According to the problems of the existing emotion recognition algorithms, which are not rich in emotion information, weak in feature representation and not high in recognition accuracy, this paper proposes a multimoda...
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The proceedings contain 23 papers. The topics discussed include: solar powered auto irrigation monitoring system with plant health indication using imageprocessing;large language model and artificial intelligence bas...
ISBN:
(纸本)9798331508692
The proceedings contain 23 papers. The topics discussed include: solar powered auto irrigation monitoring system with plant health indication using imageprocessing;large language model and artificial intelligence based human conversation agent;to enhance graph-based retrieval-augmented generation (RAG) with robust retrieval techniques;automatic timetable generation using neural networks trained by genetic algorithms;assessment of efficient and cost-effective vehicle detection in foggy weather;enhanced detection and prevention of SQL injection and cross-site scripting attacks in web applications: analyzing algorithms and threat modeling approaches;efficient duplicate question detection;and a comprehensive survey on computing services to detect stress and anxiety using smart devices.
Vehicle positioning algorithms are essential for improving traffic management and safety by accurately locating vehicles in real-time, and, thus, minimizing congestion and accidents. They also support the development ...
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ISBN:
(纸本)9798350364309;9798350364293
Vehicle positioning algorithms are essential for improving traffic management and safety by accurately locating vehicles in real-time, and, thus, minimizing congestion and accidents. They also support the development of advanced driver assistance systems and autonomous vehicles, relying on precise positioning data for safe navigation. One of the solutions involves using imageprocessingalgorithms, which can have two approaches. One approach is decentralized, in which each vehicle performs its own computing steps and determines its position concerning the other nearby vehicles. The second approach, proposed in this paper, is centralized, where each vehicle sends data to a server that uses cloud computing to process all the data in real-time. As such, vehicles can create a more comprehensive view of the driving conditions in the area by using either of these two approaches, which can help them anticipate potential hazards and make more informed decisions.
In the context of healthcare and human-computer interaction., this research study provides a thorough analysis of sophisticated computational algorithms for data classification, picture processing, and disease predict...
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To concurrently address the challenges of automatic modulation recognition (AMR) and modulation parameter estimation (MPE) in radar signals, we introduce a multi-task learning (MTL) network architecture designed for i...
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ISBN:
(纸本)9798350351040;9798350351033
To concurrently address the challenges of automatic modulation recognition (AMR) and modulation parameter estimation (MPE) in radar signals, we introduce a multi-task learning (MTL) network architecture designed for intra-pulse multi-parameter recognition through automatic noise reduction. The proposed approach initiates by generating time -frequency image (rn) directly from noisy time-domain signal using a neural network. To effectively capture relevant features from TFls, we employ a VGG-like network Subsequently, the multitask learning framework is utilized to achieve AMR and MPE simultaneously. Four typical intra-pulse modulated signals were simulated in the experiments, simulation results verify the effectiveness and reliability of the proposed algorithms.
This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient descent algorithms. Instead of concentra...
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ISBN:
(纸本)9781713899921
This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient descent algorithms. Instead of concentrating solely on the generalization error of the DNN post-training, we present a novel perspective for analyzing generalization error by investigating the contribution of each update step to the change in generalization error. This perspective enable a more direct comprehension of how the learning trajectory influences generalization error. Building upon this analysis, we propose a new generalization bound that incorporates more extensive trajectory information. Our proposed generalization bound depends on the complexity of learning trajectory and the ratio between the bias and diversity of training set. Experimental observations reveal that our method effectively captures the generalization error throughout the training process. Furthermore, our approach can also track changes in generalization error when adjustments are made to learning rates and label noise levels. These results demonstrate that learning trajectory information is a valuable indicator of a model's generalization capabilities.
With the continuous development of radar jamming methods and styles, radar systems are facing more and more complex electromagnetic environment, resulting in limited suppression performance through spatial anti-jammin...
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The paper presents a description of the developed algorithm for changing the size of a multi-element aperture of a recursive-separable five-stage filter for processing digital images generated by specialized optical s...
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The indoor imaging visible light positioning (VLP) technology based on the image sensor (IS) utilizes existing indoor lighting infrastructure to provide high-precision indoor positioning services. However, due to the ...
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Over the past decade, advancements in cellular phone technology have drastically transformed mobile phones from mere communication devices into powerful mini-computers. The integration of high-quality cameras with sma...
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