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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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Multiversion Low-Power Hardware Accelerator for the AV1 Interpolation Filters

Multiversion Low-Power Hardware Accelerator for the AV1 Inte...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Daiane Freitas Mateus Grellert Cláudio M. Diniz Guilherme Correa Video Technology Research Group (ViTech) Graduate Program in Computing (PPGC) Federal University of Pelotas (UFPel) Embedded Computing Lab (ECL) Federal University of Santa Catarina (UFSC) PGMICRO Institute of Informatics (INF) Federal University of Rio Grande do Sul (UFRGS)

One of the new tools included in the AV1 video codec is the adaptive filtering scheme used in the sample interpolation process. This scheme includes three different filter families called Regular, Sharp and Smooth, offering high flexibility for motion estimation (ME) and motion compensation (MC). However, the high number of interpolation filters also leads to greater complexity and energy consumption, since the generation of samples at sub-pixel position is a costly process. This paper proposes a low-power and high-throughput hardware accelerator focused on the AV1 interpolation filters called Multiversion Interpolation Processor (MVIP). The accelerator includes the three AV1 interpolation filter families, with versions that employ operand isolation for power reduction in unused filters. The accelerator also includes a precise MVIP assuming the MC scenario, besides two approximate versions to reduce the cost on the ME scenario. The proposed design is able to process 8K video at 50fps in MC and 2,656.14 Msamples/sec in ME, with a power dissipation of 41.30mW.

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Architectural Exploration for Energy-Efficient LMS and NLMS Adaptive Filters VLSI Design

Architectural Exploration for Energy-Efficient LMS and NLMS ...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Pedro T. L. Pereira Guilherme Paim Eduardo Costa Paulo Flores Sergio Bampi Graduate Program on Microelectronics (PGMicro) Federal University of Rio Grande do Sul (UFRGS) Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Brazil INESC-ID Instituto Superior Técnico ULisboa Portugal

Adaptive filtering plays an essential role in digital signal processing (DSP), being present in many application fields such as noise reduction, telecommunication, and audio signal processing. Notably, the least mean square (LMS) and its normalized version (NLMS) are the most used algorithms due to their simplicity and robustness, which turn them attractive to hardware solutions. This work proposes architectural solutions for LMS and NLMS algorithms targeting an energy-efficient VLSI design. We explore fully sequential, fully parallel, and semi-parallel architectures reusing the hardware as much as possible for both algorithms. It allows for investigating the number of steps necessary for each architecture, demonstrating the energy per operation, circuit area, and throughput savings. The synthesis results highlight the semi-parallel architectures LMS-2 steps and NLMS-3 steps have the best trade-off energy-speed. In addition, we proved the architecture’s energy efficiency in a harmonic canceling case study, achieving more than 60% in energy savings using both filters.

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Deviations in Representations Induced by Adversarial Attacks

arXiv

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arXiv 2022年

作者： Steinberg, Daniel Munro, Paul Intelligent Systems Program University of Pittsburgh United States School of Computing and Information University of Pittsburgh United States

Deep learning has been a popular topic and has achieved success in many areas. It has drawn the attention of researchers and machine learning practitioners alike, with developed models deployed to a variety of settings. Along with its achievements, research has shown that deep learning models are vulnerable to adversarial attacks. This finding brought about a new direction in research, whereby algorithms were developed to attack and defend vulnerable networks. Our interest is in understanding how these attacks effect change on the intermediate representations of deep learning models. We present a method for measuring and analyzing the deviations in representations induced by adversarial attacks, progressively across a selected set of layers. Experiments are conducted using an assortment of attack algorithms—on the CIFAR-10 dataset—with plots created to visualize the impact of adversarial attacks across different layers in a network. Copyright © 2022, The Authors. All rights reserved.

关键词： Network layers

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Design and Development of a Livestock Food Monitoring System

Design and Development of a Livestock Food Monitoring System

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International Carpathian Control Conference (ICCC)

作者： Rafael Martins Preisser Marçal José Geraldo Ribeiro Júnior Gustavo Campos Menezes Murillo Ferreira Dos Santos Paolo Mercorelli Master Degree Program in Automation and Systems CEFET-MG Leopoldina Brazil Department of Computing CEFET-MG Leopoldina Brazil Department of Electronics and Computing CEFET-MG Contagem Brazil Department of Electroelectronics CEFET-MG Leopoldina Brazil Institute for Production Technology and Systems (IPTS) Leuphana University of Lüneburg Lüneburg Germany

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

In the context of optimizing herd management and animal health monitoring, this study aims to enhance the understanding of livestock feeding behavior through precise monitoring of the time each animal spends at the trough. The primary objective is to optimize feed supply management while concurrently enabling early detection of potential health issues. The fundamental innovation of this study revolves around the creation of an Internet of Things (IoT) Platform, leveraging Ultra High Frequency (UHF) Radio-Frequency Identification (RFID) tags and a specialized antenna cable. This platform facilitates the meticulous recording of animals' ingress and egress times at the feeding trough, thereby providing valuable insights for improved livestock management and health monitoring.

关键词： Animals Sociology UHF antennas Agriculture Real-time systems Internet of Things Feeds

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HistAttentionNAS: A CNN built via NAS for Penile Cancer Diagnosis using Histopathological Images

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Procedia Computer Science 2025年 256卷 764-771页

作者： José Ribamar Durand R. Geraldo Braz Junior Italo Francyles Santos da Silva Rui Miguel Gil da Costa Oliveira Applied Computing Group - Federal University of Maranhão Vila Bacanga São Luís 65080-805 Brazil Graduate Program in Adult Health - PPGSAD - Federal University of Maranhão Vila Bacanga São Luís 65080-805 Brazil

Penile cancer, although rare, has an increasing mortality rate in Brazil, highlighting the need for effective diagnostic methods. Artificial Intelligence (AI) in histopathological analysis can speed up and objectify diagnosis, but designing an ideal architecture is challenging. In this study, we propose a neural architecture search (NAS) methodology for detecting penile cancer in digital histopathology images. We explored different configurations of stem blocks and the inclusion of attention mechanisms, highlighting specific preferences depending on the magnification of the images. The results showed that the NAS methodology enabled the discovery of more accurate and optimized architectures for this task, surpassing conventional models. The proposed models achieve 89.5% and 88.5% F1-Score for 40X and 100X magnification, respectively.

关键词： Penile Cancer Detection Histopathologic Classification Neural Architecture Search

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Circulogy: An AI-Enabled Blockchain-Based e-Waste Management Framework Using Non-Fungible Tokens (NFT) to Achieve Net Zero and Imply the Circular Economy

Circulogy: An AI-Enabled Blockchain-Based e-Waste Management...

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Md Mahmudul Hasan Rajesh Kumar Plamthottathil Jabed Morshed Dipto Sarkar Nazia Hameed Silvia Cirstea Faculty of Science and Engineering School of Computing and Information Science Anglia Ruskin University Cambridge UK TrackGenesis ONE Tech Hub Schoolhill Aberdeen Scotland UK Lecturer in Cyber Security Program La Trobe University Australia Research and Development Team intelliCreate University of Nottingham School of Computer Science Computing and AI Group School of Computing and Information Science (CIS)

Overconsumption of resources is a global issue. To deal with resource depletion and mitigate impending crises, the circular economy (CE) solution provides an ecosystem by reducing waste via the reuse, repair, refurbishment, and recycling the existing materials and products. However, as the complexity of supply chains is increasing an effective CE management is very crucial. We want to address this issue by performing a feasibility study with AI-enabled blockchain technology using our developed customised NFT platform, TrackGenesis NFT, along with the *** architecture for CE management to decrease transaction costs, enhance performance and communication along the supply chain, and reduce carbon footprints. Circulogy is an e-waste management system that can respond to supply chain challenges using blockchain technologies. A supply chain can get complicated very quickly, considering that each product component has its supply chain. In our proposed solution, blockchain provides a solution to this by establishing transparency in every node of the product's lifecycle and users can exchange or sell/buy NFTs. There are multiple copies of the audit trail for every transaction using blockchain, which will provide the ability to track and reuse/recycle Waste Electric and Electronic Equipment (WEEE).

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A Method Based on Deep Neural Network for Instance Segmentation of Retinal Lesions Caused by Diabetic Retinopathy

A Method Based on Deep Neural Network for Instance Segmentat...

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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Santos, Carlos Aguiar, Marilton Welfer, Daniel Silva, Marcelo Pereira, Alejandro Ribeiro, Marcelo Belloni, Bruno Computer Center Federal Institute of Education Science and Technology Farroupilha Alegrete Brazil Postgraduate Program in Computing Federal University of Pelotas Pelotas Brazil Postgraduate Program in Computer Science Federal University of Santa Maria Santa Maria Brazil Federal University of Pelotas Pelotas Brazil Federal Institute of Education Science and Technology Sul-Rio-Grandense Passo Fundo Brazil

ISBN: (纸本)9798350320282

Diabetic Retinopathy is one of the main causes of vision loss and can be identified through ophthalmological exams that aim to locate the presence of retinal lesions such as microaneurysms, hemorrhages, soft exudates, and hard exudates. The development of computerized methods to perform the instance segmentation of lesions may support in the early diagnosis of the disease. However, the instance segmentation of retinal artifacts is a complex task due to factors such as the size of objects and their morphological characteristics. This article proposes a method based on a Mask R-CNN neural network architecture to perform instance segmentation of lesions associated with diabetic retinopathy. The proposed method was trained, adjusted, and tested using the public DDR and IDRiD Diabetic Retinopathy datasets, and implemented with the Detectron2 and OpenCV libraries. The proposed method reached in the DDR dataset, using the SGD optimizer, the mAP of 0.2660 for the limit of I oU of 0.5 in the validation step. The results obtained in the experiments demonstrate that the proposed method showed promising results in the instance segmentation of fundus lesions. © 2022 IEEE.

关键词： Deep neural networks

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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AxASRE: A Novel Approach to Approximate Adder Synthesis Results Estimation.

AxASRE: A Novel Approach to Approximate Adder Synthesis Resu...

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International Conference on Dependable Systems and Networks Workshops (DSN-W)

作者： Pedro T. L. Pereira Guilherme Paim Paulo Flores Eduardo Costa Sergio Bampi Graduate Program on Microelectronics (PGMicro) Federal University of Rio Grande do Sul (UFRGS) Brazil INESC-ID Instituto Superior Técnico ULisboa Portugal Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Brazil

Thanks to the high energy efficiency achieved by approximate circuits, the exploration of approximate solutions for a wide of applications is the focus of academic and industrial research. However, defining the most appropriate approximation considering the power consumption and accuracy trade-off can be exhaustive. This paper proposes a novel approach to estimate the synthesis results for approximate adders (AxA) with different bit-width and approximation levels based on a database and simple mathematical operations to reduce the synthesis process complexity during an approximate circuit development. We implement the proposed approach to estimate the synthesis results for the Copy, Error-Tolerant Adder I (ETA-I), Lower-part-or (LOA), and Truncation (Trunc) AxAs. Comparing our estimation with real synthesis results obtained by an industrial synthesis tool, we prove the approach’s efficiency by presenting an average error on the estimated parameters (area, power and delay) less than 10% in the worst case. In addition, our approach reduces by more than 25 times the number of synthesis runs when estimating the synthesis results for all circuits in the bit width range of 2 to 64 with all possible approximation levels.

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