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In today's world, the rapid development of aviation technologies, particularly unmanned aerial vehicles (UAVs), presents new challenges and opportunities. UAVs are utilized across various industries, including scientific research, military, robotics, surveying, logistics, and postal delivery. However, to ensure efficient and safe operation, UAVs require a reliable autopilot system that delivers precise navigation control and flight stability. This paper introduces a method for controlling and adjusting UAV trajectories, which enhances accuracy in environments and tasks corresponding to the first or second level of autonomy. It outperforms the linear-quadratic method and the unmodified predictive control method by 43% and 74%, respectively. The findings of this study can be applied to the development and modernization of new UAV, as well as the advancement of new UAV motion control systems, thereby enhancing their quality and efficiency.

Predicting the correct values of stock prices in fast fluctuating high‐frequency financial data is always a challenging task. A deep learning‐based model for live predictions of stock values is aimed to be developed here. The authors' have proposed two models for different applications. The first one is based on Fast Recurrent Neural Networks (Fast RNNs). This model is used for stock price predictions for the first time in this work. The second model is a hybrid deep learning model developed by utilising the best features of FastRNNs, Convolutional Neural Networks, and Bi‐Directional Long Short Term Memory models to predict abrupt changes in the stock prices of a company. The 1‐min time interval stock data of four companies for a period of one and three days is considered. Along with the lower Root Mean Squared Error (RMSE), the proposed models have low computational complexity as well, so that they can also be used for live predictions. The models' performance is measured by the RMSE along with computation time. The model outperforms Auto Regressive Integrated Moving Average, FBProphet, LSTM, and other proposed hybrid models on both RMSE and computation time for live predictions of stock values.

Channel estimation for single-input–multiple-output systems over doubly selective channel using superimposed training and discrete prolate spheroidal basis expansion model is considered. To remove the interference from unknown data, a two-step interference cancellation scheme is proposed, where in the first step, a first-order statistics-based estimator with cyclic mean removal before transmission is proposed. In this step, only interference components corresponding to cyclic mean of data sequence are removed, which avoids the conflict between total interference elimination and symbol recovery. In the second step, a low complexity iterative interference cancellation scheme is proposed to further improve estimation performance with detected symbols at the receiver's end. Simulation results show that the proposed iterative scheme has the symbol error rate (SER) performance slightly inferior to that of the partially-data-dependent approach but with less computational complexity and outperforms the data-dependent scheme in terms of SER as well as mean-square error over doubly selective channels.

The low-cost multimodal platform BITalino is being increasingly used for educational and research purposes. However, there is still a lack of well-structured work comparing data acquired by this toolkit against a reference device, using established experimental protocols. This work intends to fill the said gap by benchmarking the performance of BITalino against the BioPac MP35 Student Lab Pro device. This work followed a methodical experimental protocol to acquire data from the two devices simultaneously. Four physiological signals were acquired: electrocardiography, electromyography, electrodermal activity and electroencephalography. Root mean square error and coefficient of determination were computed to analyse differences between BITalino and BioPac. Electrodermal activity signals were very similar for the two devices, even without applying any major signal processing techniques. For electrocardiography, a simple morphological comparison also revealed high similarity between devices, and this similarity increased after a common segmentation procedure was followed. Regarding electromyography and electroencephalography data, the approach consisted of comparing features extracted using common post-processing methods. The differences between BITalino and BioPac were again small. Overall, the results presented here show a close similarity between data acquired by the BITalino and by the reference device. This is an important validation step for all researchers working with this multimodal platform.

The arrival of molecules in molecular communication via diffusion obeys, by its nature, the binomial distribution, considering the hitting probability as the success probability. It is, however, hard to work with the binomial cumulative distribution function (CDF) when considering consecutively sent symbols as it is necessary to add the distribution. Therefore, in the literature, two approximations of the binomial distribution are used. In the present reported work, the Gaussian and Poisson approximations of the binomial model of the molecule arrival process have been analysed. Considering the distance and the number of emitted molecules, the regions in which the Poisson or Gaussian model is better in terms of root mean squared error of CDFs are investigated and the regions using numerical simulations are confirmed.

The concept of missing data is important to apply statistical methods on the dataset. Statisticians and researchers may end up to an inaccurate illation about the data if the missing data are not handled properly. Of late, Python and R provide diverse packages for handling missing data. In this study, an imputation algorithm, cumulative linear regression, is proposed. The proposed algorithm depends on the linear regression technique. It differs from the existing methods, in that it cumulates the imputed variables; those variables will be incorporated in the linear regression equation to filling in the missing values in the next incomplete variable. The author performed a comparative study of the proposed method and those packages. The performance was measured in terms of imputation time, root-mean-square error, mean absolute error, and coefficient of determination $\\lpar {\\bi R}^2\\rpar $(R2). On analysing on five datasets with different missing values generated from different mechanisms, it was observed that the performances vary depending on the size, missing percentage, and the missingness mechanism. The results showed that the performance of the proposed method is slightly better.

A wireless indoor positioning system using white LED lights is proposed. The time difference of arrival technique is employed and the phase differences between the received signals are determined to develop a positioning algorithm which can estimate the receiver location with a mean localisation error as low as 1 mm in a room of dimensions 5 × 5 × 3 m. Through simulations, it is identified that the optimum receiver height where localisation error gets minimised is between 2.5 and 3 m from the ceiling which corresponds well with the typical dimensions of a room.

Accurate channel estimation is essential for optimising intelligent reflecting surface‐assisted multi‐user communication systems, particularly in dynamic indoor environments. Conventional techniques such as least squares (LS), linear minimum mean square error (LMMSE), and orthogonal matching pursuit (OMP) suffer from noise sensitivity and fail to effectively capture spatial dependencies in high‐dimensional intelligent reflecting surface (IRS)‐assisted channels. To overcome these limitations, this work proposes a deep learning‐driven ResNet+UNet framework that refines initial LS estimates using residual learning and multi‐scale feature reconstruction. While UNet enhances channel estimation through hierarchical processing, efficiently decreasing noise and enhancing estimate accuracy, ResNet gathers spatial features. Simulation results show that the proposed method significantly outperforms existing methods across various performance metrics. In NMSE versus signal‐to‐noise ratio assessments, the proposed approach surpasses convolutional deep residual network (CDRN) by 59%, OMP by 81%, LMMSE by 114%, and LS by 115%. When IRS elements are modified, it overcomes CDRN by 60%, OMP by 78%, LS by 107%, and LMMSE by 110%. Along with this, recommended structure performs more effectively than CDRN by 39%, OMP by 44%, LS by 122%, and LMMSE by 129% across various antenna configurations. The proposed approach is particularly beneficial for augmented reality (AR) applications, where real‐time, high‐precision channel estimation ensures seamless data streaming and ultra‐low latency, enhancing immersive experiences in AR‐based communication and interactive environments. These results illustrate the proposed method's scalability and resilience, making it a suitable choice for next‐generation IRS‐assisted wireless communication networks. Effective channel estimation is vital for optimising intelligent reflecting surface‐assisted multi‐user communication systems, particularly in dynamic indoor environments. This study introduces a deep learning‐based ResNet+UNet framework that enhances initial least squares (LS) estimates by incorporating residual learning and multi‐scale feature reconstruction to reduce noise and improve spatial feature extraction. Simulation results show that the proposed approach outperforms traditional methods like LS, linear minimum mean square error, and orthogonal matching pursuit across multiple performance metrics, especially in NMSE versus signal‐to‐noise ratio evaluations.

The emergence of terahertz (THz) communication in ultra‐massive multiple‐input multiple‐output (UM‐MIMO) systems presents new challenges for accurate and efficient channel estimation, particularly under hybrid‐field propagation conditions. Conventional estimation techniques struggle to meet the demands of such high‐dimensional systems, especially in the presence of limited radio frequency (RF) chains and mixed near‐ and far‐field effects. To address these limitations, this paper proposes a deep learning‐based framework that combines a fully connected neural network (FCNN) for linear channel estimation with a convolutional neural network (CNN) for non‐linear refinement. The architecture is designed to adapt to diverse propagation environments while maintaining computational efficiency. Simulation studies based on realistic THz scenarios demonstrate that the proposed approach significantly improves estimation accuracy, achieving up to 90% reduction in normalized mean squared error (NMSE) compared to traditional and advanced estimation techniques. The robustness of the model under varying signal‐to‐noise ratios and noise power levels underscores its potential for deployment in future 6G THz communication networks. This paper introduces a deep learning‐based framework for channel estimation in terahertz (THz) ultra‐massive multiple‐input multiple‐output systems, combining fully connected neural network for linear estimation and connected neural network for non‐linear refinement. The proposed method significantly reduces normalized mean squared error by up to 90% compared to traditional techniques, showcasing its potential for 6G THz communication. Simulation results confirm its robustness across varying signal‐to‐noise ratios.

Dynamic thermal management (DTM) is applied to address the thermal problem of high performance very‐large‐scale integrated chips. The false alarm rate (FAR) can be used to evaluate the impact of full‐chip thermal field reconstruction accuracy on DTM. A low FAR relies on the accurate reconstruction of the full thermal field, especially near the temperature triggering threshold of DTM. However, little attention is currently being paid to such temperature ranges. To reduce FAR, a new full‐chip thermal field reconstruction strategy is proposed. A low‐dimensional linear model is used to accurately represent the thermal fields. The dictionary learning technology is exploited to train the model and the minimum weighted mean square error evaluation method is incorporated to improve the reconstruction accuracy near the temperature triggering threshold. A temperature sensor placement algorithm using the heuristic algorithm to solve the NP‐hard problem is also proposed. The experimental results show that the proposed strategy can reconstruct the full thermal field with a more precise accuracy near the triggering threshold and achieve the lowest FAR compared to the state of the art.