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Effective recognition of pollen grains based on parametric adaptation of the image identification model

Effective recognition of pollen grains based on parametric a...

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2020 International conference on Information Science and Communications Technologies, ICISCT 2020

作者： Ibragimovich, Jumanov Isroil Isroilovich, Djumanov Olimjon Maxmudovich, Xolmonov Sunatillo Samarkand State University Head of the Department of Information Technologies Samarkand Uzbekistan Samarkand State University Department of Information Technologies Samarkand Uzbekistan

ISBN: (纸本)9781728199696

Constructive approaches, principles and methods of image processing in conditions of a priori insufficiency, parametric uncertainty, low accuracy of information have been developed on the basis of statistical, dynamic, neural network models for identifying micro-objects. methods of point and nonlinear verification of the correspondence of the contours of the input and reference objects based on the selection, segmentation, interpolation of specific characteristics of images of pollen grains and regulation of raster parameters are proposed. Modified computational schemes for learning neural networks with forward and backward propagation of errors, regulation of interneural connections in layers, neuron weights, variable activation functions, network architecture, training samples, superposition of continuous input-output dependencies. A computer complex for identification in the C++ language in the 'CUDA' parallel computing environment has been implemented for the recognition, classification and systematization of pollen grains [1]. © 2020 IEEE.

关键词： Classification (of information)

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MiniMCTAD: Minimalist Monte Carlo Transport Architecture Design

MiniMCTAD: Minimalist Monte Carlo Transport Architecture Des...

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

作者： Siqing Fu Tiejun Li Jianmin Zhang Sheng Ma Sheng Liu National University of Defense Technology Changsha China Beihang University Beijing China

Monte Carlo (MC) method is a kind of statistical experiment method, which has been widely used in scientific research, especially in solving particle transport problems. The increase in application scale requires faster machine speeds, so we propose a novel and simple architecture, MiniMCTAD, to speed up MC Transport applications. We improve parallelism by interating more cores on a single chip, rather than improving single-threaded performance through complex on-chip designs. To achieve high-performance and low-power architecture, we design the CPU pipeline structure, execution unit, memory hierarchy and explore the design space. The most commonly used architecture simulators, gem5 and McPAT, are employed to evaluate the appropriate architecture model. The experimental results show that the performance of MiniMCTAD architecture is 1.416x that of out-of-order core architecture, and it also has less area and lower power consumption. In that same number of cores, the MiniMCTAD architecture achieves a 4.45x advantage on performance per watt and 2.78x advantage on performance per area.

关键词： Out of order Monte Carlo methods Power demand Pipelines Optimization methods Computer architecture parallel processing

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Decouple the High-Frequency and Low-Frequency Information of images for Semantic Segmentation

Decouple the High-Frequency and Low-Frequency Information of...

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IEEE International conference on Acoustics, Speech and Signal processing

作者： Lianlei Shan Xiaobin Li Weiqiang Wang Computer Vision and Multimedia Technology Laboratory University of Chinese Academy of Sciences CAS Beijing China

ISBN: (纸本)9781728176055;9781728176062

As a special kind of signal processing technology, image processing has been developed rapidly after the appearance of convolutional neural network (CNN). At present, the semantic segmentation methods are all based on CNN and ignore the advantages of traditional image processing technology. We combine the two and make them promote each other. The high frequency component of the image represents the edge part and the low frequency represents the body part. Based on this assumption, we use Fourier transform to obtain the high and low frequency component from images. Then, a multi-branch parallel network structure is designed, and the high and low frequency components are sent into two branches respectively to obtain the body and edge features. Finally, the two features are fused together through one deep feature fusion to obtain the final output. In this way, the edge information and body information are decoupled on original images and extracted separately, which not only ensures the consistency of the internal information within objects, but also strengthens the supervision of the edge part which is the most error prone area in semantic segmentation. The results on Cityscapes and KITTI fully demonstrate the effectiveness of our work.

关键词： image segmentation Fourier transforms Fuses image edge detection Semantics Signal processing Feature extraction

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Deep spatiooral attention model for grain storage temperature forecasting 26

Deep spatiooral attention model for grain storage temperatur...

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26th IEEE International conference on parallel and distributed Systems, ICPADS 2020

作者： Duan, Shanshan Yang, Weidong Wang, Xuyu Mao, Shiwen Zhang, Yuan Henan Key Laboratory of Grain Photoelectric Detection Control Key Laboratory of Grain Information Processing Control College of Information Science Engineering Henan University of Technology Zhengzhou Henan China California State University Department of Computer Science SacramentoCA95819-6021 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States

ISBN: (纸本)9781728190747

Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatiooral attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods. © 2020 IEEE.

关键词： Piles

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Transformer-Based Speech Synthesizer Attribution in an Open Set Scenario

Transformer-Based Speech Synthesizer Attribution in an Open ...

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International conference on Machine Learning and Applications (ICMLA)

作者： Emily R. Bartusiak Edward J. Delp Video and Image Processing Lab School of Electrical and Computer Engineering Purdue University West Lafayette IN

Speech synthesis methods can create realistic-sounding speech, which may be used for fraud, spoofing, and mis-information campaigns. Forensic methods that detect synthesized speech are important for protection against such attacks. Forensic attribution methods provide even more information about the nature of synthesized speech signals because they identify the specific speech synthesis method (i.e., speech synthesizer) used to create a speech signal. Due to the increasing number of realistic-sounding speech synthesizers, we propose a speech attribution method that generalizes to new synthesizers not seen during training. To do so, we investigate speech synthesizer attribution in both a closed set scenario and an open set scenario. In other words, we consider some speech synthesizers to be "known" synthesizers (i.e., part of the closed set) and others to be "unknown" synthesizers (i.e., part of the open set). We represent speech signals as spectrograms and train our proposed method, known as compact attribution transformer (CAT), on the closed set for multi-class classification. Then, we extend our analysis to the open set to attribute synthesized speech signals to both known and unknown synthesizers. We utilize a t-distributed stochastic neighbor embedding (tSNE) on the latent space of the trained CAT to differentiate between each unknown synthesizer. Additionally, we explore poly-1 loss formulations to improve attribution results. Our proposed approach successfully attributes synthesized speech signals to their respective speech synthesizers in both closed and open set scenarios.

关键词： Training Synthesizers Forensics Machine learning Transformers Fraud Speech synthesis

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CLEARER: Multi-scale neural architecture search for image restoration 34

CLEARER: Multi-scale neural architecture search for image re...

引用

34th conference on Neural Information processing Systems, NeurIPS 2020

作者： Gou, Yuanbiao Li, Boyun Liu, Zitao Yang, Songfan Peng, Xi College of Computer Science Sichuan University China TAL Education Group Beijing China

Multi-scale neural networks have shown effectiveness in image restoration tasks, which are usually designed and integrated in a handcrafted manner. Different from the existing labor-intensive handcrafted architecture design paradigms, we present a novel method, termed as multi-sCaLe nEural ARchitecture sEarch for image Restoration (CLEARER), which is a specifically designed neural architecture search (NAS) for image restoration. Our contributions are twofold. On one hand, we design a multi-scale search space that consists of three task-flexible modules. Namely, 1) parallel module that connects multi-resolution neural blocks in parallel, while preserving the channels and spatial-resolution in each neural block, 2) Transition module remains the existing multi-resolution features while extending them to a lower resolution, 3) Fusion module integrates multi-resolution features by passing the features of the parallel neural blocks to the current neural blocks. On the other hand, we present novel losses which could 1) balance the tradeoff between the model complexity and performance, which is highly expected to image restoration;and 2) relax the discrete architecture parameters into a continuous distribution which approximates to either 0 or 1. As a result, a differentiable strategy could be employed to search when to fuse or extract multi-resolution features, while the discretization issue faced by the gradient-based NAS could be alleviated. The proposed CLEARER could search a promising architecture in two GPU hours. Extensive experiments show the promising performance of our method comparing with nine image denoising methods and eight image deraining approaches in quantitative and qualitative evaluations. The codes are available at https://***/limit-scu. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： image reconstruction

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Machine learning approaches for accurate image recognition and detection for plant disease 3rd

Machine learning approaches for accurate image recognition a...

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3rd International conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

作者： Vashisht, Swati Kumar, Praveen Trivedi, Munesh C. Computer Science and Engineering Amity University NoidaUttar Pradesh India Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789811553448

In recent world where world population has reached to 7.7 billion and still increasing at a rapid rate, and it would reach to approx 9.8 billion by 2050. With the increase of population, several concerns also grow side by side in parallel, like environment, development, and food security and availability and increase in population leads to increase in demand of food. Technically, there is only 510.1 million km2 of area on earth in total. And about which very small portion is used for crop cultivations. Hence, it becomes important to make best possible use of the land available for growing crops and feed our population accordingly [1]. With the fast improvement of Internet and information technology, maintaining large records about any specific dataset and processing it according to the requirements has become very easy and efficient [2]. The recent survey predicts that 30% of jobs will be extinguished in the next nine years as the machine learning algorithms are going to replace these jobs with automation and zero human work interface machineries. Many technologies come and go in the timeline of years but these are some basic technologies which cannot be replaced or can never extinguish till the final rock on this planet [3]. In this paper, we will be analyzing different approaches of machine learning which are involved in plant disease detection. This paper will also explain the working of various approaches or algorithms and their comparative measures with other methods and technologies. © Springer Nature Singapore Pte Ltd 2021.

关键词： Cultivation

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Measurement of refractive index of optical materials using improved Brewster method 10

Measurement of refractive index of optical materials using i...

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conference on Optical Design and Testing X

作者： Lv, Jiahang Hu, Yao Li, Fei Hao, Qun Beijing Inst Technol Sch Opt & Photon Beijing Key Lab Precis Optoelect Measurement Inst Beijing 100081 Peoples R China Natl Inst Metrol Ctr Med Metril Beijing 100029 Peoples R China

ISBN: (数字)9781510639126

ISBN: (纸本)9781510639126;9781510639119

Refractive index is one of the most important physical parameters of the materials. Owe to its great influence on the working characteristics of an optical systems, high accuracy measurement is required. Many methods have been proposed such as the v-prism method, the minimum deviation angle method, and the interferometric method. However, for the restriction of the principle, the shape of the sample is required to be a parallel plate or a prism with a specific shape. The sample with only spherical or aspheric surfaces cannot be tested. In this paper, an improved Brewster method is proposed to measure the refractive index of optical materials with arbitrary shapes. Brewster law can be expressed as that the reflectivity of the P-polarized light approaches zero when it is incident in the Brewster angle, which is the inverse trigonometric value of the refractive index. In the original method, a parallel laser beam with P polarization is incident on the sample, and reflected by it to a photodiode to get the intensity. The minimum intensity position corresponding to Brewster angle can be found by changing the incident angle. The reflecting surface of the sample need to be planar to provide smooth reflective area with a size greater than the beam diameter. In the improved method, a laser probe focusing on the sample and an array detector are used instead of the parallel beam and the photodiode. The minimum intensity position can be found with image processing technology. Since the laser beam is focused on the sample, only a tiny area with a size of 10 microns for reflection is needed. Thus, whatever the shape of the sample is, the method can be used. In order to demonstrate the feasibility, samples with different shapes such as a prism, a parallel plate and a lens was tested, and the accuracy of the results could all reach the order of magnitude of 10(-3).

关键词： refractive index measurement Brewster law image processing

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A High-Performance parallel Approach to image processing in distributed Computing

A High-Performance Parallel Approach to Image Processing in ...

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International conference on Application of Information and Communication Technologies (AICT)

作者： Mekhriddin Rakhimov Doniyor Mamadjanov Abulkosim Mukhiddinov Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (数字)9781728173863

ISBN: (纸本)9781728173870

Digital image processing is an actual task in the digital communication systems, IP-telephony and video conferencing, in digital television, and video surveillance. Digital processing of large video images takes a lot of time, especially if it happens in a real-time system. And, processing speed plays an important role in recognition of objects in video images received from IP-cameras in real time. This requires the use of modern technologies, and fast algorithms that increase the acceleration of digital image processing. Acceleration problems have not been fully resolved till present. Today's realities are such that the development of accelerated image processing programs requires a good knowledge of parallel and distributed computing. Both of these areas are united by the fact that both parallel and distributed software consists of several processes that together solve one common problem. This article proposes an accelerated method for the tasks of recognizing objects in video images received from IP-cameras using parallel and distributed computing technologies.

关键词： Streaming media Video surveillance Real-time systems Acceleration distributed computing Task analysis parallel algorithms

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Provisioning with Fine-grained Affinity for Container-enabled Cloud-edge System

Provisioning with Fine-grained Affinity for Container-enable...

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

作者： Dingkun Yang Nan Zhao Hongshuang Ma Jiasheng Yang Jiangsu Electric Power Information Technology Co. Ltd Nanjing China

Containerization effectively decouples the applications and facilitates those user-defined dependencies for extreme elasticity via existing container management frameworks. However, the variance occurs among container-enabled hosts at edges, since their copies of images and libraries, and available resources are quite different. Then, even for the same container, the costs of retrieving the dependency also vary over hosts, since existing mechanism forces each host to retrieve the missing parts from the central repository only upon its own status. Although previous researches have optimized the management of dependencies, the deployment mainly focuses on the entire image. Instead, upon the fine-grained difference on dependencies, we propose to minimize the overall latency of both retrieving the missing parts and the executions under constrained resources. We propose an integer linear program and then design a randomized algorithm to select suitable hosts to deploy the containers. The probability calculated in algorithm shows the preference and the affinity of hosts, and is derived from the status of fine-grained dependencies. Via rigorous proof, the result of our algorithm is concentrated on its optimum with high probability. Extensive simulations confirm that our algorithm outperforms the alternatives up to 44% regarding the average completion time of containers.

关键词： Costs Scalability image edge detection Containers Probability Elasticity Dynamic scheduling

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