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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： M S Vinu P Kanaga Priya P Thilakaveni J M Ashforn Hermina K Prashanthini M Misba Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Engineering PSG Polytechnic College Coimbatore India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Bethlahem Institute of Engineering Karungal Nagercoil India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure

关键词： Smart cities Lighting Data visualization Collaboration Sensor systems Real-time systems Energy efficiency Internet of Things Sustainable development Intelligent sensors

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Crop and Weeds Classification in Aerial Imagery of Sesame Crop Fields Using a Patch-Based Deep Learning Model-Ensembling Method 2

Crop and Weeds Classification in Aerial Imagery of Sesame Cr...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Moazzam, S. Imran Khan, Umar S. Nawaz, Tahir Qureshi, Waqar S. National University of Sciences and Technology National Centre of Robotics and Automation Department of Mechatronics Engineering Islamabad Pakistan Technological University Dublin Department of Computer Science Dublin Ireland

ISBN: (纸本)9781665498197

Sesame (Sesamum indicum L.) is an important commercial and food crop, and its yields is limited by many insects, pests, diseases, and weeds. Autonomous aerial agrochemicals spray application on sesame fields using a drone aims to save crop from these yield limiting factors and in addition agrochemicals application quantity and site could be controlled, and human health is expected to be protected. For accurate and selective spray application, autonomous systems would need some parameters to distinguish between crop, weed and background. In this research an aerial sesame field dataset has been collected with the focus to classify patch areas of sesame and weeds present in the field. Dataset was captured using Agrocam. We have developed a patch image-based classification approach along with a novel SesameWeedNet convolutional neural network (CNN) inspired by the layer's configuration of VGG networks and depth-wise convolutions of the MobileNet. The small model contains 6 convolutional layers, and it runs faster and accurately on small patch images. Our approach breaks 1920×1080-pixel images into smaller patch images of size 45×45 pixels. After that, these small patch images are fed to a relatively small CNN for training, validation, and finally for classification. Patch based model ensemble and dataset grouping are two major parts in our methodology. Our system recommends the dataset grouping according to vegetation present in the images to enhance classification results. We have achieved accuracy up to 96.7% with our proposed method. We have tested our system under sunlight variation, in wet and dry soil conditions and at different growth stages. To the best of our knowledge, no attempt has been made to classify and treat crop and weeds in sesame fields at the post-emergence stage previously. In this research we have made the contribution of aerial sesame-weed dataset and a complete deep learning-based approach to classify weeds in sesame fields under variable lighti

关键词： Crops

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Improvement of Depth Mapping and Occlusion Removal Method Using Integral Imaging 23

Improvement of Depth Mapping and Occlusion Removal Method Us...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Sakanoue, Haruka Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose the depth mapping and occlusion removal method using integral imaging (InIm). InIm is a method to obtain 3D information by capturing and reconstructing an object from multiple viewpoints. InIm can remove occlusion using parallax obtained by photographing an object from multiple viewpoints. However, occlusion removal uses a unit matrix to calculate the number of overlaps, which results in an overall darkening of the image. To solve this problem, this paper aims to improve occlusion removal by using the matrix of the mask image to calculate the number of overlaps. InIm can provide depth mapping that shows the distance from an object as pixel values. However, when there is an occlusion in front of the object, it is difficult to provide depth mapping object due to the influence of the occlusion. To solve this problem, we propose a method of depth mapping and occlusion removal method using InIm. Our method is expected to make great progress in improving safety in the field of autonomous driving. © 2023 ICROS.

关键词： Mapping

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

ISBN: (纸本)9798350351255

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model's performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis. © 2024 IEEE.

关键词： Electronic health record

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A Novel Approach to Continual Knowledge Transfer in Multilingual Neural Machine Translation Using Autoregressive and Non-Autoregressive Models for Indic Languages

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IEEE Access 2025年 13卷 89775-89810页

作者： Sheshadri, Shailashree K. Gupta, Deepa Paul, Biswajit Siva Bhavani, J. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India DRDO Centre for Artificial Intelligence and Robotics CV Raman Nagar Bangalore India

Recent progress in multilingual pre-trained models has significantly improved translation quality for Indic languages. However, extending these models to new languages via fine-tuning or retraining remains computationally costly and often leads to parameter interference, degrading performance on previously learned or typologically distant languages. While continual learning offers a promising alternative for incremental language addition, its application in Indic contexts is still limited and faces challenges in generalization across diverse linguistic settings. To overcome these issues, we propose a Continual Knowledge Transfer (CKT) framework for efficient and scalable multilingual adaptation. CKT is realized in both autoregressive (MNMT) and non-autoregressive (Switch-GLAT) architectures, yielding two variants: MNMT+CKT and Switch-GLAT+CKT. Rather than retraining the entire model, CKT freezes the multilingual base and updates only parameters relevant to the newly added *** innovations include gradient-based knowledge pruning, sequential teacher integration, and dynamic vocabulary expansion for minimizing interference and maximizing cross-lingual retention. Comprehensive evaluations on the IN22-Conv and IN22-Gen benchmark datasets demonstrate that both MNMT+CKT and Switch-GLAT+CKT consistently outperform established baselines, such as IndicTrans2, Google Translate, GPT-4-32K, LLaMA-2-17B, and NLIP-LAB-IITH. The proposed multi-step distillation approach, MNMT+CKT, consistently outperforms conventional fine-tuning and Knowledge Transfer (MNMT+KT) strategies for incremental adaptation of linguistically diverse Indic languages. On IN22-Conv, BLEU improvements range from +4.93 (Kashmiri → English) to +11.48 (Assamese → English) and similar improvements are seen for IN22-Gen. The method also achieves substantial reductions in trainable parameters—19.80% (Nepali) to 66.87% (Kannada)—while enabling up to 4x faster inference when integrated with the Switch-GLAT ar

关键词： Neural machine translation

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Photon-Counting Integral Imaging Using Bayesian Estimation with Depth Camera Information 23

Photon-Counting Integral Imaging Using Bayesian Estimation w...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Uchino, Hideaki Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose a method for visualizing object information under low light conditions using photon-counting integral imaging and depth images as prior information. To visualize 3D objects under low-light conditions, computational photon-counting imaging may be used. It is possible to estimate photons using statistical methods such as the Poisson distribution. Therefore, photon-counting integral imaging can visualize 3D images under low-light conditions by maximum likelihood estimation (MLE). However, MLE does not consider prior information so it may not be an accurate estimation. In addition, it is difficult to obtain accurate depth information as the distance increases due to rounding errors caused by computational reconstruction. Therefore, we propose Bayesian estimation such as a maximum a posteriori estimation method that uses as a priori information images from a depth camera, which can be acquired even in low-light conditions, to improve the quality of 3D images. To evaluate image quality of the proposed method, optical experiments were conducted by calculating image quality metrics. This technology can be utilized for night vision applications, such as object recognition in security camera systems and autonomous driving, using RGB cameras and depth cameras. © 2023 ICROS.

关键词： Maximum likelihood estimation

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Multiplier analysis of Lurye systems with power signals 63

Multiplier analysis of Lurye systems with power signals

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Heath, William P. Carrasco, Joaquin Bangor University School of Computer Science and Engineering Dean St LL57 1UT United Kingdom Nancy Rothwell Building University of Manchester Control Systems and Robotics Group Department of Electrical and Electronic Engineering M13 9PL United Kingdom

ISBN: (纸本)9798350316339

Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no guarantee the closedloop system has finite incremental gain. It has been suggested in the literature that without this guarantee such a system may be critically sensitive to time-varying exogenous signals including noise. We show that multipliers guarantee the power gain of the system to be bounded and quantifiable. Furthermore power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee Lurye systems have low sensitivity to noise, provided other exogenous systems have constant steady state. We illustrate the analysis with an example where the exogenous signal is a power signal with non-zero mean. © 2024 IEEE.

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A Study on Coherence Length and Interferometry in Digital Holographic Microscopy 23

A Study on Coherence Length and Interferometry in Digital Ho...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Koga, Takahiro Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

Digital Holographic Microscopy (DHM) can display 3D profiles of microscopic objects and is expected to be used in a wide range of fields such as microbiology, microstructural investigation, and disease diagnosis. Since these fields require detailed information of the object, acquiring an accurate 3D profile is the most important. However, DHM requires several experimental equipments, and it does not define appropriate lasers for interferometers. Therefore, in this paper, we implement an experiment and find the optimum laser for the most accurate 3D profiles of interferometers by comparing 3D profiles using two types of interferometers and three types of lasers with different wavelengths. The results show that the relationship between the laser's coherence length and the optical path length of the interferometer is related to the accuracy of the 3D profiles. They also show that the accuracy of the 3D profile is higher when the coherence length is longer than the optical path length plus twice the height of the object. © 2023 ICROS.

关键词： Computational Imaging

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Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy Procedures: A Comparison of Two Resolved-Rate Controllers 46

Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Yanzhou Al-Zogbi, Lidia Liu, Jiawei Shepard, Lauren Ghazi, Ahmed Tokuda, Junichi Leonard, Simon Krieger, Axel Iordachita, Iulian Johns Hopkins University Department of Mechanical Engineering The Laboratory for Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Laboratory of Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Brady Urological Institute BaltimoreMD United States Harvard Medical School Department of Radiology Brigham and Women's Hospital BostonMA United States Johns Hopkins University Laboratory for Computational Sensing and Robotics Department of Computer Science BaltimoreMD United States

ISBN: (纸本)9798350371499

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation. © 2024 IEEE.

关键词： Biopsy

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Noise Removal in Scattering Media Enviorment Using Peploaraphy and DCLGAN 23

Noise Removal in Scattering Media Enviorment Using Peploarap...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Numata, Riki Kim, Hyun-Woo Ono, Seiya Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

Noise removal in scattering media environments such as fog and smoke are an important task in reality. For example, it can be applied in many fields such as searching under turbid water and rescuing in smoke. Therefore, the purpose of this research is to remove the noise under the scattering medium environment and to visualize the object. As a conventional method for removing noise, there is a technique called peplography. Peplograhy enables the denoising of dense fog and reconstructing the image in scattering media environments. However, peplography has a problem that complex noise exists after removing particularly deep noise, and as a result, it becomes difficult to visualize the object. This research aims to solve this problem. We propose a new denoising technology that combines peplography with DCLGAN. DCLGAN is an image transformation algorithm using deep learning and an improved model of CycleGAN which is also used in the field of noise reduction. In this study, we evaluate the conventional method and the proposed method for scattering media images. As the evaluation method, visual evaluation and numerical evaluation using the PSNR index are performed to demonstrate the effectiveness of the proposed method. We believe that the proposed method can be applied in various fields such as object visualization under turbid water and automatic driving technology with fog removed. © 2023 ICROS.

关键词： Smoke

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