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5th International Conference on Transdisciplinary AI, TransAI 2023

作者： On, Vincent Zahedi, Atena Phandthong, Rattapol California State University Bakersfield Department of Computer and Electrical Engineering and Computer Science BakersfieldCA United States Stem Cell Research Center University of California Irvine IrvineCA United States University of California Riverside Department of Molecular Cell and Systems Biology RiversideCA United States

ISBN: (纸本)9798350358018

Cellular microscopy is enhanced by computational paradigms such as image processing, computer vision, and machine learning. image segmentation is vital for quantifying cell images, enabling tracking and subsequent applications. Superpixel algorithms offer a promising solution to challenges in phase contrast image segmentation, as they group pixels with similar characteristics into meaningful regions. By preserving essential structural information and reducing complexity, superpixels improve accuracy in cell-related measurements and facilitate the study of dynamic cellular processes. This paper presents a comparison of popular superpixel algorithms applied to cellular microscopy, highlighting their contributions to image analysis and interpretation in cell biology research. © 2023 IEEE.

关键词： image segmentation

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Research on Defect Detection Algorithm for Cigarette Case Appearance Based on machine vision

Research on Defect Detection Algorithm for Cigarette Case Ap...

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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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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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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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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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Read Right: Empowering the Blind to Read the World

Read Right: Empowering the Blind to Read the World

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2024 IEEE International Conference on Signal processing and Advance Research in Computing, SPARC 2024

作者： Prasuna, Vempati Lakshmi Fathimabi, Sk. Adusumilli, Apoorva Rajesh, Bandaru Veera Venkata Velagapudi Ramakrishna Siddhartha Engineering College Information Technology Vijayawada India

ISBN: (纸本)9798350385199

In the real world, knowledge comes from books and papers. Now that information only reaches to those with clear vision. In the community there are a part of people suffering either from poor eyesight or blindness. Braille is one of the methods employed to interpret several reports or books, however obtaining all these files in braille may be prohibitively expensive as well as regularly impossible. Hence, considering these issues and flaws in existing systems this paper is designed as a simple user-friendly mobile application 'Read Right' that works on voice commands like 'Take Picture' to click an image of the document which extracts text from picture as well plays it out (audio) also providing futuristic object detection. All this is designed using Firebase ML kit which predominantly uses bitmap class for preprocessing, text-to-speech synthesizer and FirebasevisionObjectDetector, for joining machine learning capabilities into portable applications. Hence, the application stands out for its flexibility in dealing with official archives through voice. © 2024 IEEE.

关键词： Natural language processing systems

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Facial Micro-Expression Recognition using Deep Spatio-Temporal Neural Networks 32

Facial Micro-Expression Recognition using Deep Spatio-Tempor...

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Conference on Signal processing, Sensor/Information Fusion, and Target Recognition XXXII

作者： Zheng, Yufeng Blasch, Erik Univ Mississippi Med Ctr Jackson MS 39216 USA MOVEJ Analyt Fairborn OH USA

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

In the billions of faces that are shaped by thousands of different cultures and ethnicities, one thing remains universal: the way emotions are expressed. To take the next step in human-machine interactions, a machine must be able to clarify facial emotions. Allowing machines to recognize micro-expressions gives them a deeper dive into a person's true feelings at an instant which allows designers to create more empathetic machines that will take human emotion into account while making optimal decisions;e.g., these machines will be potentially able to detect dangerous situations, alert caregivers to challenges, and provide appropriate responses. Micro-expressions are involuntary and transient facial expressions capable of revealing genuine emotions. We propose to design and train a set of neural network (NN) models capable of micro-expression recognition in real-time applications. Different NN models are explored and compared in this study to design a hybrid deep learning model by combining a convolutional neural network (CNN), a recurrent neural network (RNN, e.g., long short-term memory [LSTM]), and a vision transformer. The CNN can extract spatial features (of a neighborhood within an image) whereas the LSTM can summarize temporal features. In addition, a transformer with an attention mechanism can capture sparse spatial relations residing an image or between frames in a video clip. The inputs of the model are short facial videos, while the outputs are the micro-expressions gleaned from the videos. The deep learning models are trained and tested with publicly available facial micro-expression datasets to recognize different micro-expressions (e.g., happiness, fear, anger, surprise, disgust, sadness). The results of our proposed models are compared with that of literature-reported methods tested on the same datasets. The proposed hybrid models perform the best.

关键词： Facial micro-expression Human-machine interaction Long short-term memory (LSTM) Convolutional neural network (CNN) vision transformer Deep learning

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