26 papers · ranked by Valyu relevance
Li Li, Nan Sun
With the rapid development of deep learning, researchers have gradually applied it to motor imagery brain computer interface (MI-BCI) and initially demonstrated its advantages over traditional machine learning. However, its application still faces many challenges, and the recognition rate of electroencephalogram (EEG)…
Yangde Gao, Zahoor Ahmad, Jong-Myon Kim, Yi Qin
The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional LSTM autoencoder to…
Tong Liu, Zheng Wang
The Hi-C experiments have been extensively used for the studies of mammalian genomic structures. In the last few years, spatiotemporal Hi-C has enormously contributed to the investigation of genome dynamic reorganization. However, computationally modeling and forecasting spatiotemporal Hi-C data still have not been…
Altair Agmata, Svanur Guðmundsson
Efficient fisheries management is crucial for sustaining both marine ecosystems and the economies that heavily depend on them, such as Iceland, where the fishing industry contributes significantly to national export revenues. Current fishing practices involve decisions informed by a combination of personal experience…
Coşku Can Horuz, Sebastian Otte, Martin V. Butz, Matthias Karlbauer
We introduce MinConvLSTM and MinConvGRU, two novel spatiotemporal models that combine the spatial inductive biases of convolutional recurrent networks with the training efficiency of minimal, parallelizable RNNs. Our approach extends the log-domain prefix-sum formulation of MinLSTM and MinGRU to convolutional…
Lu Xu, Yixin Ma, Rui Shi, Juanjuan Li + 2 more
'Antonio Guerrieri'] The accurate identification of channel-coding types plays a crucial role in wireless communication systems. The recognition of convolutional codes presents challenges, primarily due to their strong temporal dependencies, varying constraint lengths, and additional contamination from noise. However…
Qi Xu, Yuyuan Gao, Jiangrong Shen, Yaxin Li + 3 more
'Huajin Tang' 'Gang Pan'] Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS). Although convolutional SNNs have achieved remarkable performance on these AER…
Hong-Yun Ou, Takahiro Hasegawa, Osamu Fukayama, Eizo Miyashita
Brain–machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their…
Bin Wu, Xinyu Wu, Peng Li, Youbing Gao + 4 more
'Naofal Al-Dhahir' 'Janusz Dudczyk' 'Piotr Samczyński'] In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter…
Nirmal Joshua Kapu, Raghav Karan
—This article surveys convolution-based modelsconvolutional neural networks (CNNs), Conformers, ResNets, and CRNNs-as speech signal processing models and provide their statistical backgrounds and speech recognition, speaker identification, emotion recognition, and speech enhancement applications. Through comparative…
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…
Kun Liu, Yong Liu, Shuo Ji, Chi Gao + 3 more
'Albert Vette'] Gait phase recognition is of great importance in the development of rehabilitation devices. The advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined (LSTM-CNN) in this paper, then a gait phase recognition method based on LSTM-CNN neural network model is…
Lei Zhang
Credit evaluation is a difficult problem in the process of financing and loan for small and medium-sized enterprises. Due to the high dimension and nonlinearity of enterprise behavior data, traditional logistic regression (LR), random forest (RF), and other methods, when the feature space is very large, it is easy to…
Sweta Kumari, C Vigneswaran, V. Srinivasa Chakravarthy
Sequential decision making tasks that require information integration over extended durations of time are challenging for several reasons including the problem of vanishing gradients, long training times and significant memory requirements. To this end we propose a neuron model fashioned after the JK flip-flops in…
David W. Romero, David M. Knigge, Albert Gu, Erik J. Bekkers + 3 more
'Efstratios Gavves' 'Jakub M. Tomczak' 'Mark Hoogendoorn'] The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learning framework. However, performant CNN architectures must be tailored to specific…
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…
Authors not listed
The automatic generation of image captions in natural language is a critical and challenging task, particularly in the context of environmental monitoring and control. This paper presents a novel deep learning-driven image captioning system designed for real-time monitoring and predictive control of pollutant gas…
Sukhdeep Singh, Sudhir Rohilla, Anuj Sharma
handwriting recognition Authors: ['Sukhdeep Singh' 'Sudhir Rohilla' 'Anuj Sharma'] Deep learning expresses a category of machine learning algorithms that have the capability to combine raw inputs into intermediate features layers. These deep learning algorithms have demonstrated great results in different fields. Deep…
Lorenzo Mazzaschi, Andrew J King, Ben DB Willmore, Nicol S Harper
Memory is essential for the neural processing of natural sounds. It has been proposed that cortical memory is subserved by gated recurrency, a powerful machine learning method that enables memory of sequential dependencies. However, standard forms lack biological realism. We built a computational model using a…
Xiaoxun Zhu, Baoping Liu, Zhentao Li, Jiawei Lin + 2 more
CNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same. Extracting different signal features with a fixed convolution kernel will affect the local feature…
Oumaima Moutik, Hiba Sekkat, Smail Tigani, Abdellah Chehri + 4 more
'Rachid Saadane' 'Taha Ait Tchakoucht' 'Anand Paul' 'Loris Nanni'] Understanding actions in videos remains a significant challenge in computer vision, which has been the subject of several pieces of research in the last decades. Convolutional neural networks (CNN) are a significant component of this topic and play a…
Mikhail Kiselev, Andrey Lavrentyev
—We consider an implementation of convolutional architecture in a spiking neural network (SNN) used to classify images. As in the traditional neural network, the convolutional layers form informational "features" used as predictors in the SNN-based classifier with CoLaNET architecture. Since weight sharing contradicts…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Nelly Elsayed, Zag ElSayed, Anthony S. Maida
Long short-term memory (LSTM) is one of the robust recurrent neural network architectures for learning sequential data. However, it requires considerable computational power to learn and implement both software and hardware aspects. This paper proposed a novel LiteLSTM architecture based on reducing the LSTM…
Rongpei Gou, Jingyi Yang, Menghan Guo, Yingjun Chen + 1 more
Central nervous system (CNS) drugs have had a significant impact on human health, e.g., treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models, particularly those for designing drugs from scratch, have shown great potential for accelerating drug…
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…