As cloud data centres expand and provide more services, they consume more energy and cause challenges for the environment. To address this, there is a focus on energy-saving scheduling approaches in cloud computing. T...
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Ensuring precise registration of sensors stands as a pivotal element in integrating LiDAR and inertial data for autonomous driving. This study introduces an innovative three-phase method for extrinsic registration amo...
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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
Fire is the major disaster worldwide, and even worst condition at the village. Hence, the fire detection system or alarm should accurately locate the fire in the shortest amount of time to reduce financial loss and en...
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The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of t...
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The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.
The exponential growth of large, complex, and dynamic datasets, known as big data, required the development of robust analytical techniques beyond conventional tools. This article examines the landscape of big data pr...
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Electrocardiogram (ECG) signals play a very important role in the detection of heart irregularities. Early detection of abnormalities is essential for better patient care and improved medical outcomes. Recent years ha...
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Personal voice assistants have revolutionized how people interact with technology by enabling effortless access to a variety of services by making the user experience natural and more effective using Artificial Intell...
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In present days real-time video surveillance is a very essential aspect of establishing safety, observing traffic, and detecting violence and crimes. Intelligent video surveillance (IVS) is one of the most acknowledge...
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The precise prediction of the fundamental vibrational period for reinforced concrete(RC)buildings with infilled walls is essential for structural design,especially earthquake-resistant *** learning models from previou...
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The precise prediction of the fundamental vibrational period for reinforced concrete(RC)buildings with infilled walls is essential for structural design,especially earthquake-resistant *** learning models from previous studies,while boasting commendable accuracy in predicting the fundamental period,exhibit vulnerabilities due to lengthy training times and inherent dependence on pre-trained models,especially when engaging with continually evolving data *** predicament emphasizes the necessity for a model that adeptly balances predictive accuracy with robust adaptability and swift data *** latter should include consistent re-training ability as demanded by realtime,continuously updated data *** research implements an optimized Light Gradient Boosting Machine(LightGBM)model,highlighting its augmented predictive capabilities,realized through the astute use of Bayesian Optimization for hyperparameter tuning on the FP4026 research data set,and illuminating its adaptability and efficiency in predictive *** results show that the R^(2) score of LightGBM model is 0.9995 and RMSE is 0.0178,while training speed is 23.2 times faster than that offered by XGBoost and 45.5 times faster than for Gradient ***,this study introduces a practical application through a streamlit-powered,web-based dashboard,enabling engineers to effortlessly utilize and augment the model,contributing data and ensuring precise fundamental period predictions,effectively bridging scholarly research and practical applications.
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