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检索条件"机构=Dept. of Computer and Elec. Eng. and Computer Science"

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Design Method of Effective Passband Flat Variable FIR Filters for Weight Measurement 50

Design Method of Effective Passband Flat Variable FIR Filter...

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50th Annual Conference of the IEEE Industrial elec.ronics Society, IECON 2024

作者： Miyata, Toma Sugita, Yasunori Aikawa, Naoyuki Dept. of Elec. Elec. and Info. Eng. Kanagawa Institute of Technology Kanagawa Japan Dept. of Elec. Elec. and Info. Eng. Nagaoka University of Technology Niigata Japan Dept. of Applied Elec. Tokyo University of Science Tokyo Japan

ISBN: (纸本)9781665464543

In order to efficiently eliminate noise in the mass measurement of objects, design methods of FIR filters with high attenuation in a part of the stopband and variable characteristics in the stopband have been proposed. Recently, we have proposed a design method for variable FIR filters with a flat amplitude in the passband and equiripple in the stopband, where the magnitude of the high attenuation can be specified in dB to improve measurement accuracy. In this paper, we present a design method for a variable linear phase-variable FIR filter with a specified attenuation in a part of the stopband. The proposed design method produces a variable filter whose magnitude is greater than or equal to the specified attenuation even if the position of the specified attenuation changes. Finally, we confirm the effectiveness of the proposed method by actually designing a variable filter using the method. © 2024 IEEE.

关键词： FIR filters

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Hybrid Reinforcement Learning for Data Stream Freshness in Autonomous Vehicle Networks

Hybrid Reinforcement Learning for Data Stream Freshness in A...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ebrahimi, Dariush Ghosh, Pronab Alzhouri, Fadi De Oliveira, Thiago E. Alves Wilfrid Laurier University Dept. of Computer Science WaterlooON Canada Lakehead University Dept. of Computer Science Thunder BayON Canada Concordia University Dept. of Elec. & Computer Eng. MontrealQC Canada

ISBN: (纸本)9798350351255

Autonomous vehicles (AVs) are poised to become integral components of intelligent transportation systems, particularly within the framework of future smart cities. Traditional performance metrics such as throughput and latency fall short in adequately addressing the temporal relevance and freshness of data in critical applications such as autonomous driving and accident prevention. Consequently, this paper delves into the challeng. of reducing the Age of Information (AoI) for disseminating data streams within AV-assisted vehicular networks. Given the dynamic nature of the environment, the problem is formulated as a Markov decision process and tackled using Q-learning and DDQN, both prominent reinforcement learning (RL) algorithms. Additionally, a heuristic approach is introduced to augment the performance of the RL algorithms, expediting environmental learning convergence. The numerical findings underscore the effectiveness of the proposed methodologies in minimizing the aggregate AoI across all data streams. © 2024 IEEE.

关键词： Adversarial machine learning

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Facial Expression Recognition on Wild and Multi-Label Faces with Deep Learning

Facial Expression Recognition on Wild and Multi-Label Faces ...

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2023 International Conference on elec.rical, computer, Communications and Mechatronics eng.neering, ICECCME 2023

作者： Han, Hasan Sonmez, Elena Battini Istanbul Bilgi University Dept. of Elec. and Electr. Eng. Istanbul Turkey Istanbul Bilgi University Dept. of Computer Eng. Istanbul Turkey

ISBN: (纸本)9798350322972

The analysis of facial expressions is a powerful tool to decode nonverbal behavior in humans. Due to its importance, several studies have already been done in the past. However, facial expression recognition on wild and multi-label faces is under-investigated also due to the limited number of available databases. This paper fills in the current lack by challeng.ng the RAF-ML dataset and fixing the state-of-the-art performance of 50.5% on the "single label experiment". The proposed method is also tested in a second experiment, suggested by this work, which considers only wild faces having a dominant expression. The benchmark performance for the second trial is 56.1%. The deep-learning algorithms presented in this work are described in detail to facilitate their reproduction. © 2023 IEEE.

关键词： Face recognition

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Loss Function Analysis for DNN Iterative Decoder on Rayleigh Fading Massive Random Access 5

Loss Function Analysis for DNN Iterative Decoder on Rayleigh...

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5th World Symposium on Communication eng.neering, WSCE 2022

作者： Lu, Shan Kamabe, Hiroshi Elec. and Computer Eng. Gifu University Dept. of Elec. Gifu501-1193 Japan

ISBN: (数字)9781665450577

ISBN: (纸本)9781665450577

This work considers user activity detection and estimates the fading coefficient of massive random access on a Rayleigh block-fading AWGN channel. Only a fraction of them is active among a huge number of potential users on the massive random network at each time slot. The active user transmits a unique vector to the base station to identify and estimate the fading coefficient. We consider an iterative decoder for user activity and channel estimation and use two deep neural networks to implement them. A generalized loss function with loss coefficients is proposed for the DNN-based iterative decoder. The trend of the loss coefficients is analyzed from the point of view of NMSE performance, training convergence, and overfitting. The simulation results show that loss coefficients that satisfied the geometric progression with a common ratio of 0.5 can obtain better NMSE performance in all SNR regimes and reduce the overfitting problem in higher SNR. © 2022 IEEE.

关键词： Rayleigh fading

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Using Conceptual Blending to Teach Software Design Principles to Undergraduates 54

Using Conceptual Blending to Teach Software Design Principle...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Gaffar, Ashraf Selim, Mohamed Y. Eulenstein, Oliver Iowa State University Elec. & Comp. Eng. Dept. AmesIA United States Iowa State University Computer Science Department AmesIA United States

ISBN: (纸本)9798350351507

The domain of software design is gaining a long overdue recognition as a vital discipline within software eng.neering, necessitating innovative approaches to its teaching in undergraduate education. Despite the growing importance of software design, academic programs often treat it as a secondary skill, overshadowed by the strong emphasis on coding. Only a few schools offer a design degree or dedicated design courses. This disparity between industry demands and educational practices underscores the need for novel pedagogical strategies. In our innovative work, we discuss a new way of effectively teaching software design-by-analogy for undergraduates to help them rapidly acquire the essential skills needed to design complex software without getting entangled in complex code generation and management. Software design does not necessarily follow the same clear delineation/separation between modules and components naturally apparent in tangible eng.neering domains. We employ 'Conceptual Blending' to help students map their everyday experiences onto software design concepts. The process begins with students analyzing a simple two-arm watch to identify its user interface and create a finite state automaton for its interaction design. Success rates decline as the complexity of the watches increases, underscoring the software design challeng.s. By comparing these exercises to software interfaces, students learn to apply design techniques such as navigation modeling and prototyping, ensuring they can create intuitive, user-friendly software. © 2024 IEEE.

关键词： common sense mapping software design teaching watch

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Detecting Fake Reviews on E-commerce Platforms Using Machine Learning 3

Detecting Fake Reviews on E-commerce Platforms Using Machine...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Vashist, Ansh Keswani, Arul Pareek, Varda Jain, Tarun Manipal University Jaipur Dept. of Computer Science & Eng. India

ISBN: (纸本)9798350354348

Currently, reviews on different e-commerce websites are fake and mostly posted by the company itself to increase the reach or sale of the product itself. These reviews are mostly generated by different bots or written manually. This study provides a novel framework that combines machine learning (ML) and natural language processing (NLP) methods to identify fraudulent reviews on e-commerce platforms. The system is subjected to extensive data preparation and feature extraction, utilising cutting-edge natural language processing techniques to identify subtle linguistic patterns and emotions. By utilising supervised and unsupervised machine learning methods, together with ensemble learning for enhanced precision, the model successfully differentiates between authentic and fraudulent reviews. Experiments conducted on real-world datasets show that the system performs robustly, avoiding false positives, while maintaining high detection rates. This study makes a substantial contribution to improving the integrity of e-commerce platforms by offering workable solutions to prevent fraud and protect customer confidence. © 2024 IEEE.

关键词： Adversarial machine learning

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Use of Ball-based Movement for Omnidirectional Collaborative Carry Robot Systems 17

Use of Ball-based Movement for Omnidirectional Collaborative...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Meyer, Michael Conrad Wang, Yu Eastern Washington University Comp. Sci. and Elec. Eng. Dept. SpokaneWA United States Gonzaga University Computer Science SpokaneWA United States

ISBN: (纸本)9798331530471

Robotics as a field has taken off in recent years, with a significant focus on autonomy. An area that has a great contribution to the autonomous aspect of collaborative robots is the actual movement mechanisms they use. When two robots are used to collaboratively carry an object, their movements are critical in order to maintain stable control of the shared object. There are many factors that can affect the usefulness of one drivetrain, but the key factor for successful collaborative carry is the degrees of freedom and how freely we can control them while other degrees are or are not being controlled. After constructing robots with each of the drivetrains, the testing showed that the proposed ball-based system brought several benefits to collaborative carry robots, with drawbacks in complexity. © 2024 IEEE.

关键词： Collaborative robots

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Blockchain-Driven Decentralized Healthcare Data Management with IPFS and Elasticsearch

Blockchain-Driven Decentralized Healthcare Data Management w...

引用

2024 International Conference on computer, elec.ronics, elec.rical eng.neering and their Applications, IC2E3 2024

作者： Mittal, Kratika Kumar, Rajeev Chauhan, Naveen NIT Hamirpur Dept. of Computer Science & Eng. Hamirpur India

ISBN: (纸本)9798350388534

In response to the pressing challeng.s of security, transparency, and interoperability in healthcare data manage-ment, the proposed model emerges as a pioneering solution. This paper introduces a decentralized ecosystem, powered by blockchain, IPFS, and Elasticsearch, aimed at transforming the conventional healthcare data paradigm. Traditional systems often struggle with security concerns and lack transparency in data transactions. The proposed system addresses this gap by employing a permissioned blockchain, ensuring tamper-proof patient records. Leveraging IPFS for decentralized storage and Elasticsearch for real-time data retrieval, the platform offers a resilient and efficient solution. The significance of the proposed system lies in its user-centric design, enabling patients to manage their data securely and providing healthcare providers with swift, real-time access to critical information. This research highlights the project's novel approach, emphasizing enhanced security, transparent data transactions, and improved accessibility. The proposed system acts as a pioneer in rethinking healthcare data management, introducing a solution that harmoniously integrates with the continuously evolving demands of the digital healthcare landscape. As we navigate the complexities of modern healthcare, the proposed system heralds a new era, introducing innovative methodologies that elevate the standards of security, trans-parency, and interoperability in healthcare data management. © 2024 IEEE.

关键词： elec.ronic health record

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Multimodal Emotion Recognition Exploiting Audio, Video and Textual Features with Crossmodal Attention

Multimodal Emotion Recognition Exploiting Audio, Video and T...

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2024 IEEE International Women in eng.neering (WIE) Conference on elec.rical and computer eng.neering, WIECON-ECE 2024

作者： Das, Avishek Sarma, Moumita Sen Hoque, Mohammed Moshiul Chittagong University of Eng. & Technology Dept. of Computer Science and Eng Chattogram Bangladesh

ISBN: (纸本)9798331535476

Multimodal emotion recognition (MER) aims to identify and understand human emotions by integrating data from diverse sources such as speech signals, facial expressions, and textual content. This process requires addressing the complexities of merging varied data modalities, each of which presents unique challeng.s, including noise, variability, and inconsistencies in how emotional cues are expressed and perceived. This paper proposes a unique approach to MER by utilizing three complementary modalities-audio, video, and text-to classify emotions into distinct categories: anger, fear, joy, and sadness. The paper presents pairwise crossmodal attention to enhance the integration of unimodal features, which are then combined and processed through an artificial n eural n etwork (ANN) for classification. A key contribution of this work is the creation of the MAViT (Multimodal Audio Video Text) dataset, comprising 1,002 multimodal samples. The experimental results highlight the superior performance of the multimodal approach, achieving an F1 score of 0.62. Unimodal results were also analyzed in detail, further demonstrating the advantages of integrating data from multiple modalities to provide a more accurate and nuanced understanding of emotions. © 2024 IEEE.

关键词： Emotion Recognition

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HGDL: Heterogeneous Graph Label Distribution Learning 38

HGDL: Heterogeneous Graph Label Distribution Learning

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Yufei Lian, Heng.He, Yi Zhu, Xingquan Dept. of Elec. Eng. & Computer Sci. Florida Atlantic University Boca RatonFL33431 United States Dept. of Data Science William & Mary WilliamsburgVA23185 United States

Label Distribution Learning (LDL) has been extensively studied in IID data applications such as computer vision, thanks to its more generic setting over single-label and multi-label classification. This paper advances LDL into graph domains and aims to tackle a novel and fundamental heterogeneous graph label distribution learning (HGDL) problem. We argue that the graph heterogeneity reflected on node types, node attributes, and neighborhood structures can impose significant challeng.s for generalizing LDL onto graphs. To address the challeng.s, we propose a new learning framework with two key components: 1) proactive graph topology homogenization, and 2) topology and content consistency-aware graph transformer. Specifically, the former learns optimal information aggregation between meta-paths, so that the node heterogeneity can be proactively addressed prior to the succeeding embedding learning;the latter leverages an attention mechanism to learn consistency between meta-path and node attributes, allowing network topology and nodal attributes to be equally emphasized during the label distribution learning. By using KL-divergence and additional constraints, HGDL delivers an end-to-end solution for learning and predicting label distribution for nodes. Both theoretical and empirical studies substantiate the effectiveness of our HGDL approach. Our code and datasets are available at https://***/Listener-Watcher/HGDL. © 2024 Neural information processing systems foundation. All rights reserved.

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