In this paper, the sparse Bayesian learning (SBL) theory and underwater acoustic imaging model are the core, and the underwater acoustic imaging model and algorithm based on sparse Bayesian learning are introduced for...
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ISBN:
(数字)9798350355895
ISBN:
(纸本)9798350355901
In this paper, the sparse Bayesian learning (SBL) theory and underwater acoustic imaging model are the core, and the underwater acoustic imaging model and algorithm based on sparse Bayesian learning are introduced for the purpose of improving the resolution of underwater acoustic imaging. Focusing on the underwater acoustic imaging model, firstly, the echo signal model based on the bright spot model is established, and the observation matrices of the near-field and far-field targets are established according to the discriminant conditions of the near-field and far-field respectively. Secondly, the sparse Bayesian learning theory is introduced, and the obtained SBL algorithm is combined with the underwater acoustic imaging system to explain the sparsity in the system, and it can be seen that the accuracy of the SBL algorithm is theoretically better than that of the traditional algorithm. Finally, the derived SBL algorithm is combined with the specific underwater acoustic imaging parameters for simulation. According to the simulation results, the SBL algorithm can significantly improve the imaging resolution, and because it uses probabilistic information to quickly update the hyperparameters, it can complete the signal reconstruction with high performance, and has excellent noise immunity and universality.
Integrated Sensing and Communication (ISAC) is emerging as a pivotal technology for next-generation 6G networks, enabling simultaneous wireless communication and environmental sensing. This paper investigates the inte...
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ISBN:
(数字)9798331509859
ISBN:
(纸本)9798331509866
Integrated Sensing and Communication (ISAC) is emerging as a pivotal technology for next-generation 6G networks, enabling simultaneous wireless communication and environmental sensing. This paper investigates the integration of Reconfigurable Holographic Surfaces (RHS) and phased array antennas within ISAC systems to enhance both functionalities. The proposed system leverages RHS to dynamically manipulate electromagnetic wave reflections, improving communication coverage and sensing accuracy. A comparative analysis is conducted between RHS and phased array beamforming models, emphasizing advancements in beamforming precision, interference suppression, and spectral efficiency. Numerical simulations demonstrate that RHS-based ISAC significantly outperforms conventional phased arrays, achieving up to an 8 dB signal-to-Interference-plus-Noise Ratio (SINR) enhancement in high-element configurations. The findings highlight RHS as a scalable and efficient technology for ISAC applications, underscoring its potential for future wireless communication systems. Further research is necessary to optimize hardware implementations and develop advanced beamforming algorithms for real-world deployment.
Railway tracks are the most prominent base of train movement, any defect in the track may lead to minor/major unhappening. To minimize risk and maximize safety, regular maintenance or condition-based maintenance must ...
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ISBN:
(数字)9798350379525
ISBN:
(纸本)9798350379532
Railway tracks are the most prominent base of train movement, any defect in the track may lead to minor/major unhappening. To minimize risk and maximize safety, regular maintenance or condition-based maintenance must be performed. In this paper, a deep learning-based algorithm is proposed for automatic fault detection in railway tracks using LSTM (Long Short-Term Memory). LSTM model, an advanced version of recurrent neural networks known for their accuracy are compared with existing Deep Learning techniques to identify common railway faults, such as defects in rails and fasteners. Predictive maintenance is used to perform required maintenance before the failure of tracks by estimating RUL (Remaining Useful Life) of tracks and their components. This Proposed model is applied to the trusted source dataset which detects three defect types (DIP, XLEVEL, SURFACE). This approach enhances proactive maintenance, reduces downtime, optimizes schedules, and improves railway system reliability and safety. Our research advances predictive maintenance practices, showcasing how LSTM networks can transform railway infrastructure maintenance strategies.
Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum ma...
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ISBN:
(数字)9798350361186
ISBN:
(纸本)9798350361193
Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum machine learning algorithms for optimal financial portfolio management, presenting a novel approach implemented in Python that outperforms existing methods. The algorithm's capability to generate such substantial returns over time positions it as a groundbreaking tool for portfolio optimization in the dynamic landscape of financial markets. In the pursuit of enhancing quantum machine learning algorithms, this research focuses on the development and optimization of the QSVM algorithm. Leveraging Python for implementation, the study considers critical factors such as quantum circuit optimization, noise mitigation, and the integration of classical and quantum components to achieve superior results. The achieved portfolio performance over time not only underscores the algorithm's efficacy but also signifies a quantum advantage in financial decision-making. The implementation in Python ensures accessibility and applicability, facilitating the integration of this advanced quantum algorithm into existing financial frameworks. This research contributes to the evolving field of quantum finance, showcasing the potential of quantum machine learning in optimizing financial portfolios. The findings not only validate the superior performance of the proposed QSVM but also highlight the broader implications for the future of financial decision support systems, where quantum algorithms could play a transformative role in enhancing portfolio management strategies. The proposed Quantum Support Vector Machine (QSVM) demonstrates unparalleled success, with a remarkable Portfolio performance over time of 89.65%. This result significantly surpasses existing quantum algorithms, including Quantum Principal Component Analysis (QPCA), Quantum Boltzmann Machines (QBM), and Quantum K
Floods pose significant threats to lives, infrastructure, and the environment. Flood monitoring is crucial for effective disaster management and mitigation. Traditional methods of flood monitoring have several limitat...
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ISBN:
(数字)9798350350654
ISBN:
(纸本)9798350350661
Floods pose significant threats to lives, infrastructure, and the environment. Flood monitoring is crucial for effective disaster management and mitigation. Traditional methods of flood monitoring have several limitations including manual data collection, limited coverage, and delayed response times. In recent years, advanced remote sensing technology, coupled with the power of deep learning algorithms have greatly enhanced flood monitoring capabilities. This paper presents a comprehensive investigation into the integration of remote sensing data and deep learning algorithms for water body delineation in flood monitoring. A dataset containing 700 Sentinel-2 satellite images was used. It was divided into training, testing, and validation data, with corresponding ground truth masks. Deep learning models, including UNET, SEGNET, DeepLabv3, and RefineNet, were employed for water body delineation tasks. After thorough assessment, RefineN et emerged as the best performing model. It has exceptional accuracy and precision in outlining water bodies. Transfer learning techniques were explored to further improve the RefineN et model performance. Various backbone architectures, including ResNet50, ResNet101, MobileNetV2, and EfficientN etBO, were investigated for their compatibility with RefineN et. Experimental results revealed that EfficientN etBO served as the optimal backbone architecture, significantly enhancing the model's performance. The RefineNet model with EfficientN etBO backbone achieved remarkable results, with an accuracy of 0.9478, F1-score of 0.9162, IOU of 0.8455, and MAP of 0.8708. These findings underscore the potential of integrating remote sensing data and deep learning algorithms for flood monitoring applications. The findings from this research carry substantial importance for disaster management and emergency response efforts using neural networks.
Software diversification is an effective software protection method against reverse engineering and code reuse attacks, which can provide heterogeneous redundant execution bodies for mimetic defense mechanisms. Most e...
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ISBN:
(数字)9798350376548
ISBN:
(纸本)9798350376555
Software diversification is an effective software protection method against reverse engineering and code reuse attacks, which can provide heterogeneous redundant execution bodies for mimetic defense mechanisms. Most existing software diversification methods require access to the source code, which can provide defenders with more valuable information for devising effective defense strategies. However, due to commercial copyright and the purpose of preventing software piracy, developers often keep their software closed-source, making it difficult to access the source code. Therefore, a method called R2BF (ReCooking and Randomizing Binary File) is proposed, which combines decompilation and diversification compilation techniques to address the difficulty of obtaining the source code. This method involves diversifying the source code through compilation to achieve software diversification for binary executable files. Security and performance testing of diversified C programs has shown that diversified binary executable files can mitigate vulnerabilities resulting from code reuse, validating that this method can enhance the security of binary programs and is feasible for software protection. The diversified binary executable files are nearly identical to the original, non-diversified files in terms of file size and execution time, and may even exhibit acceleration and optimization effects in certain scenarios.
In modern wireless communication systems using multiple antenna elements, accurately identifying the signals contained in the received signal is very important. Conventional signal number estimation algorithms operate...
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ISBN:
(数字)9798350373332
ISBN:
(纸本)9798350373349
In modern wireless communication systems using multiple antenna elements, accurately identifying the signals contained in the received signal is very important. Conventional signal number estimation algorithms operate for the entire set of antenna elements, resulting in high computational complexity. In order to enhance this high computational complexity problem, this paper proposes beamspace based Akaike Information Criterion (AIC) and Minimum Description Length (MDL) algorithms based on a concentric circular array (CCA) antenna for efficient signal number estimation. In addition, computer simulation results are presented to evaluate the signal-number estimation performance of the proposed algorithm.
In Integrated Sensing and Communications (ISAC) systems assisted by Intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. Th...
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ISBN:
(数字)9798350390643
ISBN:
(纸本)9798350390650
In Integrated Sensing and Communications (ISAC) systems assisted by Intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. The estimation problem, however, becomes intricate due to complex channel coupling and specific environmental conditions. This paper addresses these challenges by formulating channel estimation as a denoising problem and proposing a novel diffusion-based framework. This approach mitigates the impact of ill-posedness, which is typically problematic for general neural network (NN)-based techniques. By modeling the joint distribution and sampling from the learned posterior distribution, we accurately recover the channel coefficients from noisy pilot-based observations. Simulation results demonstrate significant performance improvements of the proposed method compared to both least-squares and NN-based benchmarks. Moreover, thanks to the carefully designed architecture and residual prediction strategy, the number of sampling steps in the diffusion reverse process is greatly reduced, enabling real-time CSI acquisition.
The accurate prediction of students academic success holds significance in enhancing educational outcomes. The study focuses on overcoming the challenge of assessing whether a student will successfully pass the final ...
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ISBN:
(数字)9798350353068
ISBN:
(纸本)9798350353075
The accurate prediction of students academic success holds significance in enhancing educational outcomes. The study focuses on overcoming the challenge of assessing whether a student will successfully pass the final exam, taking into account a range of socio-economic and academic factors. Every student's journey is different, and it is influenced by various factors such as their socioeconomic background, academic abilities, and personal experiences. To evaluate their performance, we use various machine learning algorithms such as KNN, Naive Bayes, SVM, AdaBoost, and Naive Bayes— and assess their performance using key metrics such as ROC curve, confusion matrix, precision, recall, and F1 score. The F1 score offers a balanced assessment of precision and recall, offering insights into overall model accuracy. Precision and recall focus specifically on the model's ability to correctly identify positive instances and capture all actual positive instances, respectively. ROC curves demonstrate the balance between the rates of correctly identifying true positives and incorrectly identifying false positives, allowing for a nuanced assessment of classifier performance. The confusion matrix further breaks down true positives, true negatives, false positives, and false negatives, enhancing our understanding of algorithmic effectiveness. Through a comprehensive comparison of these classification methods, our goal is to identify the most accurate approach for predicting student outcomes, thus contributing valuable insights to educational institutions and policymakers aiming to implement effective strategies for student success and academic improvement.
In radar ground stationary or slow-moving target detection, the target detection algorithm based on High-Resolution Range Profile (HRRP) is an important technical approach. Traditional target detection algorithms base...
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ISBN:
(数字)9798331515669
ISBN:
(纸本)9798331515676
In radar ground stationary or slow-moving target detection, the target detection algorithm based on High-Resolution Range Profile (HRRP) is an important technical approach. Traditional target detection algorithms based on HRRP are difficult to apply in target detection tasks under multiple terrains. To this end, this paper proposes a feature detection algorithm based on deep learning for target polarized HRRP. This paper studies an end-to-end target detection algorithm based on prototypical networks. This algorithm utilizes prototypical networks to extract deep features from input HRRP and constructs the feature distribution of clutter. Then, it calculates the distance between the features of the input test sample and the clutter feature center, and compares it with the threshold calculated based on the false alarm probability to complete the detection. Under the backgrounds of bare soil and highway, the algorithm improves by 22% compared to the traditional energy detection algorithm for two types of vehicle targets at a signal-to-clutter ratio (SCR) of 0dB.
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