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Integrated digital image processing techniques and deep learning approaches for wheat stripe rust disease detection and grading

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Decision Analytics Journal 2023年 8卷

作者： Mumtaz, Rafia Maqsood, Muhammad Hassan Haq, Ihsan ul Shafi, Uferah Mahmood, Zahid Mumtaz, Mustafa National University of Sciences and Technology (NUST) School of Electrical Engineering and Computer Science (SEECS) Islamabad Pakistan Crop Sciences Institute National Agriculture Research Center (NARC) Islamabad Pakistan

This study proposes integrated image processing and deep learning approaches for wheat rust disease detection and grading. Wheat stripe rust is one of the diseases affecting the crop in nearly all wheat-growing countries, where humidity and high temperatures characterize the growing season. The early and prompt detection of this disease is essential for establishing timely remedial measures to contain its spread. We present several image-processing and deep-learning approaches for detecting wheat stripe rust. The image processing techniques used in this research include single-band and dual-band processing, computing the Visual Atmospheric Resistance Index (VARI), image segmentation, and image cropping. These techniques are evaluated with different deep learning approaches like transfer learning and other Convolutional Neural Networks (CNNs) architectures. The results obtained on the dataset show that processing the images to eliminate the background as much as possible before training a suitable deep learning model with a low number of epochs optimally classifies the wheat stripe rust into the target classes (i.e., healthy, resistant, and susceptible) with an overall accuracy of 84.1% on the holdout test set. Besides the comparative analysis of various image processing approaches with different deep learning models, this study suggests multiple future research directions. © 2023 The Authors

关键词： Convolutional neural network Deep learning Feature extraction Image processing Vegetation indices Wheat stripe rust disease

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A GAN-based Semantic Communication for Text without CSI

arXiv

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arXiv 2023年

作者： Mao, Jin Xiong, Ke Liu, Ming Qin, Zhijin Chen, Wei Fan, Pingyi Letaief, Khaled Ben The Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China The Department of Electronic Engineering Tsinghua University Beijing100084 China The Beijing National Research Center for Information Science and Technology The Department of Electronic Engineering Tsinghua University Beijing100084 China Hong Kong

Recently, semantic communication (SC) has been regarded as one of the most potential paradigms of 6G. Current SC frameworks require the physical layer channel state information (CSI) in order to handle the severe signal distortion induced by channel fading. Since practical CSI cannot be obtained accurately and the overhead of channel estimation cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn't require CSI. In Ti-GSC, two main modules, i.e., an autoencoder-based encoder-decoder module (AEDM) and a GAN-based non-CSI signal distortion suppression (SDS) module (GSDSM) are included where AEDM first encodes the data in the semantic dimension at the source before transmission, and then GSDSM suppresses the distortion of the received signals in both syntactic and semantic dimensions at the destination. At last, AEDM decodes the distortion-suppressed signal at the destination. As SDS only relies on learning the syntactic distribution and the semantics of the transmitted data, no prior information such as CSI is needed by GSDSM. In order to measure signal distortion, a novel loss function is proposed where two terms, i.e., a syntactic distortion loss term and a semantic distortion loss term are newly added, and a differentiable semantic measurement method is designed based on the intermediate layers of the AEDM decoder. To achieve better training results of Ti-GSC, two training schemes, i.e., the joint optimization based training (JOT) and the alternating optimization based training (AOT) are designed for the proposed Ti-GSC. Experimental results show that JOT is more efficient for Ti-GSC, and Ti-GSC outperforms conventional communication frameworks in terms of bilingual evaluation understudy (BLEU) score in both Rician and Rayleigh fading channels. Moreover, without CSI, BLEU score achieved by Ti-GSC is about 40% and 62% higher than that achieved by existing SC frameworks in Rician and Rayleigh fading, r

关键词： Semantics

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Affective Image Filter: Reflecting Emotions from Text to Images

Affective Image Filter: Reflecting Emotions from Text to Ima...

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International Conference on computer Vision (ICCV)

作者： Shuchen Weng Peixuan Zhang Zheng Chang Xinlong Wang Si Li Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing Academy of Artificial Intelligence School of Artificial Intelligence Beijing University of Posts and Telecommunications

Understanding the emotions in text and presenting them visually is a very challenging problem that requires a deep understanding of natural language and high-quality image synthesis simultaneously. In this work, we propose Affective Image Filter (AIF), a novel model that is able to understand the visually-abstract emotions from the text and reflect them to visually-concrete images with appropriate colors and textures. We build our model based on the multi-modal transformer architecture, which unifies both images and texts into tokens and encodes the emotional prior knowledge. Various loss functions are proposed to understand complex emotions and produce appropriate visualization. In addition, we collect and contribute a new dataset with abundant aesthetic images and emotional texts for training and evaluating the AIF model. We carefully design four quantitative metrics and conduct a user study to comprehensively evaluate the performance, which demonstrates our AIF model outperforms state-of-the-art methods and could evoke specific emotional responses from human observers.

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Identifying Novel Drug Targets by iDTPnd:A Case Study of Kinase Inhibitors

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 986-997页

作者： Hammad Naveed Corinna Reglin Thomas Schubert Xin Gao Stefan T.Arold Michael L.Maitland Toyota Technological Institute at Chicago ChicagoIL 60637USA Department of Computer Science National University of Computer and Emerging SciencesIslamabad 44000Pakistan 2bind GmbH D-93053 RegensburgGermany King Abdullah University of Science and Technology(KAUST) Computational Bioscience Research Center(CBRC)ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionThuwal 23955Saudi Arabia King Abdullah University of Science and Technology(KAUST) Computational Bioscience Research Center(CBRC)Biological and Environmental Sciences and Engineering(BESE)DivisionThuwal 23955Saudi Arabia Inova Center for Personalized Health and Schar Cancer Institute Falls ChurchVA 22042USA University of Virginia Cancer Center AnnandaleVA 22003USA

Current FDA-approved kinase inhibitors cause diverse adverse effects,some of which are due to the me-chanism-independent effects of these *** these mechanism-independent interactions could improve drug safety and support drug ***,we develop iDTPnd(integrated Drug Target Predictor with negative dataset),a computational approach for large-scale discovery of novel targets for known *** a given drug,we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding *** facilitate assessment of unintended targets,iDTPnd also provides a docking-based interaction score and its statistical *** confirm the interactions of sorafenib,imatinib,dasatinib,sunitinib,and pazopanib with their known targets at a sensitivity of 52%and a specificity of 55%.We also validate 10 predicted novel targets by using in vitro *** results suggest that proteins other than kinases,such as nuclear receptors,cytochrome P450,and MHC class I molecules,can also be physiologically relevant targets of kinase *** method is general and broadly applicable for the identification of protein–small molecule interactions,when sufficient drug–target 3D data are *** code for constructing the structural signatures is available at https://***/Documents/***.

关键词： Protein-drug interaction iDTPnd Kinase inhibitor Drug-binding site signature Drug repurposing

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

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eLight 2022年第1期2卷 132-142页

作者： Qian Ma Wei Gao Qiang Xiao Lingsong Ding Tianyi Gao Yajun Zhou Xinxin Gao Tao Yan Che Liu Ze Gu Xianghong Kong Qammer HAbbasi Lianlin Li Cheng‑Wei Qiu Yuanqing Li Tie Jun Cui Institute of Electromagnetic Space Southeast UniversityNanjing 210096China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Millimeter Wave Southeast UniversityNanjing 210096China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou 510330China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore University of Glasgow James Watt School of EngineeringGlasgow G128QQUK State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China

Brain-computer interfaces(BCIs),invasive or non-invasive,have projected unparalleled vision and promise for assisting patients in need to better their interaction with the *** by the BCI-based rehabilitation technologies for nerve-system impairments and amputation,we propose an electromagnetic brain-computer-metasurface(EBCM)paradigm,regulated by human’s cognition by brain signals directly and *** experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively,which can be further processed and transported by an information metasurface in automated and wireless *** wireless communications of the human minds are performed between two EBCM operators with accurate text ***,several other proof-of-concept mind-control schemes are presented using the same EBCM platform,exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning,wave modulations,and pattern encoding.

关键词： communication computer surface

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L-CoIns: Language-based Colorization With Instance Awareness

L-CoIns: Language-based Colorization With Instance Awareness

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zheng Chang Shuchen Weng Peixuan Zhang Yu Li Si Li Boxin Shi School of Artificial Intelligence Beijing University of Posts and Telecommunications National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University International Digital Economy Academy

Language-based colorization produces plausible colors consistent with the language description provided by the user. Recent studies introduce additional annotation to prevent color-object coupling and mismatch issues, but they still have difficulty in distinguishing instances corresponding to the same object words. In this paper, we propose a transformer-based framework to automatically aggregate similar image patches and achieve instance awareness without any additional knowledge. By applying our presented luminance augmentation and counter-color loss to break down the statistical correlation between luminance and color words, our model is driven to synthesize colors with better descriptive consistency. We further collect a dataset to provide distinctive visual characteristics and detailed language descriptions for multiple instances in the same image. Extensive experiments demonstrate our advantages of synthesizing visually pleasing and description-consistent results of instance-aware colorization.

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MTD: Multi-Timestep Detector for Delayed Streaming Perception

arXiv

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arXiv 2023年

作者： Huang, Yihui Chen, Ningjiang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Education Department of Guangxi Zhuang Autonomous Region Nanning China

Autonomous driving systems require real-time environmental perception to ensure user safety and experience. Streaming perception is a task of reporting the current state of the world, which is used to evaluate the delay and accuracy of autonomous driving systems. In real-world applications, factors such as hardware limitations and high temperatures inevitably cause delays in autonomous driving systems, resulting in the offset between the model output and the world state. In order to solve this problem, this paper propose the Multi-Timestep Detector (MTD), an end-to-end detector which uses dynamic routing for multi-branch future prediction, giving model the ability to resist delay fluctuations. A Delay Analysis Module (DAM) is proposed to optimize the existing delay sensing method, continuously monitoring the model inference stack and calculating the delay trend. Moreover, a novel Timestep Branch Module (TBM) is constructed, which includes static flow and adaptive flow to adaptively predict specific timesteps according to the delay trend. The proposed method has been evaluated on the Argoverse-HD dataset, and the experimental results show that it has achieved state-of-the-art performance across various delay settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

arXiv

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arXiv 2024年

作者： Li, Zongrui Lu, Zhan Yan, Haojie Shi, Boxin Pan, Gang Zheng, Qian Jiang, Xudong Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China The State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised/unsupervised NaUPS methods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP. Copyright © 2024, The Authors. All rights reserved.

关键词： Photometry

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SemanticHash: Hash Coding Via Semantics-Guided Label Prototype Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第1期2卷 42-57页

作者： Tu, Cheng-Hao Yang, Huei-Fang Yang, Shih-Min Yeh, Mei-Chen Chen, Chu-Song Academia Sinica Institute of Information Science Taipei115 Taiwan National Sun Yat-sen University Department of Information Management Kaohsiung804 Taiwan National Taiwan Normal University Department of Computer Science and Information Engineering Taipei106 Taiwan Department of Computer Science and Information Engineering MOST Joint Research Center for AI Technology and All Vista Healthcare National Taiwan University Taipei10617 Taiwan

In this article, we propose SemanticHash, a simple and effective deep neural network model, to leverage semantic word embeddings (e.g., BERT) in hash codes learning. Both images and class labels are compressed into K-bit binary vectors by using the visual (or the semantic) hash functions, which are jointly learned and aligned to optimize the semantic consistency. The K-dimensional class label prototypes-projected from semantic word embeddings-guide the hash mapping on the image side and vice versa, creating the K-bit image hash codes being aligned with their semantic prototypes and therefore more discriminative. Extensive experimental results on four benchmarks, CIFAR10, NUS-WIDE, ImageNet, and MS-COCO datasets, demonstrate the effectiveness of our approach. We also perform studies to analyze the effects of quantization and word semantic spaces and to explain the relations among the learned class prototypes. Finally, the generalization capability of the proposed approach is further demonstrated. It achieves competitive performance in comparison with state-of-the-art unsupervised and zero-shot hashing methods. Impact Statement-Hash code learning is an important technology that enables efficient image retrieval on large-scale data. While existing hashing algorithms can effectively generate compact binary codes in a supervised learning setting trained with a moderate-size dataset, they are demanding to be scalable to large datasets and do not generalize to unseen datasets. The proposed approach overcomes these limitations. Compared with state-of-the-art ones, our solution achieves 2.1% of average performance improvement on four moderate-size benchmarks and 4.7% of improvement on ImageNet, a large-scale dataset with over 1.2 M training images. With superior performance on popular benchmarks for binary hash code learning, the technology introduced performs well on cross-dataset and zero-shot (i.e., the testing concepts are unseen during training) scenarios too. Our appro

关键词： Semantics

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Intelligent backpropagated predictive networks for dynamics of the power-law fluidic model with moving wedge and flat plate

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Waves in Random and Complex Media 2023年

作者： Mahmood, Tariq Ali, Nasir Raja, Muhammad Asif Zahoor Chaudhary, Naveed Ishtiaq Cheema, Khalid Mehmood Shu, Chi-Min Milyani, Ahmad H. Department of Mathematics and Statistics International Islamic University Islamabad Pakistan Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Electronic Engineering Fatima Jinnah Women University Rawalpindi Pakistan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

A novel application of intelligent computing is presented to study the dynamics of the power-law fluidic problem (PLFP) of moving-wedge and flat-plate models using a multilayer structure of feedforward neural networks (FFNNs) optimized with the Levenberg-Marquardt method (LMM), i.e. FFNN-LMM. The reference dataset for inputs and targets of FFNNs is generated through the Adams numerical solver for the non-Newtonian power-law fluidic system in the case of dilatant, pseudo-plastic, and other thinning/thickening fluids with different values of wedge angle and velocity ratio parameters. The designed FFNNs backpropagated with LMM are constructed with the help of arbitrary samples for training, testing, and validation via mean squared error (MSE)-based figure of merit. Comparison of the proposed computing platform FFNN-LMM with reference standard solutions in terms of MSE, state transition statistics of the algorithm, error histogram studies, and regression outcomes endorse the accuracy and convergence for solving different scenarios in PLFPs. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Computational fluid dynamics

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