16 papers · ranked by Valyu relevance
Vinay Joshi, Manuel Le Gallo, Simon Haefeli, Irem Boybat + 6 more
'S. R. Nandakumar' 'Christophe Piveteau' 'Martino Dazzi' 'Bipin Rajendran' 'Abu Sebastian' 'Evangelos Eleftheriou'] In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the…
Jichong Lei, Zining Ni, Zhiqiang Peng, Hong Hu + 5 more
'Xiaoyong Fang' 'Cannan Yi' 'Changan Ren' 'Muhammad Abdul Wasaye'] As the quantity of motor vehicles and drivers experiences a continuous upsurge, the road driving environment has grown progressively more complex. This complexity has led to a concomitant increase in the probability of traffic accidents. Ample research…
Han Yu
How to enable the computer to accurately analyze the emotional information and story background of characters in Qin opera is a problem that needs to be studied. To promote the artistic inheritance and cultural emotion color dissemination of Qin opera, an emotion analysis model of Qin opera based on attention residual…
Chun-Ling Lin, Kun-Chi Wu
Background Diabetic retinopathy (DR) produces bleeding, exudation, and new blood vessel formation conditions. DR can damage the retinal blood vessels and cause vision loss or even blindness. If DR is detected early, ophthalmologists can use lasers to create tiny burns around the retinal tears to inhibit bleeding and…
Farhang Hosseini, Farkhondeh Asadi, Hamid Ahmadieh, Reza Rabiei + 8 more
Background Cystoid macular edema (CME) is a leading cause of vision loss in patients with retinitis pigmentosa (RP). The present study was aimed to CME in patients with RP using deep learning (DL) models based on the analysis of the optical coherence tomography (OCT) images. Methods In this cross-sectional study, a…
Pei Wang, Fan Luo, Lihong Wang, Chengsong Li + 2 more
Introduction Precise identification of crop insects is a crucial aspect of intelligent plant protection. Recently, with the development of deep learning methods, the efficiency of insect recognition has been significantly improved. However, the recognition rate of existing models for small insect targets is still…
Aili Wang, Minhui Wang, Haibin Wu, Kaiyuan Jiang + 1 more
LiDAR data contain feature information such as the height and shape of the ground target and play an important role for land classification. The effect of convolutional neural network (CNN) for feature extraction on LiDAR data is very significant, however CNN cannot resolve the spatial relationship of features…
Soumick Chatterjee, Faraz Ahmed Nizamani, Andreas Nürnberger, Oliver Speck
'Oliver Speck'] A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning, and magnetic resonance imaging…
Jiaojiao Chen, Haiyang Hu, Jianping Yang
The identification of plant leaf diseases is crucial in precision agriculture, playing a pivotal role in advancing the modernization of agriculture. Timely detection and diagnosis of leaf diseases for preventive measures significantly contribute to enhancing both the quantity and quality of agricultural products…
Zhou Tao, Huo Bing-qiang, Lu Huiling, Shi Hongbin + 2 more
'Ding Hongsheng'] Under the background of 18F-FDG-PET/CT multimodal whole-body imaging for lung tumor diagnosis, for the problems of network degradation and high dimension features during convolutional neural network (CNN) training, beginning with the perspective of dividing sample space, an E-ResNet-NRC (ensemble…
Shahriar Shakir Sumit, Dayang Rohaya Awang Rambli, Seyedali Mirjalili, M. Saef Ullah Miah + 1 more
'M. Saef Ullah Miah' 'Muhammad Mudassir Ejaz'] Human detection is an important task in computer vision. It is one of the most important tasks in global security and safety monitoring. In recent days, Deep Learning has improved human detection technology. Despite modern techniques, there are very few optimal techniques…
Gopalakrishnan Srinivasan, Kaushik Roy
In this work, we propose ReStoCNet, a residual stochastic multilayer convolutional Spiking Neural Network (SNN) composed of binary kernels, to reduce the synaptic memory footprint and enhance the computational efficiency of SNNs for complex pattern recognition tasks. ReStoCNet consists of an input layer followed by…
Jiadong Wu, Lun Lu, Yinan Wang, Zhiwei Li + 3 more
Spiking Neural Networks (SNNs) possess excellent computational energy efficiency and biological credibility. Among them, Spiking Convolutional Neural Networks (SCNNs) have significantly improved performance, demonstrating promising applications in low-power and brain-like computing. To achieve hardware acceleration for…
Pengfei Meng, Shuangcheng Jia, Qian Li
Sequence recognition of natural scene images has always been an important research topic in the field of computer vision. CRNN has been proven to be a popular end-to-end character sequence recognition network. However, the problem of wide characters is not considered under the setting of CRNN. The CRNN is less…
Courtney J. Spoerer, Tim C. Kietzmann, Johannes Mehrer, Ian Charest + 2 more
'Nikolaus Kriegeskorte' 'Leyla Isik'] Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. The primate visual system, by contrast, contains abundant recurrent connections. Recurrent signal flow enables recycling of limited computational resources over time, and…
Laura E. Suárez, Agoston Mihalik, Filip Milisav, Kenji Marshall + 4 more
The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how…