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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66490 条记录，以下是4941-4950 订阅

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Anti-Aliased Convolutional with Data Augmentation for Speech Emotion Recognition

Anti-Aliased Convolutional with Data Augmentation for Speech...

引用

IEEE International Conference on Consumer Electronics (ICCE)

作者： Linh-Chi Le Hoai-Bao Le Phuong Thi Le Jia-Ching Wang Dept. Computer Science and Information Engineering National Central University Taiwan Dept. Computer Science and Information Engineering Fu Jen Catholic University Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

In this work, we propose a comprehensive framework for speech emotion recognition that combines advanced data augmentation techniques and an innovative deep learning architecture to enhance model performance. Our data augmentation strategies, including noise addition, time stretching, pitch shifting, and time shifting, significantly diversify the training dataset, leading to improved generalization and robustness against real-world variations in speech. We introduce a novel deep learning model architecture based on Anti-Aliased Convolutional (AA-Conv1D) layers, which preserves spatial hierarchies and enhances the model's ability to capture fine-grained emotional nuances in speech signals. Experimental results demonstrate that our model outperforms state-of-the-art methods, achieving a weighted accuracy of 79.38%, which surpasses the performance of existing models, SepTr + LeRaC and EMix-NS.

关键词： Deep learning Training Emotion recognition Accuracy Convolution Speech recognition Speech enhancement Data augmentation Data models Robustness

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Effective Machine Learning Model for Disease Recognition and Categorization of Mango Leaves

Effective Machine Learning Model for Disease Recognition and...

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Sandeep Kumar Bhupesh Gupta Richa Grover Mohit Chhabra Computer Science & Engineering Dept. MM Engineering College MM(DU) Mullana-Ambala India Computer Science & Engineering Dept. Panipat Institute of Engineering & Technology

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

Maintaining the health and production of mango farms depends on the early detection of illnesses in mango trees. Despite the vital role agriculture plays, it remains an industry receiving limited attention from the machine-learning community. A noteworthy challenge is the lack of standardized and openly accessible agricultural datasets, impeding researchers from fully leveraging the potential of advanced computational prediction tools. The study focuses on employing deep learning and image processing techniques for diagnosing prevalent mango leaf diseases in India. The goal is to enhance both the quality and quantity of agricultural production by implementing effective disease control measures upon identifying plant diseases. The automation of plant disease detection proves particularly beneficial, reducing the monitoring efforts required by large farms. Rapid identification of leaf diseases is crucial, given that leaves serve as a plant's primary source of nutrition. Notably, none of the seven different mango leaf illnesses that have been observed in India have ever been the subject of earlier research. In this study, we provide a lightweight Resnet50 model that can reliably classify healthy mango leaves and the seven discovered illnesses on mango leaves. The performance of our proposed lightweight Resnet-50 model is evaluated against many pre-trained models, such as Exception, Resnet101, and VGG16. The findings reveal that the lightweight Resnet-50 model achieves the highest testing accuracy, reaching an impressive 95%. This underscores the model's effectiveness in automating the identification of mango leaf diseases, offering a promising solution for improving agricultural practices in India.

关键词： Productivity Plant diseases Accuracy Plants (biology) Computational modeling Agriculture Reliability

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Exploiting Impact of Eavesdropping Attacks on Secrecy Performance in WPT-based Secure Multi-hop Transmission 14

Exploiting Impact of Eavesdropping Attacks on Secrecy Perfor...

引用

14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Shim, Kyusung An, Beongku Hankyong National University School of Computer Engineering & Applied Mathematics Korea Republic of Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350335385

In this paper, we study the impact of various eaves-dropping attacks on the secrecy performance in wireless power transfer (WPT)-based secure multi-hop transmission. Since each node has a limited power supply, each node harvests energy from the power beacon before data transmission. After then, each node fully consumes the harvested energy for data transmission. Meanwhile, according to the nature of the wireless medium, eavesdroppers can overhear the confidential message at each hop. In order to exploit the relationship between network parameters and secrecy performance, we derive the closed-form expression for secrecy outage probability (SOP) with different eavesdropping attacks, respectively. From the numerical results, we show that the Monte-Carlo simulation and analysis results are tightly matched. Additionally, we evaluate and discuss the impact of the power beacon's transmit power, the number of eavesdroppers, and time switching ratio on the secrecy performance. © 2023 IEEE.

关键词： Monte Carlo methods

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Pseudo-Holographic Display of Plant Models based on Pepper's Ghost Illusion

Pseudo-Holographic Display of Plant Models based on Pepper's...

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Nobuyuki Umezu Satoshi Iijima Dept. of Computer and Information Sciences Ibaraki University Hitachi Japan Dept. of Mechanical Systems Engineering Ibaraki University Hitachi Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

We propose a new display method for visualizing flower models using a pseudo-holographic technique known as the Pepper's ghost illusion. This classic optical illusion technique creates the appearance of floating objects by reflecting them off a transparent panel, thereby producing a convincing visual effect that simulates three-dimensionality. In our approach, we utilized this illusion to display six distinct flower models obtained from a comprehensive photogrammetry archive of various plant species. These models were rendered to overlay onto a transparent glass vase, enhancing the realism of quality. To facilitate user interaction with the displayed models, we incorporated a dept. sensor (Intel D415) to track the user's hand position. A user study revealed that participants were notably attracted to the display, indicating successful capture of their attention. Feedback, however, suggested that the system requires enhancements in usability and dept. perception to improve the overall user experience. Our future work includes addressing these limitations by developing a larger display setup that could accommodate more complex interactions and by integrating motion-parallax techniques to enhance the three-dimensional view. This could be implemented using head-tracking technology, further advancing the effectiveness of the pseudo-holographic display.

关键词： Three-dimensional displays Optical feedback Glass Flowering plants Visual effects User experience Optical sensors Optical reflection Usability Photogrammetry

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A Multi-Wavelength Optical Sensing Framework for Calibration-Free Wearable Blood Pressure Monitoring

A Multi-Wavelength Optical Sensing Framework for Calibration...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tarek Hamid Patricia Flores Jane Byun Xi Chen Haoran Zhang Kyle Quinn Amanda Watson Dept. of Electrical and Computer Engineering University of Virginia Dept. of Anesthesiology University of Virginia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Blood pressure (BP) is a key indicator of cardiovascular health, with hypertension leading to significant morbidity and mortality worldwide. Continuous monitoring of BP is essential for early detection of cardiovascular disease, however current tools are either cumbersome, unreliable, or not suited for long-term use. Traditional cuff-based BP measurement, while reliable, is impractical for continuous monitoring. Recent advances using photoplethysmography (PPG) waveforms offer an alternative, but they face challenges such as limited interpretability, high computational complexity, and susceptibility to motion artifacts. In this paper, we introduce a novel multi-wavelength optical sensing framework designed for calibration-free wearable blood pressure monitoring. Our system utilizes a broad spectrum of wavelengths and interpretable features, combined with machine learning, to estimate systolic (SBP), diastolic (DBP), and mean arterial pressure (MAP). The framework was tested in a proof-of-concept study involving 9 subjects across varied postures and BP levels, demonstrating accuracy comparable to standard cuff-based techniques. This approach eliminates the need for continuous calibration and provides a scalable, interpretable solution for real-time, wearable BP monitoring.

关键词： Accuracy Machine learning Signal processing Blood pressure Robustness Optical sensors Biomedical monitoring Speech processing Monitoring Standards

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Sentiment Analysis of COVID-19 Tweets 1

Sentiment Analysis of COVID-19 Tweets

引用

1st International Conference on Informatics, ICI 2022

作者： Gupta, Isha Chatterjee, Indranath Gupta, Neha Mriirs Faculty of Computer Applications Faridabad India Tomsyong University Dept. of Computer Science Engineering Busan Korea Republic of

ISBN: (纸本)9781665437219

Sentiment analysis is the field of computer science that studies people's behaviour or attitude towards objects, events, organizations, and products. Organizations make use of this to improve their products. This paper aims to study people's behaviour towards COVID 19. We have taken data from Twitter to analyse the sentiments. The results show that people don't have positive sentiments towards this coronavirus. © 2022 IEEE.

关键词： COVID-19

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Generating Automated Assistance Mechanism in Android Programming Self-learning System Using Automatic Testing Tools

Generating Automated Assistance Mechanism in Android Program...

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2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023

作者： Syaifudin, Yan Watequlis Liliana, Dewi Yanti Kaswar, Andi Baso Fatmawati, Triana Funabiki, Nobuo Siradjuddin, Indrazno State Polytechnic of Malang Dept. of Information Technology Malang Indonesia State Polytechnic of Jakarta Dept. of Informatics Engineering Jakarta Indonesia State University of Makassar Dept. of Computer Engineering Makassar Indonesia Okayama University Dept. of Elec. and Comm. Engineering Okayama Japan State Polytechnic of Malang Dept. of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350307771

Nowadays, computer-assisted learning (CAL) has experienced a significant increase in demand for self-learning purposes with the mechanism of automated assistance to facilitate assessments and provide instant feedback to learners. Using automatic testing, test cases can be executed repeatedly, providing informative feedback to assist students in correcting the written code referring to the assignment specifications. This research experiences an automated testing mechanism in an Android programming self-learning system to serve several important functions for learning experience enhancement. By adopting the TDD method and utilizing automatic software testing tools, the testing process combines unit testing with JUnit and integration testing with Robolectric. It ensures the quality and functionality of the application under test based on learning assignment's specifications. Utilizing the assertion methods realizes the code verification referring to expected behavior and potentially produces informative feedback that can be captured to provide automated assistance. Evaluation through the test code implementation in the Basic Application learning topic shows that automatic testing tools cover the automated assistance feature in the validations of project configuration and UI components. © 2023 IEEE.

关键词： Specifications

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Tuning the Feedback Controller Gains is a Simple Way to Improve Autonomous Driving Performance

Tuning the Feedback Controller Gains is a Simple Way to Impr...

引用

UKACC International Conference on Control (CONTROL)

作者： Wenyu Liang Pablo R. Baldivieso Ross Drummond Donghwan Shin Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Dept. of Computer Science University of Sheffield Sheffield UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle's closed loop dynamics. The aim of this paper is to highlight the limitations of this approach; it is shown that re-tuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness.

关键词： Machine learning algorithms Neural networks Control systems Robustness Feedback control Control theory Adaptive control

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Cloudguard: Secure Textual Data Deduplication

Cloudguard: Secure Textual Data Deduplication

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Shaik Yacoob V. Ajay Kumar Jansi Karri M. Dhilleswar Rao Abhishek Bathula Vignesh Kommu Dept. of Computer Science & Engineering Godavari Global University Rajamahendravaram India Dept. of Computer Science & Engineering Godavari Institute of Engineering and Technology Rajahmundry Andhra Pradesh India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

In the digital age, managing the exponential growth of textual data while ensuring security in cloud environments is a significant challenge. DEDUCT introduces a secure data deduplication system utilizing Advanced Encryption Standard (AES) for both encryption and decryption processes, aimed at enhancing storage efficiency and data security. By using AES, the system ensures that textual data stored in the cloud remains confidential and protected from unauthorized access. The proposed framework delineates specific roles for cloud administrators, users, owners, and potential attackers, each with clearly defined functionalities. For instance, owners can securely upload and manage files, authorize user access, and perform file audits, while users can request access to these files under stringent security protocols. The SQL database supports these operations, maintaining the integrity and availability of the data. Studies indicate that data deduplication can reduce storage needs by 90-95%, highlighting the effectiveness of such systems in managing large-scale data efficiently while ensuring robust security measures. This approach not only enhances storage efficiency but also significantly mitigates the risks associated with data breaches, making DEDUCT a vital solution for secure cloud storage environments.

关键词： Cloud computing Protocols Databases Data security Information processing Information age Encryption Servers Standards Secure storage

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Real time air quality monitoring with fog computing enabled IoT system: an experimental study

Real time air quality monitoring with fog computing enabled ...

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2022 IEEE International Conference on Internet of Things and Intelligence systems, IoTaIS 2022

作者： Serdaroglu, Kemal Cagri Baydere, Sebnem Saovapakhiran, Boonyarith Yeditepe University Dept. of Computer Engineering Istanbul Turkey National Electronics and Computer Technology Center Nectec Pathumthani Thailand

ISBN: (数字)9798350396454

ISBN: (纸本)9798350396454

Fog computing has the benefits to handle and reduce data traffic load towards the central cloud in IoT systems. These benefits are facilitated with the help of offloaded fog services that participate in the decision making processes. Besides, fog-based systems have the potential to mitigate scalability bottlenecks that occur in cloud-based systems. In this study, we elaborate on fog based design for a scalable real time air quality monitoring and alert generation system. We established an emulation test bed with real data collected from air quality sensing nodes deployed around Bangkok and vicinity areas to understand the behavior of the proposed solution in terms of waiting time characteristics. We analyzed the performance of the system in two design scenarios;first scenario is built with the proposed fog solution and the second one is the cloud-based approach. We present the performance results revealing the advantages of the proposed model, for the number of air box nodes scaling up to 120 and the number of client nodes up to 200. © 2022 IEEE.

关键词： Fog

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