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Conference on Microwave Remote Sensing - Data processing and applications II

作者： Wang, Liya Tien, Alex MITRE Corp Mclean VA 22102 USA

ISBN: (纸本)9781510666931;9781510666948

Remote sensing scene classification has been extensively studied for its critical roles in geological survey, oil exploration, traffic management, earthquake prediction, wildfire monitoring, and intelligence monitoring. In the past, the machine Learning (ML) methods for performing the task mainly used the backbones pretrained in the manner of supervised learning (SL). As Masked image Modeling (MIM), a self-supervised learning (SSL) technique, has been shown as a better way for learning visual feature representation, it presents a new opportunity for improving ML performance on the scene classification task. This research aims to explore the potential of MIM pretrained backbones on four well-known classification datasets: Merced, AID, NWPU-RESISC45, and Optimal-31. Compared to the published benchmarks, we show that the MIM pretrained vision Transformer (ViTs) backbones outperform other alternatives (up to 18% on top 1 accuracy) and that the MIM technique can learn better feature representation than the supervised learning counterparts (up to 5% on top 1 accuracy). Moreover, we show that the general-purpose MIM-pretrained ViTs can achieve competitive performance as the specially designed yet complicated Transformer for Remote Sensing (TRS) framework. Our experiment results also provide a performance baseline for future studies.

关键词： Remote sensing classification self-supervised learning (SSL) Masked image Modeling (MIM) vision Transformer (ViTs)

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5th International Conference on Data Science, machine Learning and applications, ICDSMLA 2023

5th International Conference on Data Science, Machine Learni...

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5th International Conference on Data Science, machine Learning and applications, ICDSMLA 2023

ISBN: (纸本)9789819780303

The proceedings contain 128 papers. The special focus in this conference is on Data Science, machine Learning and applications. The topics include: Digitization of Monuments – An Impact on the Tourist Experience with Special Reference to Hampi;resume Parser Using machine Learning;IOT Based Smart Hydroponics System;comparative Study of machine Learning and Deep Learning Techniques for Cancer Disease Detection;High Thruput Modulation Approaches Used in Next Generation WiF’s Under Multi-impairments Environments with MATLAB Codes;skin Disease Detection;root Vegetable Crop Recommendation System Based on Soil Properties and Environmental Factors;deep Learning Model Development for an Automatic Healthcare Edge Computing Application;Empathetic Conversations in Mental Health: Fine-Tuning LLMs for Supportive AI Interactions;exploring Block Chain Technology with applications, and Future Prospects;a Comprehensive Review of Soft Computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead;Performance Analysis of machine Learning Algorithms on Imbalanced Datasets Using SMOTE Technique;An AI Based Nutrient Tracking and Analysis System;power Saving Mechanism for Street Lights System Using IoT;Automatic Login System Using ATTINY85 IC;forecasting Stock Prices: A Comparative Analysis of machine Learning, Deep Learning, and Statistical Approaches;smart vision Bot;robots in Logistics: Apprehension of Current Status and Future Trends in Indian Warehouses;smart Healthcare: Enhancing Patient Well-Being with IoT;Detection of B-ALL Using CNN Model and Deep Learning;a Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for image processing;a Comprehensive Survey on Enhancing Patient Care Through Deep Learning and IoT-Enabled Healthcare Innovations;attention-Based image Caption Generation.

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Neural Architecture Search for Transformers: A Survey

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IEEE ACCESS 2022年 10卷 108374-108412页

作者： Chitty-Venkata, Krishna Teja Emani, Murali Vishwanath, Venkatram Somani, Arun K. Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA Argonne Natl Lab Lemont IL 60439 USA

Transformer-based Deep Neural Network architectures have gained tremendous interest due to their effectiveness in various applications across Natural Language processing (NLP) and Computer vision (CV) domains. These models are the de facto choice in several language tasks, such as Sentiment Analysis and Text Summarization, replacing Long Short Term Memory (LSTM) model. vision Transformers (ViTs) have shown better model performance than traditional Convolutional Neural Networks (CNNs) in vision applications while requiring significantly fewer parameters and training time. The design pipeline of a neural architecture for a given task and dataset is extremely challenging as it requires expertise in several interdisciplinary areas such as signal processing, image processing, optimization and allied fields. Neural Architecture Search (NAS) is a promising technique to automate the architectural design process of a Neural Network in a data-driven way using machine Learning (ML) methods. The search method explores several architectures without requiring significant human effort, and the searched models outperform the manually built networks. In this paper, we review Neural Architecture Search techniques, targeting the Transformer model and its family of architectures such as Bidirectional Encoder Representations from Transformers (BERT) and vision Transformers. We provide an in-depth literature review of approximately 50 state-of-the-art Neural Architecture Search methods and explore future directions in this fast-evolving class of problems.

关键词： Transformers Computer architecture Convolutional neural networks Computational modeling Bit error rate Search problems Neural architecture search NAS transformers BERT vision transformers multi-head self-attention hardware-aware NAS

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Efficient Facial Expression Recognition Through Lightweight CNN Technique on Public Datasets

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SN Computer Science 2025年第1期6卷 1-13页

作者： Grover, Richa Bansal, Sandhya Department of Computer Science and Engineering Maharishi Markandeshwar Engineering College Maharishi Markandeshwar Deemed to be University Ambala Haryana Mullana India

The exploration of sentiments through facial expressions is a captivating domain with applications across security, healthcare, and human–computer interaction, where understanding sentiments is primarily about interpreting an individual's stance from a piece of text. However, in the context of non-verbal communication, it extends to the interpretation of emotions conveyed through facial expressions. This research endeavor aims to push the boundaries of machine-assisted sentiment predictions by refining models and methods for more accurate emotion recognition. A major obstacle in this pursuit is accurately identifying emotions from images not constrained by controlled conditions, often marred by poor visibility, shadows, or inconsistent lighting. Our study presents an innovative approach employing a lightweight convolutional neural network technique. This technique addresses various challenges to enhance the accuracy and reliability of sentiment analysis after incorporating pre-processing techniques such as sharpening to deal with image inconsistencies and histogram equalization to manage contrast variations, which improve image quality but include some artifacts in the image that are further resolved through the application of contrast limited adaptive histogram equalization. The technique’s effectiveness is underscored by its performance, achieving a 52% accuracy rate on raw, unconstrained images of FER-2013 and a 70% accuracy rate following the application of our pre-processing technique to the same dataset. The demonstrated proposed lightweight model not only performed exceptionally well over FER-2013 but also over CK+, RAF-DB, KDFE with an accuracy of 99.2%, 84.4%, and 94%. Furthermore, the proposed technique shows strong performance on real-time images captured via webcam. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision Facial expression recognition image pre-processing Lightweight CNN Un-controlled datasets

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Optimization of Aquaponics System Efficiency Based on Artificial Intelligence Approach 3

Optimization of Aquaponics System Efficiency Based on Artifi...

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3rd International Conference on Computing and machine Intelligence (ICMI)

作者： Liu, Jayden Jiang, William Harker Sch San Jose CA 95124 USA

ISBN: (纸本)9798350372977;9798350372984

This paper aims to design and implement an MLbased approach to learn from NeuroAqua - the AI and IoT-based aquaponics system set up in our previous research at both a lab setting and larger-scale Ouroboros Aquaponics Farm (Half Moon Bay, CA) to enhance system stability and efficiency. Utilizing the data gathered from the wireless sensors, a structured database was formed to store the aquaponics environmental conditions, water quality, nutrient components, and plant images. We used the ML model to find the important factors having the largest impact on plant growth and their optimal amount levels. First, computer vision with image processing was applied to develop auto plant growth monitoring and to measure plant growth rate as the target variable more accurately and automatically for ML. Then feature engineering on the input variables was performed to enhance model performance and accuracy for a smaller dataset. ML algorithms including Linear Regression, Bagging Regressor, Decision Tree, Random Forest, XGBoost and Artificial Neural Network were applied and evaluated based on key performance metrics. The findings show that XGBoost outperformed the other models with 91.6% accuracy and also had the lowest MAE. Random Forest came in second with 90.9% accuracy and then Bagging Regressor in third with 88.5% accuracy. Lastly, according to the feature importance analysis conducted on the best model of XGBoost, Nitrogen had the largest impact on plant growth, followed by Nitrate, Nitrite, Light, and Phosphorus. Hence the initial results would recommend to closely monitor these top important factors together with plant growth in NeuroAqua's monitoring applications.

关键词： Artificial Intelligence machine Learning Aquaponics IoT image processing

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PROGRESS ON DISTRIBUTED image ANALYSIS FROM DIGITAL CAMERAS AT ELSA USING THE RABBITMQ MESSAGE BROKER 12

PROGRESS ON DISTRIBUTED IMAGE ANALYSIS FROM DIGITAL CAMERAS ...

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12th International Beam Instrumentation Conference, IBIC 2023

作者： Switka, M. Desch, K. Gereons, T. Kronenberg, S. Proft, D. Spreitzer, A. Physikalisches Institut University of Bonn Germany

ISBN: (纸本)9783954502363

In the course of modernization of camera based imaging and image analysis for accelerator hardware and beam control at the ELSA facility, a distributed image processing approach was implemented, called FGrabbit. We utilize the RabbitMQ message broker to share the high data throughput from image acquisition, processing, analysis (e.g. profile fit), display and storage between different work stations to achieve an optimum efficacy of the involved hardware. Recalibration of already deployed beam profile monitors using machine vision algorithms allow us to perform qualitative beam photometry measurements to obtain beam sizes and dynamics with good precision. We describe the robustness of the calibration, image acquisition and processing and present the architecture and applications, such as the programming- and web-interface for machine operators and developers. © 2023 by JACoW — cc Creative Commons Attribution 4.0.

关键词： image acquisition

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Increased Leverage of Transprecision Computing for machine vision applications at the Edge

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND VIDEO TECHNOLOGY 2022年第10期94卷 1101-1118页

作者： Minhas, Umar Ibrahim Lee, JunKyu Mukhanov, Lev Karakonstantis, Georgios Vandierendonck, Hans Woods, Roger Queens Univ Belfast Belfast Antrim North Ireland Queens Univ Belfast Inst Elect Commun & Informat Technol Belfast Antrim North Ireland Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland Queens Univ Belfast Sch Elect Elect Engn & Comp Sci High Performance & Data Intens Comp Belfast Antrim North Ireland Queens Univ Belfast Inst Elect Commun & Informat Technol Ctr Data Sci & Scalable Comp Belfast Antrim North Ireland

The practical deployment of machine vision presents particular challenges for resource constrained edge devices. With a clear need to execute multiple tasks with variable workloads, there is a need for a robust approach that can dynamically adapt at runtime and which can maintain the maximum quality of service (QoS) within the available resource constraints. A lightweight approach that monitors the runtime workload constraints and leverages accuracy-throughput trade-offs on a graphics processing unit (GPU), is presented. It includes optimisation techniques that identify the configurations for each task in terms of optimal accuracy, energy and memory and management of the transparent switching between configurations. Using a neural network architecture search that statically generates a range of implementations that target a resource-precision trade-off, we explore the detection of the optimal parameters for the required QoS under specific memory and energy constraints. For an accuracy loss of 1%, we demonstrate that a 1.6x higher frame processing rate can be achieved on GPU with further improvements possible at further relaxed accuracy. In order to further improve the switching between configurations, we enhance the proposed mechanism by employing central processing units (CPUs) for offloading some of the executed frames, which helps to improve the frame rate by further 0.9%.

关键词： Edge Computing Approximate Computing Transprecision Computing machine vision

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Application of deep learning approaches for classification of diabetic retinopathy stages from fundus retinal images: a survey

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MULTIMEDIA TOOLS AND applications 2023年第14期83卷 43115页

作者： Mukherjee, N. Sengupta, S. Infosys Ltd Mysore India Aliah Univ Kolkata India

Diabetic retinopathy (DR) is an impediment of diabetes mellitus, which if not treated early may result in complete loss of vision, even without any preemptive symptoms. DR is caused by high level of glucose in the blood, causing alterations in the microvasculature of retina. However, early screening of diabetic patients through retinal fundus imaging, along with proper diagnosis and treatment can control the prevalence of DR complications. Manual inspection of pathological changes in retinal fundus images is an extremely challenging and tedious task. Therefore, computer-aided diagnosis (CAD) system is an efficient and effective method for early detection of DR and can greatly assist the ophthalmologists. CAD system encompasses DR detection and severity grading that includes detection, classification, localization and segmentation of lesions from the fundus images. Significant contributions have been made in DR severity grading using conventional image processing approaches using hand-engineered features and traditional machine-learning (ML) techniques. In the recent years, significant development of deep learning (DL) methods alleviated by the advancement of hardware computation power and efficient learning algorithms, has triumphed over the traditional ML methods in DR detection and grading tasks. Many researchers have employed the established as well as customized DL models in different DR image repositories and reported their findings. In this paper, we conduct a detailed review of the recent state-of-the-art contributions in the field of DL based DR classification by explaining their methodologies and highlighting their advantages and limitations. A detailed comparative study based on certain statistical parameters has also been conducted to quantitatively evaluate the methods, models and preprocessing techniques. In addition, the challenges in designing an efficient, accurate and robust deep-learning model for DR classification are explored in details to help t

关键词： Diabetic Retinopathy DR Stage Classification DR-related Lesions Medical image Analysis Computer-assisted Diagnosis machine Learning Deep Learning Survey

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Research on taper thread’s compensation algorithm based on machine vision considering the inclined state effect and tooth profile distortion

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Multimedia Tools and applications 2023年第29期82卷 45983-46010页

作者： Lu, Qianhai Kong, Lingfei Tian, Dongzhuang Sun, Jin Li, Longlong Gong, Chunyuan School of Mechanical and Precision Instrument Engineering Xi’an University of Technology Shannxi Xi’an710048 China Xi’an Research Institute of China Coal Technology and Engineering Group Corp Shaanxi Xi’an710077 China School of Mechanical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

Drill pipe joint’s thread quality directly affects the machining performance and the drill pipe’s service life. machine vision can quickly detect thread parameters to determine the thread processing quality, but this method has low thread measurement accuracy due to factors such as drill pipe joint inclination and tooth shape distortion. This paper proposes a thread detection compensation algorithm based on thread geometry space and thread section projection theory to promote machine vision inspection accuracy. The distortion mechanism of thread section image caused by the drill pipe joint in an inclined state is revealed and a taper thread mathematical model is proposed. The difference equation is obtained by subtracting the projected contour from the theoretical contour, and the extreme value is obtained to correct the thread contour in the inclined state. Experiments show that the thread profile angle compensation efficiency can be increased by 60% under inclined conditions, and the requirements for the placement of drill pipe joints are reduced. A good agreement of the standard measurement with the experimental data proves the effectiveness of the proposed method. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Drill pipe

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Artificial Intelligence Algorithms for Robotic Harvesting of Agricultural Produce 1

Artificial Intelligence Algorithms for Robotic Harvesting of...

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1st International Conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Kolhalkar, Nilesh R. Pandit, Anupama A. Kedar, Shridhar Ashok Yedukondalu, G. MKSSS's Cummins College of Engg. For Women Department of Mechanical Engineering MH Pune India Department of Computer Science & Engg. Pune MH Pune India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Mechanical Engineering A.P. Vijayawada India

ISBN: (纸本)9798350355611

Robotic harvesting of fruits and vegetables is an advanced technology that leverages Robotics, Artificial Intelligence, and machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations. © 2025 IEEE.

关键词： machine vision

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