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39th IMAC, A Conference and Exposition on Structural Dynamics, 2021

作者： Urban, Matthias Herfert, Daniel Gollnick, Maik Department of Structural Dynamics / Pattern Recognition Society for the Advancement of Applied Computer Science Berlin Germany

ISBN: (纸本)9783030763343

Operational deflection shape (ODS) analysis is a method to determine the vibration pattern of a structure influenced by its own operating forces and can be applied to the time and frequency domain. For rotating machinery with rapidly changing numbers of revolutions, ODS analysis provides insufficient results in the frequency domain. This work introduces the novel order ODS from the software WaveImage. This approach applies order analysis (OA) techniques to the ODS analysis. Classical OA deals with the analysis of structures which contain rotating elements such as engines or turbines. Each rotating element generates its own noise and vibration pattern which contributes to the overall vibration pattern. With OA these individual patterns can be identified and analyzed for each rotating element. During a run-up or run-down, relevant frequencies of the structure shift in relation to the current rotational speed and are therefore more difficult to evaluate. Furthermore, in OA each measuring channel is evaluated individually. The visualization of a complex geometry together with the simultaneous measurement of several measuring points is currently not applied in the field of OA. To overcome these disadvantages, a new domain was introduced to the ODS analysis. The advantage is the possibility of visualizing the ODS for a certain rotational speed. In OA, digital resampling takes place in which the original time signal is transformed into the angular range. After that the result is transferred to the target domain by Fourier transformation. To carry out the order ODS, the RPM signal must be measured in addition to the response measurement. For the validation of the order ODS approach, a structure was measured using acceleration sensors and a slow-motion video sequence. The acceleration data was used to perform a frequency ODS. To calculate the order ODS, the optical flow is used to determine the acceleration data from the recorded video. Finally the result of the frequency OD

关键词： Optical flows

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Enhancing and Adversarial: Improve ASR with Speaker labels 48

Enhancing and Adversarial: Improve ASR with Speaker Labels

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhou, Wei Wu, Haotian Xu, Jingjing Zeineldeen, Mohammad Luscher, Christoph Schluter, Ralf Ney, Hermann Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany AppTek GmbH Aachen52062 Germany

ISBN: (纸本)9781728163277

ASR can be improved by multi-task learning (MTL) with domain enhancing or domain adversarial training, which are two opposite objectives with the aim to increase/decrease domain variance towards domain-aware/agnostic ASR, respectively. In this work, we study how to best apply these two opposite objectives with speaker labels to improve conformer-based ASR. We also propose a novel adaptive gradient reversal layer for stable and effective adversarial training without tuning effort. Detailed analysis and experimental verification are conducted to show the optimal positions in the ASR neural network (NN) to apply speaker enhancing and adversarial training. We also explore their combination for further improvement, achieving the same performance as i-vectors plus adversarial training. Our best speaker-based MTL achieves 7% relative improvement on the Switchboard Hub5'00 set. We also investigate the effect of such speaker-based MTL w.r.t. cleaner dataset and weaker ASR NN. © 2023 IEEE.

关键词： Linearization

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Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers 48

Lattice-Free Sequence Discriminative Training for Phoneme-Ba...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Zijian Zhou, Wei Schluter, Ralf Ney, Hermann Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany AppTek GmbH Aachen52062 Germany

ISBN: (纸本)9781728163277

Recently, RNN-Transducers have achieved remarkable results on various automatic speech recognition tasks. However, lattice-free sequence discriminative training methods, which obtain superior performance in hybrid models, are rarely investigated in RNN-Transducers. In this work, we propose three lattice-free training objectives, namely lattice-free maximum mutual information, lattice-free segment-level minimum Bayes risk, and lattice-free minimum Bayes risk, which are used for the final posterior output of the phoneme-based neural transducer with a limited context dependency. Compared to criteria using N-best lists, lattice-free methods eliminate the decoding step for hypotheses generation during training, which leads to more efficient training. Experimental results show that lattice-free methods gain up to 6.5% relative improvement in word error rate compared to a sequence-level cross-entropy trained model. Compared to the N-best-list based minimum Bayes risk objectives, lattice-free methods gain 40% - 70% relative training time speedup with a small degradation in performance. © 2023 IEEE.

关键词： neural transducer sequence discriminative training Speech recognition

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A Grassmannian Manifold Self-Attention Network for Signal Classification 33

A Grassmannian Manifold Self-Attention Network for Signal Cl...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Wang, Rui Hu, Chen Chen, Ziheng Wu, Xiao-Jun Song, Xiaoning School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China Department of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (纸本)9781956792041

In the community of artificial intelligence, significant progress has been made in encoding sequential data using deep learning techniques. Nevertheless, how to effectively mine useful information from channel dimensions remains a major challenge, as these features have a submanifold structure. Linear subspace, the basic element of the Grassmannian manifold, has proven to be an effective manifold-valued feature descriptor in statistical representation. Besides, the Euclidean self-attention mechanism has shown great success in capturing long-range relationships of data. Inspired by these facts, we extend the self-attention mechanism to the Grassmannian manifold. Our framework can effectively characterize the spatiotemporal fluctuations of sequential data encoded in the Grassmannian manifold. Extensive experimental results on three benchmarking datasets (a drone recognition dataset and two EEG signal classification datasets) demonstrate the superiority of our method over the state-of-the-art. The code and supplementary material for this work can be found at https://***/ChenHu-ML/GDLNet. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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Generalist Segmentation Algorithm for Photoreceptors Analysis in Adaptive Optics Imaging 27th

Generalist Segmentation Algorithm for Photoreceptors Analys...

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27th International Conference on pattern recognition, ICPR 2024

作者： Kulyabin, Mikhail Sindel, Aline Pedersen, Hilde R. Gilson, Stuart Baraas, Rigmor Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany National Centre for Optics Vision and Eye Care Faculty of Health and Social Sciences University of South-Eastern Norway Kongsberg Norway

ISBN: (纸本)9783031781032

Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive optics scanning light ophthalmoscope (AOSLO) imaging enables visualization of the cones from reflections of waveguiding cone photoreceptors. While there have been significant improvements in automated algorithms for segmenting cones in confocal AOSLO images, the process of labeling data remains labor-intensive and manual. This paper introduces a method based on deep learning (DL) for detecting and segmenting cones in AOSLO images. The models were trained on a semi-automatically labeled dataset of 20 AOSLO batches of images of 18 participants for 0∘, 1∘, and 2∘ from the foveal center. F1 scores were 0.968, 0.958, and 0.954 for 0∘, 1∘, and 2∘, respectively, which is better than previously reported DL approaches. Our method minimizes the need for labeled data by only necessitating a fraction of labeled cones, which is especially beneficial in the field of ophthalmology, where labeled data can often be limited. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adaptive optics

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A New HourGlass Network for Detecting Text in Shaky and Non-shaky Video Frames 27th

A New HourGlass Network for Detecting Text in Shaky and Non-...

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27th International Conference on pattern recognition, ICPR 2024

作者： Halder, Arnab Palaiahnakote, Shivakumara Pal, Umapada Blumenstein, Michael Gornale, Shivanand S. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom University of Technology Sydney Sydney Australia Department of Computer Science Rani Channamma University Belagavi India

ISBN: (纸本)9783031784972

Shaky and non-shaky videos are quite common in real-time applications such as surveillance and monitoring vehicles and human movements in protected areas. As a result, text detection in such videos is a formidable challenge due to motion blur, noise, shaky cameras, poor quality and poor visibility. In contrast to existing text detection methods, which focus on text detection in scene images or specific types of images, the present work focuses on text detection in both shaky and non-shaky video frames. Inspired by the impressive performance of the HourGlass network for adverse situations, we explore the HourGlass network for successful text detection in shaky, non-shaky video frames and natural scene images. To improve the performance of the HourGlass network, we employ the Real-Time Model (RTMHead) for predicting text precisely and the Cross Stage Partial Network (CSPNet), which is a neck architecture for robust feature fusion. In addition, the integration of the SiLu activation function with the HourGlass network improves the discriminative power ability. To test the efficacy of the proposed method, we conducted experiments on shaky and non-shaky video frames, as well as ICDAR 2015 video frames. Furthermore, to show the effectiveness of the proposed method, we used Total-Text scene images for experimentation. The results on different datasets and a comparative study with the state-of-the-art models show that the proposed model outperforms the existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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6G-Enabled Internet of Things:Vision,Techniques,and Open Issues

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computer Modeling in Engineering & sciences 2022年第12期133卷 509-556页

作者： Mehdi Hosseinzadeh Atefeh Hemmati Amir Masoud Rahmani Pattern Recognition and Machine Learning Lab Gachon UniversitySeongnam13120Korea Department of Computer Engineering Science and Research BranchIslamic Azad UniversityTehran***Iran Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

There are changes in the development of wireless technology systems every decade.6G(sixth generation)wireless networks improve on previous generations by increasing dependability,accelerating networks,increasing available bandwidth,decreasing latency,and increasing data transmission speed to standardize communication *** purpose of this article is to comprehend the current directions in 6G studies and their relationship to the Internet of Things(IoT).Also,this paper discusses the impacts of 6G on IoT,critical requirements and trends for 6G-enabled IoT,new service classes of 6G and IoT technologies,and current 6G-enabled IoT studies selected by the systematic literature review(SLR)method published from 2018 to *** addition,we present a technical taxonomy for the classification of 6G-enabled IoT,which includes self-organization systems,energy efficiency,channel assessment,and ***,according to the articles reviewed,we consider the evaluation factors in this domain,including data transmission,delay,energy consumption,and ***,we focus on open issues and future research challenges in 6G-enabled *** mention important future challenges and directions,we can point to migration,data storage,data resource,data security,data sharing,data offloading,availability,scalability,portability,user experience,reliability,authentication,and authorization.

关键词： Internet of Things 6G wireless network machine learning artificial intelligence

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Robust Knowledge Distillation from RNN-T Models with Noisy Training labels Using Full-Sum Loss 48

Robust Knowledge Distillation from RNN-T Models with Noisy T...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zeineldeen, Mohammad Audhkhasi, Kartik Baskar, Murali Karthick Ramabhadran, Bhuvana Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany Google Llc New York United States

ISBN: (纸本)9781728163277

This work studies knowledge distillation (KD) and addresses its constraints for recurrent neural network transducer (RNN-T) models. In hard distillation, a teacher model transcribes large amounts of unlabelled speech to train a student model. Soft distillation is another popular KD method that distills the output logits of the teacher model. Due to the nature of RNN-T alignments, applying soft distillation between RNNT architectures having different posterior distributions is challenging. In addition, bad teachers having high word-error-rate (WER) reduce the efficacy of KD. We investigate how to effectively distill knowledge from variable quality ASR teachers, which has not been studied before to the best of our knowledge. We show that a sequence-level KD, full-sum distillation, outperforms other distillation methods for RNN-T models, especially for bad teachers. We also propose a variant of full-sum distillation that distills the sequence discriminative knowledge of the teacher leading to further improvement in WER. We conduct experiments on public datasets namely SpeechStew and LibriSpeech, and on in-house production data. © 2023 IEEE.

关键词： Recurrent neural networks

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A Hybrid Pipeline for the Segmentation of Eye Regions from Video Frames 2nd

A Hybrid Pipeline for the Segmentation of Eye Regions from V...

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2nd International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2021

作者： Rao, Adish Mysore, Aniruddha Guragol, Abhishek Shetty, Rajath Ajri, Siddhanth Sarkar, Poulami Srinivasa, Gowri PES Center for Pattern Recognition PES University Electronic City Campus Bengaluru560100 India Department of Computer Science and Engineering PES Center for Pattern Recognition PES University Bengaluru India

ISBN: (纸本)9789811698842

An accurate tracking and, in turn, an accurate segmentation of key regions of the eye are a sine qua non to provide users with superior quality of immersive experiences in augmented reality (AR), virtual reality (VR), and mixed reality (MR) applications. In this paper, we present the detailed rationale and research behind the design of an image processing pipeline to perform pixel-wise segmentation of eye from the background and labeling key structures, viz. sclera, iris and pupil, of the eye. The images used in this study are from a dataset provided by Facebook as a part of the OpenEDS Challenge 2020 and are sampled frames from a video capture of the eye. The pipeline we present is a hybrid of traditional image preprocessing techniques (such as histogram equalization) with application-specific augmentation (such as emulating a glare pattern) and an ensemble of five powerful, deep learning-based segmentation networks derived from the U-Net and LinkNet, followed by post-processing that harnesses temporal information. With this pipeline, we obtain a final evaluation score of 0.9641, which is well above the baseline score of 0.840 (provided by Facebook as a part of the challenge) and comparable to the top scores reported in the public domain. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pipelines

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Abstract: A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT

Abstract: A Spatiotemporal Model for Precise and Efficient F...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Ploner, Stefan B. Chen, Siyu Won, Jungeun Husvogt, Lennart Breininger, Katharina Schottenhamml, Julia Fujimoto, James G. Maier, Andreas K. Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Electrical Engineering and Computer Science Research Laboratory for Electronics Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9783658416560

Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology. OCT instruments image by raster-scanning a focused light spot across the retina, acquiring sequential cross-sectional images to generate volumetric data. Patient eye motion during the acquisition poses unique challenges: Non-rigid, discontinuous distortions can occur, leading to gaps in data and distorted topographic measurements. We present a new distortion model and a corresponding fully-automatic, reference-free optimization strategy for computational motion correction in orthogonally raster-scanned, retinal OCT volumes. Using a novel, domain-specific spatiotemporal parametrization of forward-warping displacements, eye motion can be corrected continuously for the first time. Parameter estimation with temporal regularization improves robustness and accuracy over previous spatial approaches. We correct each A-scan individually in 3D in a single mapping, including repeated acquisitions used in OCT angiography protocols. Specialized 3D forward image warping reduces median runtime to © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： Optical tomography

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