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15th International Conference Elektro, ELEKTRO 2024

作者： Zgank, Andrej Donaj, Gregor Vlaj, Damjan University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350372359

This paper analyzes how emotional speech influ-ences the quality assessment process. The objective metrics for speech quality assessment are usually evaluated with neutral speech recordings, but human interaction frequently involves emotions. Those can modify speech signals' acoustic and phonetic characteristics largely, which are essential from the objective speech quality assessment perspective. The proposed analysis was carried out with the English and Slovenian Interface emotional speech database, which includes two neutral and six emotional speaking styles. Speex codec was used to simulate the degradations, which were then assessed with the lTV - T P.862 PESQ objective metric. The results' analysis showed that in both languages surprise, in English fear, and in Slovenian disgust influenc the speech quality assessment significantly. © 2024 IEEE.

关键词： Quality control

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An Efficient Federated Learning Framework for IoT Intrusion Detection 100

An Efficient Federated Learning Framework for IoT Intrusion ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Chen, Yushen Fang, Fang Wang, Boyu Zhang, Lan Western University Department of Computer Science London Canada Western University Department of Electrical and Computer Engineering London Canada Clemson University Department of Electrical and Computer Engineering Clemson United States

ISBN: (纸本)9798331517786

The exponential growth of the Internet of Things (IoT) ecosystems has raised significant cybersecurity concerns. Deep learning (DL)-based methods have shown promising performance in detecting potential cyber threats in IoT networks. However, as these methods often involve data centralization, they can pose serious data privacy issues for IoT users and increase the communication burden of local networks. Federated learning (FL), as a distributed learning paradigm, enables privacy-preserving training of IoT intrusion detection models by requiring only model updates from IoT devices. However, the resource-constrained nature of IoT devices can significantly decrease FL training efficiencies, such as increased training latency and delayed convergence speed. Moreover, the data heterogeneous issues of IoT devices can also impact the accuracy and robustness of the trained model. To address these challenges, we propose an efficient FL framework, FedKD-Prox, based on federated proximal (FedProx) and knowledge distillation (KD). To improve the prediction accuracy within a limited time budget, the proposed framework aims to efficiently exploit the computation capability of the IoT trainers, reduce the communication overhead of FL, and alleviate the impact of heterogeneous data issues. The simulation results show that FedKD-Prox achieves higher accuracy and improves the robustness of the trained intrusion detection model. © 2024 IEEE.

关键词： Federated learning

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Automatic Waterfowl and Habitat Detection using Drone Imagery and Deep Learning

Automatic Waterfowl and Habitat Detection using Drone Imager...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Zhao, Andrew Fratila, Andrea Zhang, Yang Zhai, Zhenduo Liu, Zhiguang Shang, Yi University of Illinois Computer Science Department United States University of Missouri Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350324136

The integration of drone technology and Artificial Intelligence (AI) has opened up new possibilities for wildlife conservation and habitat monitoring. In this paper, we present a new system for efficiently and accurately analyzing waterfowl populations and classifying their habitats over large natural areas using drone imagery and deep learning (DL). Given a sequence of drone images captured by a drone along a flight path, the system utilizes customized deep learning models for waterfowl detection and counting, Meta's SAM for image segmentation and customized deep learning models for segment classification, and ChatGPT to generate text-based survey reports. Several image overlap detection methods were developed and compared with. Our experimental results show accurate waterfowl and habitat detection results and improvement over previous work, providing efficient and accurate data analysis for wildlife conservation efforts. © 2024 IEEE.

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Time-delay Correction Control Strategy for HVDC Frequency Regulation Services

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2027-2037页

作者： Yuqing Dong Kaiqi Sun Jinning Wang Shunliang Wang He Huang Tianqi Liu Yilu Liu School of Electrical Engineering Shandong UniversityJinan 250061China School of Electrical Engineering Shandong UniversityJinan 250061China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA.

With the advancements in voltage source converter(VSC)technology,VSC based high voltage direct current(VSCHVDC)systems provide system operators with a prospective approach to enhance system operating stability and *** addition to long-distance transmission,the VSC-HVDC system can also provide multiple ancillary services,such as frequency regulation,due to its power ***,if a time delay exists in the control signal,the VSC-HVDC system may bring destabilizing influences to the system,which will decrease the system resilience under the *** order to reduce control deviation caused by time delay,in this paper,a small signal model is first conducted to analyze the impact of time delay on system *** a time-delay correction control strategy for HVDC frequency regulation control is developed to reduce the influence of the time *** control performance of the proposed time-delay correction control is verified both in the established small signal model and the runtime simulation in a modified IEEE 39 bus *** results indicate that the proposed time-delay correction control strategy shows significant improvement in system stability.

关键词： Frequency regulation control HVDC small signal analysis time delay time-delay correction control strategy

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Detection of Novel Cyberattacks Using Multi-Binary Classifiers 34

Detection of Novel Cyberattacks Using Multi-Binary Classifie...

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34th Annual International Conference on Collaborative Advances in Software and Computing, CASCON 2024

作者： Gaudreault, Jean-Gabriel Branco, Paula School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada

ISBN: (纸本)9798331504830

Intrusion Detection Systems (IDSs) are an essential part of modern-day cybersecurity defenses, enabling the detection of potential malicious activities within network and computer systems. Although multiple techniques have been employed to implement IDSs, supervised machine learning approaches have historically proven particularly effective due to their capacity to leverage large amounts of data and maintain a generally low false alarm rate. However, a significant limitation of these approaches is their inability to detect novel threats, such as zero-day attacks, that were not present in the training data. In this study, we introduce a novel supervised approach based on binary classifiers that can detect these previously unseen attacks while also maintaining a high detection rate and low false alarm rate through the use of a filtering mechanism. We empirically demonstrate the effectiveness of our proposed technique on multiple intrusion detection datasets, showing that it is able to detect unseen attacks and perform multiclass classification while outperforming traditional supervised methods. © 2024 IEEE.

关键词： Network intrusion

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Near-field Source Localization in 3-D Using Two Parallel Centrally Symmetric Unfold Coprime Array

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4738-4749页

作者： Chen, Hua Li, Junjie Yang, Songjie Liu, Wei Eldar, Yonina C. Yuen, Chau Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu610054 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel Nanyang Technological University School of Electrical and Electronics Engineering Singapore639798 Singapore

Most near-field (NF) localization algorithms cannot deal with the underdetermined case, while those which can are computationally expensive due to employment of fourth-order cumulants. In this work, a low-complexity solution is provided for underdetermined three-dimensional (3-D) NF localization, by employing second-order statistics with a tailored array configuration named two parallel centrally symmetric unfold coprime (TPSC) array. Its implementation can be divided into three stages. Firstly, the proposed algorithm constructs two cross-correlation matrices based on the received array data, which eliminates the non-linear range-related information of NF signals. Secondly, covariance and vectorization operations are applied to these two cross-correlation matrices to form a virtual array with extended aperture. Finally, the two-dimensional (2-D) angle parameters are estimated by the sparse and parametric approach (SPA) and a phase retrieval operation, and then the one-dimensional (1-D) range parameter is achieved by the multiple signal classification (MUSIC) algorithm. One specific feature is that the estimated angle and range parameters are matched automatically. An analysis of the properties of the TPSC array is provided, and an optimal parameter configuration is derived, given that the total number of array elements is fixed. Simulation results demonstrate that the designed TPSC array can achieve underdetermined 3-D NF localization, and deliver enhanced estimation capabilities, surpassing those of established algorithms. © 2002-2012 IEEE.

关键词： Multiple signal classification

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A Grid-Based Random Erasing Approach for Enhanced Image Classification 4

A Grid-Based Random Erasing Approach for Enhanced Image Clas...

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4th IEEE International Conference on Data science and computer Application, ICDSCA 2024

作者： Li, Qingyi School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (纸本)9798350368239

In modern image classification tasks, the data is insufficient due to time or money cost constraints, which affects the generalization and accuracy of the model. This paper proposes a random erasure method based on gridded images, aiming to expand the dataset to improve the generalization ability and classification accuracy of the model while reducing the damage to the original structure of the image caused by continuous erasure. This paper uses YOLOv8n-cls, YOLOv8mcls, and ResNet50 as models and selects CIFAR-10, CIFAR-100, FER2013, and mini-ImageNet as datasets for comparative experiments. The Top-1 accuracy, Top-5 accuracy, and loss function curves on the validation set are used to analyze the effect of random erasing on each model's performance on various datasets. The findings demonstrate that lightweight models greatly increase accuracy on simple datasets like CIFAR-10. However, to effectively use the enhancing effect of random erasing on complicated datasets, more sophisticated models are required. This study shows the impact of random erasing based on gridded images on improving the generalization ability and classification accuracy of the model in image classification tasks. It points out the limitations of this method and potential future optimization directions to better handle more complex classification tasks. ©2024 IEEE.

关键词： Image classification

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JPEG Reversible Data Hiding via Block Sorting Optimization and Dynamic Iterative Histogram Modification

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IEEE Transactions on Multimedia 2025年 27卷 3729-3743页

作者： Li, Fengyong Wang, Qiankuan Cheng, Hang Zhang, Xinpeng Qin, Chuan Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Fuzhou University School of Mathematics and Statistics Fuzhou Fujian350108 China Fudan University School of Computer Science Shanghai200433 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

JPEG reversible data hiding (RDH) refers to covert communication technology to accurately extract secret data while also perfectly recovering the original JPEG image. With the development of cloud services, a large number of private JPEG images can be efficiently managed in cloud platforms by embedding user ID or authentication labels. Nevertheless, data embedding operations may inadvertently disrupt the encoding sequence of the original JPEG image, resulting in severe distortion of the host image when it is re-compressed to JPEG format. To address this problem, this paper proposes a new JPEG RDH scheme based on block sorting optimization and dynamic iterative histogram modification. We firstly design a block ordering optimization strategy by combining the number of zero coefficients and the quantization table values of non-zero coefficients in a DCT block. Subsequently, a dynamic iterative histogram modification scheme is proposed by considering the local features and embedding capability of histograms generated from different texture images. According to the given payloads, we introduce different parameters to control the iterations of two-dimensional histogram and then adaptively generate the optimal histogram modification mapping, which can realize low JPEG file size increments by guaranteeing most of the AC coefficients unchanged as much as possible. Numerous experiments have shown that our scheme can achieve an effective balance among embedding capacity, visual quality, file size increment, computational complexity, and outperforms the state-of-the-arts in terms of the above metrics. © 1999-2012 IEEE.

关键词： Embeddings

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A Custom Generative AI Chatbot as a Course Resource

A Custom Generative AI Chatbot as a Course Resource

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2024 ASEE Annual Conference and Exposition

作者： Ai, Yutong Baveja, Maya Girdhar, Akanksha O'Dell, Melina Deorio, Andrew Department of Electrical Engineering and Computer Science University of Michigan United States

Generative AI (GenAI) chatbots have taken the world by storm with their ability to summarize information and produce complex natural language *** the same time, the demand for computer science education has grown *** developed a custom GenAI chatbot and released it to over 700 students to assist with a course programming *** tool was trained on the project specification, lecture content, lab materials, and past course forum posts relevant to the *** course is a high-enrollment upper-level computer science elective at a large public research *** project is a simple distributed system using processes, threads, and *** goal of the tool was to provide an additional course resource that supplies valuable information to students comparable to advice they would receive from course *** measured the effectiveness of our tool with a student survey, evaluation by course instructors, and comparison with a state-of-the-art general purpose *** overall survey data indicated high rates of correctness and helpfulness in the Bot *** found that hallucination was not common, and most incorrect responses were identifiable by *** Bot also performed better than general purpose bots for project-specific *** experience can provide insights for faculty using GenAI to assist students in their courses.A customized chatbot can be helpful to students and augment traditional course resources. © American Society for engineering Education, 2024.

关键词： Students

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Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach 18

Contactless Fingerprint Biometric Anti-Spoofing: An Unsuperv...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Adami, Banafsheh Hosseinzadehketilateh, Mohammad Reza Karimian, Nima West Virginia University Computer Science and Electrical Engineering United States

ISBN: (纸本)9798350364132

Contactless fingerprint recognition offers a higher level of user comfort and addresses hygiene concerns more effectively. However, it is also more vulnerable to presentation attacks, such as photo-paper, paper printout, and various display attacks, making it more challenging to implement in biometric systems compared to contact-based modalities. Limited research has been conducted on presentation attacks in contactless fingerprint systems, and these studies have encountered challenges in terms of generalization and scalability since both bonafide samples and presentation attacks are utilized during the training model. Although this approach appears promising, it lacks the ability to handle unseen attacks, which is a crucial factor for developing PAD methods that can generalize effectively. We introduced an innovative anti-spoofing approach that combines an unsupervised autoencoder with a convolutional block attention module to address the limitations of existing methods. Our model is trained exclusively on bonafide images without exposure to any spoofed samples during the training phase. It is then evaluated against various types of presentation attack images in the testing phase. The scheme we proposed has achieved an average BPCER of 0.96% with an APCER of 1.6% for presentation attacks involving various types of spoofed samples. © 2024 IEEE.

关键词： Biometrics

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