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TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution

IEEE Transactions on Image Processing 2024年 33卷 738-752页

作者： Xiao, Yi Yuan, Qiangqiang Jiang, Kui He, Jiang Lin, Chia-Wen Zhang, Liangpei Wuhan University School of Geodesy and Geomatics Wuhan430079 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300 Taiwan Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China

Transformer-based method has demonstrated promising performance in image super-resolution tasks, due to its long-range and global aggregation capability. However, the existing Transformer brings two critical challenges for applying it in large-area earth observation scenes: (1) redundant token representation due to most irrelevant tokens;(2) single-scale representation which ignores scale correlation modeling of similar ground observation targets. To this end, this paper proposes to adaptively eliminate the interference of irreverent tokens for a more compact self-attention calculation. Specifically, we devise a Residual Token Selective Group (RTSG) to grasp the most crucial token by dynamically selecting the top-k keys in terms of score ranking for each query. For better feature aggregation, a Multi-scale Feed-forward Layer (MFL) is developed to generate an enriched representation of multi-scale feature mixtures during feed-forward process. Moreover, we also proposed a Global Context Attention (GCA) to fully explore the most informative components, thus introducing more inductive bias to the RTSG for an accurate reconstruction. In particular, multiple cascaded RTSGs form our final Top-k Token Selective Transformer (TTST) to achieve progressive representation. Extensive experiments on simulated and real-world remote sensing datasets demonstrate our TTST could perform favorably against state-of-the-art CNN-based and Transformer-based methods, both qualitatively and quantitatively. In brief, TTST outperforms the state-of-the-art approach (HAT-L) in terms of PSNR by 0.14 dB on average, but only accounts for 47.26% and 46.97% of its computational cost and parameters. The code and pre-trained TTST will be available at https://***/XY-boy/TTST for validation. © 1992-2012 IEEE.

关键词： Remote sensing

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Hybrid Machine Learning Model for Face Recognition Using SVM

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computers, Materials & Continua 2022年第8期72卷 2697-2712页

作者： Anil Kumar Yadav R.K.Pateriya Nirmal Kumar Gupta Punit Gupta Dinesh Kumar Saini Mohammad Alahmadi IES College of Technology Bhopal462044India Maulana Azad National Institute of Technology Bhopal462003India Department of Information Technology Manipal University JaipurJaipur303007India Department of Computer and Communication Engineering Manipal University JaipurJaipur303007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

Face recognition systems have enhanced human-computer interactions in the last ten ***,the literature reveals that current techniques used for identifying or verifying faces are not immune to *** Component Analysis-Support Vector Machine(PCA-SVM)and Principal Component Analysis-Artificial Neural Network(PCA-ANN)are among the relatively recent and powerful face analysis *** to PCA-ANN,PCA-SVM has demonstrated generalization capabilities in many tasks,including the ability to recognize objects with small or large data *** from requiring a minimal number of parameters in face detection,PCA-SVM minimizes generalization errors and avoids overfitting problems better than ***-SVM,however,is ineffective and inefficient in detecting human faces in cases in which there is poor lighting,long hair,or items covering the subject’s *** study proposes a novel PCASVM-based model to overcome the recognition problem of PCA-ANN and enhance face *** experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.

关键词： Face recognition system(FRS) face identification SVM discrete cosine transform(DCT) artificial neural network(ANN) machine learning

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DeepSight: Enhancing Deepfake Image Detection and Classification through Ensemble and Deep Learning Techniques 5

DeepSight: Enhancing Deepfake Image Detection and Classifica...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Manju, T. Kalarani, S. St. Joseph's Institute of Technology Information Technology Tamil Nadu Chennai India PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350367171

in today's digital age, deepfake images poses a significant threat to multimedia content authenticity and integrity. Detecting and classifying deepfake images with high accuracy is crucial to addressing this growing challenge. Deepfake images, generated using advanced machine learning techniques, have become a significant concern due to their potential to deceive individuals and manipulate information. This paper presents DeepSight, an innovative approach that combines ensemble learning techniques with deep learning models to enhance deepfake image detection and classification. DeepSight leverages the diversity of multiple classifiers trained on distinct feature representations extracted from the input image data. The framework begins with an initial assessment to determine whether the image has been manipulated. Subsequently, the image goes through deep feature extraction using Convolutional Neural Networks (CNNs). The resulting feature vectors are then classified using Random Forest, KNearest Neighbors, and XGBoost, with hyper-parameter optimization. Experimental evaluations on benchmark datasets demonstrate DeepSight's superior performance compared to state-of-the-art methods, achieving higher accuracy and robustness across various deepfake generation techniques and quality levels. Notably, DeepSight achieves the highest accuracy of 97.5% when utilizing DenseNet and XGBoost. Overall, DeepSight represents a significant advancement in deepfake image detection and classification. It provides a reliable solution to address deceptive visual content in digital environments. © 2024 IEEE.

关键词： Contrastive Learning

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Dimensional Measurement System Using Canny Edge Detection Algorithm

Dimensional Measurement System Using Canny Edge Detection Al...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Bhatele, Priyanka Shivankar, Ishan Chogule, Shrey Saraswat, Shivansh Patel, Shraddha Sabut, Shreya Nale, Shreya Vishwakarma Institute of Technology Depatment of Information Technology Pune India Vishwakarma Institute of Technology Depatment of Computer Engineering Pune India Vishwakarma Institute of Technology Department of Electronics & Telecomminication Enginnering Pune India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vishwakarma Institute of Technology Depatment of Chemical Engineering Pune India

ISBN: (纸本)9798350306927

In the realm of industrial and scientific applications, accurate dimensional measurement plays a pivotal role in ensuring quality control and process optimization. This is a vital topic of computer vision problems. This study presents a project that uses reference markers to measure height and width of objects from their pictures rather than using conventional methods. Project model managed to achieve around 99.9% accuracy, whereas this was not provided in other works. © 2023 IEEE.

关键词： Python

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A Smart Automatic Agent Operating System Using Screen Analysis and Remote Control

A Smart Automatic Agent Operating System Using Screen Analys...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Lim, Zi-Yi Liao, Hsien-Chou Department of Information and Communication Engineering Chaoyang University of Technology Taichung Taiwan Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this century, more automated production has been driven by high demand. As a result, more automated production machines are required to meet market demands. Unfortunately, most machine screen activities still need humans to control manually, and most of the operations are simple and repeated. The human eye must constantly assess the production situation and control the keyboard and mouse to operate machinery to do excessively repetitive tasks. We proposed a smart automated agent operating system called 'iScript' to upgrade the old machine to a more automated machine. 'iScript' is a software solution that uses image recognition techniques to automate agent operation by automatically controlling the keyboard and mouse to perform agent operations using preset scripts. The agent operation system is also integrated with a modular software design. The essential modules include the Steps Control Module (SCM), Function Plug-in Module (FPM), Variable Module (VM), Condition Control Module (CCM), and Operator Control Module (OCM). The modular design makes the script setting highly expandable. It can set customized scripts for varied purposes and applications dynamically, making production process management more accessible and efficient and accomplishing many tiresome tasks necessary for industrial automation upgrades. The proposed smart automatic agent operating system has been applied to several industrial applications, and the cases of application are shared in this research. © 2022 IEEE.

关键词： Automation

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MCFTNet: Multimodal Cross-Layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy National Biodiversity Future Center NBFC Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral imaging (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification token (CLS token) that is randomly initialized and often struggles to generalize effectively. In this article, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in-depth experiments on commonly used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshones them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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SPUFChain: Permissioned Blockchain Lightweight Authentication Scheme for Supply Chain Management Using PUF of IoT

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IEEE Access 2025年 13卷 88662-88682页

作者： Assaqty, Mohammad Iqbal Saryuddin Gao, Ying Alqarni, Abeer D. Khan, Siraj Chakraborty, Mohuya Nur Siddiqi, Hafied Ahmad, Sadique South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Universitas Nahdlatul Ulama Indonesia Faculty of Engineering and Computer Science Jakarta10320 Indonesia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Research Institute for Future Media Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China University of Engineering and Management Cybersecurity Centre of Excellence Kolkata700160 India Riyadh12435 Saudi Arabia

The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a defense countermeasure that exploits the exclusive, unrepeatable features of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The distinctive proof of identity of IoT devices in the supply chain is employed by means of PUFs as inviolable IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its efficiency to resist several threat scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing lightweig

关键词： Supply chain management

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A Way to Design Fog Computing Model for 5G Network using Vanet 4

A Way to Design Fog Computing Model for 5G Network using Van...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Maruthamuthu, R. Patel, Neeraj Yawanikha, T. Jayasree, S. Alsalami, Zaid Subbarao, S.P.V. Madanapalle Institute of Technology & Science Computer Applications Andhrapradesh Madanapalle India Ies Institute of Technology and Management Ies University Computer Science & Engineering Department M.P Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College India Karpagam Institute of Technology Department of Information Technology Coimbatore641105 India The Islamic University of Babylon College of Technical Engineering Department of Computers Techniques Engineering Babylon Iraq Sreenidhi Institute of Science and Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350360165

Intelligent cities rely heavily on smart automobiles, providing portable computing as well as communication capabilities via a wireless link. By Mobile spontaneous groups, computer fog acts as a middleman between automobiles and the web, giving them access to storing, retrieving and abilities to network. With VANET, which stands processing the request to the vehicle takes fewer minutes since it is a latency-sensitive tech. The most well-known problems with fogged and VANETs are congestion and delays. In this study, we created a priority-based fogged approach to intelligent metro automobile journeys, which lowers fog compute time and postpone, to address such issues. 5G localised Multi-Access edge information workstations was also used to enhance the computer fog infrastructures in meeting timely and The caliber of Level standards, which has greatly reduced the latency and wait. In lieu of the study conducted utilizing simply a web-based design, we achieved a 20% reduction in data time. Additionally, we achieved a 35% reduction in CPU usage as compared to the use of a public cloud platform. © 2024 IEEE.

关键词： 5G mobile communication systems

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Enhanced Breast Cancer Relapse Prediction using Ensemble Learning Techniques 4

Enhanced Breast Cancer Relapse Prediction using Ensemble Lea...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Tripathi, Mukesh Kumar Ananya, Meka Murthy, V.N.L.N. Shashank, Tumma Reddy, Jale Subash Attaluri, Kaushal Vardhaman college of Engineering Department of Computer Science & Engineering Hyderabad India Vardhaman college of Engineering Department of Information Technology Hyderabad India Pegasystems Worldwide India Pvt Ltd Machine Learning/AI Team Hyderabad India

ISBN: (纸本)9798331540364

Breast cancer remains a significant health concern for women, and accurate prediction of recurrence is vital for effective treatment and improved outcomes. This research investigates the application of advanced ensemble learning techniques to enhance the reliability of breast cancer recurrence predictions. By combining Deep Neural Networks (DNN) and Artificial Neural Networks (ANN) with traditional machine learning methods, the proposed approach demonstrates significant improvements in accuracy, precision, sensitivity, specificity, F1-scores, and areas under the curve (AVCs). The study utilizes two breast cancer relapse datasets, UMCIO and WPBC, to evaluate the performance of the ensemble learning models. The results highlight the effectiveness of the proposed approach in accurately predicting breast cancer recurrence. This research contributes to the advancement of breast cancer diagnosis and treatment by providing a valuable tool for clinicians to assess the risk of recurrence and tailor treatment plans accordingly. © 2024 IEEE.

关键词： Prediction models

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Performance Evaluation of LoRa 923 MHz for the Internet of Things 10

Performance Evaluation of LoRa 923 MHz for the Internet of T...

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10th IEEE information technology International Seminar, ITIS 2024

作者： Juledi, Angga Putra Huda, Moh. Khoridatul Dwi Putra, Muhammad Taufik Delsi Samsumar, Lalu Prasetyo Adi, Puput Dani Nurdiansyah, Yanuar Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu City Indonesia Malang East Java Malang Indonesia Universitas Pendidikan Indonesia Department of Computer Engineering Bandung Indonesia Universitas Teknologi Mataram Department of Information Technology Mataram City Indonesia Research Center for Telecommunication Bandung Indonesia University of Jember Faculty of Computer Science Indonesia

ISBN: (纸本)9798331521295

This research focuses on enhancing the performance of data transmission in the Internet of Things (IoT) by leveraging LoRa technology. Specifically, it utilizes the LILYGO 923 MHz LoRa32 board, which integrates the ESP32 microcontroller with a LoRa module and additional devices like a 0.96-inch OLED display. This board also features a serial converter interface, simplifying the programming process, and uses a micro USB connection for both power and uploading code. One of the goals of this research is to enable the transmission of images using LoRa, though currently, only image conversion during transmission is implemented. The study includes reliability testing of the LoRa module over a distance of 10 km. The results show that the Packet Delivery Ratio (PDR) for Spreading Factor (SF) 7 is 50%, while for SF 12, it improves to 85%. Additionally, the Received Signal Strength Indicator (RSSI) measurements at a distance of 1000 meters with 3 Non-Line-of-Sight (NLoS) conditions yielded values of -120 dBm, -90 dBm, and -78 dBm. These results highlight the potential of LoRa technology in improving IoT data transmission, especially for long-range applications. © 2024 IEEE.

关键词： Image enhancement

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