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Design of JPEG2000 image Compression System Based on ADV212 for Satellites

Design of JPEG2000 Image Compression System Based on ADV212 ...

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IEEE International conference on Control Science and Systems Engineering (CCSSE)

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

ISBN: (数字)9798331517199

ISBN: (纸本)9798331517205

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.

关键词： Space vehicles image coding Satellites System performance Transform coding Systems architecture Hardware Software Field programmable gate arrays Standards

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Third International conference on Electronics and Communication;Network and Computer Technology, ECNCT 2021

Third International Conference on Electronics and Communicat...

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3rd International conference on Electronics and Communication;Network and Computer Technology, ECNCT 2021

ISBN: (纸本)9781510652101

The proceedings contain 119 papers. The topics discussed include: research on error and correction system of electric energy experimental metering device based on configuration design;two applications of machine learning on COVID-19;optimal control of slow traffic at intersections based on green building materials and signal processing technology;design of off-line detection device for spare parts of shipborne satellite communication antenna servo system;a garbage collection method based on integrity and mobile communication technology;mimic honeypot based on dual mimicry mechanism;the application value of deep convolution level set in ABUS image analysis of varicose vein of lower extremity;reliability optimization of distribution network based on the interaction between electric vehicles and distributed generations;and design of meter-reading recognition system for intelligent converter station based on machine vision technology.

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Access control based on roles and groups: Case study Building a secure Smart Hospital using BPMS

Access control based on roles and groups: Case study Buildin...

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International conference on Advanced Technologies for signal and image processing (ATSIP)

作者： Daassa Asma Machhout Mohsen Sonia Ayachi Ghannouchi Aguili Taoufik Electronics and Microelectronics Laboratory >Faculty of Sciences Monastir National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Electronics and Microelectronics Laboratory Faculty of Sciences University of Monastir Monastir Tunisia ISG Sousse University of Sousse Sousse Tunisia ENSI RIADI Laboratory University of Mannouba Mannouba Tunisia Department of Information and Communications Technology National Engineering School of Tunis SYSCOM Laboratory University of Tunis El Manar Tunis Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

In today’s e-health applications, controlling access to patients’ medical information is a real concern. In particular, the control of access has been identified as one of the requirements for the security of these systems. In a BPMS, this security requirement is not adequately addressed: it is represented by a simple actor filter where a task is performed by a particular user. However, to ensure the confidentiality of the data used in each task, this is not sufficient. This research paper explores the idea of integrating an access control filter into a BPMS, specifically the Bonita BPMS. A case study is performed for the validation of this idea. More specifically, the COVID-19 healthcare process of a Tunisian hospital is taken into account, for which we have been able to apply the proposed solution by implementing this process in Bonita and providing real-time access control based on groups and roles. This article explores the access control in a BPMS for smart hospitals. We address the problem of access control of personal information of patients and doctors by using a BPMS systems, in the Intelligent assistance system for a COVID-19 crisis unit (SMART2C).

关键词： Access control COVID-19 Hospitals image processing Buildings Process control Information filters

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Towards Green VAE: A Light Pixel-weighting Technique to Enhance Variational AutoEncoder

Towards Green VAE: A Light Pixel-weighting Technique to Enha...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Cheng Zhong Junlin Wu Ziming Feng Boan Chen Junchi Yan Dept. of CSE MOE Key Lab of AI Credit Card Center China Merchants Bank School of AI Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Variational autoencoders (VAEs) has been a popular generative model for its effectiveness, mathematical foundation, and its impact to other approaches in deep generative learning. For its relatively light-weights and easiness for training, compared with Generative Adversarial Networks (GANs) or other large-scale model e.g. Diffusion, VAEs become a viable tool in cost-efficient generative applications especially considering the so-called green AI. However, in comparison to GANs, the performance of VAEs in generating realistic images is still inferior to the state-of-the-art generative adversarial network (GAN). In this paper, we argue that this problem is at least partly due to the irrational reconstruction in VAE that all pixels are equally weighted, which is harmful to the generating ability (or density estimate). Motivated by this, we propose to compute the weights of pixels. First, we formulate the problem of finding the appropriate weights into an optimal problem, and then give an analytical solution. Moreover, we propose a method to apply the computed weights of pixels into the training pipeline with almost no computation overhead which fits with the spirit of green AI for more energy-saving AI especially for deep learning models. Experiments on MNIST, Fashion-MNIST and CIFAR-10 show that our method can significantly improves VAE in terms of FID.

关键词： Training Deep learning Autoencoders Pipelines Speech enhancement signal processing Generative adversarial networks Mathematical models Artificial intelligence image reconstruction

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A Novel Self-Supervised Contrastive Learning Framework for Masked EEG Motor imagery Modeling

A Novel Self-Supervised Contrastive Learning Framework for M...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kunkun Zhang Qianwei Zhou Haigen Hu College of Computer Science and Technology Zhejiang University of Technology Hangzhou PR China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Electroencephalography (EEG) is vital for brain-computer interfaces (BCIs) due to its non-invasive approach and high temporal resolution data capabilities, amid challenges such as data scarcity and the need for extensive labeling. Significant inter-individual variability in EEG signals further limits model generalization. Concurrently, the use of self-supervised pre-training, particularly through masked modeling, is gaining traction in time series analysis to mitigate labeling costs. Although this method involves reconstructing masked signal from unmasked series, random masking can disrupt critical temporal variations, complicating effective representation learning. We thus introduce SSL-MEMI, a novel self-supervised contrastive learning framework for masked EEG motor imagery modeling, integrating Domain Adaptive Alignment (DAA) and Multi-View Temporal-spatial Attention module (MTSA) to effectively handle EEG variability. This framework utilizes manifold-based masking to reconstruct original sequences from masked series, thereby enhancing classification accuracy. When tested on the BCI Competition IV and High Gamma datasets, SSL-MEMI outperforms existing methods, achieving top accuracies and demonstrating superior domain adaptation through reduced Global ${\mathcal{A}}$-distance scores. This study advances EEG classification and indicates broader applications for self-supervised learning in biomedical signal processing. The source code is available at https://***/KunKun-Zhang/***.

关键词： Manifolds Adaptation models Accuracy Time series analysis Contrastive learning Brain modeling Motors Electroencephalography Labeling image reconstruction

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Development Of A Deep Learning Model To Classify x-Ray Of Covid-19, Normal And Pneumonia-Affected Patients 7

Development Of A Deep Learning Model To Classify X-Ray Of Co...

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7th IEEE International conference on signal and image processing applications, ICSIPA 2021

作者： Law, Boon Kai Lin, Lih Poh Department of Electrical and Electronics Engineering Tunku Abdul Rahman University College Kuala Lumpur Malaysia

ISBN: (纸本)9781665435925

Pneumonia is commonly seen in several diseases, including Covid-19 that has put countries under lockdown today [1]. Other than antigen rapid test kit (RTK) and reverse transcription-polymerase chain reaction (RT-PCR), an alternative method to detect COVID-19 is through the examination of patients' chest radiography (CxR). However, the results of manual inspections may be false and the misdiagnosis could lead to fatal consequences such as delayed treatment and death. The manual inspection can be inconsistent, inaccurate and may differ from different individuals due to different perspectives. Often, Covid-19 xrays are misinterpreted as bacterial pneumonia. With the advancement of technology, this issue can be overcome by developing a Convolutional Neural Network (CNN) model to categorize x-ray of normal, pneumonia-affected and COVID-19 patients via deep learning. In this work, various CNN models (ResNet-50, ResNet-101, Vgg-16, Vgg-19 and SqueezeNet) were trained with the public databases that contain a combination of 1345 viral pneumonia, 1200 COVID-19 in addition to 1341 regular CxR images. The transfer learning method was employed, aided by image augmentation for training and validation of ResNet-50, ResNet-101, Vgg-16 and Vgg-19 architectures. Meanwhile, SqueezeNet was trained from scratch to investigate the importance of transfer learning to the model. The highest training accuracy achieved in this study was 97.38% by the VGG-16 model using a learning rate of 0.01 whereas the highest weighted average accuracy achieved was 94% by the VGG-16 model using a learning rate of 0.01 and the VGG-19 model using a learning rate of 0.001. The reliability and high accuracy of the CNN model would open a new avenue for the diagnosis of Covid-19. © 2021 IEEE

关键词： COVID-19

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Guess What I Think: Streamlined EEG-to-image Generation with Latent Diffusion Models

Guess What I Think: Streamlined EEG-to-Image Generation with...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Eleonora Lopez Luigi Sigillo Federica Colonnese Massimo Panella Danilo Comminiello Dept. Information Engineering Electronics and Telecommunications (DIET) Sapienza University of Rome Italy

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues. Most of the literature has focused on fMRI-to-image tasks as fMRI is characterized by high spatial resolution. However, fMRI is an expensive neuroimaging modality and does not allow for real-time BCI. On the other hand, electroencephalography (EEG) is a low-cost, non-invasive, and portable neuroimaging technique, making it an attractive option for future real-time applications. Nevertheless, EEG presents inherent challenges due to its low spatial resolution and susceptibility to noise and artifacts, which makes generating images from EEG more difficult. In this paper, we address these problems with a streamlined framework based on the ControlNet adapter for conditioning a latent diffusion model (LDM) through EEG signals. We conduct experiments and ablation studies on popular benchmarks to demonstrate that the proposed method beats other state-of-the-art models. Unlike these methods, which often require extensive preprocessing, pretraining, different losses, and captioning models, our approach is efficient and straightforward, requiring only minimal preprocessing and a few components. The code is available at https://***/LuigiSigillo/GWIT.

关键词： Neuroimaging Adaptation models Visualization Streaming media Functional magnetic resonance imaging Diffusion models Brain modeling Electroencephalography Real-time systems Spatial resolution

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Multi-Task Joint 3D Swin Transformer Learning for Segmentation and Classification of Hyperspectral Medicine images

Multi-Task Joint 3D Swin Transformer Learning for Segmentati...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Dong Zhang Meijun Sun College of Intelligence and Computing Tianjin University Tianjin China Institute of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral images had made many applications in the medical field with their rich spectral information. However, there were currently problems with feature extraction based on hyperspectral images, especially in extracting contextual feature information from spectral bands, and a single convolutional kernel may restrict the receptive field and inadequately capture the sequential properties of the data. Meanwhile, due to the large data volume of hyperspectral images, current medical hyperspectral images focus more on individual segmentation or classification. This paper proposed a 3D swin transformer with multi-task joint learning framework, to simultaneously learn multiple tasks for hyperspectral tongue images. Based on the 3D swin transformer model, the framework regards cross-band context feature learning of hyperspectral images as a sequence-to-sequence prediction process. The 3D transformer encoder used as the basic framework for shared feature extraction, and set up corresponding decoders for prediction according to different visual tasks such as segmentation and classification. We conducted simultaneous segmentation and classification tasks of the tongue coating region on the hyperspectral image of tongue images. The results showed that the proposed model had good results in single tasks of segmentation and classification, and performed better than other multi-task convolutional neural network models.

关键词： Training image segmentation Visualization Tongue Three-dimensional displays Transformers Feature extraction Multitasking Hyperspectral imaging Biomedical imaging

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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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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Guohua Lv Yanlong xu Yongbiao Gao Guixin Zhao Guotao Wang xiangcheng Sun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China Shandong Key Laboratory of Ubiquitous Intelligent Computing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Representation learning Three-dimensional displays Attention mechanisms Convolution Streaming media Multitasking Speech processing image fusion Information exchange Hyperspectral imaging

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Focal Modulation Based End-to-End Multi-Label Classification for Chest x-Ray image Classification

Focal Modulation Based End-to-End Multi-Label Classification...

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IEEE signal processing and Communications applications (SIU)

作者： Şaban Öztürk Tolga Çukur Elektrik ve Elektronik Mühendisligi Bölümü Bilkent Üniversitesi Ankara Türkiye Elektrik ve Elektronik Mühendisligi Bölümü Amasya Üniversitesi Amasya Türkiye Ulusal Manyetik Rezonans Araştırma Merkezi (UMRAM) Bilkent Üniversitesi Ankara Türkiye

Chest x-ray imaging is of critical importance in order to effectively diagnose chest diseases, which are increasing today due to various environmental and hereditary factors. Although chest x-ray is the most commonly used device for detecting pathological abnormalities, it can be quite challenging for specialists due to misleading locations and sizes of pathological abnormalities, visual similarities, and complex backgrounds. Traditional deep learning (DL) architectures fall short due to relatively small areas of pathological abnormalities and similarities between diseased and healthy areas. In addition, DL structures with standard classification approaches are not ideal for dealing with problems involving multiple diseases. In order to overcome the aforementioned problems, firstly, background-independent feature maps were created using a conventional convolutional neural network (CNN). Then, the relationships between objects in the feature maps are made suitable for multi-label classification tasks using the focal modulation network (FMA), an innovative attention module that is more effective than the self-attention approach. Experiments using a Chest x-ray dataset containing both single and multiple labels for a total of 14 different diseases show that the proposed approach can provide superior performance for multi-label datasets.

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