检索结果-内蒙古大学图书馆

Australian Microwave Symposium (AMS)

作者： Shaghayegh Chamani Xiaojing Lv Meisam Esfandiari Trevor S Bird Yang Yang School of Electrical and Data Engineering University of Technology Sydney (UTS) Ultimo NSW Global Big Data Technology Centre University of Technology Sydney (UTS) Ultimo NSW

ISBN: (数字)9798350379969

ISBN: (纸本)9798350379976

This paper presents a novel microwave twin-port coaxial probe integrated with a coplanar waveguide (CPW) transmission line for non-invasive skin cancer detection. Operating at approximately 14 GHz, the probe utilizes both reflection and transmission-based analyses. The design was evaluated through full-wave simulations, and the results demonstrate the probe’s capability to differentiate the dielectric properties of various materials.

关键词： Waveguide transitions Reflection Coplanar waveguides Sensors Dielectrics Probes Skin cancer

来源：评论

学校读者我要写书评

暂无评论

M3amba: CLIP-driven Mamba Model for Multi-modal Remote Sensing Classification

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Cao, Mingxiang Xie, Weiying Zhang, Xin Zhang, Jiaqing Jiang, Kai Lei, Jie Li, Yunsong The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The School of Electrical and Data Engineering the University of Technology Sydney Australia

Multi-modal fusion holds great promise for integrating information from different modalities. However, due to a lack of consideration for modal consistency, existing multimodal fusion methods in the field of remote sensing still face challenges of incomplete semantic information and low computational efficiency in their fusion designs. Inspired by the observation that the visual language pre-training model CLIP can effectively extract strong semantic information from visual features, we propose M3amba, a novel end-to-end CLIP-driven Mamba model for multi-modal fusion to address these challenges. Specifically, we introduce CLIP-driven modality-specific adapters in the fusion architecture to avoid the bias of understanding specific domains caused by direct inference, making the original CLIP encoder modality-specific perception. This unified framework enables minimal training to achieve a comprehensive semantic understanding of different modalities, thereby guiding cross-modal feature fusion. To further enhance the consistent association between modality mappings, a multi-modal Mamba fusion architecture with linear complexity and a cross-attention module Cross-SS2D are designed, which fully considers effective and efficient information interaction to achieve complete fusion. Extensive experiments have shown that M3amba has an average performance improvement of at least 5.98% compared with the state-of-the-art methods in multi-modal hyperspectral image classification tasks in the remote sensing field, while also demonstrating excellent training efficiency, achieving a double improvement in accuracy and efficiency. © 2025 IEEE. All rights reserved.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems

arXiv

引用

arXiv 2025年

作者： Hieu, Nguyen Quang Hoang, Dinh Thai Nguyen, Diep N. Alsheikh, Mohammad Abu Kuhn, Carlos C.N. Alem, Yibeltal F. Radwan, Ibrahim School of Electrical Data Engineering University of Technology SydneyNSW2007 Australia School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia Faculty of Science & Technology University of Canberra CanberraACT2617 Australia

Full 3D human pose reconstruction is a critical enabler for extended reality (XR) applications in future sixth generation (6G) networks, supporting immersive interactions in gaming, virtual meetings, and remote collaboration. However, achieving accurate pose reconstruction over wireless networks remains challenging due to channel impairments, bit errors, and quantization effects. Existing approaches often assume error-free transmission in indoor settings, limiting their applicability to real-world scenarios. To address these challenges, we propose a novel deep learning-based framework for human pose reconstruction over orthogonal frequency-division multiplexing (OFDM) systems. The framework introduces a two-stage deep learning receiver: the first stage jointly estimates the wireless channel and decodes OFDM symbols, and the second stage maps the received sensor signals to full 3D body poses. Simulation results demonstrate that the proposed neural receiver reduces bit error rate (BER), thus gaining a 5 dB gap at 10−4 BER, compared to the baseline method that employs separate signal detection steps, i.e., least squares channel estimation and linear minimum mean square error equalization. Additionally, our empirical findings show that 8-bit quantization is sufficient for accurate pose reconstruction, achieving a mean squared error of 5×10−4 for reconstructed sensor signals, and reducing joint angular error by 37% for the reconstructed human poses compared to the baseline. © 2025, CC BY.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

An Attention Guided Lightweight Network-Based Scheme for Anxiety Detection Using Multimodal Analysis of Single-Channel Wearable ECG and RSP Sensor Signals

IEEE Sensors Letters

引用

IEEE Sensors Letters 2025年第5期9卷

作者： Saha, Utsab Sammya, Swojan Datta Saha, Puja Fattah, Shaikh Anowarul Shahnaz, Celia Bangladesh University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka1205 Bangladesh BRAC University School of Data and Sciences Dhaka1212 Bangladesh Washington University Department of Electrical and Systems Engineering St. LouisMO63130 United States

This letter presents an attention-guided, lightweight deep learning (DL) network-based approach that utilizes electrocardiogram (ECG) and respiration (RSP) sensor signals to detect various stages of anxiety. For accurate detection, an effective attention mechanism has been incorporated into our proposed DL baseline architecture with a multiobjective loss function. Our proposed model has proven to be highly effective, with minimal trainable parameters and a very simple structural design, achieving an impressive accuracy of 98.67% on a publicly available benchmark dataset in predicting four different anxiety classes. The proposed model has been thoroughly tested using various data window durations, different loss functions, and attention mechanisms. Finally, it has been demonstrated that the proposed architecture, incorporating adaptive attention and a multiobjective loss function, outperforms existing methods in anxiety stages detection. © 2017 IEEE.

关键词： Wearable sensors

来源：评论

学校读者我要写书评

暂无评论

Hybrid Deepfake Image Detection: A Comprehensive dataset-Driven Approach Integrating Convolutional and Attention Mechanisms with Frequency Domain Features

arXiv

引用

arXiv 2025年

作者： Anan, Kafi Bhattacharjee, Anindya Intesher, Ashir Islam, Kaidul Fuad, Abrar Assaeem Saha, Utsab Imtiaz, Hafiz Department of Electrical and Electronic Engineering BUET Dhaka1205 Bangladesh School of Data and Sciences BRAC University Dhaka1212 Bangladesh

Effective deepfake detection tools are becoming increasingly essential over the last few years due to the growing usage of deepfakes in unethical practices. There exists a diverse range of deepfake generation techniques, which makes it challenging to develop an accurate universal detection mechanism. The 2025 Signal Processing Cup (DFWild-Cup competition) provided a diverse dataset of deepfake images, which are generated from multiple deepfake image generators, for training machine learning model(s) to emphasize the generalization of deepfake detection. To this end, we proposed an ensemble-based approach that employs three different neural network architectures: a ResNet-34-based architecture, a data-efficient image transformer (DeiT), and an XceptionNet with Wavelet Transform to capture both local and global features of deepfakes. We visualize the specific regions that these models focus for classification using Grad-CAM, and empirically demonstrate the effectiveness of these models in grouping real and fake images into cohesive clusters using t-SNE plots. Individually, the ResNet-34 architecture has achieved 88.9% accuracy, whereas the Xception network and the DeiT architecture have achieved 87.76% and 89.32% accuracy, respectively. With these networks, our weighted ensemble model achieves an excellent accuracy of 93.23% on the validation dataset of the SP Cup 2025 competition. Finally, the confusion matrix and an Area Under the ROC curve of 97.44% further confirm the stability of our proposed method. © 2025, CC BY.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

RIS-Assisted ISAC Systems for Robust Secure Transmission With Imperfect Sense Estimation

引用

IEEE Transactions on Wireless Communications 2025年第5期24卷 3979-3992页

作者： Jiang, Chengjun Zhang, Chensi Huang, Chongwen Ge, Jianhua Niyato, Dusit Yuen, Chau Xidian University State Key Laboratory of Integrated Service Networks Xi’an710071 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Nanyang Technological University College of Computing and Data Science Nanyang Avenue 639798 Singapore Nanyang Technological University School of Electrical and Electronics Engineering Nanyang Avenue 639798 Singapore

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems for robust physical layer security (PLS) schemes. Traditionally, eavesdroppers (Eves) typically avoid interacting with the base station, making it challenging to obtain their relevant information, which limits the implementation of PLS. Fortunately, the sensing information obtained by ISAC can contribute to the design of PLS. Therefore, leveraging imperfect sensing estimation and employing dedicated radar signals as artificial noise, we formulate an RIS-assisted joint active and passive beamforming design problem to maximize the sum secrecy rate while satisfying the user’s quality of service constraints, the transmission power constraints, and the sensing signal strength requirements. To make the problem tractable, we first derive the bound for Eve’s channel state information uncertainty region based on security approximations. Subsequently, we employ the S-procedure and the symbolic-deterministic methods to transform the infinite number of inequalities. We then utilize the first-order Taylor expansion, the second-order cone methods, and the successive convex approximation to address the nonconvexity problem, leading to an efficient suboptimal solution obtained by an iterative algorithm. Finally, the simulation results demonstrate the significant potential of the sensing function in enhancing security and the effectiveness of the proposed robust scheme in flexibly balancing communication and sensing quality. © 2002-2012 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Real-Time Speech Emotion Recognition Using Hybrid Transformer Networks for Mental Health Monitoring

Real-Time Speech Emotion Recognition Using Hybrid Transforme...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Durga, P. Janani Basha, C. H. Hussaian Christopher, Nelson Kennedy Babu Senthilkumar, S. Kumar, N. Rathina Kavitha, M. E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India Sr University Department of Electrical and Electronics Engineering Telangana Hanumakonda50637 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Sns College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore641107 India

ISBN: (纸本)9798331509828

This study proposes a hybrid method to multi dialect speech recognition using the transfer learning with transformer-based architecture. The primary objective of this method is to enhance the automatic speech recognition (ASR) across the different dialectal variations of specific language. The transfer learning methods are utilized to modify the base model that was trained on high resource language to manage the multiple dialects with limited labelled data for each dialect. This is achieved by using pre trained transformer methods such as Wav2Vec2.0 and Whisper. An approach which employs dialect specific embeddings is presented. It enables the method to capture data and differentiate between prosodic elements and phonetic that are distinctive to a variety of dialects. data augmentation technique are utilized in order to overcome the limited availability of labelled data for low resource dialects. These methods are utilized to increase the efficiency of training set and enhance the robustness of the method. Evaluation of the method is carried out using multi dialect speech datasets which include Arabic MGB-3 and common voice. These datasets represent a variety of dialects that are spoken in various linguistic regions. The result of this study indicates that the transformer-based method is effective in managing difficulties of dialectal variances while the reliability is reduced on different datasets. Different real-world applications are provided by the proposed method for improving voice enabled applications. Furthermore, this method leads the way for the development of ASR method that are more accurate and independent in locations that have a significant linguistic variation. © 2025 IEEE.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Deep learning at the service of metaheuristics for solving numerical optimization problems

引用

Neural Computing and Applications 2025年 1-36页

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Thieu, Nguyen V. Gandomi, Amir H. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Bangladesh Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 United States Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Faculty of Computer Science Phenikaa University Yen Nghia Hanoi Ha Dong12116 Viet Nam Department of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms. © The Author(s) 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Protocol Design for Irregular Repetition Slotted ALOHA with Energy Harvesting to Maintain Information Freshness

Protocol Design for Irregular Repetition Slotted ALOHA with ...

引用

IEEE Conference on Wireless Communications and Networking

作者： Khac-Hoang Ngo Diep N. Nguyen Thai-Mai Dinh Thi Department of Electrical Engineering (ISY) Linköping University Linkoping SE Sweden School of Electrical and Data Engineering University of Technology Sydney Australia Faculty of Electronics and Telecommunications VNU University of Engineering and Technology Hanoi Vietnam

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We investigate an internet-of-things system where energy-harvesting devices send status updates to a common receiver using the irregular repetition slotted ALOHA (IRSA) protocol. Energy shortages in these devices may lead to transmission failures that are unknown to the receiver, disrupting the decoding process. To address this issue, we propose a method for the receiver to perfectly identify such failures. Furthermore, we optimize the degree distribution of the protocol to enhance the freshness of the status updates. Our optimized degree distribution mitigates the adverse effects of potential transmission failures. Numerical results demonstrate that, despite energy-harvesting constraints, IRSA can achieve a level of information freshness comparable to systems with unlimited energy.

关键词： Potential energy Performance evaluation Protocols Receivers Decoding Batteries Energy harvesting

来源：评论

学校读者我要写书评

暂无评论

Software-Defined Number Formats for High-Speed Belief Propagation

引用

IEEE Transactions on Emerging Topics in Computing 2025年

作者： Molahosseini, Amir Sabbagh Lee, Junkyu Vandierendonck, Hans School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Institute for Analytics and Data Science University of Essex United Kingdom

This paper presents the design and implementation of Software-Defined Floating-Point (SDF) number formats for high-speed implementation of the Belief Propagation (BP) algorithm. SDF formats are designed specifically to meet the numeric needs of the computation and are more compact representations of the data. They reduce memory footprint and memory bandwidth requirements without sacrificing accuracy, given that BP for loopy graphs inherently involves algorithmic errors. This paper designs several SDF formats for sum-product BP applications by careful analysis of the computation. Our theoretical analysis leads to the design of 16-bit (half-precision) and 8-bit (mini-precision) widths. We moreover present highly efficient software implementation of the proposed SDF formats which is centered around conversion to hardware-supported single-precision arithmetic hardware. Our solution demonstrates negligible conversion overhead on commercially available CPUs. For Ising grids with sizes from 100×100 to 500×500, the 16- and 8-bit SDF formats along with our conversion module produce equivalent accuracy to double-precision floating-point format but with 2.86× speedups on average on an Intel Xeon processor. Particularly, increasing the grid size results in higher speed-up. For example, the proposed half-precision format with 3-bit exponent and 13-bit mantissa achieved the minimum and maximum speedups of 1.30× and 1.39× over single-precision, and 2.55× and 3.40× over double-precision, by increasing grid size from 100×100 to 500×500. © 2013 IEEE.

关键词： Digital arithmetic

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：