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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Kostantinos Tatas Michalis Gemenaris Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

Neural network implementations on FPGAs have received significant attention from the research community due to their superior performance in tasks such as computer vision and the need to perform them on edge devices. Most efforts have focused on architecture optimizations such as pruning and operations such as the multiplications and additions with less attention on the ANN activation function. In this paper a hardware implementation of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. A digital system architecture is designed with the purpose of reducing the delay of implementation and minimizing the LUTs in the FPGA. The designed architecture is described using Very High-Speed Integrated Circuits Hardware Description Language (VHDL). The proposed method utilizes a known piecewise linear approximation of the sigmoid function, and additionally exploits properties of fixed-point binary number representation to efficiently reduce delay and the amount of LUTS required Compared with previously published implementations of the same function and FPGA technology, the proposed method is shown to achieve a reduction between 33% and 95% in LUTs with no reduction in accuracy.

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Prediction of Parkinson’s disease based on artificial neural networks using speech datasets

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13571-13584页

作者： Liu, Wei Liu, Jierong Peng, Tao Wang, Guojun Balas, Valentina Emilia Geman, Oana Chiu, Hung-Wen School of Information and Communication Engineering Hainan University Hainan Haikou570228 China School of Computer Science and Cyber Engineering Guangzhou University Guangdong Guangzhou510006 China College of Biomedical Information and Engineering Hainan Medical University Street Hainan Haikou571199 China Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad310130 Romania Department of Health and Human Development Stefan cel Mare University of Suceava Suceava720229 Romania Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan

Parkinson’s disease (PD) is a progressive disorder of the nervous system that affects movement. Early prediction of PD can increase the chances of earlier intervention and delay the onset of the disease. Vocal impairment is one of the most important signs in the early stages of PD. Therefore, PD detection based on speech analysis and vocal patterns has attracted significant attention recently. In this paper, we propose a vowel-based artificial neural network (ANN) model for PD prediction based on single vowel phonation. Firstly, we propose a novel multi-layer neural network based on speech features to predict PD. The speech samples from 48 PD patients and 20 healthy individuals are processed into four types: vowel, number, word, and short sentence. Secondly, we establish ANN models with single-type speech samples versus combinations of multi-type speech samples, respectively. Comparative experiments demonstrate that the single-type vowel model is superior to other single-type models as well as multi-type models. Finally, we build a vowel-based ANN model for PD prediction and evaluate its performance. Extensive experiments demonstrate that the proposed model has a prediction accuracy of 91%, sensitivity of 99%, specificity of 82%, and area under the receiver operating characteristic curve (AUC) of 91%, which is superior to the performance of previous methods. Overall, this study demonstrates that the proposed model can provide good classification accuracy for predicting PD and can improve the rate of early diagnosis. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Network layers

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SkinSAM: Adapting the Segmentation Anything Model for Skin Cancer Segmentation

SkinSAM: Adapting the Segmentation Anything Model for Skin C...

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Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment

作者： Hu, Mingzhe Li, Yuheng Yang, Xiaofeng Department of Computer Science and Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Emory University AtlantaGA United States Department of Radiation Oncology Winship Cancer Institute School of Medicine Emory University AtlantaGA United States

ISBN: (纸本)9781510671621

Skin cancer is a prevalent and potentially fatal disease that requires accurate and efficient diagnosis and treatment. Although manual tracing is the current standard in clinics, automated tools are desired to reduce human labor and improve accuracy. However, developing such tools is challenging due to the highly variable appearance of skin cancers and complex objects in the background. In this paper, we present SkinSAM, a fine-tuned model based on the Segment Anything Model that showed outstanding segmentation performance. The models are validated on HAM10000 dataset which includes 10015 dermatoscopic images. While larger models (ViT_L, ViT_H) performed better than the smaller one (ViT_b), the finetuned model (ViT_b_finetuned) exhibited the greatest improvement, with a Mean pixel accuracy of 0.945, Mean dice score of 0.8879, and Mean IoU score of 0.7843. Among the lesion types, vascular lesions showed the best segmentation results. Our research demonstrates the great potential of adapting SAM to medical image segmentation tasks. © 2024 SPIE.

关键词： Diagnosis

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Accelerated Image Stitching Via Parallel Computing for UAV Applications

Accelerated Image Stitching Via Parallel Computing for UAV A...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Ramirez, Rick Korah, John Bhandari, Subodh Nguyen, Tu Chen, Yuqi Le, Du D. Computer Science Department California State Polytechnic University PomonaCA91768 United States Aerospace Engineering Department California State Polytechnic University PomonaCA91768 United States Computer Science Department University of California IrvineCA92697 United States Informatics Department University of California IrvineCA92697 United States Mathematics and Computer Science Department Mt. San Antonio College CA91789 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

In this work, we formulate an accelerated image fusion algorithm for Unmanned Aerial Vehicle (UAV) application which is based on image stitching using invariant features. By utilizing parallel computing techniques for distributed and shared memory architectures, we have improved both the run time of the feature-based image stitching procedure, and the quality of the results when compared to sequential processing. The images used to generate the panorama were obtained from a UAV at a frame rate of 30Hz and a resolution of 3840×2160. As the UAV traverses across the landscape, a video frame buffer is collected and sent to a multi-core Central Processing Unit (CPU) where individual CPU cores perform image fusion in parallel. Our algorithm can be applied to any multi-core CPU (or cluster of multi-core CPU architectures) with support for Graphics Processing Unit (GPU) accelerations. The procedure was tested on various hardware platforms including edge computing devices, ground stations, and high-performance computing environments. The results show that as we increase the number of available CPU cores, there is a decrease in the runtime by as much as 7x under the distributed memory design. There is also a decrease in runtime during the feature detection stage of the fusion process under the shared memory design by a factor of 10x. Finally, by incorporating GPU acceleration at various stages of the stitching procedure, we can decrease the runtime by another factor of 2x. Furthermore, as we increase the number of threads utilized in the shared memory design, we find that there is a significant improvement in the quality of the panorama. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Graphics processing unit

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Multi-Criteria Decision-Making Approach for Laptop Selection: A Case Study 3

Multi-Criteria Decision-Making Approach for Laptop Selection...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Channi, Harpreet Kaur Sandhu, Ramandeep Faiz, Mohammad Islam, Sardar M. N. Chandigarh University Gharuan Department of Electrical Engineering Punjab Mohali India School of Computer Science and Engineering Lovely Professional University Punjab Jalandhar India Victoria University Applied Informatics Research Melbourne Australia

ISBN: (纸本)9798350302288

This article uses the well-known multi-criteria decision-making (MCDM) theory to determine which laptop is the best among those that are currently on the market. To illustrate the importance of decision-making in a class of 25 students, data on laptop features was gathered for this case study. The following eight factors were considered while choosing an option: price, weight, RAM, camera megapixels, screen size, clock speed, operating system, and laptop appearance. SAW and WPM determine the optimal laptop alternative. To rank laptop qualities, identical weights are applied. The suggested work displays laptop rankings derived by SAW and WPM methods. Future engineers and management may benefit significantly from this straightforward decision-making process. More important attributes/criteria and alternatives may be taken into account, and there are a variety of weighting techniques from which to choose. The study emphasizes the importance of incorporating user preferences and real-time market data to ensure personalized and up-to-date laptop recommendations. Through this approach, users can make well-informed decisions that align with their specific needs and environmental considerations. © 2023 IEEE.

关键词： Decision making

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Tree Data Structures and Efficient Indexing Techniques for Big Data Management: A Comprehensive Study 26

Tree Data Structures and Efficient Indexing Techniques for B...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Samoladas, Dimitrios Karras, Christos Karras, Aristeidis Theodorakopoulos, Leonidas Sioutas, Spyros Department of Computer Engineering and Informatics University of Patras Rion Greece Department of Management Science and Technology University of Patras Koukouli Patras Greece

ISBN: (纸本)9781450398541

In the modern era where data is produced from multivariate sources, there is an urge to handle such data in an efficient yet effective manner. Therefore, applications that necessitate such capabilities shall make use of data structures and indexing mechanisms that can perform fast index operations along with low complexity as per insertion, deletion, and search. In this work, we survey B+ Tree, QuadTree, kD Tree, R Tree, and others along with efficient indexing techniques for big data management in order to provide a generic overview of the field to readers. Ultimately, we provide some indexing experiments as per insert operations and response times. © 2022 ACM.

关键词： Information management

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The Comparison of Image Enhancement Methods for A Realtime Under Water Vision System of The Break Water Implementation 24

The Comparison of Image Enhancement Methods for A Realtime U...

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24th International Electronics Symposium, IES 2022

作者： Marta, Bayu Sandi Muhammad Amin, A. Sari, Dewi Mutiara Armono, Haryo Dwito Politeknik Elektronika Departement of Informatics and Computer Engineering Negeri Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Marine Engineering Ocean Engineering Department Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

The implementation of computer vision in the field of underwater image is currently very popular. However, Indonesia's vision system for breakwater work has not yet been implemented. The work of the vision system is still manual using divers [1]. In addition, the application of computer vision in underwater images has a big obstacle because the resulting image in an underwater environment has poor color and contrast. This poor color and contrast are due to a density nearly 1000 times denser than air. This density directly affects the light transmission, which affects the resulting image, which has less color and contrast than the image taken through the air. So it takes image processing such as Image Enhancement and Image Color Restoration [2]. Another issue is building a real-time system. If the system that is built is not real-time, this makes the possibility of data reading errors being significant, and the movement of information is slow [3]. This research presents methods regarding image enhancement and color restoration built in the Graphical User Interface (GUI). The methods applied in this research are Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Multi-Scale Retinex with Color Restoration (MSRCR). Those methods are applied to the images produced by the two cameras and run on the single board processor and laptop for the comparison. The result by using 71 underwater images shows that for Underwater Color Image Quality Evaluation (UCIQE) value of the Histogram Equalization (HE) Method yields 20%, meanwhile for Contrast Limited Adaptive Histogram Equalization (CLAHE) yields 8%, and the Multi-Scale Retinex with Color Restoration (MSRCR) method yields 72%. © 2022 IEEE.

关键词： Image enhancement

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Evaluation Metrics for Automated Typographic Poster Generation

arXiv

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arXiv 2024年

作者： Rebelo, Sérgio M. Merelo, J.J. Bicker, João Machado, Penousal University of Coimbra CISUC/LASI – Centre for Informatics and Systems the University of Coimbra Department of Informatics Engineering Portugal Department of Computer Engineering Automatics and Robotics and CITIC University of Granada Spain

Computational Design approaches facilitate the generation of typographic design, but evaluating these designs remains a challenging task. In this paper, we propose a set of heuristic metrics for typographic design evaluation, focusing on their legibility, which assesses the text visibility, aesthetics, which evaluates the visual quality of the design, and semantic features, which estimate how effectively the design conveys the content semantics. We experiment with a constrained evolutionary approach for generating typographic posters, incorporating the proposed evaluation metrics with varied setups, and treating the legibility metrics as constraints. We also integrate emotion recognition to identify text semantics automatically and analyse the performance of the approach and the visual characteristics *** Codes 68W50 © 2024, CC BY.

关键词： Quality control

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Tomato Disease Classification Using CNN 4th

Tomato Disease Classification Using CNN

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Archanaa, N. Daniel, V. Vifert Jenuben Divya, S. Raja, Kalpana Oviya, I.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Sciences Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

ISBN: (纸本)9789819736898

Plant disease detection using various machine learning techniques is a very popular research area. Many promising results have been obtained, but only a few specific applications can still make farmers’ lives easier. This research focuses on the important task of identifying tomato diseases at an early stage to prevent their spread and protect crop yields more broadly, emphasizing the idea that disease prevention will more important than treatment. Leveraging the capabilities of convolutional neural networks (CNN), we developed a model that diagnoses eight distinct tomato diseases using only their images. Diseases listed include bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, and tomato yellow leaf curl virus. An integral part of our approach involves sophisticated image prepossessing and enhancement techniques, ensuring data quality and model robustness. Feature extraction plays a key role in transforming raw image data into recognizable patterns. The architecture of the CNN has been meticulously designed, consisting of multiple layers with a notable focus on the ReLU activation function and other relevant hyper-parameters. Our main dataset comes from PlantVillage, known for synthesizing rich and diverse plant images. After rigorous training and validation, the CNN model showed exceptional accuracy of 96.88% in tomato disease identification. However, challenges such as overfitting, image quality variability, and class imbalances were encountered but were resolved through strategic data augmentation, careful CNN overlay. In summary, our research presents a practical solution for farmers and agricultural entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fruits

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3D Object Pose Estimation Using Local Features Based for Industrial Appliance 25

3D Object Pose Estimation Using Local Features Based for Ind...

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25th International Electronics Symposium, IES 2023

作者： Pratama, Alfan Rizaldy Dewantara, Bima Sena Bayu Sari, Dewi Mutiara Pramadihanto, Dadet Politeknik Elektronika Negeri Surabaya Center for Research and Innovation on Advanced Transportation Electrification Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Object localization in the industrial field plays an important role to obtain the pose information of an object. For supporting the localization works, 3D point cloud data is used as its primacy containing the shape of an object. Despite its rapid development, the problems faced by each object used are heterogeneous. In this research, Time-of-Flight (ToF) camera is used to obtain the data considering its capability to provide a small form factor and sufficient point cloud data. As the one of most popular objects in the industrial field, fitting pipe is used in whole systems. Start from obtaining the data using a ToF camera, then followed by preprocessing which eliminate Non-measured data and point outside of the threshold range. Segmentation by separating the object with its planar, and separating each other into individual objects done by RAndom SAmple Consensus (RANSAC) and Density-based Spatial Clustering Application with Noise (DBSCAN). Before the pose estimation is applied, CNN is used to recognize the object. By using Fast Point Feature Histogram (FPFH) with SAmple Consensus Initial-Alignment (SAC-IA) as the coarse pose estimation and Iterative Closest Point (ICP) as the refinement pose estimation, average rotation error in Euclidean space (roll, pitch, yaw) as 0.47 degree, in translation is 4.17 mm. Also, we estimate the size of the pipe which has an average error of 5.30 mm. © 2023 IEEE.

关键词： Cameras

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