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Machine Learning-driven Antenna Selection Strategies for Fading MIMO systems 2

Machine Learning-driven Antenna Selection Strategies for Fad...

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2nd IEEE international conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350390179

In this study, we investigate better performing antenna selection strategies in wireless communication systems based on machine learning algorithms. Reinforcement learning as well as neural networks among others are analysed for different metrics such a bit error rate (BER), signal-to-noise ratio (SNR) and data throughput. These results show that these methods are working well against the dynamic changing of channel conditions to provide reliable and efficient. The computational complexity analysis suggests affordable overhead, allowing the feasibility of real-time implementation. Although this method can simulate the scenario in a real environment, it is not scalable due to simulation fidelity and antenna array. On the one hand, future research will refine and expand upon these ML models with real-time adaptation capabilities - On the other hand significant field trials are needed to put this work into practice for specific applications making sure that we develop robust adaptive wireless communication systems in practice. © 2024 IEEE.

关键词： Adversarial machine learning

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Deployment of Proposed EfficientNeXt on NXP *** 8M Mini 6

Deployment of Proposed EfficientNeXt on NXP *** 8M Mini

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6th international conference on Information and Computer Technologies, ICICT 2023

作者： Deokar, Abhishek El-Sharkawy, Mohamed Purdue School of Engineering and Technology Indianapolis Department of Electrical and Computer Engineering Indianapolis United States

ISBN: (纸本)9798350300956

Convolutional Neural Networks make tasks of computer vision like image classification and object tracking possible. The advances in accelerator hardware have made the progress in neural networks possible. Accelerator hardware is prevalent on desktops and high-end computing systems and may not always be available on low compute devices deployed on the Edge of internet of Things. The capabilities of neural network need to be ported to hardware that can run without accelerators. Benchmark setting neural networks like EfficientNet are too heavy for deployment on systems with low compute capabilities and can benefit from reduction in their memory footprint and optimized to improve their inference times. To this end we propose the design of EfficientNeXt and demonstrate its inference capabilities with reduced memory footprint (by 56%), increased accuracy and reduced inference time (by 30%) on an ARM based device. © 2023 IEEE.

关键词： internet of things

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6th international conference on Soft Computing and signal Processing, ICSCSP 2023

6th International Conference on Soft Computing and Signal Pr...

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6th international conference on Soft Computing and signal Processing, ICSCSP 2023

ISBN: (纸本)9789819986279

The proceedings contain 54 papers. The special focus in this conference is on Soft Computing and signal Processing. The topics include: Stock Price Prediction Using LSTM, CNN and ANN;IoT-based Smart Wearable Devices Using Very Large Scale Integration (VLSI) technology;plant Disease Detection and Classification Using Artificial Intelligence Approach;camera and Voice Control-based Human–Computer Interaction Using Machine Learning;optimal Crop Recommendation by Soil Extraction and Classification Techniques Using Machine Learning;ioT-based Smart Irrigation System in Aquaponics Using Ensemble Machine Learning;Performance Evaluation of MFSK Techniques Under Various Fading Environments in Wireless Communication;prediction of Breast Cancer Using Feature Extraction-based Methods;review and Design of Integrated Dashboard Model for Performance Measurements;anomaly Detection in Classroom Using Convolutional Neural Networks;Efficient VLSI Architectures of Multimode 2D FIR Filter Bank using Distributed Arithmetic Methodology;implementation of an Efficient image Inpainting Algorithm using Optimization Techniques;A Systematic Study and Detailed Performance Assessment of SDN Controllers Across a Wide Range of Network Architectures;Classification and Localization of Objects Using Faster RCNN;an image Processing Approach for Weed Detection Using Deep Convolutional Neural Network;detection of Leaf Black Sigatoka Disease in Enset Using Convolutional Neural Network;detecting Communities Using Network Embedding and Graph Clustering Approach;Deep Learning Approaches-based Brain Tumor Detection Using MRI images—A Comprehensive Review;Predicting Crop Yield with AI—A Comparative Study of DL and ML Approaches;An Empirical Analysis of Lung Cancer Detection and Classification Using CT images;trust and Secured Routing in Mobile Ad Hoc Network Using Block Chain.

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6th international conference on Soft Computing and signal Processing, ICSCSP 2023

6th International Conference on Soft Computing and Signal Pr...

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6th international conference on Soft Computing and signal Processing, ICSCSP 2023

ISBN: (纸本)9789819984503

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Robust JPEG image Steganography Using Wavelet Domain SVD and Adaptive QIM 8

Robust JPEG Image Steganography Using Wavelet Domain SVD and...

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8th international conference on signal and image Processing, ICSIP 2023

作者： Liu, Jing Song, Xiaofeng Li, Guopeng Han, Kun Xi'An University of Posts & Telecommunications Information Network Center Xi'an710121 China National University of Defense Technology Test Center Xi'an710106 China

ISBN: (纸本)9798350397932

Massive images in internet networks provides rich cover objects for covert communication using digital steganography. However, the lossy processing such as image compressing and image scaling often take place in some network platforms, which makes the existing image steganography algorithms designed for lossless environment unusable for lossy network platforms. based on singular value decomposition (SVD) in stationary wavelet domain, a robust and adaptive image steganography algorithm is proposed, which can resist lossy image compression and extract the embedded secret message correctly. First, stationary wavelet transformation is performed for cover image, and the low-frequency subband is divided into non-overlapping blocks, and SVD is performed for each block;second, considering the stability of maximum singular value, the cover elements are extracted by applying a adaptive quantization index modulation (QIM) process on the maximum singular values;third, the embedding distortion function is defined according to the influence of the cover element change on the inter-block and intra-block wavelet coefficients, and the secret messages are embedded using syndrome-trellis code (STCs);finally, the adaptive QIM is used to embed the secret elements and generate the stego image. The experimental results show that the proposed steganography algorithm has better robustness and stronger anti-detection capabilities. © 2023 IEEE.

关键词： Singular value decomposition

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A Deep Fake Detection System Using Diffusion Model based on Graph based image Segmentation 5

A Deep Fake Detection System Using Diffusion Model Based on ...

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5th international conference on Machine Learning and Intelligent systems, MLIS 2023

作者： Al-Din, Basim Najim Jaber, Karimpour Mahan, Farnaz Computer Science Department University of Tabriz 29 Bahman Blvd Tabriz*** Iran

ISBN: (纸本)9781643684468

With the development of technology and the increase in the need to use the internet and the transmission of personal data and save it on the cloud and personal computers and with the increase in security risks represented by penetrations and cyber threats, and among these threats are those represented by the use of deep learning techniques to produce deep fake images that are mostly used to violate cyber security through threats such as the ransom threat or publishing fake news and other cyber risks, so there was an urgent need to establish systems to detect deep forgery in images or videos, in this paper a system was proposed to detect deep forgery based on the principle of using the diffusion model based on the graph based on image segmentation where the system transforms the image to graph and then segment it into three areas which are fake, real and background. And do purification of the adversarial content in these areas using diffusion technology, and then identify the fake areas in the image and use GCN in order to distinguish between real and fake images. In order to evaluate the proposed method, it was compared with the other methods on different data sets (CELEB-DF (V2) data set, Face Forensics++ data set, and wild deep fake). The result shows that the proposed system achieved better results than the approaches in the literature. © 2023 The authors and IOS Press.

关键词： Personal computers

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FedDistillSC: A Distributed Semantic Communication System based on Federated Distillation 23

FedDistillSC: A Distributed Semantic Communication System Ba...

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Xing, Huanlai Ma, Huaming Xiao, Zhiwen Zhao, Bowen Peng, Jincheng Luo, Shouxi Feng, Li Xu, Lexi School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Research Institute China United Network Communications Corporation Beijing China

ISBN: (纸本)9798350325959

In this paper, we propose a federated-distillation-based distributed semantic communication system for image classification tasks, called FedDistillSC, which considers multiple transmitters with varying channel conditions. After local training with public and private datasets, the local models of each transmitter are knowledge distilled by weighted aggregation based on the classification probabilities generated at the receiver to achieve knowledge sharing without exposure of private data, improving the semantic extraction capability of each transmitter. Moreover, our method does not upload/download model parameters, thus saving bandwidth resources and avoiding data security problems caused by model parameter leakage. Simulation results show that the proposed FedDistillSC exhibits excellent Top-1 accuracy performance under three channels. Especially for those transmitters with low signal-to-noise ratio, knowledge distillation could signiflcantly improve their semantic extraction capability. © 2023 IEEE.

关键词： Federated Learning image Classification Knowledge Distillation Semantic Communication

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Comparison of image quality of intra-frame high dynamic range infrared imaging with multiple integration time arrangements

Comparison of image quality of intra-frame high dynamic rang...

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2024 conference on Spectral technology and Applications, CSTA 2024

作者： Hao, Yingjian Zhang, Jiqing Yao, Libin Chen, Nan Pu, Yuesheng Zhu, Fang Shi, Yifeng Kunming Institute of Physics Yunnan Kunming650223 China

ISBN: (纸本)9781510683082

Intra-frame HDR imaging with multiple integration times effectively expands the dynamic range of infrared focal plane array systems at the cost of image quality decreasing due to the loss of spatial resolution. To obtain a positive strategy for improving the intra-frame HDR image quality, a comparison of infrared images obtained from six different integration time arrangements is made. Four image quality assessment metrics, peak-signal-to-noise ratio, structural similarity index measure, natural image quality evaluator and perception-based image quality evaluator are calculated for comparative analysis. The comparison result shows that the 1/3 2/4 arrangement with four different integration times exhibits the best improvement in image quality and demonstrates that the 1/3 2/4 arrangement has excellent visualization effect in high-quality intra-frame HDR imaging applications. © 2024 SPIE.

关键词： Infrared imaging

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Enhancing image Quality to Improve Medical image Classification: Application to Nuclear Medicine Planar images

Enhancing Image Quality to Improve Medical Image Classificat...

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18th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Boukhennoufa, Ouassim Zerhouni, Noureddine Comas, Laurent Nicod, Jean-Marc Boulahdour, Hatem SUPMICROTECH CNRS Institut FEMTO-ST BesançonF-25000 France CHUBesançn Médecine Nucléaire BesançonF-25000 France Université de Franche-Comté SINERGIES BesançonF-25000 France

Nuclear Medicine images are obtained by injecting small amounts of radio-tracers into the body to track specific organs. Particular cameras detect radiations emitted from the radio-tracers resulting in images that visualize the function of the organs rather than their structure. The association of the cameras and radio tracers causes low resolution and low signal-to-noise ratio, therefore, the images are often of poor quality. image Quality Enhancement (IQE) is one possible solution to this problem as it improves the clarity of the images by removing noise and correcting distortions. In this paper, we propose a methodology based on artificial intelligence (AI) with the integration of an IQE step for the detection of normal/abnormal parathyroid glands. Two different IQE techniques are employed, one based on a statistical filter and the other on AI. The enhanced images are processed with a Convolutional Neural Network (CNN), and Lasso regression for features extraction and selection. Finally, several AI models are used for binary image classification. The obtained results achieved an accuracy of 83% in distinguishing normal/abnormal parathyroid glands. IQE step significantly improves the accuracy of the AI model by 16.9% over the initial accuracy of 71%, demonstrating the importance of IQE in assessing image classification performance. © 2025 by SCITEPRESS– Science and technology Publications, Lda.

关键词： Nuclear medicine

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FPGA-based Implementation of the Quantized Neural Network for image Super-Resolution 8

FPGA-Based Implementation of the Quantized Neural Network fo...

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8th international conference on image, Vision and Computing, ICIVC 2023

作者： Ruan, Zhihao Xiao, Shanzhu Deng, Qiuqun Tao, Huamin Wu, Jiadong Zhang, Huan College of Electronic Science and Technology National University of Defense Technology Chang Sha China

ISBN: (纸本)9798350335231

The convolutional neural network(CNN)-based method for image super-resolution(SR) reconstruction has been becoming an important part in the fields of image processing such as security monitoring, object tracking. However, the huge computational complexity of neural networks make it shackled to satisfy the requirements for power consumption and complexity of reconstruction systems in specific application circumstances. In this paper, we propose a structure-unsophisticated and parameter-slight CNN model, and design a quantization scheme that allows inference to be performed by using integer-only arithmetic to alleviate parameter storage and communication pressure in hardware. Furthermore, convolution loop unroll and parallel acceleration analysis are performed for the model under the premise of board resources to optimize hardware inference. The results show that when the quantization precision is 8 bits, memory requirement of parameters is reduced by 75%, and the inference peak signal-to-noise ratio(PSNR) value loss is less than 1%. Quantization model was performed on the Xilinx Zynq7035 platform with inference losses not exceeding 2%, while hardware resources consumption is greatly reduced. © 2023 IEEE.

关键词： Acceleration

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