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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Chaudhari, Pranav Vicente-Sola, Alex Basu, Amlan Manna, Davide L. Kirkland, Paul Caterina, Gaetano Di Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow United Kingdom

In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial Neural Networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking Neural Networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages. © 2024 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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Large-size Geiger-mode Avalanche Photodiode quenched by Dynamic Memristor

Large-size Geiger-mode Avalanche Photodiode quenched by Dyna...

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2024 IEEE Photonics Conference, IPC 2024

作者： Zheng, Jiyuan Zhao, Jizhe Yang, Yubo Liu, Yinjie Hua, Xiayang Wang, Lai Hao, Zhibiao Guo, Anran Luo, Yi Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Tsinghua University Department of Electronic Engineering Beijing100084 China Cetc Chips Technology Group CO. Ltd Cetc No.44 Research Institute Department of Solid State Image Sensor Chongqing401332 China

This study explores a Dynamic Memristor (DM) for quenching large (3mm) Geiger-mode avalanche photodiodes. Results show significant improvement in count rate, jitter, and afterpulsing using DM quenching compared to a c... 详细信息

ISBN: (纸本)9798350361957

关键词： Avalanche photodiodes

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Sign Language Recognition using Spiking Neural Networks

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Procedia Computer Science 2024年 235卷 2674-2683页

作者： Pranav Chaudhari Alex Vicente-Sola Amlan Basu Davide L. Manna Paul Kirkland Gaetano Di Caterina Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow UK

关键词： Spiking Neural Networks American Sign Language Sign Language Recognition Deep Learning

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Blessemflood21: Advancing Flood Analysis with a High-Resolution Georeferenced Dataset for Humanitarian Aid Support

Blessemflood21: Advancing Flood Analysis with a High-Resolut...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vladyslav Polushko Alexander Jenal Jens Bongartz Immanuel Weber Damjan Hatic Ronald Rösch Thomas März Markus Rauhut Andreas Weinmann Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. Computer Vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep Learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery.

关键词： Deep learning Training Semantic segmentation Human in the loop sensors Floods Task analysis

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BLESSEMFLOOD21: ADVANCING FLOOD ANALYSIS WITH A HIGH-RESOLUTION GEOREFERENCED DATASET FOR HUMANITARIAN AID SUPPORT

arXiv

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arXiv 2024年

作者： Polushko, Vladyslav Jenal, Alexander Bongartz, Jens Weber, Immanuel Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Hochschule Darmstadt Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

关键词： Floods

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Improving Accuracy of Optical Sorters Using Closed-Loop Control of Material Recirculation

Improving Accuracy of Optical Sorters Using Closed-Loop Cont...

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American Control Conference (ACC)

作者： Jonathan Vieth Marcel Reith-Braun Albert Bauer Florian Pfaff Georg Maier Robin Gruna Thomas Längle Harald Kruggel-Emden Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory Karlsruhe Institute of Technology Karlsruhe Germany Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Karlsruhe Institute of Technology Karlsruhe Germany Chair of Mechanical Process Engineering and Solids Processing (MVTA) Technical University of Berlin Berlin Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation (IOSB) Karlsruhe

Optical sorting is a key technology for the circular economy and is widely applied in the food, mineral, and recycling industries. Despite its widespread use, one typically resorts to expensive means of adjusting the accuracy, e.g., by reducing the mass flow or changing mechanical or software parameters, which typically requires manual tuning in a lengthy, iterative process. To circumvent these drawbacks, we propose a new layout for optical sorters along with a controller that allows re-feeding of controlled fractions of the sorted mass flows. To this end, we build a dynamic model of the sorter, analyze its static behavior, and show how material recirculation affects the sorting accuracy. Furthermore, we build a model predictive controller (MPC) employing the model and evaluate the closed-loop sorting system using a coupled discrete element–computational fluid dynamics (DEM–CFD) simulation, demonstrating improved accuracy.

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GridSort: image-based Optical Bulk Material Sorting Using Convolutional LSTMs

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IFAC-PapersOnLine 2023年第2期56卷 4620-4626页

作者： Marcel Reith-Braun Albert Bauer Maximilian Staab Florian Pfaff Georg Maier Robin Gruna Thomas Längle Jürgen Beyerer Harald Kruggel-Emden Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory Karlsruhe Institute of Technology 76131 Karlsruhe Germany Chair of Mechanical Process Engineering and Solids Processing Technische Universität Berlin 10587 Berlin Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation 76131 Karlsruhe Germany

Optical sorters separate particles of different classes by first detecting them while they are transported, e.g., on a conveyor belt, and subsequently bursting out particles of undesired classes using compressed air nozzles. Currently, the most promising results are achieved by predictive tracking, a multitarget tracking approach based on extracted midpoints from area-scan camera images that analyzes the particles’ motion and activates the nozzles accordingly. However, predictive tracking requires expert knowledge for setup and preceding object detection. Moreover, particle shapes are only considered implicitly, and the need to solve an association problem rises the computational complexity of the algorithm. In this paper, we present GridSort, an image-based approach that forecasts the scene at the nozzle array using a convolutional long short-term memory neural network and subsequently extracts nozzle activations, thus circumventing the aforementioned weaknesses. We show how GridSort can be trained in an unsupervised fashion and evaluate it using a coupled discrete element–computational fluid dynamics simulation of an optical sorter. We compare our method with predictive tracking in terms of sorting accuracy and demonstrate that it is an easy-to-apply alternative while achieving state-of-the-art results.

关键词： Monitoring of product quality and control performance Neural networks in process control Machine learning methods and applications Artificial intelligence in mining, minerals and metals Process monitoring and fault diagnosis

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Large-size Geiger-mode Avalanche Photodiode quenched by Dynamic Memristor

Large-size Geiger-mode Avalanche Photodiode quenched by Dyna...

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IEEE Photonics Conference (IPC)

作者： Jiyuan Zheng Jizhe Zhao Yubo Yang Yinjie Liu Xiayang Hua Lai Wang Zhibiao Hao Anran Guo Yi Luo Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China Department of Solid State Image Sensor CETC No.44 Research Institute CETC Chips Technology Group CO. LTD Chongqing P.R. China

ISBN: (数字)9798350361957

ISBN: (纸本)9798350361964

关键词： Memristors Avalanche photodiodes Jitter Timing Photonics

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Scale adaptive block target tracking based on multi-layer convolution features and Kernel correlation filter

Scale adaptive block target tracking based on multi-layer co...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Zhang, Ting Hu, Dong Zhang, Jing Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Target tracking

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Joint DBF and SAO parallel filtering based on multithread load balancing

Joint DBF and SAO parallel filtering based on multithread lo...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Ma, Hao Hu, Dong Li, Yi Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Textures

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