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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Arthur Cardozo Morgana M. A. Rosa Rafael Soares Eduardo Costa Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

This work proposes an ultra-low-energy ECG data compression with VLSI DHWT-based (discrete Haar wavelet transform) architecture to enable storage and transmission in resource-constrained environments. We present original, pruned, and approximate DHWT (ODHWT, PDHWT, and AxDHWT, respectively) hardware architectures for ECG data compression at ultra-high energy efficiency. Our best proposal employing the AxDHWT hardware architecture requires just five additions only. Using a PDHWT technique to improve energy efficiency observes the evolution of the signal-to-noise ratio and the ultimate impact on the ECG data compression application. The DHWT-based configurations architecture proposal achieves a minimum compression ratio of 0.125 (i.e., 1/8) and a PRD (percent root difference) <1.34. We implemented the VLSI architectures in 65nm CMOS technology in maximum and fixed frequencies and two different supply voltages. The AxDHWT occupies a die area of 0.64mm 2 . The measured total power is $0.534\mu {\mathrm {W}}$, the higher energy-savings among the ECG data compression architectures.

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New Energy-Efficient 3-2 and 4-2 Approximate Adder Compressors Topologies

New Energy-Efficient 3-2 and 4-2 Approximate Adder Compresso...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Rodrigo Lopes Leonardo Antonietti Morgana M. A. Rosa Eduardo Costa Rafael Soares Sergio Bampi Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil PPGC & PGMICRO - Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

Addition units are present across many computational kernels inherent in various error-tolerant applications, including machine learning, signal, image, and video processing. Notably, adder compressors are the target when high speed and low power are the main design concerns. This work introduces energy-efficient structures of 3-2 approximate adder compressors (Ax3-2) and three distinct versions of 4-2 approximate adder compressors (Ax4-2). We compared our proposed Ax3-2 and Ax4-2 compressors with state-of-the-art energy-efficient approximate adder compressors (AxACs). Both Ax3-2 and Ax4-2 models passed through rigorous tests as standalone units. Furthermore, we integrated the Ax4-2 models into an essential application kernel appearing in video coding, i.e., the Sum Squared Differences (SSD) video accelerator. We introduce Ax3-2 and Ax4-2 that yield a new Pareto front concerning both energy-quality and area-quality trade-offs, thereby demonstrating a marked improvement over the prevailing state-of-the-art energy-efficient AxACs.

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Medical Image Segmentation Model for Highly Constrained Environments 1

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International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024

作者： Kong, Youngbin Kim, Kwangtai Jeong, Seoi Lee, Kyu Eun Kong, Hyoun-Joong Interdisciplinary Program in Bioengineering Graduate School Seoul National University Seoul Korea Republic of Department of Transdisciplinary Medicine Seoul National University Hospital Seoul Korea Republic of Department of Surgery Seoul National University Hospital and College of Medicine Seoul Korea Republic of Department of Medicine Seoul National University College of Medicine Seoul Korea Republic of Innovative Medical Technology Research Institute Seoul National University Hospital Seoul Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University College of Medicine Seoul Korea Republic of

ISBN: (数字)9783031818547

ISBN: (纸本)9783031818530

Medical image segmentation is a crucial step for accurate diagnosis and treatment planning, as it provides quantitative information about anatomical structures and pathological lesions in various clinical scenarios. However, the existing methodologies have limitations in terms of their generalizability and computational efficiency. In this study, we propose SwiftMedSAM, an ultra-lightweight prompt-based general model, to enable efficient medical image segmentation even in resource-constrained environments. Based on LiteMedSAM, we significantly reduced the model size and computational complexity through the hyperparameter optimization of the image encoder and mask decoder components. SwiftMedSAM showed remarkable performance across various imaging modalities enabling real-time inference in resource-limited computing environments. It achieved a validation score of 0.75 demonstrating that SwiftMedSAM outperformed the existing methodologies in terms of the trade-off between accuracy and efficiency. Owing to its unprecedented generalizability and low computational cost, SwiftMedSAM is expected to enable high-quality medical image analysis in resource-constrained settings, thereby contributing to advancements in precision medicine and telemedicine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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Fuels Demand Forecasting: Identifying Leading Feature Sets, Prediction Strategy, and Regressors

Fuels Demand Forecasting: Identifying Leading Feature Sets, ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jonas Krause Alexandre C. A. Beiruth Jean P. Barddal Alceu S. Britto Vinicius M. A. Souza Graduate Program in Informatics (PPGIa) Centro Integrado de Soluções em Inteligência Artificial (CISIA) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba PR Brazil

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Fuels are crucial for any country's development and economy, impacting various sectors such as transportation, industry, and electricity generation. Accurate prediction of monthly fuel demand can improve supply chain management, strategic decision-making, and financial planning for businesses while helping governments develop decarbonization policies and estimate pollutant emissions. This paper explores machine learning models to forecast fossil fuels and biofuel demand 12 months ahead, using univariate time series data representing the historical sales of 27 Brazilian states, one of the world's leading producers and consumers of fuels. We evaluate different time series feature sets, machine learning regression models, and prediction strategies to address the complexity of fuel sales influenced by factors such as economic conditions and geopolitical events. Our comprehensive evaluation aims to determine an effective setting for predictive models in the fuel domain. Our results show that popular feature extractors for time series, such as Catch22 and TsFresh, cannot improve the original data representation for most forecasting models. Although focused on Brazil, our findings apply to other countries, since the trained models do not rely on external variables, such as micro and macroeconomic indicators.

关键词： Supply chain management Biological system modeling Transportation industry Time series analysis Machine learning Predictive models Feature extraction Data models Macroeconomics Fuels

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Analysis of the Brazilian Climate Data Influence on Soybean Pricing by Using Long Short-Term Memory Models

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SSRN 2024年

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Dal Molin, Viviane Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical and Computer Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Department of Academic Informatics Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Graduate Program in Production and Systems Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil

This work uses machine learning to verify whether the Brazilian climate influences international soybean price prediction. For this, climatic data, dollar quotes, inflation, and annual soybean production were collected from different data sources. Then, the collected data were pre-processed and grouped by Brazilian state. Afterward, data from the five most productive Brazilian states are chosen, such as Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS). Afterward, predictive models are built using the long short-term memory (LSTM) method, which is applied in each chosen state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest values of root mean squared error (RMSE) and mean absolute error (MAE). Regarding predictors trained without climate data, the RS and PR obtained the lowest values of RMSE and MAE, respectively. After conducting statistical hypothesis tests, no significant difference was found among models, stating, with 95% certainty, that, in this study, the Brazilian climatic data did not influence the model performance for predicting soybean prices. On the other hand, one of the main contributions of work is conducting an unprecedented study about soybean price prediction using Brazilian climatic data. © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Utilization Extract, Transform, Load for Developing Data Warehouse in Education Using Pentaho Data Integration 4

Utilization Extract, Transform, Load for Developing Data War...

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4th International Conference on Electrical, Electronics, informatics, and Vocational Education, ICE-ELINVO 2021

作者： Barahama, A.D. Wardani, R. Department of Electronics and Informatics Engineering Education Post-Graduate Program Universitas Negeri Yogyakarta Jalan Colombo 01 Daerah Istimewa Yogyakarta 55281 Indonesia

The utilization of data warehouses in various fields is an absolute necessity. A data warehouse is a database that contains large amounts of data that aims to help organizations, fields, and institutions specifically for decision making. Data warehouses can produce important information in the future. Loading data from various sources and processed through an ETL (Extract, Transform, Load) process that displays data consistently is the basis for creating a data warehouse architecture. The development of a data warehouse in education will provide significant benefits for the progress of education. Integration of data and processing results stored in the data warehouse can be the basis for evaluating better planning. Development of data warehouse adopt the multidimensional modelling method which consists of four stages: select the business process, declare the grain, select dimensions, and identify facts. This stage produces a data warehouse architecture and influences and contributes to the advanced information technology in education. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Data warehouses

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Interacting with Cultural Heritage Through Shape Representation Techniques in 3D Modeling Environments 8th

Interacting with Cultural Heritage Through Shape Representat...

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8th European-Mediterranean Conference, EuroMed 2020

作者： Moralıoğlu, Begüm Aktaş, Begüm Architectural Design Computing Graduate Program Istanbul Technical University Institute of Informatics Istanbul Turkey Department of Architecture Altınbas University Istanbul Turkey

ISBN: (纸本)9783030730420

Three-dimensional (3D) representation of cultural heritage in a digital environment is coming into prominence for documentation of geometric and semantic details and the interpretation of shape representation from academic studies to commercial practices. To interpret cultural and historical shapes, the use of the computational medium is expanding with developing technologies. Advanced 3D design and development applications appear every day with distinct functions. However, not every technology provides designers a convenient medium to create, explore, and think naturally. In that sense, digital design mediums and tools become important to enhance the interactivity level with cultural heritage for designers through different interfaces. Particularly, the utilities of design interfaces are essential for preserving reconstructing ruined cultural heritage in the digital design mediums. In this study, we investigate the designers’ creative design process in the context of shape representation methods within the possibilities of two different 3D design interfaces as a computer and virtual reality (VR) interfaces. At first, we conducted a shape exploration exercise to generate the 3D model pieces of the Dark Church in Cappadocia. And then, we used the generated 3D model pieces in a shape representation exercise to observe the seeing, imagining, and acting abilities of designers in computer and VR environments. © 2021, Springer Nature Switzerland AG.

关键词： 3D modeling

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Soybean Price Prediction by Using Lstm Models Based on Brazilian Climatic Data

SSRN

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SSRN 2024年

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Molin, Viviane Dal Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Department of Academic Informatics Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Graduate Program in Production and Systems Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil

关键词： Long short-term memory

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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

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Computers in Biology and Medicine 2025年 185卷 109541-109541页

作者： Le, Van-The Malik, Muhammad-Shahid Lin, Yi-Jing Liu, Yu-Chen Chang, Yan-Yun Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan Department of Computer Sciences Karakoram International University Gilgit-Baltistan 15100 Pakistan

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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