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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20484 条记录，以下是251-260 订阅

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Hybrid Framework for Medical image Classification: Integrating Quantum DE and Deep Learning

Hybrid Framework for Medical Image Classification: Integrati...

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2024 ieee international conference on Intelligent signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Dixit, Abhishek Mani, Ashish Gorbachev, Sergey School of Mathematics and Big Data Chongqing University of Education China Amity School of Engineering and Technology Amity University Department of EEE Noida India

ISBN: (纸本)9798350379525

This paper 1 presents an innovative hybrid framework for medical image classification, combining Quantum Differential Evolution (QDE) with deep learning techniques. Initially, wavelet and Gaussian filters are applied in the preprocessing stage to enhance image features. Otsu thresholding is then used for image segmentation to identify regions of interest. The main novelty of this approach is the integration of the Quantum Differential Evolution algorithm, an optimization technique inspired by quantum computing principles. This framework utilizes QDE's global exploration abilities to optimize key features from segmented medical images. These optimized features are subsequently input into a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture for accurate classification. The core innovation of this approach lies in employing Quantum Differential Evolution for an efficient and adaptive optimization process, enhancing feature extraction. Experimental results highlight the effectiveness of this Quantum Differential Evolution-based framework, demonstrating improved classification accuracy and robustness. This model represents a significant advancement in medical image classification, illustrating the benefits of merging quantum-inspired optimization with deep learning. This unique hybrid methodology contributes to the progress of medical image analysis and classification systems. © 2024 ieee.

关键词： Convolutional neural networks

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ADAPTXRAY: VISION TRANSFORMER AND ADAPTER IN X-RAY imageS FOR PROHIBITED ITEMS DETECTION 31

ADAPTXRAY: VISION TRANSFORMER AND ADAPTER IN X-RAY IMAGES FO...

引用

2024 international conference on image Processing

作者： Huang, Yaobin Gao, Hongxia Li, Xiaomeng South China Univ Technol Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Prohibited items detection refers to the non-contact inspection of passenger baggage for potential threats through X-ray image. Since the uncertainty of artificial security screening, previous research has mainly concentrated on direct transfer by universal detection frameworks based on natural image and design in enhance or aware module with salient features like edge and color. With the increasing complexity in both categories and quantities of X-ray security inspection, the unreliability of direct transfer and complication of taskspecific design make the existing algorithms difficult to reliably and efficiently adapt the complex security inspection. To address this challenge, we propose the Adapter in X-ray (AdaptXray), which firstly explores pre-trained Vision Transformer with powerful representation and Parameter Efficient Transfer Learning method applying for prohibited items detection. Specifically, we design Color Prior Extractor to perceive local prior features from different color spaces. Subsequently, we develop Global-aware Self-Adapter to adaptively perceive and optimize the global universal features in the backbone. Additionally, we propose Local-aware Interactive Adapter to incorporate prior knowledge into the pretrained backbone. Thorough experimentation on two public baggage datasets, namely OPIXray and PIDray, demonstrates that the effectiveness of our proposed method, outperforming the existing renown CNN-based detection approaches.

关键词： Prohibited items detection X-ray image Deep learning Vision Transformer Parameter Efficient Transfer Learning

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The Method of Industrial internet image Super-resolution based on Transformer 16

The Method of Industrial Internet Image Super-resolution Bas...

引用

16th ieee international conference on signal Processing (ICSP)

作者： Liu, Lin Yu, Yingjie Wang, Juncheng Jin, Yi Zeng, Yuqiao China Automot Technol & Res Ctr Co Ltd Automot Data China Tianjin Co Ltd Tianjin Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China

ISBN: (数字)9781665460569

ISBN: (纸本)9781665460569

As a fundamental task in the field of computer vision, super-resolution reconstruction has a wide range of applications on the industrial internet. However, most of the existing super-resolution reconstruction methods are based on convolutional neural networks for feature extraction, which have the problems of low model representation efficiency and insufficient explanation of the extraction process, which limit the performance of the model in industrial internet scenarios. In recent years, although transformers have been proposed to solve the above problems well and achieve good results in image classification tasks, there is still room for improvement in the adaptability of super-resolution reconstruction tasks. Therefore, in this paper, an improved transformer-based image super-resolution reconstruction model is designed to effectively improve the performance of the image super-resolution reconstruction model. Experiments show that the adaptation of the Transformer mechanism on the super-resolution reconstruction task in this paper can practically improve the performance of the model on public datasets and industrial internet scenario datasets.

关键词： Super-resolution Reconstruction Transformer Technologies Industrial internet

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IoT based Localization and Tracking by Using MIMO Antenna technology 4

IoT Based Localization and Tracking by Using MIMO Antenna Te...

引用

4th international conference on signal Processing and Communication, ICSPC 2023

作者： Prakash, P. Manoj, G. Immanuel.j, Samson Karunya Institute of Technology and Sciences Ece Department Coimbatore India

ISBN: (纸本)9798350300772

Multiple aspects of our life could benefit from the IoT. A component of the 'connected house' is the internet of Things (IoT). All the programs below are the ones that need to be localized in some form. When applied to the first, IoT allows for the accurate localization of anything, even living creatures and humans. The second component tracks mobile targets such as toddlers and the elderly. When it comes to home safety and surveillance, smart homes offer features like geolocation and monitoring. A localization system is able to track almost anywhere or anything by combining information from multiple sources, such as wired and wireless networks. As time goes on, the internet of Things (IoT) becomes increasingly important and influences our daily lives in tangible ways. There is growing interest in the field of navigation IoT-based systems due to their widespread use in context-aware and localizationaware applications such as tracking, healthcare, and security. This subject has received a lot of attention as sensor technology and computational capacities improve. This study presents a brief overview of the localization techniques used by internet of Things-based indoor navigation systems at present. © 2023 ieee.

关键词： internet of things

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Skin Cancer Detection using image Processing and Machine learning 3

Skin Cancer Detection using Image Processing and Machine lea...

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3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Rajkumar, S. Jaiswal, Shivam Jenila Livingston, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

Skin cancer, with melanoma being the most perilous type, has a low survival rate. Detecting melanoma early can greatly enhance survival prospects. Skin cancer detection technology consists of the key components: image preprocessing (involving tasks like hair removal, noise reduction, and resizing), segmentation (used to isolate the region of interest), feature extraction from the segmented image, and image classification based on extracted features. In the proposed system section Convolution Neural Network (CNN) has been used as a base algorithm for training my model followed by training my model on ResNet and DenseNet and based on the evaluation results. Fine-tuning has been applied to adjust the learning rate dynamically. Different methods can be used for segmentation. Some commonly used segmentation algorithms are threshold in the histogram, etc., features extraction from the segmented image, and classification of the image from the features set extracted from the segmented image. © 2024 ieee.

关键词： Diseases

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Supervised Machine Learning Method based Modulation Formats Classification for WDM systems 8

Supervised Machine Learning Method based Modulation Formats ...

引用

8th ieee international conference on image and signal Processing and their Applications, ISPA 2024

作者： Kebaili, Rima Driz, Samia Fassi, Benattou University of Djillali Liabes Telecommunications and Digital Signal Processing Laboratory Faculty of Electrical Engineering Sidi Bel Abbes22000 Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309249

Recently, Machine Learning (ML) play a significant role in the design and optimization of new generation optical systems that are intelligent, adaptive, and efficient, enabling a wide range of new applications in the field of optical telecommunications. In this work, ML algorithm is proposed to classify the two common intensity modulation formats, such as Non-Return-to-Zero (NRZ) and Return-to-Zero (RZ) used in Wavelength Division Multiplexing (WDM) system. ML-based modulation classification involves to classify a signal based on its features that can include statistical features, such as the mean and variance of the signal, spectral features such as the power spectral density (PSD), autocorrelation function, constellation diagram, Bit Error Rate (BER), Q-Factor and signal-to-Noise Ratio (SNR). The proposed system was trained on a dataset of signals with the known modulation schemes, NRZ and RZ which is pre-processed to extract features, notably BER, Q- Factor and SNR. Once the dataset is prepared, an ML algorithm was trained and will output a predicted modulation format based on the learned relationship between the features and the modulation format to classify new signals. The obtained results based on Pearson correlation matrix and VIF (Variance Inflation Factor) factor indicate that from a dataset of Q-Factor as a function of the distance link, the ML-based modulation classification help in optimizing the design of WDM systems by allowing for more precise control and tuning of the used modulation format. © 2024 ieee.

关键词： Bit error rate

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OffloaDNN: Shaping DNNs for Scalable Offloading of Computer Vision Tasks at the Edge 44

OffloaDNN: Shaping DNNs for Scalable Offloading of Computer ...

引用

44th ieee international conference on Distributed Computing systems (ICDCS)

作者： Puligheddu, Corrado Varshney, Nancy Hassan, Tanzil Ashdown, Jonathan Restucciat, Francesco Chiasserinit, Carla Fabiana Politecn Torino Turin Italy Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA Air Force Res Lab Wright Patterson AFB OH USA Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9798350386066;9798350386059

Emerging mobile applications often require the execution of computer vision (CV) tasks based on compute- and memory-intensive deep neural networks (DNNs). Although offloading CV tasks to edge servers can decrease resource consumption at the mobile devices, it poses the challenge of handling multiple concurrent tasks with limited computing and memory capacity. In stark opposition with the existing state of the art, we tackle this challenge by jointly optimizing (i) the utilization of resources at the edge, among which memory - so far widely overlooked - and the radio resources used for task offloading;(ii) which and how many offloaded tasks should be executed;and (iii) the structure of the DNNs. First, we formulate the DNN for scalable Offloading of Tasks (DOT) problem, prove that it is NP-hard, and envision a weighted-tree-based heuristic solution, named OffloaDNN, that efficiently solves the DOT problem. We evaluate OffloaDNN through extensive numerical analysis using state-of-the-art image classification ResNet-18, as well as real-world experiments on the Colosseum emulator. The numerical results show that, in small-scale scenarios, OffloaDNN matches the optimum very closely, and, in larger-scale scenarios, increases the number of admitted offloaded tasks by 26.9% with respect to the state of the art, while saving 82.5% memory and 77.4% per-inference computing time. The numerical results are confirmed by the real-world validation on Colosseum.

关键词： image classification

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Neural Network Approach to Channel estimation in Wireless Communication 10

Neural Network Approach to Channel estimation in Wireless Co...

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10th ieee international conference on Electronics, Computing and Communication Technologies (ieee CONECCT)

作者： Tangudu, Narendranath Durga Kumar, Swaraj Vankayala, Satya Kumar Yoon, Seungil Samsung R&D Inst Stand Res Grp Bengaluru India Samsung R&D Inst Call SW Grp Network Call & OAM Team Bengaluru India Samsung Elect OAM SW R&D Grp Suwon South Korea

ISBN: (纸本)9798350385939;9798350385922

To meet the high throughput and stringent Quality of Service (QoS) requirements of the fifth and the sixth generation (5G and 6G) users, operators have explored technologies such as Multi-Input Multi-Output (MIMO), massive MIMO (mMIMO), Base Station (BS) densification, Non-Orthogonal Multiplex Access (NOMA) etc. The Central Unit (CU) and Distributed Unit (DU) split architectures defined in Open Radio Access Network (ORAN) and 3rd Generation Partnership Project (3GPP), gives the possibilities to explore the software-based methods, which were restricted earlier due to hardware-centric systems. With software defined Radio Access Network (RAN) at cloud and artificial intelligence being a promising domain for solving various problems, these two can be leveraged in exploring to solve or optimize various existing challenges like channel estimation in the wireless receivers, which is very computationally expensive. In this work, we attempted to improve the performance of the compute intensive module channel estimation using machine learning architecture. Convolutional Neural Network (CNN) based image processing architectures, Super Resolution CNN (SRCNN) and Denoising CNN (DnCNN) were implemented for channel estimation module, along with various combinations of spatial data arrangements of the input signal. The spatial data arrangement of complex and real components of input signal outperformed and yielded better accuracy compared to traditional methods such as estimated Minimum Mean Square Error (MMSE) and Approximate Linear Minimum Mean Square Error (ALMMSE).

关键词： Channel estimation convolutional neural network image super resolution image denoising machine learning

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Parking Space Monitoring - A LoRa based Approach 11

Parking Space Monitoring - A LoRa Based Approach

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11th international conference on internet of Everything, Microwave Engineering, Communication and Networks, IEMECON 2023

作者： Mallikarjun, B.C. Anantha Krishna, G. Mahantesha, B.V. Monish, K.T. Kishore, E. Siddaganga Institute of Technology Dept. of Ete Karnataka state Tumkur India

ISBN: (纸本)9781665475129

A huge growth of big cities, the number of vehicles are increased significantly and large buildings and parking lots have been built. Smarter parking systems need to be developed to assist drivers in finding nearby parking spaces available. Various research efforts have been shown in developed countries in such system implementation. There are four main types of systems of parking management: wired sensor based, image based, counter based, and based on wireless sensors. This paper combines computer vision and LoRa wireless technology and worked on image-based system to find empty slot in the parking lot. Following the first edge detection category, includes front/rear pixel rate, closed contour density, and edge density. For all car parks to determine if the vehicle exists or not and to update this information to the user using wireless technology in a way that saves power and performance offline while minimizing system maintenance costs. © 2023 ieee.

关键词： image processing

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A Secure and Distributed Decision based Handover Scheme for Low Earth Orbit Satellites in 6G internet 21

A Secure and Distributed Decision Based Handover Scheme for ...

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21st international Bhurban conference on Applied Sciences and technology, IBCAST 2024

作者： Arshad, Muhammad Liu, Jianwei Usman, Muhammad Khalid, Waqar School of Cyber Science and Technology Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (纸本)9798331516680

The space internet offers wireless communication services at any location and anytime. Therefore, satellite communication plays a vital role in the 6G internet. However, the rapid movement of LEO satellites leads to quick transfers between ground stations. Previous research has focused on increasing authentication security ahead of handover while minimizing signalling and computation complexity. The rapid movement of satellites could lead to signaling problems and potential security risks at handover operation. Efficient and secure LEO satellite handover operation is essential to achieve broad coverage in 6G internet. This study proposes a secure and decentralized LEO satellite handover mechanism with ground stations. In the approach, LEO satellites make predictable decisions based on predictable trajectory, signal strength, and link reliability. Following predicted decisions, blockchain applications are utilized to accomplished a secure handover mechanism. The generated simulation outcomes demonstrate that the proposed scheme effectively manages the handover of LEO satellites to a different ground station in satellite communication. The performance analysis demonstrates less computing cost and signal overhead in handover operation. © 2024 ieee.

关键词： Satellite communication systems

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