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Time hopping spread spectrum (THSS) system and chaotic system have been studied as candidates of the anti-jamming (AJ) systems with substantial advantages. However, THSS systems suffer from the strict synchronization prerequisite. In order to overcome the shortcomings of the THSS system and utilize the profits of the non-coherent chaotic system, we first propose a novel TH-NRDCSK system to enhance the bit error rate (BER) performance of the conventional non-coherent THSS system and relieve the strict synchronization issue of coherent THSS systems. Then, we analyze the performance of the proposed system under additive white Gaussian noise (AWGN) channel and jamming environment. Finally, we implement the simulation to demonstrate the consistency of the analysis and the simulation results and to disclose the effects of the system parameters on system's AJ performance. Moreover, the performance of the proposed system is compared to that of conventional counterparts to look at the benefits of integrating chaotic and THSS systems. The simulation results show that the proposed system significantly outperforms conventional counterparts in a practical environment.

In this paper, we consider the fundamental and most studied non-coherent differential chaotic shift keying system under a pulse jamming environment. We devote our effort to propose a novel correlation receiver with nonlinearity blanking for the considered system to improve its anti-jamming performance. In addition and importantly, we also propose a mathematical framework to derive an optimal blanking threshold for the proposed receiver. The proposed nonlinearity blanking mechanism basically blocks the received signals with a larger amplitude than the blanking threshold, while the signals with a smaller amplitude are not affected at all. We then show that the proposed receiver with optimal blanking threshold significantly outperforms the conventional correlation counterpart.

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability.

In this paper, we consider an overlay cognitive radio network, in which a secondary transmitter (ST) is willing to relay the information of a primary transmitter toward a primary receiver. In return, ST can access the licensed band to send its own information superimposed with the primary signal to a secondary receiver. The power-limited ST uses a power splitting protocol to harvest energy from its received signal to increase its transmit power. We analyze the performance of the primary and the secondary systems under independent Nakagami-m fading by deriving their corresponding outage probabilities in integral-based expressions. In addition, by considering the high signal-to-noise ratio, we obtain very tight closed-form approximations of the outage probabilities. Thereafter, by further analyzing the approximations, we reveal novel insights on the diversity orders and coding gains of the two systems. Our analytical results are validated through extensive Monte-Carlo simulations.

This paper considers the DCSK system under jamming environment. We consider both fast and slow switching pulse jamming (PJ). We derive approximations of the system bit error rates (BERs) in analytic expressions containing only well-known special functions, from which we show that for both jamming cases increasing the spreading factor can enhance the BER. In addition, we reveal that under the fast switching PJ, the BER does not depend on the jamming duty cycle ρ when ρ ≤ 0.5, however when ρ > 0.5, increasing its value degrades the BER. Moreover, we find out that under the slow switching PJ, increasing ρ may either enhance or degrade the BER.

In this paper, we consider amplify-and-forward (AnF) cooperative systems under correlated fading environments. We first present a brief overview of existing works on the effect of channel correlations on the system performance. We then focus on our main contribution which is analyzing the outage probability of a multi-AnF-relay system with the best relay selection (BRS) scheme under a condition that two channels of each relay, source-relay and relay-destination channels, are correlated. Using lower and upper bounds on the end-to-end received signal-to-noise ratio (SNR) at the destination, we derive corresponding upper and lower bounds on the system outage probability. We prove that the system can achieve a diversity order (DO) equal to the number of relays. In addition, and importantly, we show that the considered correlation form has a constructive effect on the system performance. In other words, the larger the correlation coefficient, the better system performance. Our analytic results are corroborated by extensive Monte-Carlo simulations.

This article explores the ethical challenges encountered by auditors in Vietnam and applies the fraud triangle model to analyze the factors that contribute to fraud among auditors in the country. The study involves a survey of managers and auditors from Vietnamese auditing firms, and the data is analyzed using SPSS 25 software. The findings indicate that perceived pressure is the most influential factor, followed by perceived opportunity and rationalization. Based on these results, the author suggests recommendations to mitigate the negative effects of these factors and enhance the professional ethics of Vietnamese auditors. This research contributes to a better understanding of ethical issues among Vietnamese auditors and the application of the fraud triangle model. [Extracted from the article]