In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety,...
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In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.
In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,ex...
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In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time *** proposed system is based on Commodity WiFi and is easy to *** WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel *** feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.
In response to the urgent need for renewable energy development and variability management due to escalating population growth, rising energy demands, and diminishing natural reserves, this research focuses on optimiz...
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Cervical cancer is one of the most widespread malignancies affecting women’s health worldwide today. However, the task of detection is particularly difficult due to the complex background of the cervical smear, where...
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Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the pot...
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ISBN:
(数字)9798350362244
ISBN:
(纸本)9798350362251
Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satellite networks with their onboard computing capabilities for edge inference. This paper explores LEO scenarios where multiple remote sensing edge AI inference tasks concurrently process data from a single source. However, due to there being parts with the same functions between different AI applications, traditional monolithic edge AI architecture must be deployed repeatedly and falls short in efficiently harnessing the heterogeneous resources of LEO satellite networks. To solve this problem, we utilize the microservice architecture to decouple a single AI application into several independent microservices to reuse these same functions. However, due to the high latency caused by multiple microservices’ communication, we need to design a deployment strategy to fully utilize resources to reduce the service latency. We present a microservice deployment model to minimize the total service latency across all AI applications and meet resource constraints with the constraints of hardware, energy, and memory limitations. This latency optimization problem is rewritten as a Markov decision process (MDP) to effectively deal with the challenge posed by the time-varying transmission rate caused by satellite mobility. To increase the training data utilization, we employ a Proximal Policy Optimization (PPO) based reinforcement learning algorithm to meet the dynamic environment challenge. Finally, we obtain a sub-optimal solution with minimal accuracy loss and an acceptable solution time.
A single study has addressed actuator failure reconstruction for the One-sided Lipschitz (OSL) family of nonlinear systems. The predicted fault vector in that work does not provide any insight into the underlying prob...
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ISBN:
(纸本)9781665482622
A single study has addressed actuator failure reconstruction for the One-sided Lipschitz (OSL) family of nonlinear systems. The predicted fault vector in that work does not provide any insight into the underlying problematic physical characteristics of the system, which is a significant shortcoming. In this work, we offer a way for estimating the incorrect physical parameters of actuators using an adaptive observer strategy. To demonstrate the utility of the suggested method, a numerical example and simulation research are provided.
This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectros...
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This paper explores the application of genetic algorithms to optimize ARX-Laguerre model parameters. It outlines the ARX-Laguerre modeling process and genetic algorithm principles. A fitness function is chosen and the...
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ISBN:
(数字)9798350389838
ISBN:
(纸本)9798350389845
This paper explores the application of genetic algorithms to optimize ARX-Laguerre model parameters. It outlines the ARX-Laguerre modeling process and genetic algorithm principles. A fitness function is chosen and the impact of noise on it is addressed. A new methodology for optimizing Laguerre poles using genetic algorithms is proposed. It is used in modeling a real 2-Degree-of-Freedom helicopter offering valuable insights for real-world applications.
The mining-beneficiation wastewater treatment is highly complex and *** factors like influent quality,flow rate,pH and chemical dose,tend to restrict the effluent effectiveness of miningbeneficiation wastewater *** ox...
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The mining-beneficiation wastewater treatment is highly complex and *** factors like influent quality,flow rate,pH and chemical dose,tend to restrict the effluent effectiveness of miningbeneficiation wastewater *** oxygen demand(COD)is a crucial indicator to measure the quality of mining-beneficiation *** COD concentration accurately of miningbeneficiation wastewater after treatment is essential for achieving stable and compliant *** reduces environmental risk and significantly improves the discharge quality of *** paper presents a novel AI algorithm PSO-SVR,to predict water *** optimization of our proposed model PSO-SVR,uses particle swarm optimization to improve support vector regression for COD *** generalization capacity tested on out-of-distribution(OOD)data for our PSOSVR model is strong,with the following performance metrics of root means square error(RMSE)is 1.51,mean absolute error(MAE)is 1.26,and the coefficient of determination(R2)is *** compare the performance of PSO-SVR model with back propagation neural network(BPNN)and radial basis function neural network(RBFNN)and shows it edges over in terms of the performance metrics of RMSE,MAE and R2,and is the best model for COD prediction of mining-beneficiation *** is because of the less overfitting tendency of PSO-SVR compared with neural network *** proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater *** addition,PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.
The spiking neural networks (SNN) benefits from low power consumption, very good signal-to-noise ratio and the ability to model rigorously the physiology of the biological neural areas. In robotics, the SNN can be use...
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ISBN:
(数字)9798350365955
ISBN:
(纸本)9798350365962
The spiking neural networks (SNN) benefits from low power consumption, very good signal-to-noise ratio and the ability to model rigorously the physiology of the biological neural areas. In robotics, the SNN can be used in different applications including motion control where the neural module drives the actuators based on the information from sensors. The most suitable type of sensing devices in biomimetic robotics are the neuromorphic sensors (NS) with spiking output which can include an optical transmitter for wireless connectivity. Considering that the reduced energy consumption is a critical characteristic for the SNN, in this work we evaluate the possibility of using photovoltaic (PV) panels to power the NS with optical output. The focus is on the recently introduced type of NS with integrated force sensing resistor (FSR) that uses a module based on a light emitting diode (LED) to generate optical pulses. We measured the responses of this NS with the load mass when it is powered by a PV panel, and the results show that the NS operates in nominal conditions despite the slight variations of the used supply voltage. Moreover, the NS is able to transmit optical pulses which frequency depends on the load mass.
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