26 papers · ranked by Valyu relevance
Mukhammed Garifulla, Juncheol Shin, Chanho Kim, Won Hwa Kim + 8 more
Recently, the amount of attention paid towards convolutional neural networks (CNN) in medical image analysis has rapidly increased since they can analyze and classify images faster and more accurately than human abilities. As a result, CNNs are becoming more popular and play a role as a supplementary assistant for…
Jimmy Bao
Post-hurricane damage assessment is crucial towards managing resource allocations and executing an effective response. Traditionally, this evaluation is performed through field reconnaissance, which is slow, hazardous, and arduous. Instead, in this paper we furthered the idea of implementing deep learning through…
Matteo De Matola, Giorgio Arcara
Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come…
Ravi Raj, Andrzej Kos, Stefanos Kollias
Convolutional neural networks (CNNs), a type of artificial neural network (ANN) in the deep learning (DL) domain, have gained popularity in several computer vision applications and are attracting research in other fields, including robotic perception. CNNs are developed to autonomously and effectively acquire spatial…
Romano Weiss, Sanaz Karimijafarbigloo, Dirk Roggenbuck, Stefan Rödiger + 1 more
'Stefan Rödiger' 'Thomas Mohr'] Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when…
Ladislav Karrach, Elena Pivarčiová, Hocine Cherifi
Artificial neural networks can solve various tasks in computer vision, such as image classification, object detection, and general recognition. Our comparative study deals with four types of artificial neural networks-multilayer perceptrons, probabilistic neural networks, radial basis function neural networks, and…
Rahib Abiyev, Murat Arslan
In the paper, a vision-based vehicle identification system is proposed for autonomous intelligent car driving. The accurate detection of obstacles (vehicles) during intelligent car driving allows avoiding crashes, preventing accidents, saving people’s lives and reducing harm. The vehicle detection system, which uses…
Guta Tesema Tufa, Fitsum Assamnew Andargie, Anchit Bijalwan
Convolutional neural network (CNN) training often necessitates a considerable amount of computational resources. In recent years, several studies have proposed for CNN inference and training accelerators in which the FPGAs have previously demonstrated good performance and energy efficiency. To speed up the processing…
Danny da Costa, Rainer Goebel, Mario Senden
The distribution of retinal ganglion cells in primate visual systems portrays a densely distributed central region, with an incrementally decreasing cell density as the angle of visual eccentricity increases. This results in a non-uniform sampling of the retinal image that resembles a wheelbarrow distortion. We propose…
Saddam Hussain Khan, Rashid Iqbal
Deep Convolutional Neural Networks (CNNs) have significantly advanced deep learning, driving breakthroughs in computer vision, natural language processing, medical diagnosis, object detection, and speech recognition. Architectural innovations including 1D, 2D, and 3D convolutional models, dilated and grouped…
Mustafa Bayram Gücen
In this study, SoftReMish, a new activation function designed to improve the performance of convolutional neural networks (CNNs) in image classification tasks, is proposed. Using the MNIST dataset, a standard CNN architecture consisting of two convolutional layers, max pooling, and fully connected layers was…
Mukesh Chowdary Madineni, Mario Vega, Xiaokun Yang, Arman Roohi
This paper presents a parameterizable design generator on convolutional neural networks (CNNs) using the Chisel hardware construction language (HCL). By parameterizing structural designs such as the streaming width, pooling layer type, and floating point precision, multiple register-transfer level (RTL) implementations…
Abolfazl Younesi, Mohsen Ansari, MohammadAmin Fazli, Alireza Ejlali + 2 more
Challenges, and Future Trends Authors: ['Abolfazl Younesi' 'Mohsen Ansari' 'MohammadAmin Fazli' 'Alireza Ejlali' 'Muhammad Shafique' 'Jörg Henkel'] Abstract—In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image…
Wenhao Tang, Junding Sun, Shuihua Wang, Yudong Zhang
In recent years, the rapid development of deep learning has led to a wide range of applications in medical image classification. The variants of neural network models with ever-increasing performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Alexander Muacevic, John R Adler, Shreya Roy, Prachi Nagrale
This review aims to assess the anatomy of the human eye with a focus on exploring opportunities to mimic certain functionalities of photoreceptors in the optical system. This can help restore vision issues in people who had normal vision earlier, but their vision was impaired due to reasons that damaged parts of the…
Christian Tsvetkov, Gaurav Malhotra, Benjamin D. Evans, Jeffrey S. Bowers
Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large datasets of random patterns. We verify that human learning on such tasks…
Authors not listed
Obtaining quantitative information about residence time behavior (i.e., the residence time distribution function) in realistic experimental systems is oftentimes experimentally challenging and numerically complex. The conventional way is to conduct very simple pulse or step tracer experiments or construct elaborate…
Oliver W. Layton, Scott T. Steinmetz
Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the…
Amitava Sarkar
Convolutional Layers using Karatsuba Ofman Multiplier Authors: ['Amitava Sarkar'] Abstract—A new architecture of CNN hardware accelerator is presented. Convolutional Neural Networks (CNNs) are a subclass of neural networks that have demonstrated outstanding performance in a variety of computer vision applications…
Hojin Jang, Frank Tong
Whenever a visual scene is cast onto the retina, much of it will appear degraded due to poor resolution in the periphery; moreover, optical defocus can cause blur in central vision. However, the pervasiveness of blurry or degraded input is typically overlooked in the training of convolutional neural networks (CNNs). We…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…
Hongyang Dong, Simon D.M. Jacques, Winfried Kockelmann, Stephen W. T. Price + 10 more
Hongyang Dong 3 , Simon D.M. Jacques 1 , Winfried Kockelmann 4 , Stephen W. T. Price 1 , Robert Emberson 5 , Dorota Matras 6,7 , Yaroslav Odarchenko 1 , Vesna Middelkoop 10 , Athanasios Giokaris 1 , Olof Gutowski 8 , Ann-Christin Dippel 8 , Martin v. Zimmermann 8 , Andrew M. Beale 3 , Keith T. Butler 9 , Antonis…
Benjamin Hoar, Weitong Zhang, Shuangning Xu, Rana Deeba + 3 more
For decades, employing cyclic voltammetry for mechanistic investigation demands manual inspection of voltammograms. Here we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a electrochemical probable mechanism among five of the most common ones in homogenous…
Florentine Klepel, Rainer Goebel
To investigate the relationship of perception and imagery, we model the visual ventral stream with an encoder and decoder part with the help of capsule networks. The proposed network consists of V1 and V2 from CorNet-Z, as well as the Capsule Network architecture with the routing by agreement algorithm for V4 and IT.…