With the acceleration of industrialization in China, environmental pollution is becoming more and more serious. Therefore, the environmental protection is imminent. At present, informationengineeringtechnology has b...
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With the acceleration of industrialization in China, environmental pollution is becoming more and more serious. Therefore, the environmental protection is imminent. At present, informationengineeringtechnology has b...
With the acceleration of industrialization in China, environmental pollution is becoming more and more serious. Therefore, the environmental protection is imminent. At present, informationengineeringtechnology has been developing swiftly and applied to many aspects of social life. To apply informationengineeringtechnology to environmental protection has proved to be an inevitable choice to conform to the development trend of the times. This paper will elaborate the internal relationship between informationengineeringtechnology and environmental protection, and summarize the application of informationengineeringtechnology in environmental protection. In addition, this paper will also state the future development trend of the application of informationengineeringtechnology in the field of environmental protection.
Joint communication-caching-computing resource allocation in wireless inland waterway communications enables resource-constrained unmanned surface vehicles(USVs) to provision computation-intensive and latencysensitive...
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Joint communication-caching-computing resource allocation in wireless inland waterway communications enables resource-constrained unmanned surface vehicles(USVs) to provision computation-intensive and latencysensitive tasks forward beyond fifth-generation(B5G) and sixth-generation(6G) era. The power of such resource allocation cannot be fully studied unless bidirectional data computation is properly managed. A novel intelligent reflecting surface(IRS)-assisted hybrid UAV-terrestrial network architecture is proposed with bidirectional tasks. The sum of uplink and downlink bandwidth minimization problem is formulated by jointly considering link quality, task execution mode selection, UAVs trajectory, and task execution latency constraints. A heuristic algorithm is proposed to solve the formulated challenging problem. We divide the original challenging problem into two subproblems, i.e., the joint optimization problem of USVs offloading decision, caching decision and task execution mode selection, and the joint optimization problem of UAVs trajectory and IRS phase shift-vector design. The Karush–Kuhn–Tucker conditions are utilized to solve the first subproblem and the enhanced differential evolution algorithm is proposed to solve the latter one. The results show that the proposed solution can significantly decrease bandwidth consumption in comparison with the selected advanced algorithms. The results also prove that the sum of bandwidth can be remarkably decreased by implementing a higher number of IRS elements.
Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. Howeve...
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Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.
The underlying vertical components represented by vehicleto-everything networks will largely accelerate the advance of the 6th generation wireless communications [1]. In this context, a plethora of Internet-of-Vehicle...
The underlying vertical components represented by vehicleto-everything networks will largely accelerate the advance of the 6th generation wireless communications [1]. In this context, a plethora of Internet-of-Vehicles(IoV) applications have increasingly permeated our daily lives with the development of advocated intelligent connected vehicles [2].
In today’s era, smartphones are used in daily lives because they are ubiquitous and can be customized by installing third-party apps. As a result, the menaces because of these apps, which are potentially risky for u...
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Human emotions are intricate psychological phenomena that reflect an individual’s current physiological and psychological *** have a pronounced influence on human behavior,cognition,communication,and ***,current emot...
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Human emotions are intricate psychological phenomena that reflect an individual’s current physiological and psychological *** have a pronounced influence on human behavior,cognition,communication,and ***,current emotion recognition methods often suffer from suboptimal performance and limited scalability in practical *** solve this problem,a novel electroencephalogram(EEG)emotion recognition network named VG-DOCoT is proposed,which is based on depthwise over-parameterized convolutional(DO-Conv),transformer,and variational automatic encoder-generative adversarial network(VAE-GAN)***,the differential entropy(DE)can be extracted from EEG signals to create mappings into the temporal,spatial,and frequency information in *** enhance the training data,VAE-GAN is employed for data augmentation.A novel convolution module DO-Conv is used to replace the traditional convolution layer to improve the network.A transformer structure is introduced into the network framework to reveal the global dependencies from EEG *** the proposed model,a binary classification on the DEAP dataset is carried out,which achieves an accuracy of 92.52%for arousal and 92.27%for ***,a ternary classification is conducted on SEED,which classifies neutral,positive,and negative emotions;an impressive average prediction accuracy of 93.77%is *** proposed method significantly improves the accuracy for EEG-based emotion recognition.
This study examines the use of experimental designs, specifically full and fractional factorial designs, for predicting Alzheimer’s disease with fewer variables. The full factorial design systematically investigates ...
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Aided by device-to-device(D2D) connections, unmanned aerial vehicle(UAV) can significantly enhance the coverage of wireless communications. In this paper, we consider a data collection system with the assistance of D2...
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Aided by device-to-device(D2D) connections, unmanned aerial vehicle(UAV) can significantly enhance the coverage of wireless communications. In this paper, we consider a data collection system with the assistance of D2D, where two fixed-wing UAVs as aerial base stations cooperatively serve the ground devices. To accommodate more devices, we propose two effective algorithms to establish the multi-hop D2D connections. Then, the user scheduling, UAV trajectory, and transmit power are jointly optimized to maximize the energy efficiency, which is a non-convex problem. Accordingly, we decompose it into three subproblems. The scheduling optimization is first converted into a linear programming. Then, the trajectory design and the transmit power optimization are reformulated as two convex problems by the Dinkelbach method. Finally, an iterative algorithm is proposed to effectively solve the original problem. Simulation results are presented to verify the effectiveness of the proposed scheme.
State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embe...
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State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.
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