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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： David Montero César Aybar Chaonan Ji Guido Kraemer Maximilian Söchting Khalil Teber Miguel D. Mahecha Remote Sensing Centre for Earth System Research (RSC4Earth) Leipzig University Institute of Earth System Science & Remote Sensing Leipzig University German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Image Processing Laboratory Universitat de València Image and Signal Processing Group Leipzig University Department of Remote Sensing Helmholtz Centre for Environmental Research (UFZ)

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature of ESDCs unlocks significant opportunities for Artificial intelligence (AI) applications. By providing well-organised data, ESDCs are ideally suited for a wide range of sophisticated AI-driven tasks. An automated framework for creating AI-focused ESDCs with minimal user input could significantly accelerate the generation of task-specific training data. Here we introduce cubo, an open-source Python tool designed for easy generation of AI-focused ESDCs. Utilising collections in SpatioTemporal Asset Catalogs (STAC) that are stored as Cloud Optimised GeoTIFFs (COGs), cubo efficiently creates ESDCs, requiring only central coordinates, spatial resolution, edge size, and time range.

关键词： Earth Instruments Training data Geoscience and remote sensing Spatiotemporal phenomena Sensors Task analysis

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Application-specific compression of large MD data preserving physical characteristics

Application-specific compression of large MD data preserving...

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ieee symposium on Large Data Analysis and Visualization (LDAV)

作者： Patrick Gralka Sebastian Grottel Guido Reina Thomas Ertl Department of Mathematics Instituto Politecnico Nacional Mexico Department of Physics Universidad Autonoma Metropolitana Iztapalapa Mexico Department of Physics Instituto Politecnico Nacional Mexico

ISBN: (纸本)9781479924363

Application areas like physics or thermodynamics often require simulations of very large data sets, up to the order of 10 12 particles or even larger, to obtain results relevant for realistic industrial processes. Persisting such data is too costly, prohibiting interactive visual analysis in a classical post-processing fashion. Thus, analysis is restricted to statistical aggregation or visual in-situ exploration, both requiring an inkling of the results beforehand. We alleviate this issue by applying an application-optimized lossy compression. Reducing the size while at the same time preserving relevant physical characteristics of the data allows for accessibility on workstations and practical long-term storage. The compression is achieved by generating a density volume that is processed using wavelet decomposition, quantization and run-length encoding. Our reconstruction of particle data ensures the restoration of physically relevant properties. It employs a model based on stochastic distributions complemented by further adjustments. We evaluate the precision of the reconstruction for several data sets and a wide range of compression variants to show the effectiveness and user-adjustable trade-offs of the presented method.

关键词： Data models Quantization (signal) Wavelet transforms image reconstruction Springs Encoding computational modeling

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Research on Inertial Navigation Simulation Based on AirSim

Research on Inertial Navigation Simulation Based on AirSim

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Shanchuan Huang Xiaofeng He Jun Mao Lilian Zhang Yunxiao Lv College of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Simulating unmanned systems in a virtual environment has become a crucial approach for the development of navigation technologies. This paper presents a drone simulation platform based on AirSim. It analyzes the deficiencies of the system's built-in inertial navigation simulation sensors and designs a method to generate inertial navigation simulation data based on the true state values of the drone, through the inverse process of inertial navigation calculations. Finally, the algorithm is tested through simulation flight experiments, confirming the feasibility of the approach proposed in this paper.

关键词： Laser radar Simulation Noise Virtual environments Inertial navigation Sensor systems Sensors Gyroscopes Informatics Drones

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Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

Part-level Action Parsing via a Pose-guided Coarse-to-Fine F...

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ieee International symposium on Circuits and Systems

作者： Xiaodong Chen Xinchen Liu Wu Liu Kun Liu Dong Wu Yongdong Zhang Tao Mei Universiy of Science nd Technoogy of Chin Hefei ChinJD.co Beijing ChinJD.co Beijing ChinJD.co Beijing ChinJD.co Beijing ChinUniversiy of Science nd Technoogy of Chin Hefei ChinJD.co Beijing Chin

ISBN: (数字)9781665484855

ISBN: (纸本)9781665484862

Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural Networks (CNNs), with coarse video-level class labels. These methods can only output an action class for the video, but cannot provide fine-grained and explainable cues to answer why the video shows a specific action. Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. To this end, we propose a coarse-to-fine framework for this challenging task. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Moreover, to balance the accuracy and computation in part-level action parsing, we propose to recognize the part-level actions by segment-level features. Furthermore, to overcome the ambiguity of body parts, we propose a pose-guided positional embedding method to accurately localize body parts. Through comprehensive experiments on a large-scale dataset, i.e., Kinetics-TPS, our framework achieves state-of-the-art performance and outperforms existing methods over 31.10% ROC score.

关键词： Circuits and systems Detectors Streaming media signal processing computational efficiency Convolutional neural networks Task analysis

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Face Recognition Based on Multi-source Superpixel Fusion

Face Recognition Based on Multi-source Superpixel Fusion

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Xiaoxiang Xu Fanzhang Li Li Zhang School of Computer Science and Technology Soochow University Suzhou Jiangsu china Soochow University Suzhou Jiangsu china

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Face recognition is an important field of artificial intelligence. Single face recognition is one of the indispensable and challenging directions. There have been many existing single face recognition algorithms, such as PCA, 2DPCA, LDA, SVD, HMM, maximum likelihood linear regression, neural network and so on. These algorithms mainly solve the problem of single face recognition from three perspectives: expanding samples, expanding feature space and creating a learning general framework. The general performance of the algorithm for learning the general framework and extending the feature space is better than that of the method for expanding samples. In this paper,a face recognition learning algorithm based on multi-source superpixel fusion is proposed, which combines extended feature space and learning general framework. This algorithm mainly combines the proposed multi-source fusion representation model with super-pixel face features, and uses the theory of multi-source fusion model to solve the shortcomings of super-pixel face features. In particular, LBP algorithm is introduced into super-pixel segmentation to reduce the impact of illumination, rotation and occlusion on superpixel segmentation. Finally, the feasibility and validity of the super-pixel face recognition learning algorithm based on multi-source fusion model are demonstrated by simulation experiments.

关键词： Face recognition computational modeling Neural networks Maximum likelihood linear regression Hidden Markov models Lighting Principal component analysis

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Error Resilience Analysis for Systematically Employing Approximate Computing in Convolutional Neural Networks

Error Resilience Analysis for Systematically Employing Appro...

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Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Hanif, Muhammad Abdullah Hafiz, Rechan Shafique, Muhammad Vienna Univ Technol Inst Comp Engn Vienna Austria Informat Technol Univ Dept Elect Engn Lahore Pakistan

ISBN: (纸本)9783981926309

Approximate computing is an emerging paradigm for error resilient applications as it leverages accuracy loss for improving power, energy, area, and/or performance of an application. The spectrum of error resilient applications includes the domains of image and video processing, Artificial intelligence (AI) and Machine Learning (ML), data analytics, and other Recognition, Mining, and Synthesis (RMS) applications. In this work, we address one of the most challenging question, i.e., how to systematically employ approximate computing in Convolution Neural Networks (CNNs), which are one of the most compute-intensive and the pivotal part of AI. Towards this, we propose a methodology to systematically analyze error resilience of deep CNNs and identify parameters that can be exploited for improving performance/efficiency of these networks for inference purposes. We also present a case study for significance-driven classification of filters for different convolutional layers, and propose to prune those baring the least significance, and thereby enabling accuracy vs. efficiency tradeoffs by exploiting their resilience characteristics in a systematic way.

关键词： Resilience Hardware Convolutional neural networks Memory management Artificial intelligence Quantization (signal) computational efficiency

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Frequency-Domain Assisted Fundus image Enhancement Network Based on the Mamba Model

Frequency-Domain Assisted Fundus Image Enhancement Network B...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Bin Luo Yang Wen Jianhua Ji Wenming Cao Ping Li Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronic and Information Engineering Shenzhen University Shenzhen China College of Electronic and Information Engineering Shenzhen University Shenzhen China Department of Computing and School of Design The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Enhancing fundus images is crucial for early diagnosis and monitoring of retinal diseases. Although CNN and Transformer-based methods have made great progress, CNNs struggle with long-range dependencies, and Transformers face challenges with large model sizes and low computational efficiency. To address these issues, Selective Structured State-Space Models (SSMs) offer an efficient solution with linear complexity. However, standard Mamba models still suffer from local pixel information loss and channel redundancy, limiting their performance in fundus image enhancement. We propose MambaFE, which incorporates localized refinement and an advanced channel attention mechanism to resolve challenges such as pixel detail loss and excessive channel information. Considering that retinal structures play a vital role in fundus analysis, retaining these structures during the enhancement process is crucial. As retinal structures are predominantly located within the high-frequency components of fundus images, we introduce a frequency-guided approach to capture reliable representations of these features, ensuring improved retention of essential information during enhancement. Comprehensive experimental findings indicate that the proposed MambaFE outperforms contemporary fundus image enhancement techniques, significantly enhancing the quality of clinically low-quality and synthetically degraded fundus images, while offering robust support for real-world medical applications.

关键词： image quality Attention mechanisms computational modeling Redundancy Retina Transformers State-space methods image enhancement Standards Diseases

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DiCE-M: Distributed Code Generation and Execution for Marine Applications - An Edge-Cloud Approach

DiCE-M: Distributed Code Generation and Execution for Marine...

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ieee/ACM symposium on Edge Computing (SEC)

作者： Giuseppe Coviello Kunal Rao Gennaro Mellone Ciro Giuseppe De Vita Srimat Chakradhar NEC Laboratories America Inc. Princeton NJ

ISBN: (数字)9798350378283

ISBN: (纸本)9798350378290

Edge computing has emerged as a transformative technology that reduces application latency, improves cost efficiency, enhances security, and enables large-scale deployment of applications across various domains. In environmental monitoring, systems such as MegaSense[49], use low-cost sensors to gather and process real-time air quality data through edge-cloud collaboration, highlighting the critical role of edge computing in enabling scalable, efficient solutions. Similarly, marine science increasingly requires real-time processing and analysis of marine data from remote, resource-constrained environments. In this paper, we extend the power of edge computing by integrating it with Generative Artificial intelligence(GenAI),specifically large language models (LLMs), to address challenges in marine science applications. We propose DiCE-M (Distributed Code generation and Execution for Marine applications), a robust system that uses LLM to generate distributed code for marine applications and then utilizes a runtime to efficiently execute it on an edge+cloud computing infrastructure. Specifically, DiCE-M leverages edge computing to execute lightweight AI models locally on unmanned surface vehicles(USVs)while offloading complex tasks to the cloud, thus balancing computational load and enabling realtime monitoring in marine environments. We use marine litter identification as an example application to demonstrate the utility of DiCE-M. Our results show that DiCE-M reduces latency by more than 2X when marine litter is not detected and cuts cloud computing costs by more than half compared to traditional cloud-based approaches. By selectively cropping and transmitting relevant image portions, DiCE-M further improves bandwidth efficiency, making it a reliable and cost-effective solution for deploying AI-drivenapplications on resource-constrained USVs in dynamic marine environments.

关键词： Cloud computing Costs Codes Spectral efficiency image edge detection computational modeling Real-time systems Oceanography Vehicle dynamics Edge computing

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EEG channel selection-based binary particle swarm optimization with recurrent convolutional autoencoder for emotion recognition

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BIOMEDICAL signal processing AND CONTROL 2023年第1期84卷

作者： Kouka, Najwa Fourati, Rahma Fdhila, Raja Siarry, Patrick Alimi, Adel M. Natl Engn Sch Sfax ENIS Univ Sfax Res Grp Intelligent Machines BP 1173 1173 Sfax 3038 Tunisia Univ Paris Est Lab Image Signaux & Syst Intelligents LISSI F-94400 Creteil France Univ Johannesburg Fac Engn & Built Environm Dept Elect & Elect Engn Sci Johannesburg South Africa

Electroencephalography (EEG) signals can demonstrate the activities of the human brain and recognize different emotional states. Emotion recognition based on full EEG channels leads to the use of redundant data and increases the hardware complexity. This paper presents a channel selection method based on a new Binary Many-Objective Particle Swarm Optimization with Cooperative Agents (BMaOPSO-CA). More specifically, we perform unsupervised feature learning with recurrent convolutional layers based on autoencoder architecture directly from clean EEG signals. Extensive validation on three public effective benchmarks, i.e. DASPS, DEAP, and SEED which are different in channel number used stimuli, and participant ratings, was carried out with subject independent scheme. As result, the experimental study highlights the optimal electrode locations related to emotions, leading to analyze the relationship between specific brain regions and emotions.

关键词： Unsupervised EEG feature learning Convolutional LSTM Channel selection Binary particle swarm optimization Local learning strategy

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Hidden Challenge in Deep-Learning Real-Time Object Detection on Edge Devices

Hidden Challenge in Deep-Learning Real-Time Object Detection...

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Midwest symposium on Circuits and Systems (MWSCAS)

作者： Marcus F. Nicolas Dalila B. Megherbi Department of Electrical and Computer Engineering UMASS Lowell CMINDS Research Center Lowell USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

As the need arises for edge intelligence, which combines edge-computing with artificial intelligence, efficient and accurate algorithms are increasingly needed. Edge computing allows AI applications to be executed in real-time, closest to hardware devices/systems, thus reducing latency that could be introduced if traversing the cloud or sent to some storage unit. The aim is the needed real-time information processing, among other benefits. Notwithstanding the advantages of Edge-AI, a challenge that is not generally addressed in many such real-time detection and recognition DL models is the effect of an object rotation on real-time object detection accuracy. In particular, object label uncertainty can be introduced in rotated object detection because the detected shape is not foreseen. The focus of this paper is to highlight how real-time object detection can be affected by rotation, a problem of importance in the field. We selected a particular architecture, Efficientdet, and implemented it on a Raspberry Pi 4 to study this rotational object detection handicap. This paper also aims to increase awareness and tender possible solutions in hopes that future researchers and engineers can design DL models on edge computing devices that will significantly diminish the impacts of an object rotation on its real-time object detection accuracy.

关键词： Accuracy Uncertainty image edge detection computational modeling Object detection Real-time systems Hardware

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