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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Ning, Xinzhi Zhang, Shutao Xue, Ye Zheng, Xi Shi, Qingjiang Chang, Tsung-Hui Tongji University School of Software Engineering Shanghai China Pengcheng Lab Shenzhen China The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Huawei Technologies Networking and User Experience Lab Shenzhen China

ISBN: (纸本)9781665496261

The design of an effective codebook is crucial for the fifth generation (5G) systems, particularly in the context of massive multiple-input-multiple-output (MIMO) and millimeter wave communication. However, existing codebook design methods that rely on the instantaneous channel impulse response (CIR) face challenges in adapting to dynamic channels that undergo small-scale fading changes. To address this issue, this paper proposes a novel two-stage codebook design framework based solely on the reference signal receiving power (RSRP), which provides large-scale statistical information about environment attenuation. The proposed approach involves two stages: first, the angular power spectrum (APS) is estimated through sparse Bayesian learning (SBL), which uses the statistical relationship between APS and RSRP. Second, an iterative clustering and optimization algorithm is used to determine the optimal codebook based on the estimated APS. Experimental results demonstrate that the proposed codebook design framework with a smaller number of beams can adapt to localized environment well and achieves better performance than the classical DFT codebook. © 2023 IEEE.

关键词： MIMO systems

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Evaluation of Cloud Recovery System for Financial Applications Based on Hadoop 7

Evaluation of Cloud Recovery System for Financial Applicatio...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Lu, Li Lu, Tong Sali, Aduwati School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Department of Computer and Communication Systems Engineering Faculty of Engineering Selangor Serdang43400 Malaysia Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

The rise of cloud-based disaster recovery systems has reshaped disaster recovery practices. Integrated into the Hadoop framework with core components like Hadoop Distributed File System (HDFS), MapReduce, and Yet Another Resource Negotiator (YARN), these systems ensure data integrity and business continuity during crises while enhancing data reliability and operational efficiency, surpassing traditional recovery systems. This study presents a comprehensive comparative analysis of traditional and Hadoop-integrated cloud recovery systems, focusing on hardware architecture and performance. Architecturally, it highlights the stark differences between conventional models and Hadoop-based innovations. The results show that the Hadoop-backed recovery system exhibits remarkable improvement, reducing Recovery Time Objective (RTO) from over 18 seconds to under 7 seconds while preserving data integrity as per Recovery Point Objective (RPO) standards. This finding underscores the academic importance of evaluating cloud-based disaster recovery within the Hadoop context, offering insights into its transformative potential for disaster recovery strategies. © 2024 IEEE.

关键词： data integrity

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Multi-stability analysis and energy control for 4D hyperchaotic system with hidden attractors

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The European Physical Journal Special Topics 2023年第6期233卷 1401-1412页

作者： Ma, Shaojuan Wang, Meibo Ma, Xiaoyan Li, Hufei School of Mathematics and Information Science North Minzu University Yinchuan China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan China

Multi-stability and control of hyperchaotic system are researched in this paper. Firstly, a new 4D hyperchaotic system containing hidden attractors is modeled. Secondly, the coexistence of different attractors is confirmed and transient hyperchaotic phenomena is found as the parameters are changed. Then, the Hamiltonian energy function of the system is calculated and the energy feedback controller is designed to control the system. Finally, numerical simulations are performed to verify the validity of the results.

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CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework for Tumor Prognostic Prediction 39

CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： An, Yajun Chen, Jiale Lin, Huan Liu, Zhenbing Feng, Siyang Zhang, Hualong Lan, Rushi Liu, Zaiyi Pan, Xipeng School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China School of Artificial Intelligence and Big Data Chongqing College of Finance and Economics Chongqing402160 China Southern Medical University Guangzhou510080 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China

ISBN: (纸本)157735897X

Cancer is a leading cause of death worldwide due to its aggressive nature and complex variability. Accurate prognosis is therefore challenging but essential for guiding personalized treatment and follow-up. Previous research often relied on single data sources, missing the opportunity to combine various types of patient information for more comprehensive survival predictions. To address these challenges, we propose a two-stage fusion method named Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework (CA-MLIF). In the first stage, we propose a CA mechanism for real-time feature updates and cross-modal mutual learning to capture rich semantic information. In the second stage, we design a novel multimodal low-rank interaction fusion method for survival prediction. Specifically, we present modal attention mechanism (MAM) for feature filtration, low-rank multimodal fusion (LMF) for model complexity reduction, and optimal weight concatenation (OWC) for maximizing feature integration. Extensive experiments on two public datasets TCGA-GBMLGG and TCGA-KIRC, as well as a multi-center in-house lung adenocarcinoma (LUAD) dataset validate the effectiveness of CA-MLIF, which demonstrate that our method outperforms existing approaches in survival prediction under both pathology-gene fusion and CT-pathology fusion scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning to rank

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Three Preemption Approaches towards EDF Scheduling for Homogeneous Multiprocessors

Three Preemption Approaches towards EDF Scheduling for Homog...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Peng Wu Zhi Li Zhicai Zhang Tao Yan Lu Chen Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China School of Computer Science and Technology Hainan University Haikou China

When scheduling a set of real-time tasks, researchers can choose between preemptive and non-preemptive algorithms. However, these algorithms each have their own advantages and drawbacks, necessitating specific analysis in different contexts. In contrast to these two extremes, researchers have proposed the concept of limited-preemption algorithms. Such limited-preemption algorithms serve as feasible alternatives to the former two approaches. Currently, research on this algorithm is based on a homogenous computing *** this paper, we investigate the fixed-preemption point method in limited-preemption algorithms. We compare the impact of limited-preemption EDF algorithm on the performance of homogenous multiprocessors under different fixed-preemption strategies. We introduce a preemptive strategy where high-priority tasks can only be preempted at the most suitable fixed-preemption points, and we provide an effective schedulability analysis for this strategy. The EDF scheduling algorithm based on the best-fit fixed preemption strategy is called the Best Preemptive EDF(BP-EDF). We have introduced an effective schedulability analysis for BP-EDF. Experimental results indicate that the strategy with the most suitable fixed-preemption points demonstrates superior schedulability compared to the other two strategies. Furthermore, its performance in terms of task preemption frequency closely approaches that of the strategy employing fixed-preemption at the lowest priority points.

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Zero-shot multi-speaker accent TTS with limited accent data

Zero-shot multi-speaker accent TTS with limited accent data

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Mingyang Zhang Yi Zhou Zhizheng Wu Haizhou Li Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore

In this paper, we present a multi-speaker accent speech synthesis framework. It can generate accented speech of unseen speakers using only a limited amount of accent training data. Without relying on the accent lexicon, the proposed network is able to learn the accent phoneme embedding via a simple model adaptation. In specific, a standard multi-speaker speech synthesis is first trained with native speech. Then, an additional neural network module is appended for adaptation to map the native speech to the accented speech. In the experiments, we have synthesized English speech with Singapore and Hindi accents. Both objective and subjective evaluation results successfully confirm that our proposed technique with phoneme mapping is effective to generate high-quality accent speech for unseen speakers.

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LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement

arXiv

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arXiv 2024年

作者： Jiao, Siwen Fang, Yangyi Peng, Baoyun Chen, Wangqun Veeravalli, Bharadwaj Yang, Xulei National University of Singapore Singapore Tsinghua University China Agency for Science Technology and Research Singapore Advanced Institute of Big Data Beijing China

Recent advancements in Visual Language Models (VLMs) have made them crucial for visual question answering (VQA) in autonomous driving, enabling natural human-vehicle interactions. However, existing methods often struggle in dynamic driving environments, as they usually focus on static images or videos and rely on downsampling to manage computational costs. This results in the loss of critical details and the difficulty in effectively integrating spatial and temporal information, undermining fine-grained perception and temporal coherence essential for effective decision-making. To tackle these challenges, we introduce LaVida Drive, a novel and efficient VQA framework for autonomous driving. LaVida Drive seamlessly integrates temporal data while maintaining high-resolution inputs for detailed visual perception. It optimizes spatial processing by retaining high-resolution data for intricate details and using lower-resolution inputs for temporal analysis to focus on motion-related features, thereby boosting computational efficiency. Our method achieves an impressive 168-fold token compression while attaining optimal performance, a significant improvement over traditional approaches. The core of LaVida Drive consists of two modules: the Query-aware Token Selection module and the Spatial-Temporal Token Recovery and Enhancement module. The former dynamically selects the most relevant visual tokens based on semantic alignment with the input query, reducing the token count from high-resolution spatial input. The latter ensures smooth and coherent interactions between spatial and temporal information, preserving contextual continuity across frames. Extensive experiments on various autonomous driving question-answering benchmarks show that LaVida Drive significantly reduces visual tokens, enhances efficiency, and improves overall performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

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Defence technology（防务技术） 2022年第12期18卷 2150-2159页

作者： Jing-Tao Sun Jing-Ming Li Qiu-Yu Zhang School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China School of Management Science and Engineering Anhui University of Finance and EconomicsBengbuAnhui230030China School of Computer and Communication Lanzhou University of TechnologyLanzhouGansu730050China

Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,***,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of ***,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature ***,the events knowledge graph is constructed by means of sparse representation and l1 *** experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding *** experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.

关键词： Text event mining Knowledge graph of events Mutual information among neighbor domains Sparse representation

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Design and Promotion of Cost-Effective IOT-Based Heart Rate Monitoring

Design and Promotion of Cost-Effective IOT-Based Heart Rate ...

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2022 International Conference on Cloud Computing, Internet of Things, and Computer Applications, CICA 2022

作者： Moshayedi, Ata Jahangir Kolahdooz, Amin Roy, Atanu Shuvam Rostami, Seyyed Ali Latifi Xie, Xiaoyun School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China Faculty of Technology School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou China

ISBN: (纸本)9781510656673

The term telemedicine was first used in the 1920s, although used many years ago and has continued to evolve today. Medical diagnoses usually require visual information, but remote display systems have recently become a special place due to the constant unavailability of the treating physician or the remoteness of medical centers and the constant need of some patients for round-the-clock care. In this article, an Arduino-based heart rate information system is designed and implemented. Due to the reasonable price and easy accessibility of the created system in the fraction, it has many applications. The results of the designed system showed the system's capabilities to track and know the person's heart rate. © 2022 SPIE.

关键词： Cost effectiveness

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Enhancing Alzheimer Disease Diagnosis: Integrating Gabor Convolutional Neural Network with Conventional CNNs 2

Enhancing Alzheimer Disease Diagnosis: Integrating Gabor Con...

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2nd International Conference on Intelligent Perception and Computer Vision, CIPCV 2024

作者： Addo, Daniel Al-Antari, Mugahed A. Zhou, Shijie Ashalley, Eric Muoka, Gladys Wavinya Nartey, Obed T. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Sejong University Seoul AISSLab Department of Artificial Intelligence and Data Science Korea Republic of University of Electronic Science and Technology of China Institute of Fundamental and Frontier Science Chengdu610054 China ChengduUniversity of Technology of China Sino-British Collaborations Erxianqiao Campus Chengdu610059 China

ISBN: (纸本)9798350360097

Alzheimer's disease (AD) presents a formidable challenge in healthcare, characterized by its progressive nature and the absence of definitive diagnostic markers. This study introduces an innovative method to enhance AD detection by integrating advanced technology with established techniques. We have developed a hybrid model that combines Gabor Convolutional Neural Networks (GCNN) with traditional Convolutional Neural Networks (CNNs) to improve feature extraction from MRI images, thereby achieving a more precise diagnosis of AD. The efficacy of our model is evidenced by its high-performance metrics: an accuracy of 98.58%, a sensitivity of 98.17%, a precision of 97.11%, and robust empirical support. This approach benefits from the use of augmented data and the unique capabilities of GCNN. Comprehensive evaluations, including confusion matrix analysis and ROC curve assessments, confirm the model's proficiency in accurately classifying various stages of dementia. By enhancing diagnostic accuracy, this innovative approach holds the potential to improve patient outcomes, facilitate better care, and enable more timely interventions in the treatment of Alzheimer's disease. © 2024 IEEE.

关键词： Convolutional neural networks

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