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A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy

A Targeted Sampling Strategy for Compressive Cryo Focused Io...

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

作者： D. Nicholls J. Wells A. W. Robinson A. Moshtaghpour M. Kobylynska R. A. Fleck A. I. Kirkland N. D. Browning Department of Mechanical Materials and Aerospace Engineering University of Liverpool UK Distributed Algorithms Centre for Doctoral Training University of Liverpool UK Correlated Imaging Group Rosalind Franklin Institute Harwell Science and Innovation Campus Didcot UK Centre for Ultrastructural Imaging King’s College London London UK Department of Materials University of Oxford UK Physical and Computational Science Directorate Pacific Northwest National Laboratory Richland USA Sivananthan Laboratories Bolingbrook IL USA

Cryo Focused Ion-Beam Scanning Electron Microscopy (cryo FIB-SEM) enables three-dimensional and nanoscale imaging of biological specimens via a slice and view mechanism. The FIB-SEM experiments are, however, limited by a slow (typically, several hours) acquisition process and the high electron doses imposed on the beam sensitive specimen can cause damage. In this work, we present a compressive sensing variant of cryo FIB-SEM capable of reducing the operational electron dose and increasing speed. We propose two Targeted Sampling (TS) strategies that leverage the reconstructed image of the previous sample layer as a prior for designing the next subsampling mask. Our image recovery is based on a blind Bayesian dictionary learning approach, i.e., Beta Process Factor Analysis (BPFA). This method is experimentally viable due to our GPU-based implementation of BPFA. Simulations on artificial compressive FIB-SEM measurements validate the success of proposed methods: the operational electron dose can be reduced by up to 20 times. These methods have large implications for the cryo FIB-SEM community, in which the imaging of beam sensitive biological materials without beam damage is crucial.

关键词： Scanning electron microscopy Microscopy Machine learning Signal processing Nanoscale devices Acoustic beams Sensors

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Task Distribution Method for Index Classification based on Color Segmentation in Remote Sensing 4

Task Distribution Method for Index Classification based on C...

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4th International conference on Advanced Information Technologies, ICAIT 2020

作者： Htun, Yan Naing Aye, Bawin Aung, Aung Higher Education Center Department of Computer Technology Pyin Oo Lwin Myanmar

ISBN: (纸本)9781728183640

Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallel image processing and load balancing under the circumstance of multi-Tasks and multi-processors in remote sensing. Task distribution method can speed up computation and improve efficiency and perform larger computations which are not possible on single processor system. The tasks are distributed based on the segmentation of color and the Support Vector Machine (SVM) is used to classify the indices of the input image and intends to design and improve the color segmentation based task distribution method for index classification using machine learning. In the system, the RGB satellite image is used as an input image and the output is the four indices of forest, building, road and land The results of the system are more accurate and less time consumption than non-distributed computing methods. It is implemented in MATLAB platform with parallel computation toolbox because the system can solve computationally and data-intensive tasks using multicore processors and clusters of computer. © 2020 IEEE.

关键词： image enhancement

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Cross-subject Drowsiness Recognition Model Based on the Multi-scale parallel Convolution of EEG Channels

Cross-subject Drowsiness Recognition Model Based on the Mult...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Songquan Li Rongbo Zhu Jun Yuan Shaohua Wan College of Informatics Huazhong Agricultural University Wuhan China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Wuhan China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

Cross-subject electroencephalogram (EEG) drowsiness recognition is currently one of the most efficient methods. However, the traditional cross-subject approaches overlook the correlation between channel sub-features and effectively utilize the potential connection between frequency features and drowsiness, making it difficult to extract shared features from differentiated subjects. This paper proposes a novel deep learning model called CPCNN to address the challenging of cross-subject drowsiness recognition without calibration and improve accuracy. CPCNN obtains delta frequency band features related to drowsiness from sub-features in the channel dimension, effectively combining the advantages of delta frequency bands in EEG with the richness of channel features. To capture the common features of drowsiness, a channel slicing module is proposed, which divides the spatial and channel features extracted by the deep separable convolution module into multiple sub-features along the channel dimension. In addition, to further improve cross-subject recognition performance, a multi-scale parallel group convolution is designed to capture four key frequency sub-feature groups within the delta frequency band. The experimental results show that CPCNN boosts the recognition performance, significantly outperforms all strong baselines. On balanced and unbalanced public datasets, the average accuracy of CPCNN is 81.49% and 78.26%, respectively, surpassing the excellent baseline method by 3.14% and 0.56%.

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Optimal Placement of Grid-Connected Solar Photovoltaic Systems Using Artificial Intelligence methods 14

Optimal Placement of Grid-Connected Solar Photovoltaic Syste...

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14th International conference on Developments in eSystems Engineering (DeSE)

作者： Ansari, Sam Hamid, Abdul-Kadir Al Hindawi, Noor Ahmad Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9781665408882

Solar energy is a sustainable, clean, and free energy source used to supply heat, electricity, and even fuel and chemical energy to residential, commercial, and industrial centers. The problems associated with fossil resources and the consequences of environmental and global climate change have created good opportunities for solar energy to compete with fossil fuels, especially in countries with high radiation potential. Scientific and technical weaknesses, variations in irradiance level due to climate and seasonal changes, radiation angles, geographical location, etc., have confined the solar energy-related applications. The optimal placement of the photovoltaic (PV) power stations providing maximum performance is critical to be addressed. This study intends to empower grid-connected solar PV systems by investigating various constraints and influencing factors related to the location of solar farms. We exploit selected machine learning algorithms such as k-means, k-medoids, fuzzy c-means (FCM) methods, as well as a new proposed algorithm based on an image processing approach to optimize the system by detecting the appropriate clusters and site locations. Finally, by performing several simulations, we compare the results and the efficiency of the algorithms. Our results further designate the significant superiority of the proposed techniques.

关键词： clustering data mining grid-connected solar photovoltaic systems image processing machine learning

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Optimization of a Similarity Performance on Bounded Content of Motion Histogram by Using distributed Model 1st

Optimization of a Similarity Performance on Bounded Content ...

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1st International conference of Advanced Computing and Informatics, ICACIN 2020

作者： Saoudi, El Mehdi Adoui El Ouadrhiri, Abderrahmane Jai Andaloussi, Said Ouchetto, Ouail Department of Mathematics and Computer Sciences Faculty of Science Computer Science Modeling Systems and Decision Support Lab Hassan II University of Casablanca Casablanca Morocco

ISBN: (纸本)9789811560477

In this paper, a content-based video retrieval (CBVR) system called Bounded Coordinate of Motion Histogram version 2 (BCMH v2) was processed on a distributed computing platform by using Apache Hadoop framework and a real-time distributed storage system using HBase. In fact, the amount of multimedia data is growing exponentially. Most of this data is available in image and video models. Analyzing huge data involves complex algorithms, which leads to challenges in optimizing processing time and data storage capacity. Many content-based video retrieval systems are suitable for processing large video dataset, but they are limited by the computational time and/or storage on a single machine. Thus, this paper presents the effectiveness of the proposed method with the distributed computing platform and its evaluation on the HOLLYWOOD2 video dataset. The experimental results demonstrate the good performances of the proposed approach. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphic methods

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On Modifications and Performance of the Hypre BoomerAMG Library Application to Elliptic and Saddle-Point Problems 1

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18th International Scientific conference on parallel Computational Technologies, PCT 2024

作者： Evstigneev, N.M. Ryabkov, O.I. Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9783031733727

ISBN: (纸本)9783031733741

The Algebraic Multigrid methods (AMG) on multiple Graphics processing Units (GPUs) and their application to elliptic and saddle-point problems are considered in the paper. The main goal is to compare different implementations of this approach. The multiple GPU AMG hinges on the Hypre BoomerAMG library. The latter is incorporated into our cross-platform library, SCFD, which is based on a template metaprogramming approach. The benefit of such a design is that internal ParCSR matrix structures, partitioning of matrices and vectors, and internal buffers are all available to us in an abstract way. This allows us to maximize the performance of the BoomerAMG library by removing intermediate layers of logic and host-device copies in coordinate matrix format. We discuss and review several aspects of such a design. The library is compared against our fork of the AMGCL library on a single GPU. The problem is then considered on distributed memory machines with multiple GPUs, and its performance and acceleration are analyzed for both elliptic and saddle-point problems. Also, we examine the influence of the global numbering of variables (problem-partitioning variants) on the performance and acceleration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Graphics processing unit

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Efficient Gender Recognition with Deep Learning Models on Balanced Facial image Datasets

Efficient Gender Recognition with Deep Learning Models on Ba...

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International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Kishan Poddar Bhanu Sharma Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

From security to marketing to human-computer interaction, gender recognition from facial photographs is a basic task in many disciplines. This effort intends to develop an appropriate gender recognition model using a balanced dataset including 1,292 images equally distributed between men and women. Training, validation and testing sets were created from diverse age groups, ethnicities and environments of the dataset to ensure proper evaluation. Transfer learning methods were used to accelerate training and increase generalization using ResNet50, a deep learning model developed on convolutional neural networks (CNN). Designed for the specific difficulty of gender recognition, the pre-trained model was developed on leveraging characteristics obtained from a large dataset to achieve amazing accuracy with minimal data and processing resources. Both suitable for classification issues, the model was optimized using cross-entropy loss and Adam optimizer. Performance evaluation was shown by remarkable accuracy of 97% coupled with outstanding precision, recall and F1-scores including both genders. Low rates of misclassification help to establish even more the dependability of the model by use of the confusion matrix. These results show the viability of the approach and its use in pragmatic gender recognition programs.

关键词： Training Deep learning Accuracy image recognition Face recognition Transfer learning Data models Convolutional neural networks Security Testing

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Syntactic Enhanced Projection Network for Few-Shot Chinese Event Extraction 14th

Syntactic Enhanced Projection Network for Few-Shot Chinese E...

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14th International conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Feng, Linhui Qiao, Linbo Han, Yi Kan, Zhigang Gao, Yifu Li, Dongsheng Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9783030821470;9783030821463

Few-shot learning event extraction methods gain more and more attention due to their ability to handle new event types. Current few-shot learning studies mainly focus on English event detection, which suffering from error propagation due to the identify-then-classify paradigm. And these methods could not be applied to Chinese event extraction directly, because they suffer from the Chinese word-trigger mismatch problem. In this work, we explore the Chinese event extraction with limited labeled data and reformulate it as a few-shot sequence tagging task. To this end, we propose a novel and practical few-shot syntactic enhanced projection network (SEPN), which exploits a syntactic learner to not only integrate the semantics of the characters and the words by Graph Convolution Networks, but also make the extracted feature more discriminative through a cross attention mechanism. Differing from prototypical networks which may lead to poor performance due to the prototype of each class could be closely distributed in the embedding space, SEPN learns to project embedding to space where different labels are well-separated. Furthermore, we deliberately construct an adaptive max-margin loss to obtain efficient and robust prototype representation. Numerical experiments conducted on the ACE-2005 dataset demonstrate the efficacy of the proposed few-shot Chinese event extraction.

关键词： Few-shot learning Chinese event extraction Syntactic embedding Conditional Random Field Robust loss

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Segmentation of Medical images Using Deep Learning and Texture Enhancement Based on Fractional Derivative Operators

Segmentation of Medical Images Using Deep Learning and Textu...

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International conference on Perspective Technologies and methods in MEMS Design (MEMSTECH)

作者： Yaroslav Sokolovskyy Denys Manokhin Olha Mokrytska Department of Computer Design Systems Lviv Polytechnic National University Lviv Ukraine Department of Information Systems Ivan Franko National University of Lviv Lviv Ukraine

ISBN: (数字)9798350378627

ISBN: (纸本)9798350378634

This paper explores the task of automatic intracranial hemorrhage (ICH) segmentation based on computer tomography (CT) data, with potential applications in biomedical engineering. The main focus is, on improving segmentation accuracy by incorporating texture enhancement techniques based on fractional order derivatives. The study looks at the segmentation of ICH using $\mathrm{U}-\mathrm{Net}$, a deep learning model that is widely used in the field of the segmentation of medical images. The training process employs a parallel algorithm using CUDA technology. Afterwards, an investigation is conducted into a texture enhancement technique that relies on Riesz fractional order derivatives. The goal is to capture intricate details and subtle textures with the potential to enhance segmentation accuracy. To assess the impact of this preprocessing method on the automatic ICH segmentation problem, the U-Net model undergoes retraining and validation. The texture-enhanced images are analyzed to interpret the obtained results. The findings indicate a subtle yet discernible enhancement in accuracy, as gauged by the Jaccard and Dice coefficients. This emphasizes the auspicious potential of the explored texture enhancement method in improving intracranial hemorrhage segmentation within the realm of biomedical engineering.

关键词： Deep learning Training image segmentation Accuracy Graphics processing units Artificial neural networks Hemorrhaging

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2nd International conference on Computing, Communication, and Learning, CoCoLe 2023

2nd International Conference on Computing, Communication, an...

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2nd International conference on Computing, Communication, and Learning, CoCoLe 2023

ISBN: (纸本)9783031569975

The proceedings contain 25 papers. The special focus in this conference is on Computing, Communication, and Learning. The topics include: Yolo and RetinaNet Ensemble Transfer Learning Detector: Application in Pavement Distress;stock Market Prediction Performance Analysis by Using Machine Learning Regressor Techniques;nearest Neighbor and Decision Tree Based Cloud Service QoS Classification;object Recognition Through Content Based Feature Extraction and Classification of Sounds in IoT Environment;detection of Malicious Activity on Credit Cards Using Machine Learning;agent-Based Modeling for Optimum Location Selection: Enhancing Day-Ahead Market Analysis for Microgrid Integration;a Comparative Approach for Skin Cancer Detection Using Artificial Bee Colony;a Hybrid Approach to Enhance Software Quality by Sentiment Analysis of Developer;unsupervised Sentiment Analysis of Amazon Fine Food Reviews Using Fuzzy Logic;sentiment Analysis of Crypto Currency Trading Applications in India Using Machine Learning;Graph Similarity Join (GSJ) Approach to Detect Near Duplicate Text Documents;three Factor Authentication Scheme for Telecare Medical Information System;an Effective Threshold Based Technique for Retinal image Blood Vessel Segmentation on Fundus image Using Average and Gaussian Filters;substantiation System Using Facial Recognition and Principal Component Analysis;impact of Stress on Faculties’ Attitudes: A Study of Higher Education Institutes;dynamic Cross-Layer Communication Design for Multi-objective Optimization in Wireless Sensor Networks;Implementation of Weighted parallel Interference Cancellation Technique for Multi User Detection in UWA Communication;analysis and Comparison of Firefly Algorithm for Measuring Convergence Rate in distributed Environment;A Comparative Study on Social Behavior of Tribes and Non Tribes in ITDA Paderu by Using Network Science;raster Big Data processing Using Spark with GeoTrellis.

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