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A variable structure model of neurons’ application in computer vision

Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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A Photonic Front End for High Resolution Blue Spectral Sensor in 45 nm SOI SiPh Process

A Photonic Front End for High Resolution Blue Spectral Senso...

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Annual IEEE Northeast Workshop on Circuits and systems (NEWCAS)

作者： Md Nabil Shehtab Dhrubo Jason Case Agung Julius Robert Karlicek Mona Hella Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY

ISBN: (数字)9798350361759

ISBN: (纸本)9798350361766

A photonic front end for a blue spectral sensor in a standard silicon photonic process is presented. The detector is capable of identifying multiple spectral peaks within the blue wavelength range (410 nm ~ 500 nm), The front end consists of grating structures with different grating pitches, followed by tapered waveguides. From the waveguide, the optical power is evanescently coupled to a silicon photodetector. To further improve the overall optical collection for the photo detector, an optical power combining network is also designed and optimized. The designs are fabricated using GlobalFoundries' 45SPCLO technology. The measurement results demonstrate the effect of varying the grating pitch in detecting different spectral peaks within the blue region, with peaks demonstrating responsivities ranging between 32~135 mAIW, depending on the grating pitch and the reverse bias.

关键词： Optical filters Biomedical optical imaging Optical device fabrication Optical detectors Silicon photonics Optical coupling Gratings

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Domain Adaptation for Offline Reinforcement Learning with Limited Samples

arXiv

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arXiv 2024年

作者： Chen, Weiqin Mishra, Sandipan Paternain, Santiago Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States

Offline reinforcement learning (RL) learns effective policies from a static target dataset. Despite state-of-the-art (SOTA) offline RL algorithms being promising, they highly rely on the quality of the target dataset. The performance of SOTA algorithms can degrade in scenarios with limited samples in the target dataset, which is often the case in real-world applications. To address this issue, domain adaptation that leverages auxiliary samples from related source datasets (such as simulators) can be beneficial. In this context, determining the optimal way to trade off the source and target datasets remains a critical challenge in offline RL. To the best of our knowledge, this paper proposes the first framework that theoretically and experimentally explores how the weight assigned to each dataset affects the performance of offline RL. We establish the performance bounds and convergence neighborhood of our framework, both of which depend on the selection of the weight. Furthermore, we identify the existence of an optimal weight for balancing the two datasets. All theoretical guarantees and optimal weight depend on the quality of the source dataset and the size of the target dataset. Our empirical results on the well-known Procgen Benchmark substantiate our theoretical contributions. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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A Patient Specific Algorithm for Plasmodium Malaria Detection on Cell Images

A Patient Specific Algorithm for Plasmodium Malaria Detectio...

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2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

作者： Narayanan, Barath Narayanan De Silva, Manawaduge Supun Hardie, Russell C. University of Dayton Research Institute Sensor and Software Systems Division Dayton United States University of Dayton Department of Electrical and Computer Engineering Dayton United States

ISBN: (纸本)9798350338782

computer-aided detection of plasmodium malaria on cell images from digital microscopy provides an invaluable second opinion to medical experts. Traditionally, well-established deep learning algorithms are widely used to detect plasmodium, but these techniques have not been deployed due to the uninterpretable nature of the decisions made by the network. To address this, we present an ensemble method that fuses the predictions based on the class activation mapping (CAM) results from multiple networks via a novel selector network. We study the performance on a publicly available dataset of cell images by using three convolutional neural network architectures, Xception, ResNet50, and InceptionV3,to produce CAM results for the selector network and performance comparison. Our proposed approach helps enhance doctors' trust and outperforms the traditional methods by 1%. © 2023 IEEE.

关键词： Diseases

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Automated analysis of fundus images for the diagnosis of retinal diseases: a review

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Research on Biomedical engineering 2024年第1期40卷 225-251页

作者： Berto, Alexa Scarpa, Fabio Tsiknakis, Nikos Manikis, Georgios Fotiadis, Dimitrios I. Marias, Kostas Scarpa, Alberto D-Eye s.r.l. Padua Italy Department of Information Engineering University of Padova Padua35131 Italy Heraklion70013 Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina45115 Greece Department of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina45110 Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion71004 Greece

Purpose: Colour fundus images are widely used in diagnosis treatment decision of several retinal diseases such as diabetic retinopathy (DR), glaucoma and age-related macular degeneration (AMD). These very common conditions must be detected early and monitored to prevent progression and avoid permanent damage. Fundus images revealed to be useful also for the diagnosis of cataracts. In recent years, various automatic diagnostic support methods have been proposed in the literature, with the aim of facilitating widespread screening and obtaining quantitative, objective, and reproducible information. Methods: In this review paper, an overview of traditional, machine learning and modern deep learning techniques for ophthalmic disease diagnosis (i.e. glaucoma, DR, AMD, and cataract) using retinal fundus images is presented. In addition, various publicly available image datasets used for such purposes are described. Results: The current main challenges and findings are identified, as well as common aspects and discrepancies between the various methods developed for the various diseases. Conclusion: The overview of what has been done about all pathologies, rather than only on a specific one, could favour a migration of the best solutions and, hopefully, the development of a more precise and clinically useful automatic analysis of all pathologies. Important critical insights and research trends are also discussed. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2023.

关键词： Pathology

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CUPREDS: Multi-sensor Point Cloud Mapping for Local Navigation

CUPREDS: Multi-sensor Point Cloud Mapping for Local Navigati...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Carlos Tojal Luis Conde Bento Paulo Peixoto Institute of Systems and Robotics University of Coimbra Coimbra Portugal ESTG Polytechnic Institute of Leiria Leiria Portugal Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Robot mapping usually benefits from the combination of several sensors. However, most algorithms for Simultaneous Localization And Mapping using point clouds only consider a single point cloud as input. There are also scenarios where SLAM is not needed and a simple local map representation without probabilities is preferred, for example due to throughput requirements. For these scenarios, software is needed that combines multiple point clouds into a unified, short-lived, robot-centric representation. Mapping software is only one tool in the software stack and should not compromise other components, which is why its performance-oriented design is of utmost importance. CUPREDS presents a ROS package for local mapping that prioritizes robustness and performance, with minimal overhead for its internal mechanisms.

关键词： Point cloud compression Simultaneous localization and mapping Navigation Software architecture Software algorithms Throughput Software

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain systems

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IEEE Transactions on Knowledge and Data engineering 2025年第07期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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Multi-Sensor Aircraft Classification

Multi-Sensor Aircraft Classification

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Bolton, Sarah Dill, Richard Grimaila, Michael R. Hodson, Douglas D. Air Force Institute of Technology Department of Electrical and Computer Engineering Wright-Patterson AFBOH United States Air Force Institute of Technology Department of Systems Engineering and Management Wright-Patterson AFBOH United States

ISBN: (纸本)9798350327595

Automatic Dependent Surveillance-Broadcast (ADS-B) is a useful tool to for air traffic controllers, military and other sources that are invested in understanding a national or global air picture. While it is highly available, it can sometimes lack integrity due to hacking, spoofing or, even, unintentional inaccuracies in the broadcast. Unlike primary radar, ADS-B's lack of trustworthiness makes it not feasible to rely on it alone. Fusing other data sources with ADS-B can help confirm the accuracy of the broadcasts or allow ADS-B to act as a surrogate for primary radar and bolster the information that primary radar can provide. This paper presents an effective method of using ADS-B data as a surrogate for primary 3D radar by combining the kinematic information that ADS-B data provides with weather and aircraft images to make predictions about aircraft characteristics. © 2023 IEEE.

关键词： Automatic Dependent Surveillance-Broadcast classification machine learning Multivariate Long Short-Term Memory - Fully Convolutional Network open-source data sensor fusion

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Computationally efficient driving cycle based design and optimisation for variable air gap axial flux machines

Computationally efficient driving cycle based design and opt...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Weissitsch, Gabriel Klink, David Bramerdorfer, Gerd Heins, Greg Johannes Kepler University Linz Institute of Electric Drives and Power Electronics Linz Austria Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350316445

Axial flux permanent magnet machines provide a unique set of possibilities and challenges to electric machine designers when compared to their radial flux counterparts. By varying the air gap width between the rotor and stator a mechanical rotor flux linkage control is made possible. This paper introduces a computationally efficient method of evaluating axial flux machines with air gap variation. The investigated machine is designed as a traction drive for a student racing series. Two different design scenarios are presented, comparing optimisation results from a single operating point and static air gap model to a variable air gap, driving cycle based design. A validation design is chosen for an in-depth investigation and a prototype motor is built and tested at a range of air gaps. This experimental data is compared to FEA results to validate the model. © 2023 IEEE.

关键词： air gap variation axial flux permanent magnet machine finite element (FE) multiobjective optimisation

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