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Variational Bayesian Learning based Joint Localization and Channel Parameter Estimation in Wireless Sensor Networks with Distance-dependent Noise

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 6877-6891页

作者： Li, Yunfei Luo, Yiting Tan, Weiqiang Li, Chunguo Ma, Shaodan Yang, Guanghua Anhui Polytechnic University Department of Electrical Engineering Wuhu China Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China Southeast University School of Information Science and Engineering Nanjing China Purple Mountain Laboratories Nanjing China University of Macau State Key Lab. of Internet of Things for Smart City and the Dept. of Elec. and Computer Engineering Taipa China Jinan University School of Intelligent Systems Science and Engineering Zhuhai519070 China

This paper focuses on the challenge of jointly optimizing location and path loss exponent (PLE) in distancedependent noise. Departing from the conventional independent noise model used in localization and path loss exponent estimation problems, we consider a more realistic model incorporating distance-dependent noise variance, as revealed in recent theoretical analyses and experimental results. The distance-dependent noise introduces a complex noise model with unknown noise power and PLE, resulting in an exceptionally challenging nonconvex and nonlinear optimization problem. In this study, we address a joint localization and path loss exponent estimation problem encompassing distance-dependent noise, unknown parameters, and uncertainties in sensor node locations. To surmount the intractable nonlinear and non-convex objective function inherent in the problem, we introduce a variational Bayesian learning-based framework that enables the joint optimization of localization, path loss exponent, and reference noise parameters by leveraging an effective approximation to the true posterior distribution. Furthermore, the proposed joint learning algorithm provides an iterative closed-form solution and exhibits superior performance in terms of computational complexity compared to existing algorithms. computer simulation results demonstrate that the proposed algorithm approaches the performance of the Bayesian Cramer-Rao bound (BCRB), achieves localization performance comparable to the (maximum likelihood-Gaussian message passing) ML-GMP algorithm in some cases, and outperforms the other comparison algorithm in all cases. © 1967-2012 IEEE.

关键词： Iterative methods

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Fine-Tuning of Conditional Transformers Improves the Generation of Functionally Characterized Proteins

Fine-Tuning of Conditional Transformers Improves the Generat...

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17th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

作者： Nicolini, Marco Malchiodi, Dario Cabri, Alberto Cavalleri, Emanuele Mesiti, Marco Paccanaro, Alberto Robinson, Peter N. Reese, Justin Casiraghi, Elena Valentini, Giorgio AnacletoLab Dept. of Computer Science University of Milan Italy ELLIS European Laboratory for Learning and Intelligent Systems Germany Environmental Genomics and Systems Biology Bioscience Lawrence Berkeley National Laboratory United States - FGV Rio de Janeiro Brazil

Conditional transformers improve the generative capabilities of large language models (LLMs) by processing specific control tags able to drive the generation of texts characterized by specific features. Recently, a similar approach has been applied to the generation of functionally characterized proteins by adding specific tags to the protein sequence to qualify their functions (e.g., Gene Ontology terms) or other characteristics (e.g., their family or the species which they belong to). In this work, we show that fine tuning conditional transformers, pre-trained on large corpora of proteins, on specific protein families can significantly enhance the prediction accuracy of the pre-trained models and can also generate new potentially functional proteins that could enlarge the protein space explored by the natural evolution. We obtained encouraging results on the phage lysozyme family of proteins, achieving statistically significant better prediction results than the original pre-trained model. The comparative analysis of the primary and tertiary structure of the synthetic proteins generated by our model with the natural ones shows that the resulting fine-tuned model is able to generate biologically plausible proteins. Our results confirm and suggest that fine-tuned conditional transformers can be applied to other functionally characterized proteins for possible industrial and pharmacological applications. © 2024 by SCITEPRESS–science and Technology Publications, Lda.

关键词： Distribution transformers

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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Traffic Sign Detection and Recognition Using YOLOv5 and Its Versions 1

Traffic Sign Detection and Recognition Using YOLOv5 and Its ...

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1st IEEE International Conference on Cognitive Mobility, CogMob 2022

作者： Lebumfacil, Aaron Joaquin Abu, Patricia Angela Ateneo De Manila University Ateneo Laboratory for Intelligent Visual Environments Dept. of Information Systems and Computer Science Quezon City Philippines

ISBN: (纸本)9781665476317

A Traffic Sign Detection and Recognition (TSDR) System, which helps navigate vehicles through computer vision and human-machine communication, has to perform quickly as vehicles using them travel at high speeds. During this study, a speedy one-stage detector such as YOLOv5, a deep learning model, was chosen to dive into. This study explores creating a TSDR model by comparing four different versions of YOLOv5, namely YOLOv5 Nano, Small, Medium, and Large. This study was accomplished by first creating a new traffic sign dataset. The four versions of the YOLOv5 algorithm were then trained with a 75-25 train validation split, and 24 models were created. Afterwards, the models were tested on a test set, and their metrics were tallied. Results showed that YOLOv5 Medium and Large offer a 10% increase in accuracy performance when compared to YOLOv5 Small, but due to the slower detection speed of YOLOv5 Large, the YOLOv5 Medium models are a better fit when it comes to the detection of traffic signs when prepared by a relatively small dataset. This study provides an overview of the performance of the different YOLOv5 versions in traffic sign detection and recognition that aims to contribute to the improvement of traffic sign detectors. © 2022 IEEE.

关键词： computer vision

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Lightweight Networks for COVID-19 Detection from Chest X-Ray Images inside a Low-Tier Android Device

Lightweight Networks for COVID-19 Detection from Chest X-Ray...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Bacad, Dave Jammin A. Abu, Patricia Angela R. Ateneo de Manila University Ateneo Laboratory for Intelligent Visual Environment Dept. of Information Systems and Computer Science Quezon City Philippines

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

The efforts to inoculate majority of the population have been slower than expected and this is especially true for lower income countries. This problem has caused a lot of worries and further accentuates the importance of timely and effective mass testing considering the emergence of newer variants. The RT-PCR is still the gold standard diagnostic test for COVID-19 detection, but its limitations has led researchers and scientists to explore supplementary screening methods. One effective tool to consider is Chest X-Ray (CXR) imaging and combining it with deep learning has piqued attention from the artificial intelligence (AI) community. To further contribute to this research area, this work focuses on creating, evaluating, and comparing lightweight and mobile-phone-suitable COVID-detecting models. These transfer learning models together with their corresponding dynamic-range quantized versions are first tested according to their classification performance. Afterwards, the models are pushed in a low-tier phone to measure their resource consumption and inference timings. Results show that the utilization of EfficientNetB0 and MobileNetV3 (Small & Large) architectures for transfer learning without any quantization can produce at least 91 % overall average accuracy for 3-class classification scheme. For systems requiring more efficient models, using the quantized versions of the transfer learning models particularly with EfficientNetB0 and MobileNetV3Large as foundation can render at most 0.79 % accuracy loss but still show more than 95% f1-scores for the COVID-19 class. © 2022 IEEE.

关键词： COVID-19

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Multimodal Temporal Modeling of Emotion using Physiological Signals

TechRxiv

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TechRxiv 2023年

作者： Hinduja, Saurabh Bilalpur, Maneesh Jivnani, Liza Vimalkumar Canavan, Shaun Dept. of Psychology University of Pittsburgh United States Dept. of Intelligent Systems Program University of Pittsburgh United States Dept. of Computer Science University of South Florida United States

Modeling of human emotion is a challenging problem that can require multiple signals types, as well as contextual information that has been obtained over time. Considering this, in this paper we present our approach, based on physiological signals, to the Emotion Physiology and Experience Collaboration (EPIC) challenge at Affective Computing & intelligent Interaction (ACII), 2023. In total there are four scenarios that we model: 1) across time;2) across subjects;3) across elicitor;and 4) across version. To tackle this challenge, we propose to use a physiological fusion-based approach to solve each scenario. Along with this, we give a detailed analysis of the evaluated physiological signals and personalized predictions for each subject are shown. Our proposed approach shows encouraging results with the lowest root mean square error achieved for scenario 4 (across version) for both valence and arousal on the challenge test set. © 2023, CC BY.

关键词： Physiology

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A Web-Based Interface that Leverages Machine Learning to Assess an Individual’s Vulnerability to Brain Stroke

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IEEE Access 2025年 13卷 87950-87964页

作者： Bhandari, Divyansh Agarwal, Arnav Reena Roy, R. Priyatharshini, Rajaram Cristian, Rodriguez Rivero Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Cardiff Metropolitan University Cardiff School of Technologies Centre for Engineering Research in Intelligent Sensors and SystemsComputer Science Wales United Kingdom Cardiff Metropolitan University Academic Associate Cardiff School of Technologies Cardiff United Kingdom Universitat Politècnica de Catalunya Industrial and ICT Systems Engineering Dept. of Mining Spain

Cerebral stroke is a major global health issue, contributing to high mortality and long-term disability. Early identification of individuals at high risk of stroke can significantly improve preventive care outcomes. We present a web-based stroke risk assessment tool that uniquely combines an accessible user interface with robust machine learning modeling. The proposed platform leverages a novel combination of SMOTE oversampling and logistic regression to address class imbalance in patient health records, improving the detection of stroke risk factors over existing methods. We compare a range of algorithms – including traditional classifiers and deep learning models – and report comprehensive performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) for each. Our best model (logistic regression with SMOTE and standard scaling) achieves 93.2% accuracy with a substantially higher F1-score for the stroke-positive class than other models, indicating improved sensitivity to stroke cases. To bridge the gap between complex predictive models and end-users, we deploy this model in an intuitive web interface (built with Streamlit) that non-technical individuals and healthcare providers can easily use. This interface requires no specialized knowledge, preserves user privacy by avoiding any data storage, and provides clear explanations of results. By offering a practical and transparent tool for stroke risk screening, our work advances health informatics with an emphasis on accessibility, interpretability, and early intervention. Potential applications range from personal health self-assessment to integration in clinical workflows for preventive care, ultimately aiming to improve public health outcomes through early detection and intervention in stroke. © 2013 IEEE.

关键词： Health insurance

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Small Data, Big Impact: A Multi-Locale Bone Fracture Detection on an Extremely Limited Dataset Via Crack-Informed YOLOv9 Variants

Small Data, Big Impact: A Multi-Locale Bone Fracture Detecti...

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Frontiers of Information Technology (FIT)

作者： Ammar Ahmed Abdul Manaf Ali Shariq Imran Zenun Kastrati Sher Muhammad Daudpota Dept. of Computer Science (IDI) Intelligent Systems and Analytics (ISA) Research Group NTNU Gjøvik Norway Dept. of Computer Science Sukkur IBA University Sukkur Pakistan Dept. of Informatics Linnaeus University Växjö Sweden

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

Automated wrist fracture recognition has become a crucial research area due to the challenge of accurate X-ray interpretation in clinical settings without specialized expertise. With the development of neural networks, YOLO models have been extensively applied to fracture detection recently. However, detection models can struggle when trained on small datasets, which is often the case in medical scenarios. In this study, we utilize an extremely small multi-region fracture dataset and hypothesize that the structural similarities between surface cracks and bone fractures can allow YOLOv9 with a generalized efficient layer and programmable gradient information control to transfer knowledge effectively. We show that pre-training YOLOv9 on surface cracks rather than on COCO, which is how YOLO models are typically pre-trained, and fine-tuning it on the fracture dataset yields substantial performance improvements. We also show that knowledge gained from the surface cracks requires fewer epochs to converge and minimizes overfitting. We achieved state-of-the-art (SOTA) performance on the newly released FracAtlas dataset, surpassing the previously established benchmark. Our approach improved the mean average precision (mAP) score by 7% and sensitivity by 13%.

关键词： YOLO Wrist Sensitivity Neural networks Bones Surface cracks Information technology X-ray imaging Overfitting Biomedical imaging

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Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection 2

Applying Pre-trained Multilingual BERT in Embeddings for Imp...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Rahman, Md Abdur Shahriar, Hossain Wu, Fan Cuzzocrea, Alfredo University of West Florida Dept. of Intelligent Systems and Robotics PensacolaFL United States University of West Florida Center for Cybersecurity PensacolaFL United States Tuskegee University Department of Computer Science TuskegeeAL United States University of Calabria Idea Lab Rende Italy

ISBN: (纸本)9798331529529

Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities. © 2024 IEEE.

关键词： Embeddings

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Scalability in Autoencoder-based OFDM Communication System

Scalability in Autoencoder-based OFDM Communication System

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Seizan Tsugawa Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 ㏈ at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 ㏈ at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 ㏈, but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0 . As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication.

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