Rapid developments in digital technology have expedited the dissemination of information on social media platforms like as Twitter, Facebook, and Weibo. Unverified information can create protests and mislead the publi...
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With flexibility in maneuverability and remarkable adaptability, airborne bistatic radar system can obtain excellent detection performance for high-speed target by employing coherent integration. However, range migrat...
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With flexibility in maneuverability and remarkable adaptability, airborne bistatic radar system can obtain excellent detection performance for high-speed target by employing coherent integration. However, range migration (RM) and Doppler frequency migration (DFM) could become serious issues due to the relative motion characteristics of airborne platforms and high-speed target. Meanwhile, various unpredictable factors such as atmospheric turbulence and mechanical issues, etc., resulting in additional motion errors, would have further negative impacts on motion state and flight trajectory of airborne platforms. This phenomenon would serious consequence on coherent integration and target detection. Thus, we make contributions to tackle these limitations and enhance coherent integration and detection performance. First, we establish signal model with high-speed target in three-dimensional (3-D) space for airborne bistatic radar system, along with motion error model which simultaneously includes translational error and rotational error. Next, we articulate range history's mathematical expression and further derive echo signal model. We then propose an improved generalized Radon Fourier transform (IGRFT) method. More specifically, the purpose of IGRFT is achieving joint search for the parameters of the target motion and the parameters of motion error, to ensure high precision parameter estimation and high gain integration. However, the computational complexity surges due to the increasing of search dimensionality. To devise computationally feasible methods for practical applications, we split the high-dimensional maximization process into two disjoint problems by sequentially searching motion parameters and then motion error parameters, and this method is named GRT (generalized Radon transform)-IGRFT. Numerical simulations show that the proposed algorithms can correctly estimate parameters and achieve signal integration and target detection. Finally, we present performanc
In this study, we develop an innovative federated framework for erasable itemset mining to address the challenges of horizontal federated learning in data mining and resolve the shortcomings of the previous algorithm....
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Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's...
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Autism spectrum disease (ASD) is a neuro developmental illness that is both complicated and degenerative. A majority of known approaches use autism detection observation schedule (ADOS), pattern recognition, etc. to d...
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Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing t...
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Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde
Detection of road networks using high-resolution aerial or remote sensing imagery constitutes a significant focus within modern research efforts. Currently, deep learning models demonstrate efficiency to a certain deg...
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Agriculture plays a pivotal role in the global economy, contributing significantly to sustenance and economic growth. However, challenges such as plant diseases pose threats to food security and biodiversity. Early di...
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Marine debris has become a global problem, concerning all marine ecosystems, and humans alike. It's known to cause severe damage to the marine and coastal habitats, since a part of it washes up along the shores an...
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