Recent Advances in Conventional and Deep Learning-Based Depth Completion: A Survey

Xie, Zhihong; Yu, Xiaoxuan; Gao, Xiang; Li, Kunqian; Shen, Shuhan

doi:10.1109/tnnls.2022.3201534

Cited by 11 publications

(2 citation statements)

References 164 publications

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“…Despite their lower complexity and ease of implementation, single-input methods are less accurate due to not taking advantage of the full range of available information. They are also more prone to lower accuracy ratings when processing more complex environments and geometries [194]. [195], applied to a synthetic forestry dataset [196].…”

Section: Spatial Representations In 3d and Depth Completion Methodsmentioning

confidence: 99%

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Ferreira,

Portugal,

Andrada

et al. 2023

Robotics

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Artificial perception for robots operating in outdoor natural environments, including forest scenarios, has been the object of a substantial amount of research for decades. Regardless, this has proven to be one of the most difficult research areas in robotics and has yet to be robustly solved. This happens namely due to difficulties in dealing with environmental conditions (trees and relief, weather conditions, dust, smoke, etc.), the visual homogeneity of natural landscapes as opposed to the diversity of natural obstacles to be avoided, and the effect of vibrations or external forces such as wind, among other technical challenges. Consequently, we propose a new survey, describing the current state of the art in artificial perception and sensing for robots in precision forestry. Our goal is to provide a detailed literature review of the past few decades of active research in this field. With this review, we attempted to provide valuable insights into the current scientific outlook and identify necessary advancements in the area. We have found that the introduction of robotics in precision forestry imposes very significant scientific and technological problems in artificial sensing and perception, making this a particularly challenging field with an impact on economics, society, technology, and standards. Based on this analysis, we put forward a roadmap to address the outstanding challenges in its respective scientific and technological landscape, namely the lack of training data for perception models, open software frameworks, robust solutions for multi-robot teams, end-user involvement, use case scenarios, computational resource planning, management solutions to satisfy real-time operation constraints, and systematic field testing. We argue that following this roadmap will allow for robotics in precision forestry to fulfil its considerable potential.

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Section: Spatial Representations In 3d and Depth Completion Methodsmentioning

confidence: 99%

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Ferreira,

Portugal,

Andrada

et al. 2023

Robotics

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show abstract

“…FFT algorithm calculates the discrete Fourier transform of a sequence. FFT factorizing DFT matrix and convert into a product of sparse factors, resulting in O (N log N) from the DFT O(N2) where N represents the size of data [18,19]. We have used the python library scipy.…”

Section: Eeg Data Discretizationmentioning

confidence: 99%

Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks

Pathak,

Kashyap

2023

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The COVID-19 pandemic has precipitated an unprecedented surge in the proliferation of online E-learning platforms, designed to cater to a wide array of subjects across all age groups. However, a paucity of these platforms adopts a learner-centric approach or validates user learning, underscoring the need for effective E-learning validation and personalized learning recommendations. This paper addresses these challenges by implementing an innovative approach that leverages real-time electroencephalogram (EEG) signals collected from learners, who don neuro headsets while partaking in online courses. These EEG signals are subsequently classified using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) deep learning models, with the intent of discerning the efficacy of the E-learning process. The proposed models have yielded promising classification accuracies of 68% and 97% for the CNN and LSTM models, respectively, demonstrating their rapidity and precision in classifying E-learning EEG signals. Thus, these models hold substantial potential for application in similar E-learning validation scenarios. Furthermore, this study introduces an automated framework designed to track the learning curve of users and furnish valuable recommendations for E-learning materials. The presented approach, therefore, not only validates the E-learning process but also aids in optimizing the learning experiences on E-learning platforms.

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Evaluating E-learning Engagement Through EEG Signal Analysis with Convolutional Neural Networks

Pathak,

Kashyap

2024

Lecture Notes in Networks and Systems

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Recent Advances in Conventional and Deep Learning-Based Depth Completion: A Survey

Cited by 11 publications

References 164 publications

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks

Evaluating E-learning Engagement Through EEG Signal Analysis with Convolutional Neural Networks

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