Dynamic Forward Error Correction Coding to Avoid Detection in Airborne Tactical Networks

Airborne tactical networks are complex networks that connect various aircraft platforms via datalink systems. In the fierce battlefield, airborne tactical networks are under the risk of hostile node attack, causing route failures and packet losses. Reliable communication in airborne tactical networks is thus calling for an efficient node protection scheme design for routing strategies. Due to the inherent uncertainty of war, the enemy’s node attack is unpredictable, making it intractable to select the optimal node set to deploy defense countermeasures under limited resource. To hedge against such uncertainty, we propose an Attack-Oblivious Node Protection Strategy (AONPS) for survivable routing in airborne tactical networks. Through a crafty-designed randomized algorithm, AONPS can select an appropriate node set to protect. Both theoretical derivation and simulation experiments demonstrate that the proposed AONPS can significantly enhance routing survival rate without accurate knowledge of the targeted attack nodes of the enemy, and achieve performance comparable to that of the ideal node protection strategy.

In this paper, we present the design of a new packet loss concealment (PLC) algorithm based on Dynamic Forward Error Correction (FEC) to improve speech quality degradation caused by packet losses. It is to be implemented for the standard ITU-T AMR-WD G.722.2 codec. The performance proposed scheme is compared with the original method embedded in the standard ITU-T G.772.2. To evaluate its performance, the perceptual evaluation of speech quality (PESQ) and enhanced modified bark spectral distortion (EMBSD) tests under a variety of packet loss rates are used and the obtained results prove that the proposed PLC based Dynamic FEC is better than the concealment method embedded in the ITU-T G.722.2 for speakers (female and male). The performance measures confirm that our PLC method based Dynamic FEC is superior than the one embedded in the standard ITU-T G.722.2.

In high-temperature testing scenarios that rely on contact, fine-wire thermocouples demonstrate commendable dynamic performance. Nonetheless, their thermal inertia leads to notable dynamic nonlinear inaccuracies, including response delays and amplitude reduction. To mitigate these challenges, a novel dynamic error correction approach is introduced, which combines a Continuous Restricted Boltzmann Machine, Deep Belief Network, and Physics-Informed Neural Network (CDBN-PINN). The unique heat transfer properties of the thermocouple’s bimetallic structure are represented through an Inverse Heat Conduction Equation (IHCP). An analysis is conducted to explore the connection between the analytical solution’s ill-posed nature and the thermocouple’s dynamic errors. The transient temperature response’s nonlinear characteristics are captured using CRBM-DBN. To maintain physical validity and minimize noise amplification, filtered kernel regularization is applied as a constraint within the PINN framework. This approach was tested and confirmed through laser pulse calibration on thermocouples with butt-welded and ball-welded configurations of 0.25 mm and 0.38 mm. Findings reveal that the proposed method achieved a peak relative error of merely 0.83%, superior to Tikhonov regularization by −2.2%, Wiener deconvolution by 20.40%, FBPINNs by 1.40%, and the ablation technique by 2.05%. In detonation tests, the corrected temperature peak reached 1045.7 °C, with the relative error decreasing from 77.7% to 5.1%. Additionally, this method improves response times, with the rise time in laser calibration enhanced by up to 31 ms and in explosion testing by 26 ms. By merging physical constraints with data-driven methodologies, this technique successfully corrected dynamic errors even with limited sample sizes.

Neural networks along with expert adaptive regulators and systems with associative memory form the basis of intelligent technologies for information management and processing [1], [2]. This paper introduces an algorithm for the dynamic measurement error correction based on a neural network (NN) inverse model of a measuring transducer (MT). It uses the reduction principle in the compensating filter for proper operation with high order MTs and noises filtering based on a NN model of an IIR filter. In this regard, the development of dynamic models of measuring systems based on recurrent NN and algorithms for dynamic measurement data processing using NN technologies is one of the promising directions for the development of the modern measuring equipment intellectualization. Successful solution to this task will significantly improve the metrological characteristics and efficiency of existing MTs without significant material costs due to deep mathematical processing of the measurement results.

In this work, we present a dynamic channel coding scheme for robust transmission of JPEG2000 codestreams over Mobile Ad-hoc Networks (MANET). The proposed system, based on dynamic Forward Error Correction (FEC) rate allocation allied to layered Unequal Error Protection (UEP), is implemented according to the main recommendations of Wireless JPEG2000 standard final draft. We demonstrate that dynamic FEC rate allocation outperforms a priori FEC rate allocation. Going straightforward we validate the proposed scheme by deriving interesting results from Motion JPEG2000 video streaming over real MANET data traces.This application, developed within the European IST WCAM project, validates wireless JPEG2000 standards recommendations and is a step toward guaranteeing Quality of Service (QoS) in wireless JPEG2000 based data streaming systems.

Error control coding improves reliability and efficiency of wireless communication systems. This research delves into the latest advancements in error control coding schemes for wireless communication systems, with a specific focus on their application within the domain of Low Power Wide Area Networks (LPWANs) such as Narrowband Internet of Things (NB-IoT) systems, LoRa energy-efficient hardware design, random access control systems, and wireless sensor networks. In the context of LPWANs, particularly NB-IoT systems, we investigate the adaptive coding and modulation (ACM) in NB-IoT systems, which adapts coding rates and modulation techniques to changing channel circumstances. Comprehensive analysis of the literature review shows that the proposed approaches are more energy efficient and less error-prone than fixed coding and modulation schemes. Using Cell Design Methodology (CDM), Single Error Correction (SEC) codes are optimized for energy efficiency. Power and energy efficiency have improved in standard CMOS and CDM logic systems. This impacts IoT devices, memory storage, and security systems. Next, Forward Error Correction (FEC) is examined for ALOHA-based random-access control systems wherein packet loss rate and spectral efficiency metrics are mathematically calculated. FEC is beneficial, especially when time and frequency synchronization diverge. Monte Carlo simulations studied in the literature shows that FEC improves satellite communication systems. This review establishes benefits of FEC, notably in satellite communications systems. In addition, Wireless Sensor Networks (WSNs) error control techniques and network reliability and performance are examined along with Convolutional, Turbo, Low density parity check codes(LDPC), and Polar error correction coding (ECC) methods. We learn about their benefits, drawbacks, and implementation challenges by assessing their efficacy, efficiency, and applicability in wireless sensor networks (WSN) applications. This study further highlights advancements of energy-efficient error control coding when, integrated into network designs alongwith the trade-offs between efficacy and intricacy to help improve wireless communication networks. Finally, this study summarizes current error control coding approaches for wireless communication systems. We provide insights into improving reliability, energy conservation, and efficacy by examining ACM, CDM, FEC, and error correction coding (ECC) across varied domains including LPWAN and IoT technologies with earnest hope to facilitate researchers, engineers, and professionals working in the field of error control coding in wireless communication.

The analysis of dynamic measurements provides numerous challenges that significantly limit the use of existing calibration facilities and mathematical methodologies. The article deals with the structure of the measuring system containing the model of a primary measuring transducer and realizing the modal control of its dynamic characteristics. On this basis the approach to optimal adjustment of dynamical parameters adaptive to the measuring transducer output noises is developed. The result of digital experiment confirms the efficiency of the developed genetic algorithm of the dynamic measurement error correction. It is concluded, that deep mathematical processing of measurement results, performed after the completion of the experiment, reduces the estimate of the dynamic measurement error 37.8%.

Sea level is an important variable for studying water cycle and coastal hazards under global warming. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as a relatively new technique for monitoring sea level variations, leveraging signals from GNSS constellations. However, dynamic height errors, primarily caused by non-stationary sea surfaces, compromise the precision of GNSS-IR sea level retrievals and necessitate robust correction. In this study, we propose a new method to correct the dynamic height error by integrating the commonly used tidal analysis method and the cubic spline fitting method. The proposed method is applied to the GNSS-IR sea level retrievals from multiple systems and multiple frequency bands at two coastal GNSS stations, MAYG and HKQT. At MAYG, the results show that our method significantly reduces the Root Mean Square Error (RMSE) of the GNSS-IR sea level retrievals by 42.1% (11.4 cm) to 15.7 cm, performing better than the single tidal analysis method (16.5 cm) and the cubic spline fitting method (21.4 cm). At HKQT, our method improves the accuracy by 21.5% (3.1 cm) to 10.3 cm, which is still better than that of the tidal analysis method (11.3 cm) and the cubic spline fitting method (12.4 cm). Compared to the tidal analysis method and the cubic spline fitting method, our method maintains high retrieval retention while enhancing precision. The effectiveness of our method is further validated in the two storm surge events caused by Typhoon Hato and Typhoon Mangkhut in Hong Kong.

High-quality and high-performance real-time interactive video streaming requires both keeping the highest data transmission rate and minimizing data packet loss to achieve the best possible streaming quality. TCP-friendly rate control (TFRC) is the most widely recognized mechanism for achieving relatively smooth data transmission while competing fairly with TCP flows. However, because its data transmission rate depends largely on packet loss conditions, high-quality real-time streaming suffers from a significant degradation of streaming quality due to both a reduction in the data transmission rate and data packet losses. This paper proposes the dynamic probing forward error correction (DP-FEC) mechanism that is effective for high-quality real-time streaming to maximize the streaming quality in a situation in which competing TCP flows pose packet losses to the streaming flow. DP-FEC estimates the network condition by dynamically adjusting the degree of FEC redundancy while trying to recover lost data packets. It effectively utilizes network resources and adjusts the degree of FEC redundancy to improve the playback quality at the user side while minimizing the performance impact of competing TCP flows. We describe the DP-FEC algorithm and evaluate its effectiveness using an NS-2 simulator. The results show that by effectively utilizing network resources, DP-FEC enables to retain higher streaming quality while minimizing the adverse condition on TCP performance, thus achieving TCP friendliness.

This article offers a comparison of short‐term forecasting ability of five demand systems with an application to U.S. meat consumption. Four static demand systems (AIDS, Rotterdam, AIM, and DGM) and a dynamic Vector Error Correction Model (VECM) are considered. We tested the equality of mean square forecast errors. We also investigated the possibility of forecast encompassing among competing models. In general, the dynamic VECM model performed best, followed by the simple causal DGM model. Among three static systems, the AIDS model slightly leads the competition. Furthermore, this article provides the first evidence in literature on whether imposition of homogeneity restrictions on a cointegration space can improve the forecast accuracy of a VECM model: it does when it holds. [EconLit citations: Q10; C53]. © 2003 Wiley Periodicals, Inc. Agribusiness 19: 505–523, 2003.

With the ever-growing importance of stock markets, an overwhelming number of studies have been carried out worldwide to investigate the link which exists between stock market development and economic growth. However, studies analyzing this link are scant in the African region. As such, this chapter uses a dynamic panel vector error correction model (PVECM) to analyze the relationship among stock market development, banking development, and economic growth in a unified framework for the period 1988–2011. The results suggest that stock market development plays an important role in generating gains in terms of economic growth both in the short run and in the long-run within the sample of African countries under consideration.

Institutional and market frictions impose costs on the reallocation of labor from low to high productivity sectors, leading to suboptimal allocations and a loss in aggregate labor productivity. Using cross-country sector-level data, we use a dynamic panel error correction model to compute the speed of sectoral labor adjustment, as well as the contribution of structural reforms in governance, labor and product markets, trade and openness, and the financial sector to lowering the costs of labor reallocation. We find that, on average, sectoral employment shares converge towards equilibrium allocations, closing about 13.7 percent of labor productivity gaps each year; this speed of labor adjustment varies across sectors and income groups. On structural reforms, we find a significant association between more efficient labor reallocation and financial market liberalization, less bureaucracy, strong judicial and regulatory environment, trade liberalization, better education and more flexible labor and product markets.

In developing economies, banks act as a conduit for the efficient mobilization of financial resources from the surplus sectors for effective allocation to the deficit sectors for productive investment that will in turn lead to economic growth. Thus, the study is aimed at evaluating whether development in the banking sector intermediation process in the form of an increase in the number of branches, credit to private sectors, intermediation efficiency and total assets stimulated economic growth in Nigeria during the period 1987⎯2018. The study employed the Johansen cointegration test, dynamic ordinary least square regression and error correction model in determining the relationship between the variables. The results of the cointegration test confirmed the existence of a long-run relationship between banking sector development indicators and economic growth in Nigeria; whereas in the short-run only number of bank branches and bank’s total assets have a positive and significant impact on economic growth, signifying that much of Nigeria’s superior growth performance is attributed to an increase in the number of bank branches and growth in the bank’s total assets. Going by the above findings, the study recommends that the Central Bank of Nigeria should monitor the activities of Commercial Banks in terms of branch distribution to ensure a wider spread and even distribution and also review the asset base of banks from time to time so as to measure up to international standards in order to increase investment and economic growth.

Determinants of Government Bond Spreads in New EU Countries

Improved capture of ultra-high-rate GNSS co-seismic displacement waveforms with dynamic stress error correction for general geodetic GNSS receivers