Mississippi State University

About: Mississippi State University is a education organization based out in Starkville, Mississippi, United States. It is known for research contribution in the topics: Population & Catfish. The organization has 14115 authors who have published 28594 publications receiving 700030 citations. The organization is also known as: The Mississippi State University of Agriculture and Applied Science & Mississippi State University of Agriculture and Applied Science.

Learning Sensor-Specific Spatial-Spectral Features of Hyperspectral Images via Convolutional Neural Networks

Abstract: Convolutional neural network (CNN) is well known for its capability of feature learning and has made revolutionary achievements in many applications, such as scene recognition and target detection. In this paper, its capability of feature learning in hyperspectral images is explored by constructing a five-layer CNN for classification (C-CNN). The proposed C-CNN is constructed by including recent advances in deep learning area, such as batch normalization, dropout, and parametric rectified linear unit (PReLU) activation function. In addition, both spatial context and spectral information are elegantly integrated into the C-CNN such that spatial-spectral features are learned for hyperspectral images. A companion feature-learning CNN (FL-CNN) is constructed by extracting fully connected feature layers in this C-CNN. Both supervised and unsupervised modes are designed for the proposed FL-CNN to learn sensor-specific spatial-spectral features. Extensive experimental results on four benchmark data sets from two well-known hyperspectral sensors, namely airborne visible/infrared imaging spectrometer (AVIRIS) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed C-CNN outperforms the state-of-the-art CNN-based classification methods, and its corresponding FL-CNN is very effective to extract sensor-specific spatial-spectral features for hyperspectral applications under both supervised and unsupervised modes.

Nematode effector proteins: an emerging paradigm of parasitism

Abstract: Phytonematodes use a stylet and secreted effectors to modify host cells and ingest nutrients to support their growth and development. The molecular function of nematode effectors is currently the subject of intense investigation. In this review, we summarize our current understanding of nematode effectors, with a particular focus on proteinaceous stylet-secreted effectors of sedentary endoparasitic phytonematodes, for which a wealth of information has surfaced in the past 10 yr. We provide an update on the effector repertoires of several of the most economically important genera of phytonematodes and discuss current approaches to dissecting their function. Lastly, we highlight the latest breakthroughs in effector discovery that promise to shed new light on effector diversity and function across the phylum Nematoda.

Integrated Optimal Formation Control of Multiple Unmanned Aerial Vehicles

Abstract: In this paper, we investigate the formation control of multiple unmanned aerial vehicles (UAVs), specifically unmanned aircraft, in an obstacle-laden environment. The main contribution of this paper is to integrate the formation control, trajectory tracking, and obstacle/collision avoidance into one unified optimal control framework. A nonquadratic avoidance cost is innovatively constructed via an inverse optimal control approach, which leads to an analytical, distributed, and optimal formation control law. The stability and optimality of the closed-loop system are proven. In addition, the proposed optimal control law is dependent only on the information from the local neighbors, rather than all UAVs' information. Simulation of multiple UAVs' formation flying demonstrates the effectiveness of the integrated optimal control design with desired behaviors including formation flying, trajectory tracking, and obstacle/collision avoidance.

Widespread methane leakage from the sea floor on the northern US Atlantic margin

Abstract: Methane emissions from the sea floor affect ocean chemistry and can reach the atmosphere. Observations from the northern US Atlantic margin reveal about 570 gas plumes at water depths between 50 and 1,700 m.