For successful operation, any video-based Intelligent Transportation systems (ITS) application requires real-time road traffic information or characteristics such as speed, density, average delay, categorization, and ...
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ISBN:
(纸本)9798350362800;9798350362794
For successful operation, any video-based Intelligent Transportation systems (ITS) application requires real-time road traffic information or characteristics such as speed, density, average delay, categorization, and so on. this paper proposes the Multiple Contiguous Virtual Layer (MCVL), a robust and unique vehicle identification framework that estimates any macroscopic traffic characteristics using computer vision algorithms on traffic video. this work mainly focuses on estimating a new parameter known as Histogram Differenced Value (HDV) for MCVL, which uses spatial color information to reveal substantial differences in traffic condition. Several benchmark traffic video datasets are used to test the performance and accuracy of estimations utilizing the proposed framework, withthe results being discussed. the results indicate that using the proposed HDV parameter, the accuracy of the vehicle recognition process is improved withthe combination of lowered computing cost of MCVL.
the increasing adoption of electric vehicles (EVs) has brought about significant challenges in managing the associated charging infrastructure. this paper presents a novel methodology for constructing a knowledge grap...
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ISBN:
(纸本)9798350379860;9798350379877
the increasing adoption of electric vehicles (EVs) has brought about significant challenges in managing the associated charging infrastructure. this paper presents a novel methodology for constructing a knowledge graph tailored to the EV charging ecosystem, integrating multi-source data to enhance decision-making and optimize charging operations. By leveraging the structured and interconnected nature of knowledge graphs, this work addresses critical issues such as data interoperability, scalability, and quality maintenance in the dynamic landscape of EV charging. the proposed knowledge graph framework enables advanced analysis of user behavior, efficient management of charging stations, and improved energy distribution strategies. Deployed on Ontotext GraphDB, the EV charging knowledge graph is demonstrated through real-world usage scenarios, showcasing its potential to significantly impact the operational efficiency and sustainability of EV charging networks.
Euclidean distance (ED) calculation is essential in wireless communication systems and signal processing applications. this paper presents a low-complexity, high-throughput method for computing Euclidean distances. th...
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ISBN:
(数字)9798350359091
ISBN:
(纸本)9798350359107;9798350359091
Euclidean distance (ED) calculation is essential in wireless communication systems and signal processing applications. this paper presents a low-complexity, high-throughput method for computing Euclidean distances. the central idea is to utilize the in-memory computing (IMC) technique, performing computations via an array of memristor devices. Specifically, multiplications and additions are carried out using the inherent properties of memristor devices, following Ohm's law and Kirchhoff's current law. this innovative approach boosts flexibility through memristor programming and enhances processing efficiency by reducing computational complexity and latency, surpassing conventional implementation methods.
SAR (Synthetic Aperture Radar) technology has found extensive utilization in the field of ship detection. yet due to its unique imaging mechanism and noise characteristics, issues such as missed and false detections p...
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ISBN:
(纸本)9798350379860;9798350379877
SAR (Synthetic Aperture Radar) technology has found extensive utilization in the field of ship detection. yet due to its unique imaging mechanism and noise characteristics, issues such as missed and false detections persist in multi-scale ship detection within complex sea regions. To tackle these challenges, multi-scale feature fusion, a proven effective method in image processing, has been broadly adopted in ship detection tasks. this paper introduces a novel multi-scale feature fusion model tailored for detecting ships in SAR imagery. Initially, to capture more intricate and abundant characteristics of smaller targets, a feature fusion layer with a dimension of 160x160 is incorporated into the neck network, ultimately enhancing the detection precision of targets across multiple scales. Furthermore, the RepBlock module is integrated to alleviate computational demands while bolstering the model's generalization performance and prediction accuracy. Finally, we conducted a comprehensive series of experiments on two publicly available SAR datasets: SSDD and HRSID. the results of these experiments underscore that our proposed method delivers impressive performance in detecting ships within SAR images, marking improvements in robustness.
Automatic Modulation Recognition (AMR) is a signal processing technique used to evaluate unknown modulation types without prior information such as link characteristics and modulation parameters. It holds a crucial po...
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ISBN:
(纸本)9798350379860;9798350379877
Automatic Modulation Recognition (AMR) is a signal processing technique used to evaluate unknown modulation types without prior information such as link characteristics and modulation parameters. It holds a crucial position in the management, monitoring, and regulation of wireless communication systems. In this paper, a novel radio recognition scheme based on Coordinate Attention Mechanism Network (CAnet), Multi-Head Attention Mechanism (MHA), and Long Short-Term Memory Networks (LSTM) is proposed for Cognitive Radio (CR) signals, named CA-ML. the proposed scheme utilizes the effective information of signal nonlinearity to identify multiple signals. the computational and time complexities of the proposed scheme are analyzed. For practical scenarios, the scheme can reduce model parameters and data volume while keeping recognition accuracy. the proposed scheme is theoretical analyzed and evaluated through a large number of simulation experiments. Compared with other schemes based on deep learning, the proposed scheme can achieve superior performance and classification accuracy.
Honeypots are tools to help identify attackers' intrusion techniques, patterns and behaviors into IT systems. Among such systems, database management systems (DBMSs) are traditionally targeted software, as they st...
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Pilot's physiological state monitoring and early warning is a key component in ensuring flight safety and mission success. In this study, a pilot physiological parameter monitoring and early warning system was des...
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ISBN:
(纸本)9798350379860;9798350379877
Pilot's physiological state monitoring and early warning is a key component in ensuring flight safety and mission success. In this study, a pilot physiological parameter monitoring and early warning system was designed to collect and analyze key physiological indicators such as respiratory rate, blood pressure and heart rate of pilots in real time. the system uses precise algorithms and efficient data processing methods to monitor and warn pilots of physiological loads in different mission states so that timely intervention can be made when potential risks are detected. the application of the system is expected to significantly improve the safety and security capabilities of pilots and the efficiency of mission execution. At the same time, this study provides a program design idea for pilot operational capability assessment that can provide critical information and data to support military decision-making, training, and equipment configuration. By accurately assessing the operational capabilities of pilots in different phases and situations, it is possible to improve operational effectiveness, optimize the use of resources and increase overall military strength.
Nowadays, posting sarcastic text or visual content on platforms like WhatsApp, Twitter, and Facebook has become a popular style, allowing individuals to avoid directly expressing pessimism, thereby indirectly conveyin...
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ISBN:
(纸本)9798350379860;9798350379877
Nowadays, posting sarcastic text or visual content on platforms like WhatsApp, Twitter, and Facebook has become a popular style, allowing individuals to avoid directly expressing pessimism, thereby indirectly conveying their thoughts or intentions. As more people express their opinions online, the need to develop effective models for accurate multimodal sarcasm detection is growing. However, most existing methods have deficiencies in extracting effective features from non-text modalities, which can result in the neglect of some sarcasm-related contextual information. Additionally, during the fusion phase of different modalities, simply using straightforward feature concatenation may introduce redundant information and noise. To address these challenges, a new model called Dual-Perceiving Network (DPN) was developed. It includes a dual-view perception module, which enables the text modality to fully perceive boththe local and global features of the image modality, thereby obtaining context and background information conducive to constructing inconsistencies. Additionally, it employs an adaptive fusion method to effectively reduce the impact of redundant information. Extensive experiments comparing the proposed DPN with other multimodal sarcasm detection methods demonstrate its superior performance on public datasets.
the domain of Recommender systems (RS) plays a pivotal role in tailoring user experiences across digital platforms, yet it is fraught with pressing privacy issues due to the aggregation of user data in centralized rep...
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ISBN:
(纸本)9798350379860;9798350379877
the domain of Recommender systems (RS) plays a pivotal role in tailoring user experiences across digital platforms, yet it is fraught with pressing privacy issues due to the aggregation of user data in centralized repositories. this study introduces a groundbreaking framework designed to bolster the privacy and security of RS by amalgamating Federated Learning (FL) and Differential Privacy (DP) through an innovative Perturbed User-Based Interaction Matrix. By leveraging FL, our framework decentralizes the model training process, ensuring that user data remains confined to local devices and is not transferred to central servers. Simultaneously, DP is employed to inject controlled noise into the model updates, thereby mitigating the risk of inadvertent data leakage during the aggregation phase. the approach further integrates Laplace noise to obscure individual user contributions, achieving an effective equilibrium between privacy and utility. Empirical evaluation using the MovieLens dataset reveals that our method preserves robust recommendation accuracy, achieving a Precision@10 score of 0.82, while maintaining a commendable privacy budget (epsilon) of 1.0. this research provides a viable pathway for the deployment of privacy-enhanced recommendation systems, successfully addressing privacy concerns without sacrificing the utility of the recommendations.
To address the challenges of image degradation and the limitations of single-domain information, this paper proposes a fusion network that integrates both frequency-domain and spatial-domain information for infrared a...
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ISBN:
(纸本)9798350379860;9798350379877
To address the challenges of image degradation and the limitations of single-domain information, this paper proposes a fusion network that integrates both frequency-domain and spatial-domain information for infrared and visible image fusion. We begin by designing an image fusion network that combines the phase components of the visible image with its amplitude spectrum, effectively processing the high, low and mid-frequency. this method achieves the fusion of salient targets and the preservation of visual quality in the frequency domain. Additionally, an information complementarity network is introduced to address spatial domain differences. this network features an innovative compensation mechanism that addresses the texture disparities between the fused image. Furthermore, the network utilizes implicit degradation estimation during feature fusion. this approach helps the fusion model identify and optimize various potential degradation factors, thereby improving the extraction and aggregation of important content in degraded scenes. the experiment demonstrates our proposed method achieves the superior performance against other comparison methods.
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