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Signal processing Algorithms, Architectures, Arrangements and applications (SPA)

作者： Tomasz Grzywalski Dick Botteldooren Yanjue Song Nilesh Madhu Department of Information Technology Ghent University Ghent Belgium Department of Electronics and Information Systems IDLab Ghent University - imec Ghent Belgium

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

This work addresses the problem of extracting sounds that are unexpected in an audio stream and stand out because of their spectrotemporal characteristics. In human auditory scene analysis, such sounds are referred to as (sensory) salient. Previous research initiatives are mostly limited to the detection of presence of salient sounds and identification of their temporal localization within the signal. Other approaches aim at developing classifiers that detect fixed, predetermined categories of salient sounds. In contrast, this work aims at developing a solution capable of suppressing all background (non-salient) sounds from an audio stream, preserving, to the best extent possible, the salient sounds without any distortion. An additional assumption is that the algorithm should not be limited to any particular category of salient sound events. This challenging task is realized in two steps, both being novel contributions of this work. In the first step, a large-scale dataset of clean background samples and clean salient sound samples is created by automatically processing publicly available resource of field recordings. In the second step, a deep neural network (U-Net) trained to predict complex ideal ratio mask, a method typically used for speech enhancement, is adopted and evaluated in the context of salient sound extraction. The results of conducted experiments indicate potential high efficacy of the proposed solution and indicate directions for future research.

关键词： Training Location awareness Time-frequency analysis image analysis Signal processing algorithms artificial neural networks Speech enhancement Predictive models Recording Research initiatives

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Transformation Parameters Estimation for Medical image Registration Using CEL-DNN

Transformation Parameters Estimation for Medical Image Regis...

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Advancement in Renewable Energy and Intelligent Systems (AREIS), International Conference on

作者： M. Priya S Perumal Sankar TocH Institute of Science and Technology Ernakulum Kerala India Department of ECE TocH Institute of Science and Technology Ernakulum Kerala India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

image registration has become a major medical image computing technology over the past ten years, with applications ranging from computer-assisted therapy and surgery to computer-assisted diagnosis. A medical image registration model based on a Cross-Entropy-based Deep neural Network (CEL-DNN) is presented in this paper. First, pre-processing is done on fixed and moving images, removing noise and enhancing contrast. Next, the features of both images are retrieved and then matched using the Mahalanobis Distance-based Brute-Force Matcher (MD-BFM) approach. FIS is used to estimate the transformation parameters based on the matched features. The final aligned image is obtained by applying the parameters using the CEL-DNN model over the moving image. To show the proposed model's superior performance, it is finally benchmarked against existing models.

关键词： image registration Renewable energy sources Sensitivity Deformation Noise Surgery artificial neural networks Feature extraction Robustness Medical diagnostic imaging

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Effective and Efficient Intracortical Brain Signal Decoding with Spiking neural networks

arXiv

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arXiv 2024年

作者： Fu, Haotian Zhang, Peng Yang, Song Zhang, Herui Wang, Ziwei Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China

A brain-computer interface (BCI) facilitates direct interaction between the brain and external devices. To concurrently achieve high decoding accuracy and low energy consumption in invasive BCIs, we propose a novel spiking neural network (SNN) framework incorporating local synaptic stabilization (LSS) and channel-wise attention (CA), termed LSS-CA-SNN. LSS optimizes neuronal membrane potential dynamics, boosting classification performance, while CA refines neuronal activation, effectively reducing energy consumption. Furthermore, we introduce SpikeDrop, a data augmentation strategy designed to expand the training dataset thus enhancing model generalizability. Experiments on invasive spiking datasets recorded from two rhesus macaques demonstrated that LSS-CA-SNN surpassed state-of-the-art artificial neural networks (ANNs) in both decoding accuracy and energy efficiency, achieving 0.80-3.87% performance gains and 14.78-43.86 times energy saving. This study highlights the potential of LSS-CA-SNN and SpikeDrop in advancing invasive BCI applications. © 2024, CC BY.

关键词： Neurons

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An Innovative Intelligent Solution Incorporating artificial neural networks for Medical Diagnostic Application 6

An Innovative Intelligent Solution Incorporating Artificial ...

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6th International Conference on image Information processing, ICIIP 2021

作者： Thakral, Manish Jain, Ayur Kadyan, Virender Jain, Anurag UPES Dehradun Master of Technology in CSE School of Computer Science India University of Petroleum and Energy Studies School of Computer Science Dehradun India

ISBN: (纸本)9781665433617

image identification with extracting features in medical applications has proven to be a significant obstacle in recent years. For medical doctors, diagnosing illnesses using image recognition of X-ray or scan pictures is a difficult job. A new intelligent system is created to help therapeutic uses with picture identification and feature extraction. By implementing an artificial neural network, The Picture recognition is more effective in morphological operations than fuzzy. Nvidia flow and SciPy have been used to develop the method. More than 250 data sets were used to see how the algorithms will work. Trained pictures were classified and predicted using scanned images. The study on the data set's findings successfully passed the test and the accuracy for picture identification rose to 82 percent utilizing artificial neural networks. The data was significant at reliable statistics, thus revealing the sample t's dependability tests. The ANN method of predicting picture recognition accuracy delivers much higher results in comparison to the fuzzy logic control © 2021 IEEE.

关键词： Intelligent systems

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Towards the AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data With GPUs

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2021年第3期9卷 1330-1343页

作者： Al Badawi, Ahmad Jin, Chao Lin, Jie Mun, Chan Fook Jie, Sim Jun Tan, Benjamin Hong Meng Nan, Xiao Aung, Khin Mi Mi Chandrasekhar, Vijay Ramaseshan ASTAR Inst Infocomm Res I2R Singapore 138632 Singapore

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is the user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional neural networks (CNNs) and Graphics processing Units (GPUs). FHE, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned applications. This comes at high cost as it requires enormous computing power. In this article, we show how to accelerate the performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution achieved sufficient security level (> 80 bit) and reasonable classification accuracy (99) and (77.55 percent) for MNIST and CIFAR-10, respectively. In terms of latency, we could classify an image in 5.16 seconds and 304.43 seconds for MNIST and CIFAR-10, respectively. Our system can also classify a batch of images (> 8,000) without extra overhead.

关键词： Servers Encryption Computational modeling artificial neural networks Training Deep learning privacy-preserving technologies homomorphic encryption implementation GPUs

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An Efficient Face Recognition Method Based on CNN

An Efficient Face Recognition Method Based on CNN

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Power Electronics, Computer applications (ICPECA), IEEE International Conference on

作者： Xiu He Feng Ding Guangzhou Xinhua University Guangzhou China

As a prevailing research in artificial intelligence, the application of computer vision is widely used in many fields which are closely related to people's livelihood, such as industrial automation, new retail industry, smart transportation and security monitoring. And the proposed face recognition method is a branch in the field of computer vision, it integrates neural networks, biology, image signal processing, machine learning and other fields, which promote research and cross-development among different disciplines. Hence, this paper focuses on face recognition method by using convolutional neural network(CNN), and CNN has the property of "weight sharing", which has been widely popularized in image recognition, it can greatly simplify the work of large-scale network training. The experiments demonstrate that the proposed face recognition method is successful, and the accuracy of the proposed method can be as high as 98%.

关键词： Training Computer vision image recognition image color analysis Face recognition Color Signal processing

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Development of precise forgery detection algorithms in digital radiography images using convolution neural network

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APPLIED SOFT COMPUTING 2023年 138卷

作者： El Tokhy, Mohamed S. Egyptian Atom Energy Author Engn Dept NRC Cairo Egypt

The widespread availability of forged image software necessitates the integrity verification of digital images in industrial and medical applications. Because of image manipulation, detecting small tampering and duplicated forgery from digital radiography (gamma and x-ray) images has become a research challenge, Two essential approaches are proposed for forgery detection from digital radiography images. A precise forgery detection approach with pretrained deep convolution neural networks (CNN) is conducted. Alexnet, Resnet-18 and VGG-19 are three pretrained networks for features extraction. artificial neural network (ANN) and multiclass support vector machine (MSVM) classifiers are applied for classifying the extracted features into authentic or forged. The second suggested approach depends on Haralick and Zoning extractors. These extracted features are trained and tested using the K-nearest neighbors (KNN) classifier. The suggested approaches are investigated using several manipulated industrial (gamma welding images) and medical (spine images) datasets images. Besides, these approaches are tested with several color benchmark dataset images. The results are verified using a variety of evaluation metrics. The approaches are validated through comparison with published work and high agreements are demonstrated. For digital radiography images, Alexnet pretrained network with MSVM, Resnet-18 pretrained network with ANN and Haralick extractor with KNN achieve the highest accuracy and assessment metrics. It is observed that the performance of pretrained CNN outperforms that of conventional classification algorithms in respect of accuracy with computational time. The developed approaches allow for the precise detection of forgery regions in x-ray and gamma radiographic images as well as digital images.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Algorithms Gamma radiography Nuclear forensic Digital image processing

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SELECTIVE LOSSY image COMPRESSION FOR AUTONOMOUS SYSTEMS 22

SELECTIVE LOSSY IMAGE COMPRESSION FOR AUTONOMOUS SYSTEMS

引用

23rd Symposium on image, Signal processing and artificial Vision (STSIVA)

作者： Sood, Shreyan Ahuja, Yatharth Delhi Technol Univ Dept Appl Math New Delhi India Delhi Technol Univ Dept Elect Engn New Delhi India

ISBN: (纸本)9781665416696

The main objective of this paper was to effectively interface object detection based on Convolution neural networks (CNNs) with selective lossy image compression techniques to improve the efficiency of subsequent image operations and reduce the memory requirement for storing the images in autonomous applications of self-driving vehicles. Object detection and localization was performed using 2 state-of-the-art CNN based models from the Tensorflow 2.0 Object Detection API - Faster R-CNN ResNet152 V1 1024x1024 and CenterNet HourGlass104 1024x1024. Lossy image Compression centred around the most prominent detected object (which is preserved) is done through 3 techniques - K-Means Clustering (KM), Genetic Algorithm (GA), Discrete Cosine Transform (DCT). The compressed and preserved parts were recombined to produce the final image. Analysis of the results obtained from different models and compression techniques was carried out. It was found that DCT produced the best results on both the models.

关键词： CNN Object Detection Lossy image Compression Autonomous Systems

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Hierarchical waste detection with weakly supervised segmentation in images from recycling plants

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ENGINEERING applications OF artificial INTELLIGENCE 2024年 128卷

作者： Yudin, Dmitry Zakharenko, Nikita Smetanin, Artem Filonov, Roman Kichik, Margarita Kuznetsov, Vladislav Larichev, Dmitry Gudov, Evgeny Budennyy, Semen Panov, Aleksandr Artificial Intelligence Res Inst 32 Kutuzovsky Ave Moscow 121170 Russia Moscow Inst Phys & Technol 9 Institutsky Per Dolgoprudnyi 141701 Moscow Russia Sber AI Lab 32 Kutuzovsky Ave Moscow 117312 Russia Planetarium One Naberezhnaya Obvodnogo kanala74c St Petersburg 196084 Leningrad Oblas Russia Natl Res Univ ITMO Kronversky 49 St Petersburg 197101 Leningrad Oblas Russia

Reducing environmental pollution with household waste and emissions from the computing clusters is an urgent technological problem. In our work, we explore both of these aspects: the deep learning application to improve the efficiency of waste recognition on recycling plant's conveyor, as well as carbon dioxide emission from the computing devices used in this process. To conduct research, we developed an unique open WaRP dataset that demonstrates the best diversity among similar industrial datasets and contains more than 10,000 images with 28 different types of recyclable goods (bottles, glasses, card boards, cans, detergents, and canisters). Objects can overlap, be in poor lighting conditions, or significantly distorted. On the WaRP dataset, we study training and evaluation of cutting-edge deep neural networks for detection, classification and segmentation tasks. Additionally, we developed a hierarchical neural network approach called H-YC with weakly supervised waste segmentation. It provided a notable increase in the detection quality and made it possible to segment images, learning only having class labels, not their masks. Both the suggested hierarchical approach and the WaRP dataset have shown great industrial application potential.

关键词： Hierarchical detection Waste recognition Weakly supervised segmentation image processing Recycling plant

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Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods

arXiv

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arXiv 2024年

作者： Kovalev, Egor Bychkov, Georgii Abud, Khaled Gushchin, Aleksandr Chistyakova, Anna Lavrushkin, Sergey Vatolin, Dmitriy Antsiferova, Anastasia MSU Institute for Artificial Intelligence ISP RAS Research Center for Trusted Artificial Intelligence Lomonosov Moscow State University Russia Laboratory of Innovative Technologies for Processing Video Content Innopolis University Russia

Adversarial robustness of neural networks is an increasingly important area of research, combining studies on computer vision models, large language models (LLMs), and others. With the release of JPEG AI — the first standard for end-to-end neural image compression (NIC) methods — the question of its robustness has become critically significant. JPEG AI is among the first international, real-world applications of neural-network-based models to be embedded in consumer devices. However, research on NIC robustness has been limited to open-source codecs and a narrow range of attacks. This paper proposes a new methodology for measuring NIC robustness to adversarial attacks. We present the first large-scale evaluation of JPEG AI’s robustness, comparing it with other NIC models. Our evaluation results and code are publicly available online (link is hidden for a blind review). Copyright © 2024, The Authors. All rights reserved.

关键词： neural network models

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