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International Conference on Wireless Communications, Networking and applications (WCNA 2022)

作者： Huang, Chunhui Lu, Haihua Huang, Xiaoping Chen, Sixiao Shen, Miaojie Xiamen Tobacco Industry Co. Ltd. FJ Xiamen361022 China Ningbo Cigarette Factory China Tobacco Zhejiang Industrial Co. Ltd. Ningbo315504 China

ISBN: (纸本)9789819939503

In view of the demand for cigarette case appearance quality detection in the production process of cigarette enterprises, a machine vision-based method for detecting cigarette case appearance defects is proposed, and an appearance packaging quality line detection platform applicable to ultra-high-speed cigarette equipment is established. Haar wavelet de-noising is used for image pre-processing, and the decomposed low-frequency signal is reconstructed to obtain a compressed image. The double thresholding calculation in Canny operator is improved based on the Otsu algorithm, and the defect segmentation of the image is better achieved. Moreover, using Hu moments for defect feature extraction and training SVM classifier by sample features to realize the recognition and classification of cigarette case appearance defects. The ZB48 ultra-high-speed packaging machine was used as the test object to build a detection system, and the experimental results show that the algorithm can effectively achieve the online detection of cigarette case appearance defects. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Defects

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Reconfigurable parallel photonic matrix-vector multiplication processor based on multi-dimensional multiplexing

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OPTICS EXPRESS 2025年第9期33卷 19837-19850页

作者： Bi, Yanfeng Wu, Xingyu Fan, Chenrui Zhang, Lufan Wang, Chuan Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Beijing Normal Univ Appl Opt Beijing Area Major Lab Beijing 100875 Peoples R China

Matrix-vector multiplication (MVM) operations play an important role in applications such as data processing and artificial neural networks. To meet the growing demand for computing power, the photonic MVM processor provides what we believe to be a new computing architecture. In this paper, we propose a reconfigurable parallel MVM (RP-MVM) processor. To further improve the parallel computing dimension, wavelength division multiplexing (WDM) and digital subcarrier multiplexing (DSM) technologies were first incorporated into the photonic MVM. Compared with the traditional WDM-MVM architecture, the parallelism of RP-MVM scheme is increased by N times, where N is the carrier number of DSM signal. Moreover, the input data channel can be dynamically adjusted without changing the hardware scale, which improves the flexibility of computing system. The simulation results show that the RP-MVM scheme can achieve parallel computing operations of eight MVMs, with a computing speed of 128 GOPs. For a random 6-bit resolution data sequence, the root mean square error (RMSE) of calculation results is on the order of 1E-3. In addition, for the image edge extraction task based on Roberts operator, this scheme can realize the parallel processing of four grayscale images. Therefore, the proposed scheme provides an alternative approach for realizing a highly parallel and reconfigurable large-scale photonic MVM architecture.

关键词： machine vision Mode division multiplexing Neural networks Optical computing Variable optical attenuators Wavelength division multiplexing

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Novel vision transformer and data augmentation technique for efficient detection of monkeypox disease

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Multimedia Tools and applications 2024年 1-19页

作者： Alarfaj, Aisha Ahmed Ahmad, Salman Hakeem, Abeer M. Alabdulqader, Ebtisam Abdullah PERO, Chiara Alsubai, Shtwai Innab, Nisreen Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Post Graduate Resident Department of Urology Allama Iqbal Teaching Hospital Dera Ghazi Khan Pakistan Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Via Giovanni Paolo II 132 Fisciano Salerno Italy Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Recent technological advancements have paved the way for the optimization of medical processes, particularly automated disease detection. Moreover, the adoption of machine learning (ML) has greatly helped in automating disease detection. Such approaches can detect various diseases early, enabling timely treatment to save countless lives. Early and accurate diagnosis is very important for diseases like monkeypox, to curb its spread. Monkeypox is a viral disease caused by double-stranded DNA and can be transmitted through close contact with infected humans or animals. It’s early identification and accurate lesion diagnosis are critical to contain the disease. This study proposes an automated approach to optimize the diagnosis of monkeypox disease using a novel vision transformer, which is utilized due to its effectiveness for feature extraction. The Proposed approach’s efficiency and accuracy are tested on a public benchmark dataset comprising a variety of skin lesions of different ages and genders. In addition, data augmentation involves rotation, scaling, and flipping thereby enhancing the density of the training data set for better generalization of ML models. Experiments involve binary, as well as, multi-class classification. For the binary class, the proposed model achieves an accuracy of 97.63%, outperforming traditional ML and deep learning (DL) techniques. In the case of multi-class classification with monkeypox, measles, normal, HFMD, cowpox, and chickenpox classes, the proposed model archives an accuracy of 90.61% while precision, recall, and F1 scores are 91.39%, 89.17%, and 90.28%, respectively. Furthermore, the proposed approach shows average accuracy, precision, recall, and F1 scores of 97.54%, 96.19%, 95.16%, and 95.67%, respectively for five-fold cross-validation. Experiments demonstrate that the combination of data augmentation techniques and the vision transformer model significantly optimizes diagnostic performance. In brief, advanced DL architectur

关键词： Medical image processing

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Artificial intelligence-based droplet size prediction for microfluidic system

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INTERNATIONAL JOURNAL OF NUMERICAL METHODS FOR HEAT & FLUID FLOW 2024年第8期34卷 3045-3078页

作者： Dubey, Sameer Vishwakarma, Pradeep Ramarao, T. V. S. Dubey, Satish Kumar Goel, Sanket Javed, Arshad Birla Inst Technol & Sci Pilani Dept Mech Engn Hyderabad India Birla Inst Technol & Sci Pilani Microfluid & Nanoelect MMNE Lab Hyderabad India Elect Corp India Ltd Hyderabad India Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn Hyderabad India

PurposeThis study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet ***/methodology/approachThe photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) *** phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SVM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and R2 = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the ***/valueExperimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.

关键词： Automation image processing Microfabrication Deep neural network Deep learning model Droplet microfluidics Flow-focusing device Optical detection Lab-on-chip

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Automatic detection and identification of ground vehicles with YOLO-based deep learning using MuSES-generated OPIR synthetic imagery 2

Automatic detection and identification of ground vehicles wi...

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Artificial Intelligence for Security and Defence applications ii 2024

作者： Klein, Mark D. Edel, Zachary J. Packard, Corey D. Hendrickson, Jacob N. Gibbs, Scott C. ThermoAnalytics Inc. 23440 Airpark Blvd. CalumetMI49913 United States

ISBN: (纸本)9781510681200

Space-based sensor platforms, including both current and planned future satellites, are capable of surveilling Earth-based objects and scenes from high altitudes. Overhead persistent infrared (OPIR) is a growing surveillance technique where thermal-waveband infrared sensors are deployed on orbiting satellites to look down and image the Earth. Challenges include having sufficient image resolution to detect, differentiate and identify ground-based objects while monitoring through the atmosphere. Demonstrations have shown machine learning algorithms to be capable of processing image-based scenes, detecting and recognizing targets amongst surrounding clutter. Performant algorithms must be robustly trained to successfully complete such a complex task, which typically requires a large set of training data on which statistical predictions can be based. Electro-optical infrared (EO/IR) remote sensing applications, including OPIR surveillance, necessitate a substantial image database with suitable variation for adept learning to occur. Diversity in background scenes, vehicle operational state, season, times of day and weather conditions can be included in training image sets to ensure sufficient algorithm input variety for OPIR applications. However, acquiring such a diverse overhead image set from measured sources can be a challenge, especially in thermal infrared wavebands (e.g., MWIR and LWIR) when adversarial vehicles are of interest. In this work, MuSES™ and CoTherm™ are used to generate synthetic OPIR imagery of several ground vehicles with a range of weather, times of day and background scenes. The performance of a YOLO ("you only look once") deep learning algorithm is studied and reported, with a focus on how image resolution impacts algorithm detection/recognition performance. The image resolution of future space-based sensor platforms will surely increase, so this study seeks to understand the sensitivity of OPIR algorithm performance to overhead image resolution.

关键词： image resolution

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Drone-Assisted Infrared Thermography and machine Learning for Enhanced Photovoltaic Defect Detection: A Comparative Study of vision Transformers and YOLOv8 44th

Drone-Assisted Infrared Thermography and Machine Learning f...

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44th SGAI International Conference on Innovative Techniques and applications of Artificial Intelligence, SGAI 2024

作者： Memari, Ammar Debich, Tarek Jade University of Applied Sciences Friedrich-Paffrath-Straße 101 Wilhelmshaven26389 Germany

ISBN: (纸本)9783031779176

This paper presents a comparative study on the application of drone-assisted infrared thermography coupled with state-of-the-art machine learning models, including vision Transformers (ViTs) and YOLOv8, for efficient and accurate defect detection in Photovoltaic (PV) systems. The research outlines the methodology for on-site inspections and details the integration of drones to capture high-resolution thermal imagery, identifying minute anomalies indicative of potential system failures. By employing advanced image processing techniques and training AI models, the study compares the performance of these models in accurately identifying and classifying PV defects. A segmentation model based on TensorFlow was trained and used to detect the location of PV panels in the camera imagery, followed by the separate application of YOLOv8 and ViTs to classify defects in the detected PV panels. The dataset used to train the models was carefully curated and prepared specifically for this study, representing another contribution of this paper. This approach not only enhances detection capabilities but also streamlines the processes of data collection, analysis, and reporting, ultimately leading to improved decision-making for system operators and stakeholders. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Thermography (imaging)

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A novel image processing technique based on deep learning for water consumption detection

A novel image processing technique based on deep learning fo...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Carratu, Marco Dello Iacono, Salvatore Di Leo, Giuseppe Gallo, Vincenzo Liguori, Consolatina Pietrosanto, Antonio Univ Salerno Dept Ind Engn Via Giovanni Paolo II 132 Fisciano SA Italy

ISBN: (纸本)9781665483605

In recent years, traditional image processing techniques have seen the introduction of novel tools, able to face issues that are not always handy with classical vision algorithms. For example, classical image processing algorithms (measurement, detection of features, and many others) require a controlled environment, like illumination, target positioning, and vibration that can influence the scene for the correct operation. On the other hand, the machine learning approaches enabled image processing techniques also in non-controlled environments. One of these applications can be represented by developing a leak detector at the household level, based on processing pictures of the mechanical water meter dial. The proposed research investigates using a deep learning approach to detect the minimal movement of the water meter needles related to water leakage. In particular, a CNN was trained to correlate successive differences on the water meter dial images taken with an applied calibrated water flow. From this analysis, it is possible to detect the absence of periods with null consumption and thus detect small water losses.

关键词： Deep Learning CNN Water leakage image processing

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Leaf Lens: An Intelligent vision for Plant Disease Diagnosis using Deep Learning 2

Leaf Lens: An Intelligent Vision for Plant Disease Diagnosis...

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2nd International Conference on Artificial Intelligence and machine Learning applications, AIMLA 2024

作者： Prasad, P. Yogendra Ramu, M. Reshma, Sanjamala Priya, Rayachoty Chandana Anusha, Pasupuleti Reddy, B Likhith Dept of CSSE Tirupati India Annamacharya Institute of Technology & Sciences Dept of CSE Tirupati India

ISBN: (纸本)9798350349221

In India, where 70% of the population is involved in agriculture, accurate recognition of botanical disorders is crucial to minimize crop losses. Manual monitoring of these diseases requires significant labor, expertise in plant pathology, and time investment. This paper presents a methodology for identifying plant diseases by analyzing leaves' images. image processing extracts valuable properties and information from the images, while deep learning, [3] a part of artificial intelligence, which simplifies data preprocessing. The algorithms used can process unstructured data, including text and images, automating feature extraction and diminishing the dependance on human professionals. This automated approach facilitates informed decision-making and precise predictions based on extensive training data. The paper introduces an innovative approach to plant disease detection through deep learning, specifically using the VGG19 model, which can scrutinize intricate patterns in plant images, providing a reliable method for identifying diseases. © 2024 IEEE.

关键词： Neural network models

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Heart Signal processing Using Wavelet Analysis and Deep Learning Algorithms Based on Artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

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International Conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection 26

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtu...

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26th Irish machine vision and image processing Conference, IMVIP 2024

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

ISBN: (纸本)9781837242672

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Automobile simulators

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