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Unlimited Sampling in Phase Space

Unlimited Sampling in Phase Space

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Peiyu Zhang Ayush Bhandari Dept. of Electrical and Electronic Engg. Imperial College London UK

The Unlimited Sampling Framework (USF) is an alternative sampling and reconstruction protocol that allows for recovery of signals that are arbitrarily larger than the dynamic range of the analog-to-digital converter (ADC). In the USF, modulo non-linearity prior to sampling folds high dynamic range inputs that may potentially saturate a conventional ADC. In the recovery step, algorithms perform signal unfolding. Current theory backing the USF is predominantly focused towards Fourier bandlimited function *** generalizing the notion of bandlimitedness to a much larger class of unitary transforms, in this work, we propose Unlimited Sampling in Phase Space. The advantages are two-fold. Firstly, we generalize the USF to a much larger class of unitary transforms. Secondly, functions such us chirps are non-bandlimited in the Fourier domain but bandlimited in the phase space. This allows for practical applications that can not be handled by current, Fourier domain theory. Our main contribution in this paper is a novel modulo acquisition pipeline in phase space and a mathematically guaranteed recovery algorithm that is also backwards compatible with Fourier domain theory. computer experiments validate our theory and reinforce its potential for new application areas such as radar and optics.

关键词： Protocols Chirp Heuristic algorithms Pipelines Signal processing algorithms Transforms Optics

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DenseNet201 based Feature Extraction for Remote Sensing image Classification in Land Cover Monitoring

DenseNet201 based Feature Extraction for Remote Sensing Imag...

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Mobile Networks and Wireless Communications (ICMNWC), ieee international conference on

作者： Srinivas Aluvala Ali Ashoor Issa K. Saranya Zainab abed Almoussawi Aboothar Mahmood Shakir School of Computer Science & Artificial Intelligence SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq Department of computer science and engineering Bannari Amman institute of technology Sathyamangalam India College of Islamic Science Ahl Al Bayt University karbala Iraq The Islamic university Najaf Iraq

The expedition of the classification of the remote sensing images based on their freights have a wide range of applications in the number of fields. The manual yield interpretation for the land cover monitoring is the challenging task, as well as it has consumed much time to classify the Land Cover (LC) image. To address these challenges, this research proposed a pre-trained DenseNet201 architecture of the Deep Learning (DL) technique-based feature extraction for the land cover monitoring. This research utilized the UCM dataset which contains 2100 aerial images clipped from aerial ortho imagery is used for evaluated the performance of the proposed DenseNet201 framework. In pre-processing stage, the noise removal technique is used for removing the unwanted noise in the collected dataset. After pre-processing, the DenseNet201is used for the feature extraction and Convolutional Neural Network (CNN) is used for the classification of the LC aerial images. The proposed DenseNet201 framework achieves better results and attains the values of accuracy of 93.67%, precision of 96.28%, recall of 94.32% as well as F1-score of 94.88% when compared to the existing methods like Prototype based Memory Network (PM-Net), Pass Over (POEP) and Recurrent Neural Network-Random Forest (RNN-RF).

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New Type-2 Fuzzy Logic Algorithm Based image Segmentation for Medical Imaging applications

New Type-2 Fuzzy Logic Algorithm Based Image Segmentation fo...

引用

international conference on Smart Structures and Systems (ICSSS)

作者： S. Sangeetha B. Latha Department of Information Technology Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Anna University Chennai India Department of Computer Science and Engineering Sri Sairam Engineering College Chennai India

Medical image segmentation is a plan that has a lot of potential. The achievement of automated picture segmentation makes it simple to gather biomedical and anatomical information. In terms of the subject, more study and work have increased efficacy. In the field of image processing, this work provides a type-2 fuzzy system-based image application. computer aided diagnosis and medical image analysis both heavily rely on segmentation. This study presents a fuzzy type 2 algorithm based automated threshold selection method for the division of therapeutic pictures. It has been established that fuzzy set theory may be an effective tool for recitation surroundings when the facts are unclear or inaccurate. The algorithms have been put into practice and evaluated using brain MRI (magnetic resonance imaging) images. The outcomes have been evaluated and documented. The comparison of the controller’s efficiency of CNN has 92%.

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The Finest Convolutional Neural Network Model for Detecting Paddy Leaf Disease using image processing

The Finest Convolutional Neural Network Model for Detecting ...

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Contemporary Computing and Communications (InC4), ieee international conference on

作者： Md. Abid Ashraf Alima Begum Samia Ashraful Islam Md. Abu Naser Mojumder Department of Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh

There are over 30 different rice leaf diseases, some of which are common in Bangladesh and could cause significant economic and environmental problems. If these diseases are identified early and accurately, the damage they cause to crops and the losses they cause to farmers can be significantly mitigated. Researchers from all over the world have used a variety of methods and techniques to find diseases in rice leaves. The traditional disease detection method often needs to be more accurate and productive. Therefore, this study applies a novel approach with image processing and a convolutional neural network (CNN) to mitigate the constraint. This proposed methodology is designed to automatically sharpen the leaf images using an image filter, then apply CNN models. This work involved thirteen models in total; five models with “Resnet” architecture, four models with “Dense net” architecture, and four models based on “VGG” architecture. Here a raw dataset with 1600 images named “PaddyLeafBD” with four different classes is used, and gain the best accuracy among all of those models is 99.49% for VGG1$6_{-}$bn, Densenet-201, Densenet-161, Densenet-169, Resnet-18, Resnet-34, and Resnet-50 whereas least accuracy was 9S.4% for VGG-II and VGG-13.

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Clupiter: a Raspberry Pi mini-supercomputer for educational purposes

Clupiter: a Raspberry Pi mini-supercomputer for educational ...

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ieee international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Alonso Rodríguez-Iglesias María J. Martín Juan Touriño Computer Architecture Group CITIC Universidade da Coruña A Coruña Spain

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The main objective of this work is to bring supercomputing and parallel processing closer to non-specialized audiences by building a Raspberry Pi cluster, called Clupiter, which emulates the operation of a supercomputer. It consists of eight Raspberry Pi devices interconnected to each other so that they can run jobs in parallel. To make it easier to show how it works, a web application has been developed. It allows launching parallel applications and accessing a monitoring system to see the resource usage when these applications are running. The NAS Parallel Benchmarks (NPB) are used as demonstration applications. From this web application a couple of educational videos can also be accessed. They deal, in a very informative way, with the concepts of supercomputing and parallel programming.

关键词： Privacy Parallel programming Source coding Scalability Benchmark testing Parallel processing Supercomputers

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Open-Set Automatic Target Recognition

Open-Set Automatic Target Recognition

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Bardia Safaei Vibashan VS Celso M. de Melo Shuowen Hu Vishal M. Patel Johns Hopkins University Baltimore MD USA DEVCOM Army Research Laboratory Adelphi MD USA

Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. ATR algorithms are extensively used in real-world scenarios such as military and surveillance applications. Existing ATR algorithms are developed for traditional closed-set methods where training and testing have the same class distribution. Thus, these algorithms have not been robust to unknown classes not seen during the training phase, limiting their utility in real-world applications. To this end, we propose an Open-set Automatic Target Recognition framework where we enable open-set recognition capability for ATR algorithms. In addition, we introduce a plugin Category-aware Binary Classifier (CBC) module to effectively tackle unknown classes seen during inference. The proposed CBC module can be easily integrated with any existing ATR algorithms and can be trained in an end-to-end manner. Experimental results show that the proposed approach outperforms many open-set methods on the DSIAC and CIFAR-10 datasets. To the best of our knowledge, this is the first work to address the open-set classification problem for ATR algorithms. Source code is available at: https://***/bardisafa/Open-set-ATR.

关键词： Training Target recognition Surveillance Source coding Signal processing algorithms Speech recognition Inference algorithms

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A WGAN-GP Framework for SAR and Optical Remote Sensing image Fusion

A WGAN-GP Framework for SAR and Optical Remote Sensing Image...

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ieee international conference on Engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Akshay Ajay Amith G Gokul S Kumar R Malavika Anup Aprem Department of Electronics and Communication Engineering National Institute of Technology Calicut India

Synthetic Aperture Radar (SAR) and optical images are widely used in remote sensing applications due to their unique capabilities and complementary data. SAR can penetrate through cloud cover and adverse weather conditions, while optical images provide rich spectral information, however are affected by cloud cover. This paper proposes a novel approach for SAR and optical image fusion using the Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP). In addition, we introduce the content loss function that combines the structural similarity loss in the literature, with the pixel loss and gradient loss for optimal fusion. Experiments on the Sentinel dataset show that the proposed methodology outperforms existing state-of-art architectures based on CNN and wavelet transform. We also illustrate, how the fused image is more beneficial for both human perception and automatic computer analysis.

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Parallax-Tolerant Unsupervised Deep image Stitching

Parallax-Tolerant Unsupervised Deep Image Stitching

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international conference on computer Vision (ICCV)

作者： Lang Nie Chunyu Lin Kang Liao Shuaicheng Liu Yao Zhao Institute of Information Science Beijing Jiaotong University Beijing China Beijing Key Laboratory of Advanced Information Science and Network Beijing China University of Electronic Science and Technology of China Chengdu China

Traditional image stitching approaches tend to leverage increasingly complex geometric features (e.g., point, line, edge, etc.) for better performance. However, these hand-crafted features are only suitable for specific natural scenes with adequate geometric structures. In contrast, deep stitching schemes overcome adverse conditions by adaptively learning robust semantic features, but they cannot handle large-parallax *** solve these issues, we propose a parallax-tolerant unsupervised deep image stitching technique. First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion. It provides accurate alignment for overlapping regions and shape preservation for non-overlapping regions by joint optimization concerning alignment and distortion. Subsequently, to improve the generalization capability, we design a simple but effective iterative strategy to enhance the warp adaption in cross-dataset and cross-resolution applications. Finally, to further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks. Compared with existing methods, our solution is parallax-tolerant and free from laborious designs of complicated geometric features for specific scenes. Extensive experiments show our superiority over the SoTA methods, both quantitatively and qualitatively. The code is available at https://***/nie-lang/UDIS2.

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A Diffusion Probabilistic Model Based on Multi-Residualattention For Medical image Segmentation

A Diffusion Probabilistic Model Based on Multi-Residualatten...

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international conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Yang Peng Li Chaorong Ling Xudong Qin Fengqing Zheng Yong Qiu Lihua School of Computer Science and Engineering Chongqing University of Technology Banan China Department of Artificial Intelligence and Big Data Yibin College YiBin China Chengdu Medical University Cheng Du China Yibin City second People's Hospital YiBin China

Diffusion models are currently among the most attention- grabbing generative models, achieving revolutionary applications across multiple domains and demonstrating exceptional performance. In the application of conditional diffusion models to alter specific regions of the output image to match desired features, most methods employ V AE encoders for feature extraction from conditional images. However, V AE encoders face challenges of difficult training and large parameter sizes; simultaneously, the denoising UNet in diffusion models often leads to blurred edges in sampled images. To effectively address these issues, We have introduced ResAtMedDiff, an innovative diffusion model specifically designed for the field of medical image processing, offering a novel approach to enhance the accuracy and efficiency of image analysis. The ResAtMedDiff model consists of two core training networks: the MEUNet conditional encoder and the RsAtUNet encoder-decoder. MEUNet is responsible for efficiently encoding the conditional images and then fusing these encodings with noise image features across multiple scales. This fusion strategy significantly enhances the ability of conditional features to guide the generation of noise images. The MEUN et encoder blends the features of the original conditional image with the Gaussian noise image features of the corresponding layers of the RsAtUN et encoder, followed by the RsAtUNet decoder decoding these fused features. This process achieves the generation of image segmentation masks guided by the features of the original image as medical prior conditions, As a result, this significantly enhances the precision of lesion area segmentation in medical imaging. The experimental findings reveal that the ResAtMedDiff model surpasses existing state-of-the-art segmentation techniques in its performance, particularly in the segmentation of thyroid nodules in ultrasound images and brain tumors in MRI scans, demonstrating its superior efficacy and precis

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Cloud Identity Management Mechanisms and Issues

Cloud Identity Management Mechanisms and Issues

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international conference on Cloud Technologies and applications (CloudTech)

作者： Khalid Maidine Ahmed El-Yahyaoui Mathematics Computer Science and Information Security Laboratory (LABMIA-SI) Faculty of sciences Mohammed V University in Rabat Morroco Intelligent Processing and Security of Systems (IPSS) Faculty of sciences Mohammed V University in Rabat Morroco

It is widely recognized that cloud-based services have experienced a significant increase in popularity, accompanied by notable success. However, the security risks persist as a consequence of this progress. Cloud Service Providers (CSPs) need to challenge those security risks to make users trust their services cloud. Identity management is a key concern within the cloud environment, where several efforts have been and will continue to be made in order to ensure that no one other than the right user gets access to these services. Various identity management protocols and mechanisms have been implemented by Cloud Service Providers (CSPs). This paper provides a comprehensive overview of these commonly used protocols, including Credentials, Multi-Factor Authentication (MFA), and Single Sign-On (SSO). The main focus is placed on analyzing their features, potential security vulnerabilities, and proposed mitigations.

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