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Enhancing Lung Cancer Detection using VGG-16: Leveraging Deep Convolutional Neural Networks for Accurate Medical image Analysis 2

Enhancing Lung Cancer Detection using VGG-16: Leveraging Dee...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Rajaram, Renugadevi Midhunchakkaravarthy Selvam, Janani Vajravelu, Ashok Information Technology Lincoln University College Selangor Darul Ehsan Malaysia Lincoln University College Selangor Darul Faculty of Engineering Ehsan Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Malaysia

ISBN: (纸本)9798350366570

Lung cancer is one of the deadliest diseases a person can face. The course of medical treatment depends largely on the cancer's type and location. Early detection of cancer cells could save countless lives. To achieve this, an automated system is needed to identify malignant conditions as quickly as possible. Although many techniques have been proposed by researchers over the years, prediction accuracy remains a significant challenge. This paper introduces a method based on artificial neural networks to detect abnormal lung tissue growth. To enhance accuracy, a tool with a higher probability of detection is utilized. Medical professionals diagnose patients using histological images of biopsied tissue from potentially infected lungs. However, diagnosing various types of lung cancer is time-consuming and prone to error. Convolutional Neural Networks (CNNs) have improved the speed and accuracy of identifying and classifying different types of lung cancer, leading to better patient outcomes by helping identify the most effective treatment options. For this study, we used the 'Histopathological images' dataset from Kaggle, which includes lung adenocarcinomas, lung squamous cell carcinomas, and normal lung tissues. The key stages of this approach involve model selection, evaluation, pre-processing, and data collection. Metrics such as accuracy, precision, recall, and Fl-score were used to assess the proposed method. The CNN model achieved accuracy rates of 97.5% for training and 98.5% for validation. © 2024 IEEE.

关键词： Lung cancer

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AN ADAPTIVE SCHEDULING ALGORITHM FOR MULTI-TARGET ISAR IMAGING RADAR based ON GREEDY ALGORITHM

AN ADAPTIVE SCHEDULING ALGORITHM FOR MULTI-TARGET ISAR IMAGI...

引用

IET international Radar conference 2023, IRC 2023

作者： Yao, Huan Lou, Hou Wang, Dan Chen, Yijun School of Information Engineering Engineering University of the People's Armed Police Xi'an China Institute of Information and Navigation Air Force Engineering University Xi'an China The National Lab of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781839539954

With the increasing number of airborne targets and the shift towards clustered combat modes, radar systems are facing the challenge of dealing with a large number of targets within limited time. Effectively utilizing time resources to image as many targets as possible has become an urgent problem to address. This paper firstly investigates the impact of target motion on the quality of ISAR imaging. Subsequently, an adaptive scheduling algorithm for multi-target ISAR imaging radar based on greedy algorithm is *** algorithm begins by analyzing the changing trends in the equivalent rotational angular velocity of multiple targets relative to the radar system. This information is then used to determine the optimal observation period for each target. By considering target characteristics in conjunction with the optimal observation period, the greedy algorithm is applied to achieve optimal scheduling of multi-target imaging by the radar *** results demonstrate that the algorithm proposed in this paper can improve the success rate of radar imaging on multiple targets while ensuring imaging quality. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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Critical Analysis of ML-based Service Provisioning in the internet of Vehicles

Critical Analysis of ML-Based Service Provisioning in the In...

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Artificial Intelligence and signal Processing (AISP), INDIA, international conference on

作者： Mukund Joshi Ayush Patel Asis Kumar Tripathy School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

The internet of Vehicles (IoV) has brought a new era of innovation in transportation and mobility (T&M) in smart cities, made possible by the confluence of IoT and Machine Learning (ML) technology. The capabilities and applications of the internet of Vehicles (IoV) have been greatly expanded by the increasing adoption of IoT, 5G connectivity, and autonomous vehicles. Modern machine learning (ML) approaches used in IoV service delivery are examined in this review paper, emphasizing how they might improve multimedia communication quality of experience (QoE) in vital industries like healthcare. ML-based service provisioning enhances the functionality and customer experience of the automotive sector by delivering tailored, efficient, and predictive services by analyzing data from connected automobiles.

关键词： Technological innovation Smart cities Reviews Transportation Machine learning Medical services signal processing Quality of experience Multimedia communication internet of Vehicles

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image Recognition Methods based on Deep Learning 1

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3rd international conference on 3D Imaging Technologies - Multidimensional signal Processing and Deep Learning (3DIT-MSP and DL)

作者： Zhang, Zehua Univ Calif San Diego 9500 Gilman Dr La Jolla CA 92093 USA

ISBN: (数字)9789811924484

ISBN: (纸本)9789811924484;9789811924477

With the development of science and technology worldwide, computer technology has gradually been widely used in various fields like the military and biomedical domains. In this situation, people's requirements for image recognition are increasing. However, the efficiency of manual image recognition is very low, and image recognition has always been a weak field in computer technology. This paper will introduce various deep learning models such as feedforward neural networks, convolutional neural networks, and recurrent neural networks. We will talk about how these models work and then compare and analyze these different networks. In this paper, we will try to train a convolutional neural network from a data set. The "dogs-vs-cats" data set from Kaggle will be used. In the "dogs-vs-cats" experiment part, some basic tasks in image processing will be discussed, such as feature extraction method, which is one of the most important steps in each link of image processing. Finally, the machine should be able to distinguish between pictures of dogs and cats. Moreover, we will also discuss some preliminary applications of deep learning in image recognition.

关键词： Deep learning image recognition Machine learning

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Empirical Indoor signal Propagation of LoRa Link for IoT Applications

Empirical Indoor Signal Propagation of LoRa Link for IoT App...

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2023 international conference on Engineering technology and Technopreneurship, ICE2T 2023

作者： Mohd Yunus, Noor Hidayah Syahirah Ismail, Syaza Basarudin, Hafiz Bin Auni Izzati Aminudin, Nur Abd Razak, Mohd Raziff Azraie Mohd Azmi, Mohd Universiti Kuala Lumpur Communication Technology Section British Malaysian Institute Selangor Malaysia MOAI Managed Services Ericsson Malaysia Selangor Malaysia Universiti Tunku Abdul Rahman Centre for Communication Systems and Networks Department of Electrical and Electronics Engineering Selangor Malaysia British Malaysian Institute Selangor Universiti Kuala Lumpur Electronics Technology Section Malaysia

ISBN: (纸本)9798350304107

internet of Things (IoT) based applications have grown rapidly for a variety of uses for electronically embedded device frameworks to provide data exchange and correspondence information. Crucial factors must be determined for various associated communication networks to support the practicality of IoT applications. Feasibility in long-range wide area networks and offering low power consumption wireless technology networks are the factors that are highly considered. Long Range (LoRa) modulation technique is the most promising communication technology to support IoT applications. The paper aims to highlight the best signal coverage of LoRa links in indoor propagation for integrated and efficient IoT applications. This measurement was performed in the hallway of the concrete dormitory building at UniKL BMI, Selangor, Malaysia. Measurements were performed between two TTGO-ESP32 LoRa boards equipped with LoRa module SX1276 923 MHz transceiver chip by Semtech. Each experiment was performed by a mobile transmitter and a fixed receiver which tested in four different antenna gains of 2, 3, 5 and 12 dBi. The measured data validates that LoRa networks can be used in indoor environments for IoT applications. signal coverage is influenced by transmitter distance factors, antenna gain selection and signal propagation effects in the indoor environment. © 2023 IEEE.

关键词： Wireless sensor networks

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Cellular Metaverse: Enhancing Real-Time Communications in Virtual World

Cellular Metaverse: Enhancing Real-Time Communications in Vi...

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2023 international Technical conference on Circuits/systems, Computers, and Communications, ITC-CSCC 2023

作者： Munir, Aisha Siddiqi, Muhammad Zain Jeravongtakul, Siwanart Shah, Shashi Bajpai, Ambar Kovintavewat, Piya Wittisittikulkij, Lunchakorn Chulalongkorn University Wireless Communication Ecosystem Research Unit Department of Electrical Engineering Bangkok Thailand Atria Institute of Technology Bangalore India Advanced Signal Processing for Disruptive Innovation Research Center Nakhon Pathom Rajabhat University Faculty of Science and Technology Nakhon Pathom Thailand

ISBN: (纸本)9798350326413

In the next few years, various technologies including artificial intelligence, the internet of Things, virtual reality, and augmented reality will coexist to realize the objectives of 6G wireless systems. In this paper, we propose a digital twin (DT) prototype of conventional wireless communication systems in a metaverse/virtual environment. We develop the modelling scenario of a cellular communications set-up in the Unity platform at first. Then, we establish the real-time communication between BS/s and user/s' in a metaverse environment after introducing some features such as cell coverage area, operating frequency, and global positioning system (GPS). With the aid of GPS, we continuously monitor the user/s' position in the metaverse environment. Moreover, we use the C# scripting tool in the metaverse environment to make all the modelling units functional and interconnected. The experiment results show that the user/s' location can be localized in the digital world, creating the DT of the real cellular communications set-up. © 2023 IEEE.

关键词： Virtual reality

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PIFU-RGBD: Single-view RGB-D Pixel-aligned Implicit Function for 3D Human Reconstruction 5

PIFU-RGBD: Single-view RGB-D Pixel-aligned Implicit Function...

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5th international conference on High Performance Big Data and Intelligent systems, HDIS 2023

作者： Wang, Yingli Zhang, Liping Li, Weijun Dong, Xiaoli Li, Li Qin, Hong School of Semiconductor Science and Technology South China Normal University Guangzhou510631 China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Internet of Things for Smart City University of Macau China

ISBN: (纸本)9798350394160

Recent advances in image-based parsing of human bodies have been driven by the significant improvement in successful deep learning methods for 2D image processing. Although current methods have demonstrated outstanding global reconstruction capability, they still fail to process inherent depth ambiguity in 2D image images. In this paper, we propose PIFU-RGBD, a new pixel-aligned function representation method to reconstruct the complete and detailed 3D human from a single RGB-D image. The PIFU-RGBD method is mainly structured into two stages. The initial stage involves transforming a single RGB-D image into a single-view human point cloud, and then the single-view mesh is modeled based on the point cloud data, and the binocular view is rendered. Moving on to the second stage, the depth information and voxel alignment features of binocular view are obtained through the stereoscopic vision network and input into the implicit function estimation network. By using the Marching Cubes algorithm, a complete three-dimensional reconstruction of the human body model is obtained. It is worth noting that the RGBD images obtained by any camera can be converted into the input of unified camera parameters after processing in the first stage, which makes the depth information and voxel alignment features extracted in the second stage are camera-independent. The trained network performs depth-aware reconstruction under unified parameter settings. Compared with previous works, our proposed method can effectively improve the pose ambiguity problem of the reconstruction of human model with single view input, and significantly improve the reconstruction accuracy. Compared with the current SOTA method, which uses single-view RGB-D input to reconstruct the complete human body, the scheme proposed in this paper can reconstruct the human body model with accurate posture on the data captured by cameras with different parameters, and has the advantage of stronger generalization capability. ©2

关键词： Cameras

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Design and Implementation of Intelligent Vehicle Dual Vehicle Merging based on image Processing and Electromagnetic Induction 1

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6th international conference on Communication, Devices and Networking, ICCDN 2022

作者： Cao, Lei Hanzhong Vocational and Technical College Shaanxi Hanzhong723002 China

ISBN: (数字)9789819919833

ISBN: (纸本)9789819919826

The rapid development of urbanization in China has brought many serious "urban diseases", especially in the field of transportation. The situation and contradictions are becoming increasingly severe. For this reason, many intelligent transportation systems (ITS) based on various sensors (such as radio frequency, electromagnetic induction, infrared, and video processing) have been born in combination with the current research of various high and new technologies, especially the sensor technology of the internet of things. Double car merging is a common problem in traffic. To solve this problem, intelligent vehicles should be able to detect other vehicles and merge with them at the right time. Electromagnetic induction (EMI) has been used as a means of detecting the presence of another vehicle and its distance from the current position of the vehicle. However, there are some problems in the use of electromagnetic interference. This paper presents an intelligent system based on image processing technology. The system can overcome these problems and provide accurate information about the position of other vehicles and the distance between them and the current position of the first car or truck. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Merging

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A Novel Hyper Chaotic-Cellular Automata based Medical image Encryption Technique 3

A Novel Hyper Chaotic-Cellular Automata Based Medical Image ...

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3rd IEEE international conference on Electronic Engineering, ICEEM 2023

作者： El-Shafai, Walid Naguib, Ahmed Shokair, Mona Prince Sultan University Security Engineering Lab Computer Science Department Riyadh11586 Saudi Arabia Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt 6 October University Faculty of Electrical Engineering Egypt

ISBN: (纸本)9798350323511

The security of healthcare and telemedicine systems stands as a matter of paramount importance, necessitating extensive research. In the upcoming years, the medical industry envisions widespread adoption of advanced telemedicine applications. With healthcare smart devices becoming increasingly interconnected through the internet, enabling convenient access from any location, the protection of sensitive and private information, such as personal medical images, emerges as a critical focal point. As a result, the encryption of medical images assumes a pivotal role within the domain of telemedicine and healthcare applications. This article introduces a highly efficient cryptographic system designed to enhance the protection of medical images by harnessing the power of cellular automata techniques in conjunction with hyper-chaotic procedures. In the proposed medical image encryption technique, a layered cellular automata approach is embraced, and a pseudorandom sequence derived from a four-dimensional hyper-chaotic system is employed as the cryptographic key. The study encompasses both grayscale and color images. Several assessment criteria, including the Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), average entropy, correlation coefficients, Structural Similarity (SSIM), Feature Similarity (FSIM), and Peak signal-to-Noise Ratio (PSNR), are employed to evaluate the suggested cryptosystem. These evaluations vividly underscore the robust security performance exhibited by the proposed approach. © 2023 IEEE.

关键词： Entropy

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A New Method of Integrated Radar-Communication System Waveform Design and signal Processing based on OFDM Block Subcarrier Allocation 9th

A New Method of Integrated Radar-Communication System Wavefo...

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9th international conference on Intelligent IoT as a Service

作者： Xu, Zhan Quan, Cong Zhu, Weige Yang, Xiaolong Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Beijing 100071 Peoples R China Shanghai Inst Satellite Engn Shanghai Peoples R China

ISBN: (纸本)9783031705069;9783031705076

In the context of the internet of Vehicles (IoV) and sixth-generation (6G) wireless communication, wireless radar and communication (RadCom) systems face the dual challenges of improving spectrum utilization efficiency and lowering interference levels. The use of orthogonal frequency division multiplexing (OFDM) signals in a shared waveform system has the potential to mitigate mutual interference between communication and radar while also meeting the need for improved range resolution. Some researchers have proposed using the odd and even carriers of OFDM signals to modulate radar and communication functions, intending to achieve integration. Nevertheless, this methodology presents several concerns, such as the potential for interference between radar carriers and communication carriers and the negative impact caused by the cyclic prefix (CP) on radar performance. This study presents a new method that leverages OFDM and block subcarrier allocation techniques to enhance the integration of radar and communication system performance. The primary objective is to mitigate interference by consolidating radar and communication subcarriers while minimizing the detrimental effects of CP on radar performance by utilizing zero padding (ZP) as a replacement for CP. Furthermore, this method reduces pilot tone costs by utilizing radar data to estimate Doppler frequency offsets. It can dynamically adjust the number of radar and communication subcarriers to accommodate evolving performance demands. Simulation results indicate that in the presence of residual frequency offset, this solution enhances communication performance while slightly degrading radar performance.

关键词： RadCom OFDM Block Subcarrier Allocation

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