We propose a coarse symbol timing scheme for wireless orthogonal frequency division multiplexing (OFDM) systems using an optimal correlation-based circular-shifted preamble (CSP). Our proposed scheme is implemented by...
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We propose a coarse symbol timing scheme for wireless orthogonal frequency division multiplexing (OFDM) systems using an optimal correlation-based circular-shifted preamble (CSP). Our proposed scheme is implemented by off-line correlation-ranking and on-line processing. The on-line processing derives an optimal CSP for transmission according to the off-line correlation-ranking, and employs a delayed multiplication operation for symbol timing. The conducted simulation results show that our proposed scheme outperforms the other existing representative schemes over typical multi-path fading channels in terms of mean square error (MSE) while keeping the computational complexity low. IEEE
Artificial Intelligence (AI), with ChatGPT as a prominent example, has recently taken center stage in various domains including higher education, particularly in computer Science and Engineering (CSE). The AI revoluti...
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ISBN:
(数字)9798350394023
ISBN:
(纸本)9798350394030
Artificial Intelligence (AI), with ChatGPT as a prominent example, has recently taken center stage in various domains including higher education, particularly in computer Science and Engineering (CSE). The AI revolution brings both convenience and controversy, offering substantial benefits while lacking formal guidance on their application. The primary objective of this work is to comprehensively analyze the pedagogical potential of ChatGPT in CSE education, understanding its strengths and limitations from the perspectives of educators and learners. We employ a systematic approach, creating a diverse range of educational practice problems within CSE field, focusing on various subjects such as data science, programming, AI, machine learning, networks, and more. According to our examinations, certain question types, like conceptual knowledge queries, typically do not pose significant challenges to ChatGPT, and thus, are excluded from our analysis. Alternatively, we focus our efforts on developing more in-depth and personalized questions and project-based tasks. These questions are presented to ChatGPT, followed by interactions to assess its effectiveness in delivering complete and meaningful responses. To this end, we propose a comprehensive five-factor reliability analysis framework to evaluate the responses. This assessment aims to identify when ChatGPT excels and when it faces challenges. Our study concludes with a correlation analysis, delving into the relationships among subjects, task types, and limiting factors. This analysis offers valuable insights to enhance ChatGPT's utility in CSE education, providing guidance to educators and students regarding its reliability and efficacy.
In the current field of object detection, the YOLOv7 model demonstrates significant advantages in terms of both detection speed and accuracy. However, the model shows deficiencies in focussing on key information, and ...
In the current field of object detection, the YOLOv7 model demonstrates significant advantages in terms of both detection speed and accuracy. However, the model shows deficiencies in focussing on key information, and its loss function's sensitivity to bounding boxes results in suboptimal performance in densely populated environments and small target detection scenarios. To address these issues, this study proposes a mask target detection method based on YOLOv7. By integrating a channel attention mechanism into the YOLOv7 model, this method enhances the model's focus on important information in images, effectively reducing interference from background factors. Additionally, the method incorporates Normalized Wasserstein Distance (NWD) into the loss function to optimize the model's evaluation of small target detection. Experimental verification shows that the improved model maintains its original detection speed while increasing the overall accuracy to 92.1%, a 2.9% improvement over the original YOLOv7 model. It achieves a 3.3% increase in the mAP.5 metric. This indicates that the optimised YOLOv7 model has superior detection capabilities, allowing it to perform mask-wearing detection tasks more rapidly and accurately in natural environments.
Mostly Drugstores do not have any system that exclusively connect to access E-commerce system because the marketing concept of drugstores still use conventional store to sell their products. According to this situatio...
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PurposeThe impact of AI on healthcare is widely recognized there remains a scarcity of studies examining how doctors perceive and approach its use in medicine. This study aims to gather insights from healthcare provid...
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PurposeThe impact of AI on healthcare is widely recognized there remains a scarcity of studies examining how doctors perceive and approach its use in medicine. This study aims to gather insights from healthcare providers in Jordan concerning the advantages of integrating AI into practices, their perspectives on AI applications in healthcare, and their views on the future role of AI in replacing key tasks within health *** survey was conducted among healthcare professionals working at facilities in Jordan. An online questionnaire was used to collect data on demographics, attitudes toward using AI for tasks, and opinions on the benefits of AI adoption. Categorical variables were presented as counts and percentages, while the continuous variables were interpreted as mean and standard deviation. The associations between the determinants and the outcomes were done using one-way ANOVA. Any test with a P-value 0.05 was considered *** total of 612 healthcare professionals participated in the survey with females comprising a majority of respondents (52.8%). The majority of respondents showed optimism about AI’s potential to improve and revolutionize the field, although there were concerns about AI replacing human roles. Generally, physical therapists, medical researchers, and pharmacists displayed openness to incorporating AI into their work routines. Younger individuals aged between 18 and 40 seemed accepting of AI in the domain. A significant portion of participants believed that AI could negatively impact job opportunities and reduce the time needed for diagnosing conditions, but did not find any correlation, between responses and *** conclude, the results of this study suggest that healthcare professionals, in Jordan, hold receptive views on incorporating artificial intelligence in the medical field similar to their counterparts in developed nations. However, there is a concern about the implications of AI, on job stability a
Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the ...
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Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.
This paper introduces a physically-intuitive notion of inter-area dynamics in systems comprising multiple interconnected energy conversion modules. The idea builds on an earlier general approach of setting their struc...
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ISBN:
(数字)9798350373974
ISBN:
(纸本)9798350373981
This paper introduces a physically-intuitive notion of inter-area dynamics in systems comprising multiple interconnected energy conversion modules. The idea builds on an earlier general approach of setting their structural properties by modeling internal dynamics in stand-alone modules (components, areas) using the fundamental conservation laws between energy stored and generated, and then constraining explicitly their Tellegen’s quantities (power and rate of change of power). In this paper we derive, by following the same principles, a transformed state-space model for a general nonlinear system. Using this model we show the existence of an area-level interaction variable, intVar, whose rate of change depends solely on the area internal power imbalance and is independent of the model complexity used for representing individual module dynamics in the area. Given these structural properties of stand-alone modules, we define in this paper for the first time an inter-area variable as the difference of power wave incident to tie-line from Area I and the power reflected into tie-lie from Area II. Notably, these power waves represent the interaction variables associated with the two respective interconnected areas. We illustrate these notions using a linearized case of two lossless inter-connected areas, and show the existence of a new inter-area mode when the areas get connected. We suggest that lessons learned in this paper open possibilities for computationally-efficient modeling and control of inter-area oscillations, and offer further the basis for modeling and control of dynamics in changing systems comprising faster energy conversion processes.
Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches. A recent paper has proposed a paradigm shift calling for hypothesis-driven XAI through a conceptual f...
Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news det...
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ISBN:
(数字)9798331517878
ISBN:
(纸本)9798331517885
Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods.
Brain tumor is a leading cause of death globally and Magnetic Resonance Imaging (MRI) is a powerful tool for its diagnosis. However, the ability to extract more representative features for efficient characterization o...
Brain tumor is a leading cause of death globally and Magnetic Resonance Imaging (MRI) is a powerful tool for its diagnosis. However, the ability to extract more representative features for efficient characterization of brain tumors is still a highly targeted topic. This paper aims to study the power of different deep learning approaches such as traditional Convolutional Neural Network (CNN), transfer learning via three well-known models (VGG16, InceptionV3, and ResNet50), and convolutional auto-encoder in extracting deep representative features for efficient characterization of brain tumors. The performance evaluation was done using two public datasets of MRI images and various benchmarking evaluation metrics. Both traditional CNN and convolutional auto-encoder achieved 100% accuracy, with the auto-encoder producing the lowest loss and highly productive with balanced datasets. Transfer learning models achieved an overall performance of 99% in different evaluation metrics and were found to be preferred with unbalanced datasets.
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