检索结果-内蒙古大学图书馆

4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual processing Units VPUs, Tensor processing Units TPUs, neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection Machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey of image Steganography 2

A Comprehensive Survey of Image Steganography

引用

2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Kalaiarasi, G. Sudharani, B. Jonnalagadda, Sharon Christiana Battula, Harsha Vardhan Sanagala, Bhavana Vignans Fdn Sci Technol & Res Dept ACSE Guntur Andhra Pradesh India

ISBN: (纸本)9798350391558;9798350379990

This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical overview, this study investigates the evolution of steganography from its ancient roots to its present iterations in the digital world. Next, the study progresses towards analyzing the fundamental principles and theoretical frameworks that underpin steganographic systems, such as cryptography and digital signal processing. Finally, this study presents a thorough evaluation of contemporary steganographic technologies, which range from simple LSB (Least Significant Bit) substitution techniques to advanced adaptive algorithms and machine learning methods by including deep-learning based steganography and coverless steganography. Notably, this study identifies key challenges, including detection resistance, payload capacity, and robustness against attacks. Overall, this study presents a thorough understanding of steganography, emphasizing its significance as a versatile tool for communication in the digital era, while also highlighting the challenges that pave way for future innovations.

关键词： image steganography Information hiding Convolutional neural Network Steganography (CNN) Generative Adversarial Network Steganography (GAN) stochastic computing

来源：评论

学校读者我要写书评

暂无评论

3D detection and characterization of ALMA sources through deep learning

引用

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2023年第3期518卷 3407-3427页

作者： Delli Veneri, Michele Tychoniec, Lukasz Guglielmetti, Fabrizia Longo, Giuseppe Villard, Eric Complesso Univ Monte St Angelo INFN Sect Naples Via Cintia I-80126 Naples Italy Univ Naples Federico II Dept Elect Engn & Informat Technol Via Claudio 21 I-80125 Naples Italy ESO Karl Schwarzschild Str 2 D-85748 Garchingbei Germany Univ Naples Federico II Dept Phys Ettore Pancini Via Cintia Naples I-80126 Italy

We present a deep learning (DL) pipeline developed for the detection and characterization of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array (ALMA) data cubes. The pipeline is composed of six DL models: a convolutional autoencoder for source detection within the spatial domain of the integrated data cubes, a Recurrent neural Network (RNN) for denoising and peak detection within the frequency domain, and four residual neural networks (ResNets) for source characterization. The combination of spatial and frequency information improves completeness while decreasing spurious signal detection. To train and test the pipeline, we developed a simulation algorithm able to generate realistic ALMA observations, i.e. both sky model and dirty cubes. The algorithm simulates always a central source surrounded by fainter ones scattered within the cube. Some sources were spatially superimposed in order to test the pipeline deblending capabilities. The detection performances of the pipeline were compared to those of other methods and significant improvements in performances were achieved. Source morphologies are detected with subpixel accuracies obtaining mean residual errors of 10(-3) pixel (0.1 mas) and 10(-1) mJy beam(-1) on positions and flux estimations, respectively. Projection angles and flux densities are also recovered within 10 per cent of the true values for 80 and 73 per cent of all sources in the test set, respectively. While our pipeline is fine-tuned for ALMA data, the technique is applicable to other interferometric observatories, as SKA, LOFAR, VLBI, and VLTI.

关键词： methods: data analysis methods: numerical techniques: image processing techniques: interferometric software: simulations radio lines: galaxies

来源：评论

学校读者我要写书评

暂无评论

A Convolutional-Attentional neural Framework for Structure-Aware Performance-Score Synchronization

引用

IEEE signal processing LETTERS 2022年 29卷 344-348页

作者： Agrawal, Ruchit Wolff, Daniel Dixon, Simon Queen Mary Univ London Ctr Digital Mus London E1 4NS England Inst Res & Coordinat Acoust Mus F-75004 Paris France

Performance-score synchronization is an integral task in signal processing, which entails generating an accurate mapping between an audio recording of a performance and the corresponding musical score. Traditional synchronization methods compute alignment using knowledge-driven and stochastic approaches, and are typically unable to generalize well to different domains and modalities. We present a novel data-driven method for structure-aware performance-score synchronization. We propose a convolutional-attentional architecture trained with a custom loss based on time-series divergence. We conduct experiments for the audio-to-MIDI and audio-to-image alignment tasks pertained to different score modalities. We validate the effectiveness of our method via ablation studies and comparisons with state-of-the-art alignment approaches. We demonstrate that our approach outperforms previous synchronization methods for a variety of test settings across score modalities and acoustic conditions. Our method is also robust to structural differences between the performance and score sequences, which is a common limitation of standard alignment approaches.

关键词： Synchronization Task analysis Convolution Computer architecture Hidden Markov models Computational modeling Predictive models Performance-score synchronization audio-to-score alignment convolutional neural networks multimodal data time-series alignment stand-alone self-attention

来源：评论

学校读者我要写书评

暂无评论

AI-Supported Pneumonia Diagnosis: Performance of Deep Learning Models and Synthetic Data Generation 27

AI-Supported Pneumonia Diagnosis: Performance of Deep Learni...

引用

27th IEEE signal processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Atbas, Baran Calsskan, Efe Erdem, Oguzhan Trakya University Department of Electrical and Electronics Engineering Edirne22030 Turkey

ISBN: (纸本)9788362065486

Pneumonia, a respiratory infection that affects the lungs and leads to serious health complications, is diagnosed by physicians based on symptoms, physical exam findings and imaging tests such as chest X-rays, but these methods can be time-consuming and subjective. In this study, we propose a Convolutional neural Network (CNN) based pneumonia disease detection model utilizing X-ray images for accelerating diagnosis and initiate treatment early to prevent disease progression. Due to the insufficient number of original X-ray samples, we also produced synthetic images with our developed Deep Convolutional Generative Adversarial Network (DC-GAN) model. We comparatively evaluated the performance of our models with pretrained VGG16 and ResNet50 models on synthetic and original image datasets separately and combined. Our results show that all models performed best with the synthetic dataset and also the combined set expanded with synthetic data gave better results in all cases than using the original data alone. The average test accuracy results of all models were obtained as 93%, 89% and 91% with synthetic, original and combined datasets, respectively. Thus, it proves that the good quality of the produced synthetic images that can be used to solve the problem of medical image data need, especially in artificial intelligence-based decision support systems. When the models were compared, our CNN model achieved the best performance in all scenarios with the accuracy values of 95%, 91% and 93% with synthetic, original and combined datasets, respectively. © 2024 Division of signal processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

stochastic BINARY-TERNARY QUANTIZATION FOR COMMUNICATION EFFICIENT FEDERATED COMPUTATION 29

STOCHASTIC BINARY-TERNARY QUANTIZATION FOR COMMUNICATION EFF...

引用

IEEE International Conference on image processing (ICIP)

作者： Goutham, Rangu Afrabandpey, Homayun Cricri, Francesco Zhang, Honglei Aksu, Emre Hannuksela, Miska Tavakoli, Hamed R. Nokia Technol Espoo Finland

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

A stochastic binary-ternary (SBT) quantization approach is introduced for communication efficient federated computation;form of collaborative computing where locally trained models are exchanged between institutes. Communication of deep neural network models could be highly inefficient due to their large size. This motivates model compression in which quantization is an important step. Two well-known quantization algorithms are binary and ternary quantization. The first leads into good compression, sacrificing accuracy. The second provides good accuracy with less compression. To better benefit from trade-off between accuracy and compression, we propose an algorithm to stochastically switch between binary and ternary quantization. By combining with uniform quantization, we further extend the proposed algorithm to a hierarchical method which results in even better compression without sacrificing the accuracy. We tested the proposed algorithm using neural network Compression Test Model (NCTM) provided by MPEG community. Our results demonstrate that the hierarchical variant of the proposed algorithm outperforms other quantization algorithms in term of compression, while maintaining the accuracy competitive to that provided by other methods.

关键词： Federated learning neural network compression quantization

来源：评论

学校读者我要写书评

暂无评论

Rad4XCNN: Anew agnostic method for post-hoc global explanation of CNN-derived features by means of Radiomics

引用

COMPUTER methods AND PROGRAMS IN BIOMEDICINE 2025年 260卷 108576-108576页

作者： Prinzi, Francesco Militello, Carmelo Zarcaro, Calogero Bartolotta, Tommaso Vincenzo Gaglio, Salvatore Vitabile, Salvatore Univ Palermo Dept Biomed Neurosci & Adv Diagnost BIND I-90127 Palermo Italy Italian Natl Res Council Inst High Performance Comp & Networking ICAR CNR I-90146 Palermo Italy Univ Palermo Dept Engn I-90128 Palermo Italy

Background and Objective: In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. However, it appears that explainability is inversely proportional to accuracy. For this reason, achieving transparency without compromising predictive accuracy remains a key challenge. methods: This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power of CNN- derived features with the inherent interpretability of radiomic features. Rad4XCNN diverges from conventional methods based on saliency maps, by associating intelligible meaning to CNN-derived features by means of Radiomics, offering new perspectives on explanation methods beyond visualization maps. Results: Using abreast cancer classification task as a case study, we evaluated Rad4XCNN on ultrasound imaging datasets, including an online dataset and two in-house datasets for internal and external validation. Some key results are: (i) CNN-derived features guarantee more robust accuracy when compared against ViTderived and radiomic features;(ii) conventional visualization map methods for explanation present several pitfalls;(iii) Rad4XCNN does not sacrifice model accuracy for their explainability;(iv) Rad4XCNN provides a global explanation enabling the physician to extract global insights and findings. Conclusions: Our method can mitigate some concerns related to the explainability-accuracy trade-off. This study highlighted the importance of proposing new methods for model explanation without affecting their accuracy.

关键词： Explainable AI Radiomics Convolutional neural networks Clinical Decision Support Systems Breast cancer

来源：评论

学校读者我要写书评

暂无评论

Neuromorphic Chiplet Architecture for Wide Area Motion imagery processing 6

Neuromorphic Chiplet Architecture for Wide Area Motion Image...

引用

6th Argentine Conference on Electronics (CAE)

作者： Andreou, Andreas G. Figliolia, Tomas Sanni, Kayode Murray, Thomas S. Tognetti, Gaspar Mendat, Daniel R. Molin, Jamal L. Villemur, Martin Pouliquen, Philippe O. Julian, Pedro Etienne-Cummings, Ralph Doxas, Isidoros Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA BAE Syst London England Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350305081

We present the system architecture for real-time processing of data that originates in large format tiled imaging arrays used in wide area motion imagery ubiquitous surveillance. High performance and high throughput is achieved through approximate computing and fixed point variable precision (6 bits to 18 bits) arithmetic. The architecture implements a variety of processing algorithms in what we consider today as Third Wave AI and Machine Intelligence ranging from convolutional networks (CNNs) to linear and non-linear morphological processing, probabilistic inference using exact and approximate Bayesian methods and Deep neural Networks based classification. The processing pipeline is implemented entirely using event based neuromorphic and stochastic computational primitives. An emulation of the system architecture demonstrated processing in real-time 160 x 120 raw pixel data running on a reconfigurable computing platform (5 Xilinx Kintex-7 FPGAs). The reconfigurable computing implementation was developed to emulate the computational structures for a 2.5D System chiplet design, that was fabricated in the 55nm GF CMOS technology. To optimize for energy efficiency of a mixed level system, a general energy aware methodology is applied through the design process at all levels from algorithms and architecture all the way down to technology and devices, while at the same time keeping the operational requirements and specifications for the task at focus.

关键词： Chiplets 2.5D architecture neuromorphic processing mixed signal design event based processing wide area image processing

来源：评论

学校读者我要写书评

暂无评论

Control Theory-Inspired Model Design for Single image De-Raining

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS ii-EXPRESS BRIEFS 2022年第2期69卷 649-653页

作者： Zhou, Man Wang, Rujing Chinese Acad Sci Inst Intelligent Machines Intelligent Agr Engn Lab Anhui Prov Hefei 230031 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

While existing image de-raining methods have obtained satisfactory performance, lacking theoretical basis prevents them from further improvement. In this brief, we propose an efficient image de-raining network empowered by control theory to address this issue. To the best of our knowledge, it is the first attempt to integrate the control theory into image de-raining model design. Different from previous methods that roughly construct complicated neural networks, our method is theoretical and provides a new perspective for model design. Specifically, by mimicking the signal processing flow of state observable and controllable standard form, we expand them to two network modules, named C-IM and O-IM with every component in the module one-to-one corresponding to each operation involved in state equation. Equipped with C-IM and O-IM, our proposed network could efficiently explore and exploit the features of rain streaks in a recursive fashion. Extensive experiments demonstrate that control theory equipped method is capable of obtaining promising performance and speeding up the model training.

关键词： Mathematical model Control theory Training Rain Circuits and systems Convolution Feature extraction Control theory single image de-raining neural network

来源：评论

学校读者我要写书评

暂无评论

Robust H_∞ Deep neural Network-Based Filter Design of Nonlinear stochastic signal Systems

引用

IEEE ACCESS 2021年 9卷 165103-165119页

作者： Chen, Bor-Sen Wu, Po-Hsun Lee, Min-Yen Natl Tsing Hua Univ Dept Elect Engn Hsinchu 30013 Taiwan Yuan Ze Univ Dept Elect Engn Taoyuan 32003 Taiwan

Recently, deep neural network (DNN) schemes based on big data-driven methods have been successfully applied to image classification, communication, translation of language, speech recognition, etc. However, more efforts are still needed to apply them to complex robust nonlinear filter design in signal processing, especially for the robust nonlinear H-infinity filter design for robust state estimation of nonlinear stochastic signal system under uncertain external disturbance and output measurement noise. In general, the design problem of robust nonlinear H-infinity filter needs to solve a complex Hamilton-Jacobi-Isaacs equation (HJIE), which is not easily solved analytically or numerically. Further, the robust nonlinear H-infinity filter is not easily designed by training DNN directly via conventional big data schemes. In this paper, a novel robust H-infinity HJIE-embedded DNN-based filter design is proposed as a co-design of H-infinity filtering algorithm and DNN learning algorithm for the robust state estimation of nonlinear stochastic signal systems with external disturbance and output measurement noise. In the proposed robust H-infinity DNN-based filter design, we have proven that when the approximation error of HJIE by the trained DNN through Adam learning algorithm approaches to 0, the HJIE-embedded DNN-based filter will approach the robust nonlinear H-infinity filter of nonlinear stochastic signal system with uncertain external disturbance and output measurement noise. Finally, a trajectory estimation problem of 3-D geometry incoming nonlinear stochastic missile system by the proposed robust H-infinity HJIE-embedded DNN-based filter scheme through the measurement by the sensor of radar system with external disturbance and measurement noise is given to illustrate the design procedure and validate its robust H-infinity filtering performance when compared with the extended Kalman filter and particle filter.

关键词： Deep neural network (DNN) robust H-infinity filter nonlinear stochastic signal system extended Kalman filter particle filter Hamilton-Jacobi Isaacs equation (HJIE) co-design of H-infinity filtering and deep neural network learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：