual time series data.

Implementation of SEC-DED-DAEC Codes using Mentor Graphics

Correction of a single error Double Error Detection (SEC-DED) and Double Adjacent Error Correction (DAEC) are prominent error correction algorithms used in computer memory systems to detect and fix errors that arise during data transfer. In this research, we want to put these ideas into practice and assess how well they work through simulations and experiments. We will research the trade-offs between these strategies' efficiency and reliability and contrast them with other error correction techniques. Our research will involve designing and putting error detection and correction procedures into circuits and algorithms for DAEC and SEC-DED. We will also investigate other methods, including parallel processing and error checking mechanisms, to enhance the performance of these strategies. Our findings may also have applications in other domains where error correction is important, such as communication systems and medical equipment. They will help us understand and enhance error correction techniques used in computer memory systems.

A Time-of-Flight Image Sensor Using 8-Tap P-N Junction Demodulator Pixels.

This paper presents a time-of-flight image sensor based on 8-Tap P-N junction demodulator (PND) pixels, which is designed for hybrid-type short-pulse (SP)-based ToF measurements under strong ambient light. The 8-tap demodulator implemented with multiple p-n junctions used for modulating the electric potential to transfer photoelectrons to eight charge-sensing nodes and charge drains has an advantage of high-speed demodulation in large photosensitive areas. The ToF image sensor implemented using 0.11 µm CIS technology, consisting of an 120 (H) × 60 (V) image array of the 8-tap PND pixels, successfully works with eight consecutive time-gating windows with the gating width of 10 ns and demonstrates for the first time that long-range (>10 m) ToF measurements under high ambient light are realized using single-frame signals only, which is essential for motion-artifact-free ToF measurements. This paper also presents an improved depth-adaptive time-gating-number assignment (DATA) technique for extending the depth range while having ambient-light canceling capability and a nonlinearity error correction technique. By applying these techniques to the implemented image sensor chip, hybrid-type single-frame ToF measurements with depth precision of maximally 16.4 cm (1.4% of the maximum range) and the maximum non-linearity error of 0.6% for the full-scale depth range of 1.0-11.5 m and operations under direct-sunlight-level ambient light (80 klux) have been realized. The depth linearity achieved in this work is 2.5 times better than that of the state-of-the-art 4-tap hybrid-type ToF image sensor.

Quantum Machine Learning: A Review

Abstract: Quantum machine learning is an emerging field that aims to leverage the unique properties of quantum computing to accelerate machine learning tasks. In this paper, we review recent advances in quantum machine learning and discuss the potential applications and challenges associated with this technology. Specifically, we examine the current state of quantum machine learning algorithms, including variational quantum algorithms, quantum neural networks, and quantum generative models. We also discuss the challenges associated with practical quantum computing resources, algorithm design, and interdisciplinary collaboration. Furthermore, we highlight the potential applications of quantum machine learning in areas such as drug discovery, speech and image recognition, financial modeling, and many others. We also examine the ethical and societal implications of this technology, including the potential impact on privacy and security. Finally, we discuss future prospects for quantum machine learning, including the potential for quantum-inspired classical algorithms and the development of error correction techniques. We conclude by emphasizing the importance of interdisciplinary collaboration in the continued advancement of this field.

A Policy Rate Channel Testing of Monetary Policy Transmission Mechanism

Against the backdrop of invariant financial and economic reforms, there are presumptive changes in the confines and speed with which target variables respond to Reserve Bank of India (RBI) policy signals. The result of transmission lags from monetary policy to the real sector is unmistakable. The empirical approach used in this study is a natural progression from the VAR model videlicet, co-integration, and error correction techniques used to overcome the problem of spurious regression associated with non-stationary time-series data. After a shock induces disequilibrium, the speed and degree of adjustment return to a balanced state but with some time lag. The results indicate that in the two-step verification model, it takes approximately 2.851 months for WACMR to fully respond to a change in policy rates, whereas in the second step, it takes approximately 10.33 months for base rates to reach their complete pass-through following changes in call money market and deposit rates.

Impact of quantum noise on the training of quantum Generative Adversarial Networks

Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed. In this paper, we conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise, focusing on a simplified use case in high-energy physics. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process. Simulating a noisy quantum device classically with IBM’s Qiskit framework, we examine the threshold of error rates up to which a reliable training is possible. In addition, we investigate the importance of various hyperparameters for the training process in the presence of different error rates, and we explore the impact of readout error mitigation on the results.

Determinants of Economic Growth in Somalia: ARDL Model

Using an autoregressive distributed lag model (ARDL) and error correction technique, this paper investigates the short- and long-run relationships between exports, imports, and FDI on economic growth in Somalia. Secondary data was collected from the SESRIC website. The study spans the years 1995 to 2020. The result of the study revealed short- and long-term relationships between the variables. While FDI and exports have a negative long-term relationship with economic growth, imports have a positive long-term relationship. It also shows that FDI has a negative, short-run insignificant relationship with economic growth, whereas imports and exports have a significant, short-run relationship. Diagnostic tests were applied, and the results demonstrate that the model's validity for Somalia's economic growth is characterized by normality, the absence of serial correlation, and heteroscedasticity

The Effects of Currency Devaluation on the Bilateral Trade Balance of Sudan: Cointegration and Error-Correction Modeling

The purpose of this research paper is to investigate the effects of exchange rate, income and money supply on the trade balance of Sudan over a fifty-year period from 1970 to 2020. The empirical model for this investigation is developed using a cointegration approach and error-correction technique to specifically examine the effects of currency devaluation on the trade balance of Sudan with its major partners. The bounds test shows that the variables of interest are bound together in the long-run. The associated equilibrium correction is significant, thus confirming the existence of a long-run relationship. Moreover, to analyze the dynamic interactions of the variables included in the estimated model, variance decomposition is employed. The study results from the variance decomposition show that innovations in foreign income contribute highly to the forecast error variance of the trade balance compared with other explanatory variables in the short-run. In contrast, the domestic money supply contributes highly to the trade balance compared with other variables in the long-run. A key finding of the study suggests that the devaluation of Sudan’s currency is not an appropriate step toward improving the country's trade balance position.

Enhanced PLL-less grid synchronization algorithm amidst unbalanced and distorted three-phase grid conditions

Fast and precise tracking of grid voltage attributes is essential for the smooth and reliable control and operation of grid-side converters. The synchronous reference-frame phase-locked loop (SRF-PLL) approach is commonly used for this purpose, although it has issues related to the stability limit and sluggish transient response. Consequently, PLL-less grid synchronization approaches are becoming increasingly prevalent. In this context, in the present paper, a discrete Fourier transform-based band pass filter (DFT-BPF) is designed in the prefiltering stage for the purpose of extracting the fundamental frequency positive sequence component (FFPS) from unbalanced and distorted grid conditions. Despite being widely used, the SRF-PLL is struggling with stability issues and demands tedious tuning effort. For this objective, a simple frequency estimator has been developed, which is capable of accurately estimating the fundamental frequency. Additionally, a curve fitting based error correction technique is employed to adaptively estimate the amplitude and phase-angle of the fundamental component without using frequency feedback loop. The experimental results validate the efficacy of the proposed scheme.

A 121 dB SNDR Zoom ADC Using Dynamic Amplifier and Asynchronous SAR Quantizer

This paper presents a discrete-time zoom analog-to-digital converter (ADC) for low-bandwidth high-precision applications. It uses a coarse-conversion 5-bit asynchronous self-timed SAR ADC combined with a fine-conversion second-order delta-sigma modulator to efficiently obtain a high signal-to-noise distortion ratio (SNDR). An integrator circuit using a high-gain dynamic amplifier is proposed to achieve higher SNDR. The dynamic amplifier uses a switched tail current source to operate periodically, simplifying the common-mode feedback circuit, reducing unnecessary static current, and improving the PVT robustness. Dynamic error correction techniques, such as redundancy, chopping, and dynamic element matching (DEM) are used to achieve low offset and high linearity. And a 2-bit asynchronous SAR quantizer with an embedded feed-forward adder is used in the second-order delta-sigma modulator to reduce the quantization noise caused by redundancy, and further achieve higher energy efficiency. Simulation results show that the ADC achieves a peak SNDR of 121.1 dB in a 390 Hz bandwidth at a 200 kHz sampling clock while consuming only 170 μW from a 2.5 V supply and the core area is 0.55 mm2. This results in a Schreier figure of merit (FoM) of 184.7 dB.

Generative Adversarial Networks for DNA Storage Channel Simulator

DNA data storage systems have rapidly developed with novel error-correcting techniques, random access algorithms, and query systems. However, designing an algorithm for DNA storage systems is challenging, mainly due to the unpredictable nature of errors and the extremely high price of experiments. Thus, a simulator is of interest that can imitate the error statistics of a DNA storage system and replace the experiments in developing processes. We introduce novel generative adversarial networks that learn DNA storage channel statistics. Our simulator takes oligos (DNA sequences to write) as an input and generates a FASTQ file that includes output DNA reads and quality scores as if the oligos are synthesized and sequenced. We trained the proposed simulator with data from a single experiment consisting of 14,400 input oligo strands and 12,108,573 output reads. The error statistics between the input and the output of the trained generator match the actual error statistics, including the error rate at each position, the number of errors for each nucleotide, and high-order statistics. The code is available at <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/gyfbianhuanyun/DNA_storage_simulator_GAN</uri></monospace> .

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

In recent years, real-time video streaming has grown in popularity. The growing popularity of the Internet of Things (IoT) and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience (QoE) and performance objectives. Most researchers focused on Forward Error Correction (FEC) techniques when attempting to strike a balance between QoE and performance. However, as network capacity increases, the performance degrades, impacting the live visual experience. Recently, Deep Learning (DL) algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks. But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time. To address the previous challenge, this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics. The proposed framework contains modules such as Intelligent Content Extraction Module (ICEM), Channel Status Monitor (CSM), and Adaptive FEC (AFEC). This framework adopts the Cognitive Learning-based Scheduling (CLS) Module, which works on the deep Reinforced Gated Recurrent Networks (RGRN) principle and embeds them along with the FEC to achieve better performances. The complete framework was developed using the Objective Modular Network Testbed in C++ (OMNET++), Internet networking (INET), and Python 3.10, with Keras as the front end and Tensorflow 2.10 as the back end. With extensive experimentation, the proposed model outperforms the other existing intelligent models in terms of improving the QoE, minimizing the End-to-End Delay (EED), and maintaining the highest accuracy (98%) and a lower Root Mean Square Error (RMSE) value of 0.001.

A Unified Clock-Gated Error Correction Scheme with Three-Phase Latch-Based Pipeline for Energy-Efficient Wide Supply Voltage Range Router

Network-on-chip (NoC) has played a vital role in enabling high-speed and energy-efficient data communication among different cores in a multi-or many-core System-on-Chip (SoC). To mitigate the pessimistic timing margin reversed for severe process, voltage, and temperature (PVT) variations, we presented a wide supply voltage range router with a novel error detection and correction (EDAC) technique adopted in its latch-based pipeline for high energy efficiency. Especially, a unified clock-gated error correction scheme is presented to address dynamic timing errors under PVT variations and functional pipeline stalls when occurring allocation failure (packet fails to obtain an output port) or flow control (the next router stalls the current packet transmitting) in one cycle. Moreover, a latch-based pipeline structure with three non-overlapped clocks is adopted to further reduce the short-path padding overhead, which is significantly increased in the ultra-low voltage (ULV) regime. Consequently, we prototyped a 2 × 2 2D mesh NoC test chip with 28-nm CMOS technology. Compared with the margined baseline with a 10% VDD drop, measurement results show that the proposed router enables 1.4×, 32.4%, and 7.5% improvements in frequency, energy, and area, respectively. Furthermore, the proposed techniques can reduce packet loss by 200× and increase throughput by 19.9% for the NoC at 0.4 V, 25∘C.

Current Challenge and Limitations in Quantum Computation

This paper reviews various engineering hurdles facing the field of quantum computing. Specifically, problems related to decoherence, state preparation, error correction, and implement ability of gates are considered. Computer computation is very advanced computers used in recent days for various applications, including cryptography, optimization problems, financial modelling, route and traffic optimization, manufacturing drug, chemical research, batteries, simulation of quantum systems, and even machine learning. In a quantum computer, two qubits can also represent the exact same four states (00,01,10 or 11). It’s an exciting field with lots of potential. But it faces lots of challenges and problems like maintaining qubits, interaction with the environment can cause decoherence. However, the adventure to harness the enormous power of quantum computer systems is packed with challenges and boundaries. In this discussion, we can embark on an exploration of the current boundaries that researchers and engineers face within the world of quantum computing, losing mild on the complexities and constraints that ought to be overcome to fully free up its ability. Reacher’s are working on error correction techniques to address this issue and make quantum computations more reliable. It’s fascinating area of ongoing research. In recent development in quantum computing on 4 October 2022 The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Physics 2022 to Alain Aspect (Institute d’Optique Graduate School – Université Paris-Saclay and École Polytechnique, Palaiseau, France ), John F. Clauser ( J.F. Clauser &amp; Assoc., Walnut Creek, CA, USA ) Anton Zeilinger University of Vienna, Austria“for experiments with entangled photons, establishing the violation of Bell inequalities and pioneering quantum

Political-obsessed environment and investor sentiments: pricing liquidity through the microblogging behavioral perspective

&lt;abstract&gt; &lt;p&gt;Pakistan's political instability has pushed its economic system to the brink of collapse. Considering this political turmoil, this study addresses the behavior of liquidity providers against microblogging-opinionated information. The behavioral perspective was quantified through multiple linear regressions, the Bayesian theorem, and the vector error correction technique. Before this political crisis, sentiment indicators were linked to the liquidity-conditional cost for the same trading session. In the political uncertainty environment, pessimistic opinions were the sole concern of the liquidity providers during the same trading session. The liquidity facilitator was observed to price the liquidity in light of pessimistic sentiments. The Bayesian theorem suggested a higher posterior probability for the occurrence of the liquidity-facilitating cost in response to the pessimistic sentiments. Nevertheless, the past time series changes for the sentiment indicators were irrelevant in determining changes in cost-based liquidity for the next trading session.&lt;/p&gt; &lt;/abstract&gt;