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Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks

International Journal of Intelligent Networks 2024年第1期5卷 255-266页

作者： Ahmad, Rizwana Anwar, Dil Nashin Bany Salameh, Haythem Elgala, Hany Ayyash, Moussa Almajali, Sufyan El-Khazali, Reyad Electronic and Electrical Engineering Department University of Cambridge United Kingdom Electrical and Computer Engineering Department University at Albany-State University of New York United States Artificial Intelligence Research Center Al Ain University Al Ain United Arab Emirates Telecommunications Engineering Department Yarmouk University Irbid Jordan Department of Computing Information and Technology Chicago State University ChicagoIL United States Department of Computer Science Princess Sumaya University for Technology Amman Jordan Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates

Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy. © 2024 The Authors

关键词： Data transfer

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Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

Genre Classification of Bangla Poem Using Machine Learning a...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Pasha, Syed Tangim Islam, Ashraful Rahman, Mohammed Masudur Ahmed, Eshtiak Ahmed Foysal, Md. Ferdouse Zahangir Alam, Md Daffodil International University Department of Computing and Information System Bangladesh Independent University Center for Computational and Data Sciences Bangladesh Independent University Department of Computer Science and Engineering Bangladesh International Standard University Department of Computer Science and Engineering Bangladesh Daffodil International University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350337617

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%. © 2023 IEEE.

关键词： Semantics

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MULTI-CROSSRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction 24

MULTI-CROSSRE A Multi-Lingual Multi-Domain Dataset for Relat...

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24th Nordic Conference on Computational Linguistics, NoDaLiDa 2023

作者： Bassignana, Elisa Ginter, Filip Pyysalo, Sampo van der Goot, Rob Plank, Barbara Department of Computer Science IT University of Copenhagen Denmark TurkuNLP Department of Computing University of Turku Finland MaiNLP Center for Information and Language Processing LMU Munich Germany

ISBN: (纸本)9789916219997

Most research in Relation Extraction (RE) involves the English language, mainly due to the lack of multi-lingual resources. We propose MULTI-CROSSRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. MULTI-CROSSRE is a machine translated version of CrossRE (Bassignana and Plank, 2022a), with a sub-portion including more than 200 sentences in seven diverse languages checked by native speakers. We run a baseline model over the 26 new datasets and—as sanity check—over the 26 back-translations to English. Results on the back-translated data are consistent with the ones on the original English CrossRE, indicating high quality of the translation and the resulting dataset. © 2023 Association for Computational Linguistics.

关键词： Data assimilation

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Fast Symmetric Eigenvalue Decomposition via WY Representation on Tensor Core 23

Fast Symmetric Eigenvalue Decomposition via WY Representatio...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Shaoshuai Shah, Ruchi Ootomo, Hiroyuki Yokota, Rio Wu, Panruo Department of Computer Science University of Houston TX United States Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798400700156

Symmetric eigenvalue decomposition (EVD) is a fundamental analytic and numerical tool used in many scientific areas. The state-of-the-art algorithm in terms of performance is typically the two-stage tridiagonalization method. The first stage in the two-stage tridiagonalization is called successive band reduction (SBR), which reduces a symmetric matrix to a band form, and its computational cost usually dominates. When Tensor Core (specialized matrix computational accelerator) is used to accelerate the expensive EVD, the conventional ZY-representation-based method results in suboptimal performance due to unfavorable shapes of the matrix computations. In this paper, we propose a new method that uses WY representation instead of ZY representation (see Section 3.2 for details), which can provide a better combination of locality and parallelism so as to perform better on Tensor Cores. Experimentally, the proposed method can bring up to 3.7x speedup in SBR and 2.3x in the entire EVD compared to state-of-the-art implementations. © 2023 ACM.

关键词： Singular value decomposition

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A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU

A High-Performance Accelerator for Super-Resolution Processi...

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作者： Zhao, Wenqian Bai, Yang Sun, Qi Li, Wenbo Zheng, Haisheng Jiang, Nianjuan Lu, Jiangbo Yu, Bei Wong, Martin D. F. Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Heterogeneous Computing Center SmartMore Corporation Limited Hong Kong

Over the past few years, super-resolution (SR) processing has achieved astonishing progress along with the development of deep learning. Nevertheless, the rigorous requirement for real-time inference, especially for video tasks, leaves a harsh challenge for both the model architecture design and the hardware-level implementation. In this article, we propose a hardware-aware acceleration on embedded GPU devices as a full-stack SR deployment framework. The most critical stage with dictionary learning applied in SR flow was analyzed in details and optimized with a tailored dictionary slimming strategy. Moreover, we also delve into the programming architecture of hardware while analyzing the model structure to optimize the computation kernels to reduce inference latency and maximize the throughput given restricted computing power. In addition, we further accelerate the model with 8-bit integer inference by quantizing the weights in the compressed model. An adaptive 8-bit quantization flow for SR task enables the quantized model to achieve a comparable result with the full-precision baselines. With the help of our approaches, the computation and communication bottlenecks in the deep dictionary learning-based SR models can be overcome effectively. The experiments on both edge embedded device NVIDIA NX and 2080Ti prove that our framework exceeds the performance of state-of-the-art NVIDIA TensorRT significantly and can achieve real-time performance. © 2023 IEEE.

关键词： Graphics processing unit

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Analysis of Predictive Models for Revealing Household Characteristics using Smart Grid Data

Analysis of Predictive Models for Revealing Household Charac...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Aly, Hussein Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah College of Engineering Department of Computer Science and Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

ISBN: (纸本)9798350396782

The Smart Grid Advanced Metering Infrastructure (AMI) has revolutionized the smart grid network, generating vast amounts of data that can be utilized for diverse objectives, one of which is Household Characteristics Classification (HCC). This can help the utility provider profile their customers and tailor their services to meet customer needs. To accomplish this task, we evaluated multiple Machine learning HCC models, with a focus on CNN-based models due to their wide popularity in the field of smart grid signal classification. We evaluated 1D and 2D variants of four different CNN architectures. Our experimental analysis revealed that ResNet-based models achieved the best performance on the task of HCC. Also, we found that 2D models tends to perform better than 1D variants. © 2023 IEEE.

关键词： CNN architectures Convolutional Neural Network Electricity Data Private Smart Grid Smart Grid Data Socio-demographic Classification

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Autonomous Network Optimization and Dynamic Channel Allocation for Cognitive Radio-Based Consumer IoT

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 53-61页

作者： Abbas, Laraib Shoaib, Umar Omar, Marwan Bashir, Ali K. University of Gujrat Department of Computer Science Gujrat Pakistan Illinois Institute of Technology Information Technology and Management United States Manchester Metropolitan University Department of Computing and Mathematics Ukraine Chitkara University Institute of Engineering and Technology Center for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India

The heterogeneous environment of next-generation Consumer Internet of Things (CIoT) demands efficient resource utilization and reliable network services. On the contrary, the proliferation in the diverse nature of smart consumer IoT devices is causing spectrum scarcity and uneven utilization of available resources. Cognitive Radios (CRs) provide the most suitable solution for spectrum scarcity through dynamic spectrum access. To achieve spectral efficiency and provide consumer-centric network services we propose a novel Cognitive Radio based Autonomous Network Management framework called (CR-ANM). The framework combines the benefits of cognitive radios, Network Function Virtualization (NFV), and Software Defined Networking (SDN), to decouple the control plane from the data plane and is divided into two further operations called Dynamic Priority Determination (DPD) and Efficient Channel Allocation (ECA). DPD is responsible for determining the SUs priority using a fuzzy logic-based decision controller. Whereas ECA optimizes the channel allocation process and allocates the best available channel to SU. Which increases the channel availability by 77% and reduces the service drop rate by 81.8%. Both schemes run as Virtual Utility Functions (VUFs) on dedicated virtual machines assigned by the SDN controller. This approach increases energy efficiency for low-power consumer IoT devices and improves network reliability. © 1975-2011 IEEE.

关键词： Fuzzy logic

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Leveraging contrastive learning for enhanced node representations in tokenized graph transformers 24

Leveraging contrastive learning for enhanced node representa...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jinsong Chen Hanpeng Liu John E. Hopcroft Kun He School of Computer Science and Technology and Institute of Artificial Intelligence and Hopcroft Center on Computing Science Huazhong University of Science and Technology School of Computer Science and Technology and Hopcroft Center on Computing Science Huazhong University of Science and Technology Hopcroft Center on Computing Science Huazhong University of Science and Technology and Department of Computer Science Cornell University

ISBN: (纸本)9798331314385

While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for constructing token sequences overlooks valuable information from other nodes, hindering their ability to fully harness graph information for learning optimal node representations. To address this limitation, we propose a novel graph Transformer called GCFormer. Unlike previous approaches, GCFormer develops a hybrid token generator to create two types of token sequences, positive and negative, to capture diverse graph information. And a tailored Transformer-based backbone is adopted to learn meaningful node representations from these generated token sequences. Additionally, GCFormer introduces contrastive learning to extract valuable information from both positive and negative token sequences, enhancing the quality of learned node representations. Extensive experimental results across various datasets, including homophily and heterophily graphs, demonstrate the superiority of GCFormer in node classification, when compared to representative graph neural networks (GNNs) and graph Transformers.

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Research on liquid crystal display technology based on regional dynamic dimming algorithm 9

Research on liquid crystal display technology based on regio...

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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Yu, Zhitao Sun, Peng Zhou, Mingle Liu, Qianlong Zhao, Shilong Hisense Video Technology Co. Ltd. Qing Dao China Hisense Visual Technology Co. Ltd. Qing Dao China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In vehicle liquid crystal display (LCD) technology has attracted much attention for its wide range of applications in automotive infotainment systems. However, conventional LCD technologies have limitations in terms of energy consumption and display quality, especially in the context of the increasing demand for high contrast and low energy consumption. To address these challenges, this paper carries out a study of LCD technology based on an area dynamic dimming algorithm. The technology significantly improves the contrast ratio of LCDs and effectively reduces energy consumption by dividing the backlight module into multiple independently adjustable regions and dynamically adjusting the backlight brightness of each region according to the image content. The results show that the region dynamic dimming algorithm proposed in this paper can effectively adjust the brightness of the backlight according to the image content, achieving higher display contrast and lower energy consumption. © 2024 IEEE.

关键词： Liquid crystal displays

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Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps:An Ultra-Wide Range Dynamics for Image Encryption

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computers, Materials & Continua 2025年第5期83卷 2161-2188页

作者： De Rosal Ignatius Moses Setiadi T.Sutojo Supriadi Rustad Muhamad Akrom Sudipta Kr Ghosal Minh T.Nguyen Arnold Adimabua Ojugo Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceDian Nuswantoro UniversitySemarang50131Indonesia Department of Cyber Forensics and Information Security Behala Government Polytechnic756Upendra Nath Banerjee RoadParnasreeBehalaKolkata700060India Thai Nguyen University of Technology Thai Nguyen UniversityThai Nguyen240000Vietnam Department of Computer Science Federal University of Petroleum ResourcesEffurun330102Nigeria

Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption *** paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter *** proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance *** numerical experiments validate the effectiveness of *** proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in *** bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher *** system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force ***,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution *** approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption *** method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor *** robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential ***,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s *** results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.

关键词： Single qubit quantumchaotic quantumchaotic map quantumimage encryption quantumlogistic map quantum sine map

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