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Variable-Length Feedback Codes Over Known and Unknown Channels With Non-Vanishing Error Probabilities

IEEE Transactions on Information Theory 2025年第5期71卷 3271-3286页

作者： Yavas, Recep Can Tan, Vincent Y. F. Descartes Program CNRS@CREATE 138602 Singapore National University of Singapore Department of Computer Science Queenstown 117417 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown 119077 Singapore

We study variable-length feedback (VLF) codes with noiseless feedback for discrete memoryless channels. We present a novel non-asymptotic bound, which analyzes the average error probability and average decoding time of our modified Yamamoto-Itoh scheme. We then optimize the parameters of our code in the asymptotic regime where the average error probability ∈ remains a constant as the average decoding time N approaches infinity. Our second-order achievability bound is an improvement of Polyanskiy et al.’s (2011) achievability bound. We also develop a universal VLF code that does not rely on the knowledge of the underlying channel parameters. Our universal VLF code employs the empirical mutual information as its decoding metric and universalizes the code by Polyanskiy et al. (2011). We derive a second-order achievability bound for universal VLF codes. Our results for both VLF and universal VLF codes are extended to the additive white Gaussian noise channel with an average power constraint. The former yields an improvement over Truong and Tan’s (2017) achievability bound. The proof of our results for universal VLF codes uses a refined version of the method of types and an asymptotic expansion from the nonlinear renewal theory literature. © 1963-2012 IEEE.

关键词： White noise

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Using CNN and Transformer Model for Unimodal Speech Emotion Recognition on MELD and IEMOCAP

Using CNN and Transformer Model for Unimodal Speech Emotion ...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Aurelio, Clarence Chowanda, Andry Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

Emotion Recognition is one field that is taking the world by storm in this current age. Multimodal emotion recognition has shown promising results however, previous studies shows that recognition using speech is a field has not performed that greatly. Previous research used popular methods such as CNN, RNN, and other machine learning methods has been used to achieved good results with the help of other modality, where if speech were to be used alone the results won't be as good. A factor that contributes to the result of the speech modality not performing as well is the fact of the dataset and the way it was processed. Therefore, this research looks into what makes speech one of the lowest performing modalities by preprocessing the dataset using a different method than previous researches as well as using a CNN model paired with a Transformer model. Results achieved from this research showed an improvement when using IEMOCAP by 8.09% and MELD by 1.06%. A comparison results of using the pre-processed data with previous studies model was also done to compare the results. In conclusion, this research has explored a different preprocessing method and deep learning model that has achieved results better than previous works with the same dataset. © 2025 IEEE.

关键词： Contrastive Learning

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Experimental evaluation of MIMO configurations in indoor visible light communications under the consideration of illumination

Optics Continuum

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Optics Continuum 2025年第4期4卷 789-803页

作者： Esteban Farfán-Guillén, David Camporez, Higor Silva, Jair Adriano Lima Castellano Rodriguez, Juan Camilo Pohl, Alexandre De Almeida Prado CPGEI–Graduate Program in Electrical Engineering and Computer Science Federal University of Technology Paraná Curitiba80230–901 Brazil Graduate Program in Electrical Engineering Federal University of Espírito Santo Vitória29075-960 Brazil

Visible light communications (VLC) enable wireless data transmission using existing light-emitting diode (LED) illumination. This experimental study evaluates the performance of multiple input and multiple output (MIMO) configurations in indoor VLC systems by analyzing their impact on both communication and illumination. Various MIMO setups (2 × 2, 3 × 2, and 4 × 2) are compared against single input single output (SISO), and single input multiple outputs (SIMO) schemes, using commercial LED luminaires as transmitters. While the 4 × 2 MIMO configuration demonstrates superior overall performance, intermediate configurations like 3 × 2 or 2 × 2 MIMO offer an attractive trade-off between system complexity and performance enhancement, particularly in scenarios with space or cost constraints. The results show that MIMO configurations significantly outperform SISO systems, achieving reliable communication (BER ≤ 10−3) at practical indoor distances of up to 2 meters while maintaining illumination levels above 300 lux, where conventional SISO setups do not meet these dual requirements. These findings demonstrate the practical feasibility of MIMO-VLC systems for indoor deployments. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Frequency division multiplexing Modulation techniques Multiple input multiple output Quadrature amplitude modulation Signal processing Visible light communications

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Development of an RFID-based Children’s Disaster and Emergency PreparednessDevice 24

Development of an RFID-based Children’s Disaster and Emerge...

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8th International Conference on E-Society, E-Education and E-Technology, ESET 2024

作者： Broas, Pauline Ingrid Derama, Lovely Mae Rosales, Rosban Bacus, John Computer Engineering Program College of Engineering Education University of Mindanao Davao City Philippines

ISBN: (纸本)9798400707094

RFID Technology has grown increasingly popular over recent years due to its efficiency. This study examines the development, implementation, and impact of an RFID-based cognitive learning system focusing on child preparedness in disasters and emergencies. This study aims to design a stand-alone interactive device, fabricate an RFID-based touchscreen interactive device for children, conduct functionality testing on the accuracy of the eLearnCards and responsiveness of the LCD Touchscreen, and evaluate system accuracy and performance on children using a paired T-test. A quantitative research approach is adopted, encompassing the collection and analysis of pre-test and post-test scores from a stratified random sample of 60 children from New Matina Elementary School with an age range of 7-12 years old. The results of this study signify that the RFID-based cognitive learning system substantially contributes to enhancing students’ disaster preparedness knowledge. This study concludes that it underscores the considerable potential of RFID-based educational technology in reinforcing disaster preparedness among children. © 2024 Copyright held by the owner/author(s).

关键词： Disasters

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Breast Cancer Diagnosis via Swarm-Optimized Adaptive Neuro-Fuzzy Inference System 59

Breast Cancer Diagnosis via Swarm-Optimized Adaptive Neuro-F...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Greenlee, Aidan Palumbo, Neil F. Johns Hopkins University Electrical and Computer Engineering Program BaltimoreMD United States

ISBN: (纸本)9798331513269

This paper discusses a method for classification of breast cancer imaging data through the application of an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) for hyperparameter optimization of the ANFIS system. A robust parameter tuning method is used to select the optimal configuration for the ANFIS and PSO components without expert knowledge of the dataset. Using these methods, high classification accuracies can be achieved for both the original and diagnostic versions of the Wisconsin Breast Cancer Dataset. These results demonstrate the flexibility and potential of a joint ANFIS-PSO system for automated diagnosis while retaining system simplicity and linguistic interpretability to support clinical decision-making. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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SISE: Supersingular Isogeny-Based Symmetric Encryption 15

SISE: Supersingular Isogeny-Based Symmetric Encryption

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Min, Manki Alshamdayn, Abdullah Dakhlallah Computer Science Program Louisiana Tech University RustonLA71272 United States

ISBN: (纸本)9798331507695

In this paper, we propose the first symmetric encryption scheme based on traversals in the supersingular isogeny graph and point mapping under the 2n -isogeny, using the Legendre form of elliptic curves. In a supersingular isogeny graph, an elliptic curve can be transformed into an isogenous curve and then back to another curve with the same j-invariant by the means of a dual isogeny. However there are more than one elliptic curves with the same j-invariant and using just the dual isogenies won't lead to a correct curve mapping as well as point mapping that is required for symmetric encryption schemes. With Legendre form of elliptic curves, we define the orientation of six curves with the same j-invariant and precisely defined the rotation function as well as the standardized isogeny. The rotation as well as the standardized isogeny are used to define the accurate backtracking of elliptic curve, meaning we can move back and forth between isogenous curves. This capability naturally enabled us to design a symmetric encryption scheme which is quantum-safe due to the difficulty of the underlying problem. Our encryption scheme can be used to securely sharing short/frequently-changing secondary keys/secrets. © 2025 IEEE.

关键词： Quantum cryptography

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Seamless High Availability in MySQL Database Systems: An Advanced Implementation of ProxySQL, Orchestrator, and Keepalived

Seamless High Availability in MySQL Database Systems: An Adv...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Loa, Jeremy Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

High availability (HA) in MySQL databases remains a critical challenge, particularly due to risks such as single point of failure (SPOF) and limited scalability in existing setups. Previous studies have explored various approaches to address these issues. One study implemented ProxySQL and Orchestrator for query routing and failover but relied on a single server instance, creating a SPOF that could disrupt client access during failures. Another study implemented HAProxy to balance traffic and couple database servers with specific web applications. While effective for certain use cases, this approach reduced resource efficiency and scalability, because it relies on HTTP protocol and database server are coupled to each application, making it less suitable for dynamic, shared environments requiring high flexibility. These limitations highlight the need for a more robust and scalable HA solution. This paper proposes an improved HA architecture that eliminates SPOFs and enhances scalability by combining redundant ProxySQL instances synchronized with Keepalived managed Virtual IP (VIP) and automated failover using Orchestrator. The architecture allows shared database resources across multiple applications while decoupling them from specific application servers. Test results confirm the system's ability to maintain seamless failover, consistent query routing, and uninterrupted client access through dynamic Virtual IP reassignment to backup server. By addressing the issues of previous studies, this solution provides a practical and scalable framework to achieve high availability in the MySQL cluster by combing ProxySQL, Orchestrator, and Keepalived, making it suitable for modern enterprise environments. © 2025 IEEE.

关键词： HTTP

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Machine Learning-Based Routing Protocol in Flying Ad Hoc Networks: A Review

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computers, Materials & Continua 2025年第2期82卷 1615-1643页

作者： Priyanka Manjit Kaur Deepak Prashar Leo Mrsic Arfat Ahmad Khan Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411India Department for Program Engineering Algebra UniversityGradiscanska 24Zagreb10000Croatia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian *** dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol *** the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high *** paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and *** research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant *** research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in *** compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance *** protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive *** comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in ***,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.

关键词： FANET protocol machine learning

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Comparative Model Evaluation of Lightweight Transformer-Based and CNN-Based Architechture for Degenerative Lumbar Spine Classification

Comparative Model Evaluation of Lightweight Transformer-Base...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Limicia, Jennifer Ardelia Chandra, Wilbert Gunawan, Alexander Agung Santoso Tedjasulaksana, Jeffrey Junior School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature of MRI interpretation and the weak correlation between imaging findings and symptoms. Thus, the purpose of this study is to evaluate the performance of CNN-based architectures (VGG-16, EfficientNetB0, EfficientNetV2) and transformer-based architecture specifically Vision Transformers (ViT) in classifying lumbar spine conditions as normal, moderate, or severe. Using the RSNA 2024 Lumbar Spine Degenerative Classification dataset. The models are evaluated using the ROC-AUC and PRAUC as the performance metrics due to the dataset imbalance. Results indicate that EfficientNetB0 achieved the highest overall performance, with an average ROC-AUC of 0.784 and PR-AUC of 0.528, demonstrating strong adaptability to imbalanced datasets. EfficientNetV2 also performed competitively, while VGG-16 showed moderate effectiveness. The Vision Transformer (ViT), however, underperformed due to its reliance on larger datasets and challenges in capturing fine-grained spatial features. The findings highlight the potential of EfficientNet-based models for accurate and efficient lumbar spine diagnostics. This study underscores the potential of advanced deep learning approaches in improving diagnostic workflows for degenerative lumbar spine conditions. © 2025 IEEE.

关键词： Convolutional neural networks

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Machine Learning-Based Crop Recommendation for IoT-Enabled Smart Agriculture 24

Machine Learning-Based Crop Recommendation for IoT-Enabled S...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Sonata, Ilvico Computer Science Department Binus Graduate Program Bina Nusantara University Indonesia Jakarta Indonesia

ISBN: (纸本)9798400717161

Human food needs are increasing all the time. This condition is in line with the increasing world population and exacerbated by climate change. Agricultural products are one of the sources of food to meet food needs. The use of technology is one of the keys to success in increasing agricultural production. One of the technologies used in agricultural technology is the Internet of Things (IoT). IoT in agricultural technology can be used to monitor soil and weather conditions to ensure that these conditions are in accordance with the needs of agricultural crops. The use of machine learning technology can be combined with IoT to help predict the types of crops that are suitable for planting based on soil and weather conditions. The results of the experiment show that the machine learning model can be used to predict the right types of plants to plant based on soil and weather conditions obtained through IoT. This method can be used to increase agricultural yields. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Smart agriculture

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