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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Seidel, Henrique Da Rosa, Morgana Lopes, Rodrigo Da Costa, Eduardo Soares, Rafael Graduate Program on Computing Pelotas Brazil Graduate Program on Electronic Engineering and Computing Pelotas Brazil

ISBN: (纸本)9798331522124

We present an energy-efficient implementation of the level-2 Pruned Discrete Haar Wavelet Transform (PDHWT-2) for R-peak detection in ECG signals. Approximate computing techniques were employed, including five well-established approximate adders (AxPPA, COPY, ETA-I, LOA, and Trunc) and bit truncation (T), to reduce energy consumption while maintaining accuracy above 99% for reliable R-peak detection. By focusing on the second-level detail coefficient (D2) for peak detection and pruning the approximation coefficient, the PDHWT-2 design achieves significant reductions in computational complexity. The architecture, evaluated with ECG signals from the MIT-BIH database, maintained accuracy above 99% even with aggressive bit truncation and approximation, ensuring reliable medical-grade analysis. Integration of approximate adders (AxAs) and truncation enabled significant power and area savings. Specifically, the use of the AxPPA demonstrated power savings of up to 40% with area reductions of around 45%, making the PDHWT-2 a highly efficient solution for low-power ECG monitoring systems. © 2025 IEEE.

关键词： Image reconstruction

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Exploring Approximate Adders for Accuracy- and Energy-Quality VLSI Watermarking Systems Design 16

Exploring Approximate Adders for Accuracy- and Energy-Qualit...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Da Rosa, Morgana Da Costa, Eduardo Soares, Rafael Graduate Program on Computing Pelotas Brazil Graduate Program on Electronic Engineering and Computing Pelotas Brazil

ISBN: (纸本)9798331522124

Approximate computing (AxC) has emerged as a viable alternative to enhance computational efficiency by leveraging the intrinsic error resilience of many applications. One of the leading strategies of AxC involves exploring different approximation adder (AxA) configurations as a viable form of reducing power consumption in many applications. This work explores the use of AxAs in a hybrid watermarking technique that combines discrete Haar-Wavelet (DHWT) and discrete cosine transforms (DCT). The proposed hybrid method, HyDHWCT, integrates these transforms to improve robustness and imperceptibility in watermarking systems while optimizing for energy efficiency. We evaluate the performance of various AxAs, including Copy, ETA, LOA, Truncation (Trunc), VLSPPAs, and the AxPPA, regarding energy consumption, circuit area, and error resilience. Our results demonstrate that the AxPPA-based HyDHWCT offers superior accuracy and energy-quality trade-offs over other state-of-the-art AxAs. Specifically, the AxPPA on HyDHWCT with K = 1 achieves up to 72.85% energy savings and 88.32% area savings compared to the exact HyDHWCT while maintaining high accuracy, with a normalized cross-correlation (NC) of 0.9949 and a structural similarity (SSIM) of 0.9920 for the extracted watermark. These results make the AxPPA-based HyDHWCT a highly effective solution for robust and energy-efficient watermarking systems. © 2025 IEEE.

关键词： Energy efficiency

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An Ultra Low-Energy VLSI Approximate Discrete Haar Wavelet Transform for ECG Data Compression 30

An Ultra Low-Energy VLSI Approximate Discrete Haar Wavelet T...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Cardozo, Arthur Rosa, Morgana M. A. Soares, Rafael Costa, Eduardo Bampi, Sergio Graduate Program on Computing Pelotas Brazil Graduate Program on Electronic Engineering and Computing Pelotas Brazil Porto Alegre Brazil

ISBN: (纸本)9798350326499

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) © 2023 IEEE.

关键词： Digital storage

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Enhancing dynamic ensemble selection: combining self-generating prototypes and meta-classifier for data classification

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Neural computing and Applications 2024年第32期36卷 20295-20320页

作者： Manastarla, Alberto Silva, Leandro A. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil Computing and Informatics Faculty and Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A significant challenge in DES is efficiently estimating classifier competence for accurate prediction, especially when these techniques employ the K-Nearest Neighbors (KNN) algorithm to define the competence region of a test sample based on a validation set (known as the dynamic selection dataset or DSEL). This challenge is exacerbated when the DSEL does not accurately reflect the original data distribution or contains noisy data. Such conditions can reduce the precision of the system, induce unexpected behaviors, and compromise stability. To address these issues, this paper introduces the self-generating prototype ensemble selection (***) framework, which combines meta-learning with prototype selection. The proposed meta-classifier of *** supports multiple classification algorithms and utilizes meta-features from prototypes derived from the original training set, enhancing the selection of the best classifiers for a test sample. The method improves the efficiency of KNN in defining competence regions by generating a reduced and noise-free DSEL set that preserves the original data distribution. Furthermore, the *** framework facilitates tailored optimization for specific classification challenges through the use of hyperparameters that control prototype selection and the meta-classifier operation mode to select the most appropriate classification algorithm for dynamic selection. Empirical evaluations of twenty-four classification problems have demonstrated that *** outperforms state-of-the-art DES methods as well as traditional single-model and ensemble methods in terms of accuracy, confirming its effectiveness across a wide range of classification contexts. © The Author(s), under exclusive licence to Springer-Ver

关键词： Nearest neighbor search

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Video Quality Enhancement Using Multi-Domain Spatio-Temporal Deformable Fusion 16

Video Quality Enhancement Using Multi-Domain Spatio-Temporal...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Kreisler, Gilberto Da Silveira Junior, Garibaldi Zatt, Bruno Palomino, Daniel Correa, Guilherme Video Technology Research Group - ViTech Graduate Program in Computing - UFPel Pelotas Brazil

ISBN: (纸本)9798331522124

Lossy video compression introduces visual artifacts that degrade video quality, where deep neural networks (DNNs) are effective in enhancement. However, conventional DNN-based methods often focus on a single video compression standard, limiting their deployment in multiple cases. To overcome this issue, this study introduces a multi-domain video quality enhancement architecture based on the Spatio-Temporal Deformable Fusion (STDF) technique. This method enables the model to enhance videos compressed with multiple codecs, maintaining reliable performance across standards. After trained, the proposed architecture was tested with videos compressed by the High Efficiency Video Coding (HEVC) encoder, the Versatile Video Coding (VVC) encoder, the VP9 codec and the AOMedia Video 1 (AV1) codec. Results show an average Peak Signal-to-Noise Ratio (PSNR) improvement between 0.228 dB and 0.787 dB. © 2025 IEEE.

关键词： Video streaming

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New Energy-Efficient 3-2 and 4-2 Approximate Adder Compressors Topologies 30

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

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Lopes, Rodrigo Antonietti, Leonardo Rosa, Morgana M. A. Costa, Eduardo Soares, Rafael Bampi, Sergio Graduate Program on Computing Pelotas Brazil Graduate Program on Electronic Engineering and Computing Pelotas Brazil PPGC & PGMICRO - Informatics Institute Porto Alegre Brazil

ISBN: (纸本)9798350326499

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. © 2023 IEEE.

关键词： Adders

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An Optimized VLSI Exponential Unit Design Exploring Efficient Arithmetic Operation Strategies 30

An Optimized VLSI Exponential Unit Design Exploring Efficien...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Da Costa, Patrícia Da Rosa, Morgana Soares, Rafael Da Costa, Eduardo Bampi, Sergio Federal University of Rio Grande Do Sul Graduate Program on Microelectronics Porto Alegre Brazil Graduate Program on Computing Pelotas Brazil Graduate Program on Electronic Engineering and Computing Pelotas Brazil Porto Alegre Brazil

ISBN: (纸本)9798350326499

This work presents an optimized exponential function VLSI hardware design by Taylor series expansion. The proposed architecture implements the exponential by approximating the logic design of a 4th-order Taylor series and explores efficient CMOS arithmetic operation strategies. It implements a shift-based divider and explores an efficient 4-2 adder compressor in the adder tree. The proposal with a -7 to 11 input values range shows an output error of around 2% of MRED with a reduced energy consumption of 3.63 pJ/operation for 32-bit output. For a 64-bit output, the energy per operation of the VLSI exponential unit is 14.97pJ/op, being able to process a more comprehensive input range (i.e., -14 to 22) for a negligible mean output error of around 1.7% of MRED. © 2023 IEEE.

关键词： Adders

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Anais Brasileiros de Dermatologia: who wrote this century-old history?

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Anais Brasileiros de Dermatologia 2025年第3期100卷 527-538页

作者： Grimaldi, Helena Cargnelutti Camargo, Sandro da Silva Private Clinic RS Bagé Brazil Graduate Program in Applied Computing Federal University of Pampa RS Bagé Brazil

The Anais Brasileiros de Dermatologia (ABD), an official publication of the Brazilian Society of Dermatology, has been published since 1925. ABD is considered the most influential dermatological journal in Latin America. By 2025, the journal will mark a significant milestone, celebrating a century of history. Over this time, it has published 99 volumes and 6,299 contributions from more than 10,800 authors. To analyze the trajectory of the journal, this study employs an applied research approach characterized by descriptive objectives and a quantitative nature. This research was based on a documentary procedure that encompassed all contributions already published in the journal. The main goal of the work was to identify the most prominent authors who have contributed to the journal and to map the largest co-authorship communities. The authors hope that this research serves as a formal recognition of the researchers who have written the history of the ABD. © 2025 Sociedade Brasileira de Dermatologia

关键词： Authorship Bibliometrics Periodical Scientific communication and diffusion

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DCANon: Towards Distributed Certification Authority (CA) with Non-fungible Token (NFT) 37th

DCANon: Towards Distributed Certification Authority (CA) wit...

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Descio-Trineto, Rafael Pillon, Maurício Koslovski, Guilherme Miers, Charles Graduate Program in Applied Computing Santa Catarina State University Joinville Brazil

ISBN: (纸本)9783031290558

Distributed applications rely on Certification Authorities (CAs) entities associated with a few institutions around the world. There are countless trusted institutions on the networks, e.g., universities, governments, banks, making effective use of CAs. Although largely used, the decentralized management of CAs is an administrative challenge. In this context, the joint emergence of digital ledgers and Non-Fungible Tokens (NFTs) compose a new opportunity to effectively decentralize single assets safely. Towards Distributed Certification Authority with Non-Fungible Token (DCANon) is a distributed Certification Authority (CA) built to trusted institutions and supported by Non-Fungible Token (NFT) concepts. DCANon changes the way revocation may be find, permitting that all consortium member crawler the blockchain for revoked certificates, independently from which CA this revocation occurs. A proof-of-concept prototype was implemented with Hyperledger Fabric demonstrating the benefits of DCANon. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distributed ledger

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Computational Cost Analysis and Reduction of VVC Multiple Transform Selection 16

Computational Cost Analysis and Reduction of VVC Multiple Tr...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Camargo, Caroline Silveira, Bianca Correa, Guilherme Federal University of Pelotas - UFPel Video Technology Research Group - ViTech Graduate Program in Computing - Ppgc Pelotas Brazil

ISBN: (纸本)9798331522124

The growing demand for high-definition online videos emphasizes the need for efficient video codecs like H.266/VVC, which offer significant compression potential. However, its implementation presents challenges, particularly in terms of computational cost, as is the case of the Multiple Transform Selection (MTS) tool. This study analyzes the performance of MTS modes, showing that explicit MTS improves coding efficiency but increases encoding time, while implicit MTS offers modest efficiency gains with less computational cost. A machine learning-based approach is proposed, using decision trees, to accelerate encoder decisions for both intra and inter predicted blocks in explicit MTS, reducing encoding time by an average of 7.98%, with only a 0.89% increase in BD-rate. These results highlight the potential for optimization of explicit MTS in both intra and inter transformations. © 2025 IEEE.

关键词： Multiresolution analysis

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