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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

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Deep Fake Detection using Transfer Learning: A Comparative study of Multiple Neural Networks

Deep Fake Detection using Transfer Learning: A Comparative s...

引用

signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International conference on

作者： Abhineswari M Kurupati Sai Charan Shrikarti BN Sujithra Kanmani R Shool of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

With the proliferation of sophisticated AI techniques, the creation and dissemination of deep fake images and videos have emerged as a pressing concern in today's digital landscape. Deep fake technology employs advanced machine learning algorithms to manipulate or synthesize realistic-looking media, often with malicious intent. This study evaluates the effectiveness of various pre-trained deep learning models using transfer learning for detecting deep fake images on the Face Forensics++ dataset. The models considered include MobileN etV2, ResN et50, Inception V3, EfficientN et, Xception, NASNetMobile, and a Custom CNN. Accuracies obtained from these models are compared to assess their performance in distinguishing between real and fake images. With the highest performance in MobileNetV2 with 89% followed by ResNet50 with 83 % and other models.

关键词： Deepfakes Accuracy Computational modeling Transfer learning Neural networks Pressing signal processing

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ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE SENSOR BIAS

ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE SENSOR...

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IET International Radar conference 2023, IRC 2023

作者： Ni, Na Jiang, Qi Mao, Huafeng Zhang, Jichuan Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Radar observation of group targets has recently received much attention in the flight mechanism research and many other applications. Group targets are usually closely spaced with lots of missed detections and false alarms, making it difficult for spatial reconstruction. Multisensor systems make use of data from multiple radars to provide more accurate measurements and robust tracks than a single radar, playing an important role in the group targets observation. In the data fusion processes, multisensor measurements are associated after transformed into a global coordinate system. However, large sensor bias makes it difficult to associate measurements of closely spaced targets given outliers, and nonideal association further increase the sensor bias estimation error. In this paper, we proposed a robust joint association and registration method to simultaneously acquire sensor bias estimates and association results. A nonlinear least median of squares estimator is used to achieve better accuracy and robustness of sensor bias estimation. Simulation results show the better bias estimation and association performance of the proposed method compared to other algorithms. © The Institution of Engineering & Technology 2023.

关键词： Radar measurement

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Direct Current Motor Malfunction Detection Through Metastatic Optimized Audio Analysis and Classification

Direct Current Motor Malfunction Detection Through Metastati...

引用

International conference on Inventive Computation Technologies (ICICT)

作者： Aleksandra Bozovic Luka Jovanovic Eleonora Desnica Ljiljana Radovanovic Miodrag Zivkovic Nebojsa Bacanin "Mihajlo Pupin" Technical Faculty University of Novi Sad Zrenjanin Serbia Technical Faculty Singidunum University Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

This study investigates the integration of advanced signal processing techniques with the AdaBoost classification algorithm for the purpose of identifying malfunctions in brushless DC motors through audio analysis. Given the increasing significance of brushless DC motors in modern manufacturing and transportation, the timely detection of anomalies becomes crucial for maintaining consistent operational efficiency and preventing potential injuries. Despite regular maintenance practices, the occurrence of unexpected mechanical and electrical failures often eludes operators, leading to the potential escalation of minor malfunctions into catastrophic failures. Consequently, the implementation of a reliable and consistent monitoring system is imperative in practical applications. The proposed solution leverages audio analysis as an effective approach. Through the amalgamation of machine learning and advanced signal processing techniques, this research employs audio signal classification to discern malfunctions in brushless DC motors from audio recordings of their operations. The extraction of dominant frequencies using the Fast Fourier Transform (FFT) enhances the analysis, and the AdaBoost classifier is subsequently employed to assess the proper functioning of the motors. Recognizing the substantial impact of hyperparameter selections on classifier performance, a modified version of the Red Fox Optimization (RFO) algorithm is introduced to meet the specific demands of this study. Results demonstrate that the optimizers, when appropriately constructed, achieve perfect accuracy when applied to a real-world dataset, highlighting the potential practical applicability of the proposed methodology.

关键词： Electric potential Brushless DC motors Fast Fourier transforms signal processing algorithms Transportation Pattern classification signal processing

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Subjective and Objective VQA of Video Codecs for UHD Video Streaming

Subjective and Objective VQA of Video Codecs for UHD Video S...

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International conference on Frontiers of signal processing (ICFSP)

作者： Ioanna Valiandi Moatasim Mahmoud Stamatia Rizou Andreas S. Panayides VIDEOMICS Group CYENS Centre of Excellence Cyprus Nicosia R&D Department Singular Logic Aristotle University of Thessaloniki R&D Department Singular Logic Athens Greece

ISBN: (数字)9798350353235

ISBN: (纸本)9798350353242

In this work, we conduct a subjective video quality assessment (VQA) experiment to evaluate the performance of modern video codecs when displayed on a large (30sqm) ultra-high-definition (UHD) video wall. Our comparative study involves four encoding standards: advanced Video Coding (AVC) / H.264, High-Efficiency Video Coding (HEVC) / H.265, Versatile Video Coding (VVC) / H.266, and AOMedia Video 1 (AV1). Moreover, the study includes the evaluation in terms of correlation performance of a set of widely used full-reference objective VQA metrics against the subjective scores. Our results showcase the perceptual superiority of the VVC over rival encoding standards, aligning with the objective quality assessment and relevant literature.

关键词： Correlation Video sequences signal processing algorithms Streaming media Encoding Quality assessment Video codecs Video recording Standards High efficiency video coding

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Advancing Brain Tumor Detection: A Cutting-Edge Machine Learning Approach Leveraging CAD Systems

Advancing Brain Tumor Detection: A Cutting-Edge Machine Lear...

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International conference on Image and signal processing and their Applications (ISPA)

作者： Mohamed Amine Guerroudji Kahina Amara Nadia Zenati Zohra Ameur Centre for Development of Advanced Technologies Algeria University of Mouloud Mammeri UMMTO Tizi-Ouzou Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

In recent decades, medical imaging has emerged as a vital field in medicine, playing a crucial role in diagnosis. Computer Assisted Diagnosis (CAD) systems have become instrumental in this arena, employing sophisticated algorithms to extract crucial information from medical images. This study presents an innovative brain cancer detection system utilizing statistical classification methods. The approach involves three key stages: firstly, the identification of regions of interest through Gradient Vector Flow (GVF) Snake models and mathematical morphology techniques; secondly, the characterization of these regions using morphological and textural parameters; and finally, employing this characterization as inputs for a Bayesian network to classify malignant and benign cancer cases. Experimental validation of the proposed approach yielded impressive results, including a 100% sensitivity rate and a classification accuracy exceeding 98% for tumor segmentation. These findings underscore the high efficacy of the proposed CAD system, showcasing its potential in enhancing cancer diagnosis and patient care.

关键词： Solid modeling Sensitivity Instruments signal processing Vectors Bayes methods Topology

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Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images

Analysis of U-Net and Modified VGG16 Technique for Mitosis I...

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International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Jithy Lijo Saleema J. S Research Scholar Christ (Deemed to be University) Bengaluru Karnataka India Christ Academy Institute for Advanced Studies Bengaluru Karnataka India Department of Computer Science Christ Deemed to be University Bengaluru Karnataka India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%.

关键词： Deep learning YOLO Wireless communication Image segmentation Histopathology signal processing algorithms signal processing

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Creating an advanced Recommendation System Integrating Collaborative Filtering and Social Media Analytics for Enhanced Customer Engagement

Creating an Advanced Recommendation System Integrating Colla...

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International conference on Communications and signal processing

作者： Kathari Santosh M Sandeep Kumar M Jahnavi N Nagasubba Reddy B. Sireesha B Kiran Bala Department of MBA CMR Institute of Technology Bengaluru India RV Institute of Management Bengaluru India Cambridge Institute of Technology Bengaluru India Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The rapid expansion of online platforms necessitates sophisticated recommendation systems to enhance user engagement. Leveraging user preferences and social interactions, the system aims to provide dynamic and tailored recommendations. Traditional recommendation systems face challenges in accuracy and personalization. Collaborative filtering struggles with the cold start problem, and social-based approaches may overlook individual preferences. Addressing these drawbacks, this paper proposes a hybrid model that combines collaborative filtering and social media analytics. This paper introduces an advanced recommendation system seamlessly integrating collaborative filtering and social media analytics to deliver real-time personalized suggestions. The novelty lies in assigning appropriate weights to recommendations based on both collaborative filtering and social influence, offering a comprehensive and accurate approach to personalized suggestions. The methodology involves defining objectives, collecting and pre-processing data, implementing the hybrid recommendation system, incorporating personalization techniques, and implementing a real-time engine. Evaluation includes key metrics such as accuracy, precision as $75 \%$, recall as $80 \%$, and user engagement. $A / B$ testing and continuous optimization based on user feedback contribute to a comprehensive assessment, showcasing the hybrid model’s effectiveness. In conclusion, this paper presents an innovative hybrid recommendation system, addressing existing drawbacks through integrated collaborative filtering and social media analytics.

关键词： Analytical models Social networking (online) Collaborative filtering Scalability signal processing algorithms Reinforcement learning signal processing

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Super-resolution Underwater Acoustic Imaging Based on Sparse Bayesian Learning

Super-resolution Underwater Acoustic Imaging Based on Sparse...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Peihui Liang Wujie Wang Wusheng Tang Pan Xu Fengjian Mao Jun Wang College of Meteorology and Oceanography National University of Defense Technology Changsha China Tianjin Institute of Advanced Technology Tianjin China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In this paper, the sparse Bayesian learning (SBL) theory and underwater acoustic imaging model are the core, and the underwater acoustic imaging model and algorithm based on sparse Bayesian learning are introduced for the purpose of improving the resolution of underwater acoustic imaging. Focusing on the underwater acoustic imaging model, firstly, the echo signal model based on the bright spot model is established, and the observation matrices of the near-field and far-field targets are established according to the discriminant conditions of the near-field and far-field respectively. Secondly, the sparse Bayesian learning theory is introduced, and the obtained SBL algorithm is combined with the underwater acoustic imaging system to explain the sparsity in the system, and it can be seen that the accuracy of the SBL algorithm is theoretically better than that of the traditional algorithm. Finally, the derived SBL algorithm is combined with the specific underwater acoustic imaging parameters for simulation. According to the simulation results, the SBL algorithm can significantly improve the imaging resolution, and because it uses probabilistic information to quickly update the hyperparameters, it can complete the signal reconstruction with high performance, and has excellent noise immunity and universality.

关键词： Simulation Superresolution Imaging signal processing algorithms Sonar signal reconstruction Bayes methods Sparse matrices Underwater acoustics signal resolution

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Beamforming for 6G ISAC Systems:A Comparative Study of Reconfigurable Holographic Surfaces and Phased Array Antennas

Beamforming for 6G ISAC Systems:A Comparative Study of Recon...

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advanced Computing Technologies (ICoACT), International conference on

作者： R. Tharaniya G. Ananthi D. Kiruthika J. Rajeshkumar Department of Electronics and Communication Engineering Thiagarajar College of Engineering Madurai India School of Computing and Information Technology REVA University Banglore India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Integrated Sensing and Communication (ISAC) is emerging as a pivotal technology for next-generation 6G networks, enabling simultaneous wireless communication and environmental sensing. This paper investigates the integration of Reconfigurable Holographic Surfaces (RHS) and phased array antennas within ISAC systems to enhance both functionalities. The proposed system leverages RHS to dynamically manipulate electromagnetic wave reflections, improving communication coverage and sensing accuracy. A comparative analysis is conducted between RHS and phased array beamforming models, emphasizing advancements in beamforming precision, interference suppression, and spectral efficiency. Numerical simulations demonstrate that RHS-based ISAC significantly outperforms conventional phased arrays, achieving up to an 8 dB signal-to-Interference-plus-Noise Ratio (SINR) enhancement in high-element configurations. The findings highlight RHS as a scalable and efficient technology for ISAC applications, underscoring its potential for future wireless communication systems. Further research is necessary to optimize hardware implementations and develop advanced beamforming algorithms for real-world deployment.

关键词： Phased arrays 6G mobile communication Wireless communication Interference suppression Accuracy Array signal processing Spectral efficiency Integrated sensing and communication Hardware signal to noise ratio

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Predictive Maintenance of Railway Tracks Using LSTM

Predictive Maintenance of Railway Tracks Using LSTM

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Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE International conference on

作者： Sharad Nigam Divya Kumar Sandeep Mukherji Saurabh Singh Tomar Suryakant Shastri Preeti Gupta CSE Department MNNIT Allahabad Prayagraj India CSE Department United College of Engineering and Research Prayagraj India AI&DS Department IIMT College of Engineering Greater Noida India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Railway tracks are the most prominent base of train movement, any defect in the track may lead to minor/major unhappening. To minimize risk and maximize safety, regular maintenance or condition-based maintenance must be performed. In this paper, a deep learning-based algorithm is proposed for automatic fault detection in railway tracks using LSTM (Long Short-Term Memory). LSTM model, an advanced version of recurrent neural networks known for their accuracy are compared with existing Deep Learning techniques to identify common railway faults, such as defects in rails and fasteners. Predictive maintenance is used to perform required maintenance before the failure of tracks by estimating RUL (Remaining Useful Life) of tracks and their components. This Proposed model is applied to the trusted source dataset which detects three defect types (DIP, XLEVEL, SURFACE). This approach enhances proactive maintenance, reduces downtime, optimizes schedules, and improves railway system reliability and safety. Our research advances predictive maintenance practices, showcasing how LSTM networks can transform railway infrastructure maintenance strategies.

关键词： Accuracy Tracking signal processing algorithms Fault location Prediction algorithms Rail transportation Maintenance Safety Long short term memory Predictive maintenance

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