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OFDM with Generalized Cascade Index Modulation for Secure Communications

China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

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HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression 14

HyCoT: A Transformer-Based Autoencoder for Hyperspectral Ima...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Fuchs, Martin Hermann Paul Rasti, Behnood Demir, Begum Technische Universität Faculty of Electrical Engineering and Computer Science Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (纸本)9798331513139

The development of learning-based hyperspectral image (HSI) compression models has recently attracted significant interest. Existing models predominantly utilize convolutional filters, which capture only local dependencies. Furthermore, they often incur high training costs and exhibit substantial computational complexity. To address these limitations, in this paper we propose Hyperspectral Compression Trans-former (HyCoT) that is a transformer-based autoencoder for pixelwise HSI compression. Additionally, we apply a simple yet effective training set reduction approach to accelerate the training process. Experimental results on the HySpecNet-11k dataset demonstrate that HyCoT surpasses the state of the art across various compression ratios by over 1 dB of PSNR with significantly reduced computational require-ments. Our code and pre-trained weights are publicly avail-able at https://***/rsim/hycot. © 2024 IEEE.

关键词： Image compression

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data-Augmented Few-Shot Object Detection for Efficient Identification of Invasive Weed Seedlings 1

Data-Augmented Few-Shot Object Detection for Efficient Ident...

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1st IEEE International Conference on Advances in data-Driven Analytics and Intelligent Systems, ADACIS 2023

作者： Alli, Smaran Baidya, Sabur Carnegie Mellon University Department of Data Science PA United States University of Louisville Department of Computer Science and Engineering KY United States

ISBN: (纸本)9798350344158

Invasive weeds have continued to ravage crops across the world and with their population starkly rising, intelligent technologies need to be incorporated to mitigate the spread of these weeds. This research aims at employing Deep Learning models on invasive weed seedlings images in order to be able to detect them in images before they have grown. Due to the lack of images available on weed seedlings, more images were collected of weed seedlings, which were then combined with the limited available pre-existing images found in the Aarhus dataset. However, due to the dataset being still comparably small, a Few-Shot Object Detector (FSOD) is employed for better object detection performance. This FSOD was created through transfer learning with the universal COCO dataset. We also investigate data augmentation techniques by varying different attributes of images. We compare our proposed data-augmented FSOD with a 16-layer CNN-based object detector and also with the models without any augmentation. With experimental results, we show that the FSOD model achieves a higher mean Average Precision (mAP50) than the 16-layer CNN for both augmented (0.839 to 0.834) and non-augmented data (0.814 to 0.808). The FSOD detector also runs faster than the CNN model, and thus can be useful in real-time autonomous detection of the weed seedling and hence, in mitigating spreading of these invasive weeds. © 2023 IEEE.

关键词： Object detection

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mVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations

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IEEE Transactions on Dependable and Secure Computing 2022年 1-12页

作者： Zou, Deqing Hu, Yutao Li, Wenke Wu, Yueming Zhao, Haojun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (i.e., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design mVulPreter, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results' interpretation only by the model itself. We evaluate mVulPreter on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that mVulPreter outperforms existing state-of-the-art vulnerability detection approaches (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, StatementLSTM, SySeVR, and Devign). IEEE

关键词： Semantics

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Turmeric Leaf Disease Detection Using Optimized Deep Learning Model 2

Turmeric Leaf Disease Detection Using Optimized Deep Learnin...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Selvaraj, R. Devasena, M.S. Geetha Satheesh, T. Karpagavadivu, K. Ravindaran, F. Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Artificial Intelligence and Data Science Dr.N.G.P. Institute of Technology Coimbatore India Karpagam College of Engineering Coimbatore India

ISBN: (纸本)9781837242672

Turmeric is popular for its medicinal, culinary, and non-culinary uses. Turmeric cultivation is affected due to disease in their leaves. Farmers conventionally identify the leaf diseases and provide pesticides to avoid further loss. However, manual identification of leaf disease over large farms is time-consuming and requires human effort. Also, in several types of leaf disease, the specific type that occurs in the plant should be distinguished;thus, proper knowledge of farming is required. To simplify this and to know the type of disease, smart agricultural practices are followed worldwide. One of the intelligent techniques adopted in farming is leaf disease detection. By providing the filed image to a learning algorithm, the type of disease can be detected accurately without manual interventions. ML and DL are used in this smart agricultural practice. To improve the performance further, an optimized deep learning model is presented in this research work. A Novel gazelle optimization algorithm is combined with deep learning MobileNetV3 network to attain enhanced disease detection performance. Experimentation on our own dataset with 5000 samples, including leaf spot, leaf blight, leaf rot, and curl disease-affected leaves, the proposed model performance is evaluated and observed with a maximum accuracy of 96.8%, a precision of 97.1% over conventional disease detection models. © The Institution of engineering & Technology 2024.

关键词： Deep learning

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Plausible Heterogeneous Graph k-Anonymization for Social Networks

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Tsinghua science and Technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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Quantum-Improved Weather Forecasting: Integrating Quantum Machine Learning for Precise Prediction and Disaster Mitigation

Quantum-Improved Weather Forecasting: Integrating Quantum Ma...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Suhas, S. Divya, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Centre for Advanced Data Science Chennai India

ISBN: (纸本)9798350314946

In numerous industries, weather forecasting is essential for making informed decisions and mitigating the effects of extreme weather events. The complexity and chaos of weather systems, however, place restrictions on standard procedures, leading to errors and significant threats. We suggest the Quantum Improved Weather Forecast framework, which combines quantum machine learning methods with conventional methodologies, to solve these issues. By using quantum algorithms including quantum support vector machines, quantum neural networks, and quantum clustering, the QWF framework seeks to improve accuracy. Despite a few drawbacks, the QWF framework provides a method to revolutionize weather forecasting by enhancing prediction accuracy and facilitating improved catastrophe preparedness. It may prevent fatalities, safeguard critical infrastructure, and promote sustainable growth. © 2023 IEEE.

关键词： Climate change

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YOLOv8 Enhanced: Pioneering Accuracy in Traffic Sign Detection and Classification 9

YOLOv8 Enhanced: Pioneering Accuracy in Traffic Sign Detecti...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Thapliyal, Nitin Aeri, Manisha Namdev, Dheeraj Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science And Engineering Uttrakhand Dehradun248002 India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Iimt College of Engineering Artificial Intelligence & Data Science Uttar Pradesh Greater Noida201310 India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

This research paper considers the You Only Look Once version 8 (YOLOv8) model as a tool for traffic sign detection and classification, which is an important feature of the autonomous driving system and the intelligent transportation system. Analyzing the latest version of the YOLO series, which seeks to maintain a suitable balance between object detection speed and accuracy, is the aim of this study since precise and timely traffic sign recognition is a critical factor for road safety and traffic management. The YOLOv8 model was trained and the accuracy of it was tested using a thorough dataset of traffic signs under different environmental conditions. The approach relied on exploiting YOLOv8's advanced deep learning architecture, transfer learning, and data augmentation to improve the model performance. Statistics revealed that YOLOv8 surpasses the previous versions with its mAP of 95.2% and processing speeds that match real-time application demands. The model had high precision and recall across multiple classes of traffic signs, especially those that are unique in shape or color. This research indicates that YOLOv8 can be an effective tool to enhance the performance of autonomous driving systems by its robust sign detection and classification. The model has opened a new frontier for research on improving the model for more accuracy and further application of the model in more elaborate areas of road safety and intelligent transportation systems. © 2024 IEEE.

关键词： Object detection

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YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms for Marine Waste Recognition 1

YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms fo...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Wajdi, Achmad Zahir Wibowo, Prasetyo Sasaki, Shiori Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Faculty of Data Science Musashino University Tokyo Japan

ISBN: (纸本)9798350365191

Marine debris, the majority of which is composed of plastic (61 % to 87 %), is a significant environmental issue facing the world. Between 4.8 million and 12.7 million metric tons of plastic are thought to have entered the ocean in 2010 alone, increasing the estimated total amount of particles in the water to at least 5 trillion. Global plastic production, which reached 348 million tons in 2017, is expected to double in the next 20 years. To overcome this challenge, effective waste detection methods are needed, one of which is by utilizing computer vision, specifically the You Only Look Once (YOLO) algorithm. YOLOv9, as the latest version, offers improved object detection performance compared to its predecessor, YOLOv8. This research aims to evaluate the performance and efficiency of YOLOv9 and YOLOv8 in detecting marine waste. Using the Trash Annotations in Context dataset (TACO), We train both models and conduct tests to evaluate their accuracy and efficiency. Experimental results show that YOLOv9 has higher precision at 45% but lower recall at 6%, compared to YOLOv8, which has a precision of 33 % and a recall of 15 %. Overall, YOLOv8 shows better performance with higher mAP50 at 13% and mAP50-95 values at 10%, compared to YOLOv9, which has mAP50 at 4% and mAP50-95 values at 3%. © 2024 IEEE.

关键词： Marine Waste Neural Net-work Object Detection TACO dataset YOLO

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Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

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computer Modeling in engineering & sciences 2025年第2期142卷 1199-1231页

作者： Syed Ijaz Ur Rahman Naveed Abbas Sikandar Ali Muhammad Salman Ahmed Alkhayat Jawad Khan Dildar Hussain Yeong Hyeon Gu Department of Computer Science Islamia CollegePeshawar25120Pakistan Department of Information Technology The University of HaripurHaripur22620Pakistan College of Technical Engineering The Islamic UniversityNajaf100986Iraq School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of AI and Data Science Sejong UniversitySeoul05006Republic of Korea

Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical *** Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes *** researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of *** systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this *** image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were *** Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the ***,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.

关键词： Acute lymphoblastic bone marrow segmentation classification machine learning deep learning convolutional neural network

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