15 papers · ranked by Valyu relevance
Peishan Dai, Hanwei Sheng, Jianmei Zhang, Ling Li + 2 more
'Min Fan'] Retinal fundus image plays an important role in the diagnosis of retinal related diseases. The detailed information of the retinal fundus image such as small vessels, microaneurysms, and exudates may be in low contrast, and retinal image enhancement usually gives help to analyze diseases related to retinal…
Jungwoo Shin, HyunJin Kim, Yilun Shang
In this study, we present a novel performance-enhancing binarized neural network model called PresB-Net: Parametric Binarized Neural Network. A binarized neural network (BNN) model can achieve fast output computation with low hardware costs by using binarized weights and features. However, performance degradation is…
Huixia Lai, Lulu Zhang, Shi Zhang, Antonio Lázaro
As a technique for accelerating and stabilizing training, the batch normalization (BN) is widely used in deep learning. However, BN cannot effectively estimate the mean and the variance of samples when training/fine-tuning with small batches of data on resource-constrained devices. It will lead to a decrease in the…
Sen Yang, Xiaobao Wang, Qijuan Yang, Enzeng Dong + 2 more
'Cosimo Distante'] The single batch normalization (BN) method is commonly used in the instance segmentation algorithms. The batch size is concerned with some drawbacks. A too small sample batch size leads to a sharp drop in accuracy, but a too large batch may result in the memory overflow of graphic processing units…
Feiqing Zhang, Zhenyu Yin, Fulong Xu, Yue Li + 1 more
Rolling bearing fault diagnosis is of great significance to the safe and reliable operation of manufacturing equipment. In the actual complex environment, the collected bearing signals usually contain a large amount of noises from the resonances of the environment and other components, resulting in the nonlinear…
Songjiang Li, Shilong Wang, Peng Wang, Tamer M. Nadeem
Traffic sign detection is a crucial task in computer vision, finding wide-ranging applications in intelligent transportation systems, autonomous driving, and traffic safety. However, due to the complexity and variability of traffic environments and the small size of traffic signs, detecting small traffic signs in…
Shaode Yu, Shibin Wu, Lei Wang, Fan Jiang + 3 more
assessment CNN for BISA Authors: ['Shaode Yu' 'Shibin Wu' 'Lei Wang' 'Fan Jiang' 'Yaoqin Xie' 'Leida Li' 'You Yang'] Blind image quality assessment can be modeled as feature extraction followed by score prediction. It necessitates considerable expertise and efforts to handcraft features for optimal representation of…
Yanan Liu, Laurie Bose, Rui Fan, Piotr Dudek + 1 more
Many types of Convolutional Neural Network (CNN) models and training methods have been proposed in recent years aiming to provide efficiency for embedded and edge devices with limited computation and memory resources. The wide variety of architectures makes this a complex task that has to balance generality with…
Shangjun Ma, Wei Cai, Wenkai Liu, Zhaowei Shang + 1 more
To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution…
Xiajiong Shen, Kunying Meng, Lei Zhang, Xianyu Zuo
The neural network method can obtain a higher precision of radar echo extrapolation than the traditional method. However, its application in radar echo extrapolation is still in the initial stage of exploration, and there is still much room for improvement in the extrapolation accuracy. To improve the utilization of…
Kenway Louie, Samuel J. Gershman
Learning is widely modeled in psychology, neuroscience, and computer science by prediction error-guided reinforcement learning (RL) algorithms. While standard RL assumes linear reward functions, reward-related neural activity is a saturating, nonlinear function of reward; however, the computational and behavioral…
Li Yao
In order to improve the resolution of magnetic resonance (MR) image and reduce the interference of noise, a multifeature extraction denoising algorithm based on a deep residual network is proposed. First, the feature extraction layer is constructed by combining three different sizes of convolution kernels, which are…
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…
S. Soniya, K. C. Sriharipriya, J. Christopher Clement, Umashankar Subramaniam
In recent years, Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks, including image denoising. Image denoising focuses on reconstructing a clean image from its noise-corrupted counterpart. In this paper, we propose BIRUNet, a bilateral-filter-based noise-residue…
Seokjin Oh, Jiyong An, Kyeong-Sik Min, Nam-Trung Nguyen
Memristor crossbars can be very useful for realizing edge-intelligence hardware, because the neural networks implemented by memristor crossbars can save significantly more computing energy and layout area than the conventional CMOS (complementary metal-oxide-semiconductor) digital circuits. One of the important…