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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Narasinga Rao Miniskar Pruek Vanna-Iampikul Aaron Young Sung Kyu Lim Frank Liu Jieun Yoo Corrinne Mills Nhan Tran Farah Fahim Jeffrey S Vetter Computer Science and Mathematic Division Oak Ridge National Laboratory Oak Ridge USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Physics University of Illinois Chicago Chicago USA Scientific Computing Division Fermi National Accelerator Laboratory Chicago USA

Low latency inference has many applications in edge machine learning. In this paper, we present a run-time configurable convolutional neural network (CNN) inference ASIC design for low-latency edge machine learning. By implementing a 5-stage pipelined CNN inference model in a 3D ASIC technology, we demonstrate that the model distributed on two dies utilizing face-to-face (F2F) 3D integration achieves superior performance. Our experimental results show that the design based on 3D integration achieves 43% better energy-delay product when compared to the traditional 2D technology.

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FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradients

arXiv

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arXiv 2025年

作者： Leng, Jiaqi Ju, Yakun Duan, Yuanxu Zhang, Jiangnan Lv, Qingxuan Wu, Zuxuan Fan, Hao Haide College Ocean University of China China Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China School of Computing and Mathematic Sciences University of Leicester United Kingdom Department of Computer Engineering Qingdao City University China College of Computer Science and Technology Ocean University of China China

Surface-from-gradients (SfG) aims to recover a three-dimensional (3D) surface from its gradients. Traditional methods encounter significant challenges in achieving high accuracy and handling high-resolution inputs, particularly facing the complex nature of discontinuities and the inefficiencies associated with large-scale linear solvers. Although recent advances in deep learning, such as photometric stereo, have enhanced normal estimation accuracy, they do not fully address the intricacies of gradient-based surface reconstruction. To overcome these limitations, we propose a Fourier neural operator-based Numerical Integration Network (FNIN) within a two-stage optimization framework. In the first stage, our approach employs an iterative architecture for numerical integration, harnessing an advanced Fourier neural operator to approximate the solution operator in Fourier space. Additionally, a self-learning attention mechanism is incorporated to effectively detect and handle discontinuities. In the second stage, we refine the surface reconstruction by formulating a weighted least squares problem, addressing the identified discontinuities rationally. Extensive experiments demonstrate that our method achieves significant improvements in both accuracy and efficiency compared to current state-of-the-art solvers. This is particularly evident in handling high-resolution images with complex data, achieving errors of fewer than 0.1 mm on tested objects. Copyright © 2025, The Authors. All rights reserved.

关键词： Surface reconstruction

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Impact of predation in the spread of an infectious disease with time fractional derivative and social behavior

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第4期12卷 92-119页

作者： Soufiane Bentout Behzad Ghanbari Salih Djilali Lakshmi Narayan Guin Department of Mathematics and Informatics Belhadj Bouchaib University Center of Ain Temouchent BP 284 RP 46000Algeria Laboratoire d’Analyse Non Linaire et Mathmatiques Appliques Universit de Tlemcen TlemcenAlgeria Department of Engineering Science Kermanshah University of Technology KermanshahIran Department of Biomedical Engineering Faculty of Engineering and Naturel Sciences Bah¸ce¸sehir University 34349 IstanbulTurkey Faculty of Exact and Computer Sciences Mathematic Department Hassiba Benbouali University ChlefAlgeria Department of Mathematics Visva-Bharati Santiniketan-731235 West BengalIndia

The main purpose of this paper is to explore the influence of predation on the spread of a disease developed in the prey population where we assume that the prey has a social *** memory of the prey and the predator measured by the time fractional derivative plays a crucial role in modeling the dynamical response in a predator–prey *** memory can be modeled to articulate the involvement of interacting species by the presence of the time fractional derivative in the considered *** the purpose of studying the complex dynamics generated by the presence of infection and the time-fractional-derivative we split our study into two cases. The first one is devotedto study the effect of a non-selective hunting on the spread of the disease, where the localstability of the equilibria is investigated. Further the backward bifurcation is obtainedconcerning basic reproduction rate of the infection. The second case is for explaining theimpact of selecting the weakest infected prey on the edge of the herd by a predator onthe prevalence of the infection, where the local behavior is scrutinized. Moreover, for thegraphical representation part, a numerical simulation scheme has been achieved usingthe Caputo fractional derivative operator.

关键词： Predator–prey model herd behavior fractional derivative numerical schema backward bifurcation eco-epidemiological model

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Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis

arXiv

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arXiv 2023年

作者： Zuo, Qiankun Tian, Hao Pun, Chi-Man Wang, Hongfei Zhang, Yudong Hong, Jin The School of Information Engineering Hubei University of Economics Hubei Wuhan430205 China The Department of Computer and Information Science University of Macau 519000 China The Department of Orthopaedics and Trauma-tology University of Hong Kong 999077 Hong Kong The School of Computing and Mathematic Sciences University of Leicester LeicesterLE1 7RH United Kingdom The School of Information Engineering Nanchang University Nanchang330031 China

Effective connectivity can describe the causal patterns among brain regions. These patterns have the potential to reveal the pathological mechanism and promote early diagnosis and effective drug development for cognitive disease. However, the current methods utilize software toolkits to extract empirical features from brain imaging to estimate effective connectivity. These methods heavily rely on manual parameter settings and may result in large errors during effective connectivity estimation. In this paper, a novel brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment (MCI) analysis. To be specific, the proposed BIGG framework is based on the diffusion denoising probabilistic models (DDPM), where each denoising step is modeled as a generative adversarial network (GAN) to progressively translate the noise and conditional fMRI to effective connectivity. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Each scale concentrates on both spatial and temporal information between brain regions, enabling good quality in noise removal and better inference of causal relations. Meanwhile, the transformer-based discriminator constrains the generator to further capture global and local patterns for improving high-quality and diversity generation. By introducing the diffusive factor, the denoising inference with a large sampling step size is more efficient and can maintain high-quality results for effective connectivity generation. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model. The proposed model not only achieves superior prediction performance compared with other competing methods but also predicts MCI-related causal connections that are consistent with clinical studies. Copyright © 2023, The Authors. All rights reserved.

关键词： Brain

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Enhanced prediction of defibrillation success in out-of-hospital cardiac arrest using nonlinear ECG features and probabilistic neural network classification

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Signal, Image and Video Processing 2025年第8期19卷

作者： Shamsi, Hamed Golkarieh, Alireza Nouri, Houman Dolatabadi, Amirhosein Department of Artificial Intelligence and Data Engineering Atatürk University Yakutiye Erzurum25240 Turkey Department of Computer Science and Engineering Oakland University RochesterMI United States Department of Mathematic University of Sussex Brighton United Kingdom Department of Biomedical Engineering Science and Research Branch Islamic Azad University Tehran Iran

The present study proposes a new approach to predict the outcome of external defibrillation in out-of-hospital cardiac arrest (OHCA) patients using sophisticated nonlinear feature extraction and classification via a probabilistic neural network (PNN) of electrocardiogram (ECG) signals. We analyzed 251 samples of ECG data collected from 195 unsuccessful and 56 successful resuscitations in OHCA patients. Our proposed methodology utilized the following five advanced nonlinear dynamics features: Sample Entropy, Approximate Entropy, Detrended Fluctuation Analysis, Correlation Dimension, and Largest Lyapunov Exponent. These features were selected such that the complex dynamics of ECG signals are not missed by traditional linear analyses and subtle patterns. Then, classification has been performed using the extracted features with a PNN model, which gave great performances with a training accuracy of 97.16% and a testing accuracy of 93.33%. With very good discriminative ability, the model resulted in a high area under the curve (AUC) of 0.98 and 0.96 for both the training and testing sets, respectively. This constitutes a significant advance in the ECG-based prediction of the outcome of defibrillation and could enable better-informed decision-making in emergency cardiac care. Our approach seems clinically applicable given its high accuracy and robustness, though further validation with larger-scale studies would be desirable. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Resuscitation

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A normalized cross-correlation convolutional neural network (CNN-NCC) for exemplar-based object detection

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AIP Conference Proceedings 2023年第1期2734卷

作者： Yaya Wihardi Winda M. Kristy Erlangga Arjon Turnip Intan N. Yulita Endroyono Computer Science Education Department Faculty of Mathematic and Natural Science Education Universitas Pendidikan Indonesia Bandung Indonesia Artificial Intelligence and Big Data Research Center Padjadjaran University Sumedang Indonesia Department of Electrical Engineering Sepuluh Nopember Institute of Technology Surabaya Indonesia

Object detection in dynamic environments, such as CCTV surveillance systems and robotics, requires a special approach. New objects that have not been previously trained to the model are often captured by sensors, and it is very important to recognize them in the future. One approach that can be used to solve this problem is an exemplar-based approach. On this study, we try to build an exemplar-based object detector based on Convolutional Neural Network with Normalized Cross Correlation matching. Our proposed method did not need to re-train the model to identify unknown objects that had not been acknowledged by the model during the training stage. The results show that the model achieves 0.449 mAP on the COCO dataset.

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Algorithm Advanced Control Chaos and Stabilization Measurement Error 1

Algorithm Advanced Control Chaos and Stabilization Measureme...

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1st International Conference on computer science and Applied mathematic, ICCSAM 2018

作者： Wanayumini Zarlis, Muhammad Suwilo, Saib Furqan, Mhd. Fauzi, Rahmat Syarifah, Tengku Department Information Technology University of Asahan Indonesia Technique Engineering of Science Universitas Sumatera Utara Indonesia Mathematic of Science Universitas Sumatera Utara Indonesia Department of Computer Science Universitas Islam Negeri Sumatera Utara Medan Indonesia Department Technique Electro Universitas Sumatera Utara Indonesia Department of Economic Management University of Asahan Indonesia

Measurement error in the data sample human voice from the data 1 to 17 obtain measurement results with the algorithm Advanced Chaos Control and Stabilization through several stages of delivery of the process control and and stabilizing the data to find the value of noise and bias in the initial process control and find the value of precision and accuracy in both the stabilization process. Stable value which must be controlled ranging 58,9922dB and 60,47059dB with a standard value of 60dB. © Published under licence by IOP Publishing Ltd.

关键词： Process control

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Develop learning methods imr (integrated material realistic) alternative mathematics learning during a pandemic

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Journal of Physics: Conference Series 2021年第4期1918卷

作者： I.M. Rusmana Zainuri Wardono A.L Nulhakim Doctoral Program Universitas Negeri Semarang Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Indraprasta PGRI Jakarta Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Negeri Semarang Indonesia Department of Informatics Engineering Faculty of Engineering and Computer Science Universitas Indraprasta PGRI Jakarta Indonesia

The learning methods used by teachers in schools in Serang City are very ineffective, especially during the pandemic. This is because not integrated content of material in one subject with other subjects. Never mind between subjects, sometimes the material is not well integrated. The purpose of this study is to provide an alternative learning method during a pandemic, namely a method that integrates material with the realities of life which is abbreviated as IMR (Integrated Realistic Material). The method in this research is Research and Development (RnD) with the ADDIE model. In making this learning method according to the stages from the ADDIE study. The results of this study are in the form of learning methods that are integrated with the realities of life during the pandemic.

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Real-time UAV path-terrain collision evaluation on FPGA 4

Real-time UAV path-terrain collision evaluation on FPGA

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4th International Conference on Optimization and Applications, ICOA 2018

作者： Allaire, Francois Charles Joseph Tarbouchi, Mohammed Labonte, Gilles Roberge, Vincent Department of Electrical and Computer Engineering Royal Military College of Canada Kingston Canada Department of Mathematic and Computer Science Department of Electrical and Computer Engineering Royal Military College of Canada Kingston Canada

ISBN: (纸本)9781538642252

One of the most fundamental element of the Unmanned Aerial Vehicles (UAV) path planning is the assurance that the path planned avoids any terrain collision. The task to 'evaluate how much a UAV path is in collision with the terrain' is particularly crucial for path planners that are optimizing random generated paths. This terrain collision evaluation task can be computationally demanding and its computation time depends on the environment representation used. This paper presents a Field Programmable Gate Arrays (FPGA) based design of a UAV terrain collision evaluator that evaluates in parallel segments of the path in real-time. Its worst case computation time has a fixed upper bounded, based on the size of the map. For a 500×500 grid map, this upper bound is 1.9 μs, which is a promising result for future UAV real-time path planners. © 2018 IEEE.

关键词： Motion planning

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A computational dual-route model for processing spelling of bilingual learners in Kerala

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AIP Conference Proceedings 2022年第1期2424卷

作者： Soorya Surendran Mithun Haridas Devika Krishnan A Nirmala Vasudevan Georg Gutjahr Prema Nedungadi 1Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India 2Center for Research in Analytics & Technologies for Education (CREATE) Amrita Vishwa Vidyapeetham Amritapuri India 3Department of Mathematic Amrita Vishwa Vidyapeetham Amritapuri India 4Department of Physics Amrita Vishwa Vidyapeetham Amritapuri India

Various learning disabilities affect spelling and handwriting abilities of children. An understanding of these disabilitiesis facilitated by developing a formal model of the cognitive processes involved in spelling. One such model is the dual-route model which was originally proposed for reading but can also be applied to spelling by evaluating the cognitive steps in a different order. The model is language-dependent. To develop a spelling model for Malayalam-English bilingual learners in Kerala, South India, we conducted a spelling study with 208 second grade students from seven schools. We then fitted a Bayesian dual-route modelto the data using an expectation-maximization (EM) algorithm. The model used 4 hidden variables to represent the stages in the cognitive process and 13 observed variables to represent the various types of spelling errors. The performance of the model was evaluated using cross-validation, and it was found that the dual-route model outperforms single-route models.

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