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Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States
LiDAR airborne laser scanning enhanced forest inventory aboveground biomass forest carbon deep learning Maine New Hampshire Vermont Massachusetts Connecticut Rhode Island
2023/12/5
Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional sc...
Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning
machine learning ratio-based indices orthogonal indices Koppen–Geiger climate regionalization landscape change remote sensing landcover
2023/12/4
Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural...
Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery
Forest Farm Fire Monitoring System Deep Learning
2023/12/1
Forest fires represent one of the main problems threatening forest sustainability. Therefore, an early prevention system of forest fire is urgently needed. To address the problem of forest farm fire m...
A Hybrid Classification of Imbalanced Hyperspectral Images Using ADASYN and Enhanced Deep Subsampled Multi-Grained Cascaded Forest
hyperspectral image imbalanced data oversampling adaptive synthetic sampling tree-based classifier subsampling deep multi-grained cascade forest
2023/11/29
Hyperspectral image (HSI) analysis generally suffers from issues such as high dimensionality, imbalanced sample sets for different classes, and the choice of classifiers for artificially balanced data...
Targeted and untargeted metabolomics reveals deep analysis of drought stress responses in needles and roots of Pinus taeda seedlings
drought tolerance phytohormones fatty acids neurotransmitters sugars amino acids organic acids ?avenoids and terpenoids
2023/11/16
Drought stress is one of major environmental stresses affecting plant growth and yield. Although Pinus taeda trees are planted in rainy southern China, local drought sometime occurs and can last sever...
DEEP GREEN PANICLE1 suppresses GOLDEN2-LIKE activity to reduce chlorophyll synthesis in rice glumes
DGP1 OsGLK Chlorophyll Photosynthesis Rice Oryza sativa L.
2023/7/20
Understanding the regulation mechanisms of photosynthesis is key to improving its efficiency and, ultimately, crop yield. In this study, we report that DEEP GREEN PANICLE1 (DGP1) is involved in photos...
Comparison of partial least squares-discriminant analysis, support vector machines and deep neural networks for spectrometric classification of seed vigour in a broad range of tree species
least squares-discriminant analysis spectrometric classification seed tree species
2023/6/5
Seed vigour significantly influences the seed production and plant regeneration performance. The capability of NIR spectroscopy to identify seed vigour across multiple tree species rapidly and cost-ef...
Deep conservation of cis-element variants regulating plant hormonal responses
Cis-element variants hormonal response transcription factors(TFs) response elements
2024/2/28
Phytohormones regulate many aspects of plant life by activating transcription factors (TF) that bind sequence-specific response elements (RE) in regulatory regions of target genes. Despite their short...
Deep Learning Model Generalization in Side-Channel Analysis
Side-Channel Analysis Deep Learning Model Generalization
2019/8/30
The adoption of deep neural networks for profiled side-channel attacks provides different capabilities for leakage detection of secure products. Research papers provide a variety of arguments with res...
X-DeepSCA: Cross-Device Deep Learning Side Channel Attack
Side-channel Attacks Profiling attacks Cross-device Attack
2019/7/17
This article, for the first time, demonstrates Cross-device Deep Learning Side-Channel Attack (X-DeepSCA), achieving an accuracy of >99.9%>99.9%, even in presence of significantly higher inter-device ...
How Diversity Affects Deep-Learning Side-Channel Attacks
Side-channel attack power analysis deep learning
2019/6/6
Deep learning side-channel attacks are an emerging threat to the security of implementations of cryptographic algorithms. The attacker first trains a model on a large set of side-channel traces captur...
Simulating Homomorphic Evaluation of Deep Learning Predictions
neural networks homomorphic encryption TFHE
2019/5/31
Convolutional neural networks (CNNs) is a category of deep neural networks that are primarily used for classifying image data. Yet, their continuous gain in popularity poses important privacy concerns...
Deep Learning based Side Channel Attacks in Practice
Deep Learning based Side-Channel Attacks Data Dimensionality Data Scaling
2019/5/29
A recent line of research has investigated a new profiling technique based on deep learning as an alternative to the well-known template attack. The advantage of this new profiling approach is twofold...
Deep Learning based Model Building Attacks on Arbiter PUF Compositions
physically unclonable function machine learning deep learning
2019/5/28
Robustness to modeling attacks is an important requirement for PUF circuits. Several reported Arbiter PUF com- positions have resisted modeling attacks. and often require huge computational resources ...
DL-LA: Deep Learning Leakage Assessment: A modern roadmap for SCA evaluations
side channel leakage assessment deep learning
2019/5/21
In recent years, deep learning has become an attractive ingredient to side-channel analysis (SCA) due to its potential to improve the success probability or enhance the performance of certain frequent...