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2024 IEEE international conference on Control Science and systems Engineering, ICCSSE 2024

作者： Li, Enshi Zhou, Li Zhu, Yan National Space Science Center of Chinese Academy of Sciences Bejing China

ISBN: (纸本)9798331517199

Due to the limitations of spacecraft data processing capabilities and the data transmission rate to the ground, the image compression system is essential for imaging payloads on satellites. JPEG2000, with its advantages of high compression ratio, high signal-to-noise ratio, support for progressive transmission, and absence of mosaic distortion, is a preferred algorithm for image compression in spaceborne cameras. In response to the complexity of the JPEG2000 algorithm and the challenges of hardware implementation, this paper presents an image compression system based on the ADV212 image compression chip for realizing JPEG2000 compression. The FPGA is utilized for image buffering and control of the compression system, while the ADV212 chip performs the compression functions. The experimental results validate the high performance and compatibility of the system. This system boasts advantages such as simple hardware and software implementation, high efficiency, superior performance, short development cycle, and low cost, which makes it suitable for aerospace applications, particularly in commercial space satellites. © 2024 IEEE.

关键词： image compression

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internet of Things Enabled Blockage Detection in Micro-Irrigation System by Using Drones 5

Internet of Things Enabled Blockage Detection in Micro-Irrig...

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5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Vijayakumar, R. Divyapriya, S. Kumar, R. Satheesh Ramya, C. Sharmila, S. Sns College of Technology Department of Eee Coimbatore India Karpagam Academy of Higher Education Department of Eee Coimbatore India

ISBN: (纸本)9798350379945

Drip irrigation systems, while efficient in water usage, often suffer from clogged emitters, leading to crop loss. To address this challenge, a drone-based solution is proposed. Equipped with image processing capabilities and controlled by loT, the drone can identify clogged emitters and potentially clear obstructions. Additionally, the system can collect salty water from the irrigation system, contributing to overall water management. This integrated approach aims to optimize water utilization, enhance crop yield, and mitigate the impacts of water scarcity in agriculture. © 2024 IEEE.

关键词： Drones

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Real-Time signal Processing in IoT-based Embedded systems Using Hybrid AI-Enhanced Edge Computing

Real-Time Signal Processing in IoT-Based Embedded Systems Us...

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2024 international conference on Artificial Intelligence and Quantum Computation-based Sensor Applications, ICAIQSA 2024

作者： Rajan, Chozha P. Ahmed, A. Sarfaraz Divya, Vyshnava Nagendar, Y. Mohan, Ashwala Atheeswaran, Athiraja Bannari Amman Institute of Technology Department of AIDS Tamilnadu Erode India Sri Eshwar College of Engineering Department of CSE Tamil Nadu Coimbatore India Malla Reddy College of Engineering Department of CSE Hyderabad India SR University Department of School of Computer Science and Artificial Intelligence Telangana Warangal India Guru Nanak Institutions Technical Campus Department of ECE Telangana Ranga Reddy India Hyderabad India

ISBN: (纸本)9798331517953

The rapid expansion of the internet of Things (IoT) has necessitated real-time signal processing capabilities in embedded systems, but traditional cloud-based models often fall short in meeting the low-latency demands of these applications. To address this, we propose a hybrid AI-enhanced edge computing framework that integrates machine learning with conventional signal processing techniques, optimizing real-time processing at the edge. Our framework was tested in a smart factory environment, where edge devices equipped with lightweight AI models were employed for signal classification and processing tasks. The results demonstrate a 40% reduction in latency, with processing times as low as 50 milliseconds per signal, and an accuracy improvement of 15% over traditional methods, achieving a 92% accuracy rate in signal classification. These findings underscore the potential of hybrid AI at the edge to significantly enhance both the speed and accuracy of real-time signal processing in IoT-based embedded systems. This approach not only meets the critical latency requirements of modern IoT applications but also improves overall system reliability, making it a promising solution for applications in smart manufacturing, healthcare, and autonomous systems. Future work should explore further optimization of AI models for edge deployment and scalability across broader IoT ecosystems. © 2024 IEEE.

关键词： Smart manufacturing

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River-GEM: Generating and Enhancing Muddy Water images

River-GEM: Generating and Enhancing Muddy Water Images

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2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Pramanick, Alik Satwik, Chivukula Sairam Kumar, Sonal Daydar, Akshay Sur, Arijit Indian Institute of Technology Guwahati India

ISBN: (纸本)9798350368741

Underwater image enhancement is crucial for marine engineering and aquatic robotics. However, most recent methods have focused on ocean environments, where they trained and tested on oceanic images. As a result, these methods are less effective in river water, where relatively blurry images are produced due to extensive muddy environments. In river water-based image restoration, we have observed two main limitations: (1) the lack of datasets that accurately represent the muddy water conditions found in river environments and (2) the limited effectiveness of current enhancement models in dealing with the unique challenges of muddy water images. To address these limitations, we introduce the "River-GEM" framework, which includes (1) a dataset of highly degraded muddy water images generated using a principle component analysis-based fusion technique and (2) a novel enhancement model utilizing the depth-map guidance to learn complex contextual details of muddy water images. The proposed model captures the intricate details of the objects in degraded images by analyzing feature correlation at multiple scales. Our model shows improvements of 3.42% in SSIM and 5.04% in PSNR over existing methods on the muddy-UIEB dataset. This dataset and enhancement network marks a significant step forward in this area of research. The dataset and code will be available at: https://***/Alik033/River-GEM. © 2025 IEEE.

关键词： image enhancement Muddy image generation principle component analysis transformer

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Enhancing Data Security Using Steganography in Spatial Domain images 2

Enhancing Data Security Using Steganography in Spatial Domai...

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2nd international conference on Emerging Trends in Information technology and Engineering, ic-ETITE 2024

作者： Wahono, Dimas Rahman, Hilmy Syarif, Hisyam De La Croix, Ntivuguruzwa Jean Ahmad, Tohari Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia University of Rwanda African Centre of Excellence in Internet of Things Kigali Rwanda

ISBN: (纸本)9798350328202

The evolution of technology in transmitting information across public networks has highlighted the need to focus on data security to safeguard information. Numerous research endeavours have introduced diverse data protection approaches to enhance the security of confidential information transferred over publicly shared networks. Nevertheless, prior solutions have fallen short of delivering effectiveness to safeguard the sensitive data. To surmount this drawback, this article introduces a new data hiding approach based on steganography paradigm to improve the confidentiality of the hidden information by utilizing adjacent pixels-based difference expansion within a digital image. Experimental outcomes indicate that our method outperforms existing approaches, notably enhancing the state-of-the-art Peak signal-to-noise Ratio (PSNR). This signifies a notable advancement in visibility quality of the stego image with the proposed method. © 2024 IEEE.

关键词： National security

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A presentation attack detection network based on dynamic convolution and multi-level feature fusion with security and reliability

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FUTURE GENERATION COMPUTER systems-THE international JOURNAL OF ESCIENCE 2023年第1期146卷 114-121页

作者： Cheng, Xin Zhou, Jingmei Zhao, Xiangmo Wang, Hongfei Li, Yuqi Changan Univ Sch Informat Engn Xian 710064 Peoples R China Minist Publ Secur Traff Management Res Inst Wuxi 214151 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Xian Technol Univ Sch Elect Informat Engn Xian 710021 Peoples R China

In the internet of Things environment, most facial recognition systems are highly susceptible to face spoofing attacks. To increase the security and reliability of such systems, this paper proposes an anti-spoofing method for facial recognition systems based on optical flow and texture features. The spoofing-detection algorithm first generates the optical flow field map of the face area using the optical flow method and a face-detection method, based on two consecutive frames of the captured face video. Then, the original RGB face area image and optical flow field map are input into a two-channel convolutional neural network to extract and fuse the features of the face. Finally, based on the optical flow and texture features, this method classifies real and fake faces. In addition, to better generate the optical flow field map containing liveness information, a motion amplification algorithm is applied to enhance the 0.04-0.4 Hz signal in the video frame by 20 times. For texture and optical flow presentation, we propose a lightweight network with dynamic convolution and multi-level feature fusion blocks. This paper used the Replay Attack spoofing dataset from IDIAP, consisting of 1300 videos, for model training, verification, and testing. Experiments showed that the algorithm proposed in the paper performed well on the Replay Attack data set and achieved a half total error rate of 0.66%.(c) 2023 Elsevier B.V. All rights reserved.

关键词： internet of Things Face fraud detection Deep learning Optical flow Dynamic convolution Featurefusion

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Scanning Electron Microscope image Data Acquisition and Transmission System 6

Scanning Electron Microscope Image Data Acquisition and Tran...

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6th international conference on Intelligent Control, Measurement and signal Processing, ICMSP 2024

作者： Zhang, Yang Wang, Zuye Zhao, Yong Hefei University of Technology School of Instrument Science and Opto-Electronics Engineering Hefei China

ISBN: (纸本)9798331516550

Scanning electron microscopy (SEM) has emerged as a pivotal tool in scientific research and engineering, offering unparalleled insights into the micro- and nano-scale world. However, traditional SEM systems often entail bulky setups and complex data acquisition processes, limiting their accessibility and efficiency in various applications. This paper designs a desktop SEM image data acquisition and transmission system based on FPGA. The system is divided into two parts: signal modulation and AD sampling circuit and image data processing and transmission circuit. By designing a circuit to process SE/BSE signals, signal modulation and analog-to-digital conversion are achieved. The sampling data is processed using the internal resources of the FPGA and transmitted to the PC through the USB3.0 high-speed interface, and finally displayed as a grayscale image. The system realizes real-time collection, processing and transmission of SEM signals, providing convenience and efficiency for scientific research and engineering applications. Experimental verification shows that the system performs well and has certain application potential. © 2024 IEEE.

关键词： Analog to digital conversion

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Content based image Retrieval System Using CNN based Deep Learning Models 2

Content Based Image Retrieval System Using CNN based Deep Le...

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2nd international conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Gautam, Giriraj Khanna, Anita Dept. of Electronics and Communication Engineering Guru Ghasidas Vishwavidyalaya Koni Bilaspur495009 India

Digital information search archives on the internet and other platforms have been an essential part of our daily lives. With the use of huge amounts of digital images, it is paramount to develop efficient solutions to retrieve images seamlessly. Due to inadequate textual description, the task of automated image retrieval is quite complicated. Therefore, retrieval of images by analysing their visual content is an exciting task and research-worthy. Text based image Search (TIBR) is time consuming as initially, every image has to be linked with a keyword or text to be extracted on query search. In the proposed system, an efficient algorithm for Content based image Retrieval (CBIR) using pre-trained CNN-based Deep Learning models to extract deep features of an image has been developed, which significantly increases the performance of the image retrieval process. Convolutional Neural Networks (CNN) has evolved as an efficient deep learning solution for the CBIR systems that work in image recognition applications. The results of retrieval are evaluated in terms of precision performance parameters. Emphasis has been given to the comparison of the efficiency of the system using two different pre-trained CNN-based Deep Learning models based on VGG16 and ResNet-50 architecture. Transfer learning enables us to develop robust and efficient working systems using pre-trained models. In the proposed system, pre-trained models have been used to derive features using a deep learning CNN network trained for a large image classification dataset imageNet. This approach outperforms many contemporary CBIR systems. As most CBIR systems depend on query images and as most users of such systems are non-professional, we have developed a user interface for the built prototype systems. The efficiency of the CBIR systems with pre-trained models VGG16, and ResNet-50 are compared for better image retrieval and reliability. The proposed system has addressed a few existing problems in CBIR system de

关键词： Convolutional neural networks

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Design of rhubarb processing control system based on internet of Things 4

Design of rhubarb processing control system based on Interne...

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4th international conference on Telecommunications, Optics, and Computer Science, TOCS 2023

作者： Sun, Lijuan Wei, Jiansheng Li, Xiangwei Zhao, Xiaoqiang Tang, Jiawei School of Computer Science and Artificial Intelligence Lanzhou Institute of Technology Lanzhou730000 China Li County Rhubarb technology Co. LTD Gansu province Li County Industrial Park Longnan Longnan730000 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

In order to improve the processing quality of traditional Chinese medicine rhubarb and control the processing process of rhubarb in real time, a programmable logic control system with Ethernet and internet of things remote communication function was designed, and the corresponding programming software and configuration software were developed, which had important engineering application value for the transformation and upgrading of traditional processing equipment of rhubarb. In this paper, based on 5G Narrowband internet of Things (NBiot) technology and industrial Ethernet technology, a programmable logic control system with remote wireless communication function is designed, and the supporting programming software platform is developed to connect to the cloud wireless network. The research results were tested in Li County Rhuai technology Co., LTD. The rhuai production yield was increased by 30%, the processing quality was effectively improved, and the effectiveness of the rhuai processing process control was guaranteed. In addition, the results can provide reference for the research and development of intelligent control systems in traditional Chinese medicine preparation industry, and realize the large-scale and industrialized development of traditional Chinese medicine processing equipment. © 2024 SPIE.

关键词： internet of things

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CGNet: Classification-Guided Multi-Task Interactive Network for Hyperspectral and Multispectral image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

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2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Lv, Guohua Xu, Yanlong Gao, Yongbiao Zhao, Guixin Wang, Guotao Sun, Xiangcheng Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Ministry of Education China Beihang University Beijing China

ISBN: (纸本)9798350368741

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets. © 2025 IEEE.

关键词： Feature interaction Hyperspectral classification Hyperspectral image image fusion

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