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Academic literature on the topic 'Wide neural network'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Wide neural network"

Casper, Stephen, Xavier Boix, Vanessa D'Amario, et al. "Frivolous Units: Wider Networks Are Not Really That Wide." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 6921–29. http://dx.doi.org/10.1609/aaai.v35i8.16853.

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A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network width is increased. Recent evidence suggests that developing compressible representations allows the complexity of large networks to be adjusted for the learning task at hand. However, these representations are poorly understood. A promising strand of research inspired from biology involves studying representations at the unit level as it offers a more granular interpretation of the neural mechanisms. In order to better understand what facilitates increases in w

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Jais, Imran Khan Mohd, Amelia Ritahani Ismail, and Syed Qamrun Nisa. "Adam Optimization Algorithm for Wide and Deep Neural Network." Knowledge Engineering and Data Science 2, no. 1 (2019): 41. http://dx.doi.org/10.17977/um018v2i12019p41-46.

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The objective of this research is to evaluate the effects of Adam when used together with a wide and deep neural network. The dataset used was a diagnostic breast cancer dataset taken from UCI Machine Learning. Then, the dataset was fed into a conventional neural network for a benchmark test. Afterwards, the dataset was fed into the wide and deep neural network with and without Adam. It was found that there were improvements in the result of the wide and deep network with Adam. In conclusion, Adam is able to improve the performance of a wide and deep neural network.

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Yakovlev, A. S., E. V. Shayakberov, and V. M. Giniyatullin. "Variability of the Wide Learning Neural Network Learning Algorithm." Programmnaya Ingeneria 16, no. 3 (2025): 134–42. https://doi.org/10.17587/prin.16.134-142.

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There are a large number of datasets on the publicly available kaggle resource, from which five datasets and their corresponding structures of artificial neural networks were selected. After training neural networks, the work of the neurons of the first hidden layers was reproduced in spreadsheets. A significant number of useless neurons were found (20—60 %). A neuron is called useless if the scalar products of all instances of the training sample are less than zero. The relu activation function converts negative values to zero, therefore, such a neuron does not contribute to the work of the n

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Liu, Hengyan, Limen Zhang, Wenjun Yan, and Yajie Li. "Last Subcode Neural Network Assisted Polar Decoding." Journal of Physics: Conference Series 2356, no. 1 (2022): 012040. http://dx.doi.org/10.1088/1742-6596/2356/1/012040.

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A fast successive-cancellation decoding scheme based on neural network (NNFSCD) called last subcode neural network assisted decoding (LSNNACD) scheme for polar codes is proposed to improve decoding performance. First, a neural network node (NNN) is proposed and a neutral network model is trained to decode it. Then, the node recognizer is proposed to recognize a polar code with a NNN. Simulations with polar codes and binary phase shift keying (BPSK) shows good performance and low latency in the additive white Gaussian noise (AWGN) channel. Moreover, the proposed scheme has a wide application ra

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Eetemadi, Ameen, and Ilias Tagkopoulos. "Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships." Bioinformatics 35, no. 13 (2018): 2226–34. http://dx.doi.org/10.1093/bioinformatics/bty945.

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Abstract Motivation Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predictive models of gene expression with far reaching applications. Results We present the Genetic Neural Network (GNN), an artificial neural network for predicting genome-wide gene expression given gene knockouts and master regulator perturbations. In its core, the GNN maps existing gene regulatory information in its architecture and it uses ce

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Yang, Jia Xuan. "Ship Transportation Forecasting Based on Extension Neural Network." Applied Mechanics and Materials 241-244 (December 2012): 2055–58. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2055.

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Over the last decade, neural networks have found application for solving a wide range of areas from business, commerce, data mining and service systems. Hence, this paper constructs a new model based extension theory and neural network to forecast the ship transportation. The new neural network is a combination of extension theory and neural network. It uses an extension distance to measure the similarity between data and cluster center, and seek out the useless data, then to use neural network to forecast. When presenting a test example of prediction of ship transportation, the results verifi

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Xi, Jiangbo, Ming Cong, Okan K. Ersoy, et al. "Dynamic Wide and Deep Neural Network for Hyperspectral Image Classification." Remote Sensing 13, no. 13 (2021): 2575. http://dx.doi.org/10.3390/rs13132575.

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Recently, deep learning has been successfully and widely used in hyperspectral image (HSI) classification. Considering the difficulty of acquiring HSIs, there are usually a small number of pixels used as the training instances. Therefore, it is hard to fully use the advantages of deep learning networks; for example, the very deep layers with a large number of parameters lead to overfitting. This paper proposed a dynamic wide and deep neural network (DWDNN) for HSI classification, which includes multiple efficient wide sliding window and subsampling (EWSWS) networks and can grow dynamically acc

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Pan, Yumin. "Different Types of Neural Networks and Applications: Evidence from Feedforward, Convolutional and Recurrent Neural Networks." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 247–55. http://dx.doi.org/10.54097/6rn1wd81.

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Neural networks have achieved great process in the 90 years since they were officially introduced in 1943. Because of its wide application and huge research and development potential, this technology attracts more and more scientific and technological workers to the research of neural networks. Neural network technology is an essential component of AI development, and it is a significant indicator of a country's overall strength. In this paper, this study will demonstrate Feedforward Neural Network, Convolution Neural Network and Recurrent Neural networks and evaluate them through datasets fro

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Liang, Yunyi, Zhiyong Cui, Yu Tian, Huimiao Chen, and Yinhai Wang. "A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic-State Estimation." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (2018): 87–105. http://dx.doi.org/10.1177/0361198118798737.

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This study proposes a deep generative adversarial architecture (GAA) for network-wide spatial-temporal traffic-state estimation. The GAA is able to combine traffic-flow theory with neural networks and thus improve the accuracy of traffic-state estimation. It consists of two Long Short-Term Memory Neural Networks (LSTM NNs) which capture correlation in time and space among traffic flow and traffic density. One of the LSTM NNs, called a discriminative network, aims to maximize the probability of assigning correct labels to both true traffic-state matrices (i.e., traffic flow and traffic density

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Warstadt, Alex, Amanpreet Singh, and Samuel R. Bowman. "Neural Network Acceptability Judgments." Transactions of the Association for Computational Linguistics 7 (November 2019): 625–41. http://dx.doi.org/10.1162/tacl_a_00290.

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This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau

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Dissertations / Theses on the topic "Wide neural network"

Brande, Julia K. Jr. "Computer Network Routing with a Fuzzy Neural Network." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29685.

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The growing usage of computer networks is requiring improvements in network technologies and management techniques so users will receive high quality service. As more individuals transmit data through a computer network, the quality of service received by the users begins to degrade. A major aspect of computer networks that is vital to quality of service is data routing. A more effective method for routing data through a computer network can assist with the new problems being encountered with today's growing networks. Effective routing algorithms use various techniques to determine the most

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Yu, Xiafei. "Wide Activated Separate 3D Convolution for Video Super-Resolution." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39974.

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Video super-resolution (VSR) aims to recover a realistic high-resolution (HR) frame from its corresponding center low-resolution (LR) frame and several neighbouring supporting frames. The neighbouring supporting LR frames can provide extra information to help recover the HR frame. However, these frames are not aligned with the center frame due to the motion of objects. Recently, many video super-resolution methods based on deep learning have been proposed with the rapid development of neural networks. Most of these methods utilize motion estimation and compensation models as preprocessing to

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Viens, Francois (Joseph Lucien Francois) Carleton University Dissertation Engineering Electrical. "A neural network approach to detect traffic anomalies in a communication network." Ottawa, 1992.

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Annergren, Björn. "Log Classification using a Shallow-and-Wide Convolutional Neural Network and Log Keys." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239561.

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A dataset consisting of logs describing results of tests from a single Build and Test process, used in a Continous Integration setting, is utilized to automate categorization of the logs according to failure types. Two different features are evaluated, words and log keys, using unordered document matrices as document representations to determine the viability of log keys. The experiment uses Multinomial Naive Bayes, MNB, classifiers and multi-class Support Vector Machines, SVM, to establish the performance of the different features. The experiment indicates that log keys are equivalent to usin

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Reginbald, Ivarsson Jón. "Scalable System-Wide Traffic Flow Predictions Using Graph Partitioning and Recurrent Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254896.

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Traffic flow predictions are an important part of an Intelligent Transportation System as the ability to forecast accurately the traffic conditions in a transportation system allows for proactive rather than reactive traffic control. Providing accurate real-time traffic predictions is a challenging problem because of the nonlinear and stochastic features of traffic flow. An increasingly widespread deployment of traffic sensors in a growing transportation system produces greater volume of traffic flow data. This results in problems concerning fast, reliable and scalable traffic predictions.The

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Pakdel, Zahra. "Intelligent Instability Detection for Islanding Prediction." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/27715.

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The goal of the proposed procedure in this dissertation is the implementation of phasor measurement unit (PMU) based instability detection for islanding prediction procedures using decision tree and neural network modeling. The islanding in the power system define as a separation of the coherent group of generators from the rest of the system due to contingencies, in the case that all generators are coherent together after introducing a fault, it is called stable or non-islanding. The main philosophy of islanding detection in the proposed methodology is to use decision trees and neural network

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Di, Luca Federico. "Rivelazione del Respiro Umano da Segnali UWB con Machine Learning e Deep Neural Network." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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L'obbiettivo di questo elaborato è valutare le prestazioni di vari algoritmi di Machine Learning e Deep Learning nel distinguere tra le condizioni Non-Line-of-Sight (NLoS) e Line-of-Sight (LoS) e nel rivelare la presenza del respiro umano in condizioni NLoS, utilizzando segnali Ultra-Wide Band (UWB). A tal fine sono stati presi in considerazione quattro esperimenti: uno per la classificazione LoS-NLoS e tre per valutare l'efficacia degli stessi algoritmi nel distinguere tra le situazioni di presenza e assenza del respiro. I dati ottenuti tramite le varie campagne di misure sono stati elabora

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Dall'Olio, Daniele. "Applicazione di un algoritmo d’apprendimento basato su sistemi fuori dall’equilibrio a dati di Genome Wide Association." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18499/.

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Il fenomeno dell’apprendimento può essere studiato attraverso metodiche di Meccanica Statistica. A partire dalle Neural Networks è possibile descrivere il problema dell'apprendimento mediante un sistema di spin interagenti. Usando una descrizione all’equilibrio del sistema e sotto opportune condizioni, tale problema si dimostra computazionalmente complesso. Tuttavia, esistono algoritmi euristici in grado di risolvere lo stesso problema efficacemente. Si dimostra che questa apparente inconsistenza è dovuta al fatto che lo spazio delle soluzioni degli algoritmi euristici non coincida con quello

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Li, Vladimir. "Evaluation of the CNN Based Architectures on the Problem of Wide Baseline Stereo Matching." Thesis, KTH, Datorseende och robotik, CVAP, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192476.

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Three-dimensional information is often used in robotics and 3D-mapping. There exist several ways to obtain a three-dimensional map. However, the time of flight used in the laser scanners or the structured light utilized by Kinect-like sensors sometimes are not sufficient. In this thesis, we investigate two CNN based stereo matching methods for obtaining 3D-information from a grayscaled pair of rectified images.While the state-of-the-art stereo matching method utilize a Siamese architecture, in this project a two-channel and a two stream network are trained in an attempt to outperform the state

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Lundberg, Gustav. "Automatic map generation from nation-wide data sources using deep learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170759.

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The last decade has seen great advances within the field of artificial intelligence. One of the most noteworthy areas is that of deep learning, which is nowadays used in everything from self driving cars to automated cancer screening. During the same time, the amount of spatial data encompassing not only two but three dimensions has also grown and whole cities and countries are being scanned. Combining these two technological advances enables the creation of detailed maps with a multitude of applications, civilian as well as military.This thesis aims at combining two data sources covering most

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Books on the topic "Wide neural network"

International, Conference of the North American Fuzzy Information Processing Society (21st 2002 New Orleans La ). 2002 annual meeting of the North American Fuzzy Information Processing Society: Proceedings : NAFTIS-FLINT 2002 : June 27-29, 2002, Tulane University, New Orleans, Louisiana. IEEE, 2002.

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L, Walker Ellen, IEEE Systems, Man, and Cybernetics Society., and North American Fuzzy Information Processing Society., eds. NAFIPS '2003: 22nd International Conference of the North American Fuzzy Information Processing Society--NAFIPS : proceedings : Chicago, Illinois, USA, July 24-26, 2003. IEEE, 2003.

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Tucci, Mario, and Marco Garetti, eds. Proceedings of the third International Workshop of the IFIP WG5.7. Firenze University Press, 2002. http://dx.doi.org/10.36253/88-8453-042-3.

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Contents of the papers presented at the international workshop deal with the wide variety of new and computer-based techniques for production planning and control that has become available to the scientific and industrial world in the past few years: formal modeling techniques, artificial neural networks, autonomous agent theory, genetic algorithms, chaos theory, fuzzy logic, simulated annealing, tabu search, simulation and so on. The approach, while being scientifically rigorous, is focused on the applicability to industrial environment.

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Leshkevich, Tat'yana. Where technology ends and Man begins: a socio-humanitarian understanding of artificial intelligence. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2189092.

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The monograph examines the challenges of artificial intelligence (AI), analyzes the symbiosis of modern humans and technology, taking into account the positive and negative consequences. The fundamental limitations of the computer theory of consciousness and new configurations of subjectivity of the digital era are demonstrated. The barriers of algorithmization are revealed and the problem of subject-like qualities of neural networks is discussed. The necessity of creating a theory of consciousness based on human subjective experience is substantiated. It is addressed to a wide range of reader

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Barabási, Albert-Laszló, and Albert-Laszló Barabási. Linked: How everything is connected to everything else and what it means for business, science, and everyday life. Plume, 2003.

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1954-, Howlett Robert J., ed. Internet-based intelligent information processing systems. World Scientific, 2003.

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Gates, Bill. Shu wei shen jing xi tong: Yü si kao deng kuai di ming ri shi jie. Shang yeh zhou kan chu ban gu fen yu xian gong si, 1999.

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Gates, Bill. Business @ the speed of thought: Succeeding in the digital economy. Penguin, 2000.

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Gates, Bill. Los negocios en la era digital. Plaza & Janes, 1999.

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Gates, Bill. Wei lai shi su: Shu zi shen jing xi tong yu shang wu xin si wei. Beijing da xue chu ban she, 1999.

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Book chapters on the topic "Wide neural network"

Kabani, AbdulWahab, and Mahmoud R. El-Sakka. "Ejection Fraction Estimation Using a Wide Convolutional Neural Network." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59876-5_11.

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Paulsen, Brandon, and Chao Wang. "Example Guided Synthesis of Linear Approximations for Neural Network Verification." In Computer Aided Verification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13185-1_8.

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AbstractLinear approximations of nonlinear functions have a wide range of applications such as rigorous global optimization and, recently, verification problems involving neural networks. In the latter case, a linear approximation must be hand-crafted for the neural network’s activation functions. This hand-crafting is tedious, potentially error-prone, and requires an expert to prove the soundness of the linear approximation. Such a limitation is at odds with the rapidly advancing deep learning field – current verification tools either lack the necessary linear approximation, or perform poorly

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Shimomura, Suguru. "Reservoir Computing Based on Iterative Function Systems." In Photonic Neural Networks with Spatiotemporal Dynamics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5072-0_11.

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AbstractVarious approaches have been proposed to construct reservoir computing systems. However, the network structure and information processing capacity of these systems are often tied to their individual implementations, which typically become difficult to modify after physical setup. This limitation can hinder performance when the system is required to handle a wide spectrum of prediction tasks. To address this limitation, it is crucial to develop tunable systems that can adapt to a wide range of problem domains. This chapter presents a tunable optical computing method based on the iterati

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Shi, Zhouxing, Qirui Jin, Zico Kolter, Suman Jana, Cho-Jui Hsieh, and Huan Zhang. "Neural Network Verification with Branch-and-Bound for General Nonlinearities." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90643-5_17.

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Abstract Branch-and-bound (BaB) is among the most effective techniques for neural network (NN) verification. However, existing works on BaB for NN verification have mostly focused on NNs with piecewise linear activations, especially ReLU networks. In this paper, we develop a general framework, named GenBaB, to conduct BaB on general nonlinearities to verify NNs with general architectures, based on linear bound propagation for NN verification. To decide which neuron to branch, we design a new branching heuristic which leverages linear bounds as shortcuts to efficiently estimate the potential im

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Anandan, K., R. Shankar, and S. Duraisamy. "Soil Category Classification Using Convolutional Neural Network Long Short Wide Memory Method." In Intelligent Computing and Innovation on Data Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3153-5_14.

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Duong, Hai, ThanhVu Nguyen, and Matthew B. Dwyer. "NeuralSAT: A High-Performance Verification Tool for Deep Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-98679-6_19.

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Abstract Deep Neural Networks (DNNs) are increasingly deployed in critical applications, where ensuring their safety and robustness is paramount. We present $$_\text {CAV25}$$ CAV 25 , a high-performance DNN verification tool that uses the DPLL(T) framework and supports a wide-range of network architectures and activation functions. Since its debut in VNN-COMP’23, in which it achieved the New Participant Award and ranked 4th overall, $$_\text {CAV25}$$ CAV 25 has advanced significantly, achieving second place in VNN-COMP’24. This paper presents and evaluates the latest development of $$_\text

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Yizhuo, Wang, Li Zhonlian, Li Long, Li Runhua, Cui Xinglei, and Fang Zhi. "Prediction and Evaluation Method of Modification Effect of Large-Scale DBD Insulation Materials Based on Distributed Current Measurement and Neural Network Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4856-6_8.

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Abstract Wide dielectric barrier discharge (DBD) has broad application prospects in the modification of insulating materials, but the aging of the electrode directly affects the modification effect in the application process. As the size of the DBD device increases, the real-time evaluation of its modification effect becomes more complicated. Therefore, this paper proposes a real-time prediction and evaluation method for the modification effect of wide DBD insulation materials based on distributed current measurement and neural network model. The operating condition parameters such as DBD exci

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Hamidi Rad, Radin, Hossein ZareMehrjerdi, Yasaman Mirmohammad, Mohammad Navid Shahsavari, and Maryam Amir Haeri. "Recommendation System over Multi-layer Complex Networks: A Wide and Deep Graph Convolutional Neural Network Approach." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47724-9_30.

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Shirasaka, Sho. "Investigation on Oscillator-Based Ising Machines." In Photonic Neural Networks with Spatiotemporal Dynamics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5072-0_9.

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AbstractMoore’s law is slowing down and, as traditional von Neumann computers face challenges in efficiently handling increasingly important issues in a modern information society, there is a growing desire to find alternative computing and device technologies. Ising machines are non-von Neumann computing systems designed to solve combinatorial optimization problems. To explore their efficient implementation, Ising machines have been developed using a variety of physical principles such as optics, electronics, and quantum mechanics. Among them, oscillator-based Ising machines (OIMs) utilize sy

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Sasikaladevi, N. "Deep Fused Network for Maize Plant Disease Detection Using Wide Neural Networks and Visualization Using Explainable AI." In Artificial Intelligence of Everything and Sustainable Development. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-7202-8_12.

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Conference papers on the topic "Wide neural network"

Aranha, Ancy Princia, Anna Merine George, Vedavyasa Kamath, Ciji Pearl Kurian, and Padmashree K. S. "Design of Wide Neural Network Model for Controlling Tunable LED Luminaire: (Neural Network based Luminaire Control)." In 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2025. https://doi.org/10.1109/icaect63952.2025.10958914.

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Chanudom, Apichai, Jiraporn Thomkaew, Podjana Homhual, and Supaporn Chairat. "Brain Tumor Image Classification Based on Wide Dense Convolution Neural Network." In 2025 10th International Conference on Computer and Communication System (ICCCS). IEEE, 2025. https://doi.org/10.1109/icccs65393.2025.11069950.

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Reid, S., G. E. C. Bell, and G. L. Edgemon. "The Use of Skewness, Kurtosis and Neural Networks for Determining Corrosion Mechanism from Electrochemical Noise Data." In CORROSION 1998. NACE International, 1998. https://doi.org/10.5006/c1998-98176.

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Abstract This paper describes the work undertaken to de-skill the complex procedure of determining corrosion mechanisms derived from electrochemical noise data. The use of neural networks is discussed and applied to the real time generated electrochemical noise data files with the purpose of determining characteristics particular to individual types of corrosion mechanisms. The electrochemical noise signals can have a wide dynamic range and various methods of raw data pre-processing prior to neural network analysis were investigated. Normalized data were ultimately used as input to the final n

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Liu, Wencan, Yuyao Huang, Run Sun, Tingzhao Fu, and Hongwei Chen. "Diffraction-based on-chip optical neural network with high computational density." In JSAP-Optica Joint Symposia. Optica Publishing Group, 2024. https://doi.org/10.1364/jsapo.2024.17p_a25_6.

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The rapid advancement of artificial intelligence has led to substantial progress in various fields with deep neural networks (DNNs). However, complex tasks often require increasing power consumption and greater resources of electronics. On-chip optical neural networks (ONNs) are increasingly recognized for their power efficiency, wide bandwidth, and capability for light-speed parallel processing. In our previous work [1], we proposed on-chip diffractive optical neural networks (DONNs) to offer the potential to map a larger number of neurons and connections onto optics. To further improve the c

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Huang, Rui, Qi Wang, Jianbin Xiong, Xiangjun Dong, Jianxiang Yang, and Yipeng Wu. "Improved bearing fault diagnosis based on deep convolutional neural network with wide first-layer kernels." In 2024 6th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI). IEEE, 2024. http://dx.doi.org/10.1109/iotaai62601.2024.10692793.

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Zhou, Zinan, Keiichiro Toda, Rikimaru Kurata, Kohki Horie, Ryoichi Horisaki, and Takuro Ideguchi. "Quantitative Zernike Phase-Contrast Microscopy with an Untrained Neural Network." In JSAP-Optica Joint Symposia. Optica Publishing Group, 2024. https://doi.org/10.1364/jsapo.2024.16p_a37_3.

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In optical microscopy, Zernike phase-contrast microscopy (PCM) is a technique that transforms phase shifts in a sample to contrast in intensity by interference. Despite its wide usage in many biological and clinical applications, it fails to provide quantitative information about the specimen. One prior collaborative work [1] from our group managed to add quantitativeness to PCM by a phase retrieval algorithm based on compressive propagation. However, this algorithm relies heavily on regularization and non-trivial optimization tricks, severely limiting its generalizability and usage in practic

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Zhang, Xiaolong, Gaoyu Dai, Luqiao Yin, and Jianhua Zhang. "Efficiency and Uniformity Improvement of Micro-LED-Based Diffractive Waveguide Using Neural Network." In 2024 21st China International Forum on Solid State Lighting & 2024 10th International Forum on Wide Bandgap Semiconductors (SSLCHINA: IFWS). IEEE, 2024. https://doi.org/10.1109/sslchinaifws64644.2024.10835396.

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Gallagher, James C., Nadeemullah A. Mahadik, Robert E. Stahlbush, Karl D. Hobart, and Michael A. Mastro. "Using a Convolutional Neural Network to Map Defects in Wide Bandgap Semiconductors at a Wafer Scale." In NAECON 2024 - IEEE National Aerospace and Electronics Conference. IEEE, 2024. http://dx.doi.org/10.1109/naecon61878.2024.10670671.

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Guan, Jingyun, and Wen Yang. "Artificial Neural Network-Based (ANN) Physical Model for GaN HEMTs Based on Terminal Charge." In 2024 21st China International Forum on Solid State Lighting & 2024 10th International Forum on Wide Bandgap Semiconductors (SSLCHINA: IFWS). IEEE, 2024. https://doi.org/10.1109/sslchinaifws64644.2024.10835327.

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Zheng, Shengjie, Lang Qian, Pingsheng Li, Chenggang He, Xiaoqi Qin, and Xiaojian Li. "An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence." In 8th International Conference on Artificial Intelligence (ARIN 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121010.

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Stemming from the rapid development of artificial intelligence, which has gained expansive success in pattern recognition, robotics, and bioinformatics, neuroscience is also gaining tremendous progress. A kind of spiking neural network with biological interpretability is gradually receiving wide attention, and this kind of neural network is also regarded as one of the directions toward general artificial intelligence. This review summarizes the basic properties of artificial neural networks as well as spiking neural networks. Our focus is on the biological background and theoretical basis of s

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Reports on the topic "Wide neural network"

Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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BARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, 2021. http://dx.doi.org/10.12731/er0519.07122021.

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The auroral activity indices AU, AL, AE, introduced into geophysics at the beginning of the space era, although they have certain drawbacks, are still widely used to monitor geomagnetic activity at high latitudes. The AU index reflects the intensity of the eastern electric jet, while the AL index is determined by the intensity of the western electric jet. There are many regression relationships linking the indices of magnetic activity with a wide range of phenomena observed in the Earth's magnetosphere and atmosphere. These relationships determine the importance of monitoring and predicting ge

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Al-Qadi, Imad, Jaime Hernandez, Angeli Jayme, et al. The Impact of Wide-Base Tires on Pavement—A National Study. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-035.

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Researchers have been studying wide-base tires for over two decades, but no evidence has been provided regarding the net benefit of this tire technology. In this study, a comprehensive approach is used to compare new-generation wide-base tires (NG-WBT) with the dual-tire assembly (DTA). Numerical modeling, prediction methods, experimental measurements, and environmental impact assessment were combined to provide recommendations about the use of NG-WBT. A finite element approach, considering variables usually omitted in the conventional analysis of flexible pavement was utilized for modeling. F

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Kendall Niles, Ken Pathak, and Joe Tom. Widened attention-enhanced atrous convolutional network for efficient embedded vision applications under resource constraints. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49459.

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Onboard image analysis enables real-time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision-making critical for time-sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time-sensitive inference. This article introduces the widened att

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Irudayaraj, Joseph, Ze'ev Schmilovitch, Amos Mizrach, Giora Kritzman, and Chitrita DebRoy. Rapid detection of food borne pathogens and non-pathogens in fresh produce using FT-IRS and raman spectroscopy. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7587221.bard.

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Rapid detection of pathogens and hazardous elements in fresh fruits and vegetables after harvest requires the use of advanced sensor technology at each step in the farm-to-consumer or farm-to-processing sequence. Fourier-transform infrared (FTIR) spectroscopy and the complementary Raman spectroscopy, an advanced optical technique based on light scattering will be investigated for rapid and on-site assessment of produce safety. Paving the way toward the development of this innovative methodology, specific original objectives were to (1) identify and distinguish different serotypes of Escherichi

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Wideman, Jr., Robert F., Nicholas B. Anthony, Avigdor Cahaner, Alan Shlosberg, Michel Bellaiche, and William B. Roush. Integrated Approach to Evaluating Inherited Predictors of Resistance to Pulmonary Hypertension Syndrome (Ascites) in Fast Growing Broiler Chickens. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7575287.bard.

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Background PHS (pulmonary hypertension syndrome, ascites syndrome) is a serious cause of loss in the broiler industry, and is a prime example of an undesirable side effect of successful genetic development that may be deleteriously manifested by factors in the environment of growing broilers. Basically, continuous and pinpointed selection for rapid growth in broilers has led to higher oxygen demand and consequently to more frequent manifestation of an inherent potential cardiopulmonary incapability to sufficiently oxygenate the arterial blood. The multifaceted causes and modifiers of PHS make

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Yatsymirska, Mariya. KEY IMPRESSIONS OF 2020 IN JOURNALISTIC TEXTS. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.50.11107.

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The article explores the key vocabulary of 2020 in the network space of Ukraine. Texts of journalistic, official-business style, analytical publications of well-known journalists on current topics are analyzed. Extralinguistic factors of new word formation, their adaptation to the sphere of special and socio-political vocabulary of the Ukrainian language are determined. Examples show modern impressions in the media, their stylistic use and impact on public opinion in a pandemic. New meanings of foreign expressions, media terminology, peculiarities of translation of neologisms from English into

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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, contro

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A BAYESIAN-OPTIMIZED NEURAL NETWORK MODEL FOR SHEAR CAPACITY OF A PERFOBOND STRIP CONNECTOR IN VARIOUS TYPES OF COMPOSITE STRUCTURES. The Hong Kong Institute of Steel Construction, 2024. https://doi.org/10.18057/ijasc.2024.20.4.5.

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Perfobond strips are integral to composite steel-concrete structures or joints between precast concrete elements. However, the diverse boundary conditions and design parameters in various applications have led to numerous empirical and analytical methods to investigate their shear behavior. Existing empirical formulas often fail to accurately assess the shear capacity of perfobond strip connectors under different conditions. This study addresses this issue by developing a comprehensive prediction model for the shear capacity of perfobond strip connectors using a Bayesian-optimized artificial n

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