216 research outputs found
Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires
We used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m
spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector
Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial
resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire
data were screened to prevent extraction of unrepresentative burned area samples and combined with
surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied
over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and
compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area
product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized
scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based
region growing algorithms, the proposed approach allows for the detection and mapping of burn
scars without active fires, thus eliminating a potential source of omission error. The burned area
mapping approach presented here should facilitate the development of operational-automated
burned area algorithms, and is very straightforward for implementation with other sensorsinfo:eu-repo/semantics/publishedVersio
Deep Reinforcement Learning for Smart Queue Management
With the goal of meeting the stringent throughput and delay requirements of classified network flows, we propose a Deep Q-learning Network (DQN) for optimal weight selection in an active queue management system based on Weighted Fair Queuing (WFQ). Our system schedules flows belonging to different priority classes (Gold, Silver, and Bronze) into separate queues, and learns how and when to dequeue from each queue. The neural network implements deep reinforcement learning tools such as target networks and replay buffers to help learn the best weights depending on the network state. We show, via simulations, that our algorithm converges to an efficient model capable of adapting to the flow demands, producing thus lower delays with respect to traditional WFQ
DESiRED -- Dynamic, Enhanced, and Smart iRED: A P4-AQM with Deep Reinforcement Learning and In-band Network Telemetry
Active Queue Management (AQM) is a mechanism employed to alleviate transient
congestion in network device buffers, such as routers and switches. Traditional
AQM algorithms use fixed thresholds, like target delay or queue occupancy, to
compute random packet drop probabilities. A very small target delay can
increase packet losses and reduce link utilization, while a large target delay
may increase queueing delays while lowering drop probability. Due to dynamic
network traffic characteristics, where traffic fluctuations can lead to
significant queue variations, maintaining a fixed threshold AQM may not suit
all applications. Consequently, we explore the question: \textit{What is the
ideal threshold (target delay) for AQMs?} In this work, we introduce DESiRED
(Dynamic, Enhanced, and Smart iRED), a P4-based AQM that leverages precise
network feedback from In-band Network Telemetry (INT) to feed a Deep
Reinforcement Learning (DRL) model. This model dynamically adjusts the target
delay based on rewards that maximize application Quality of Service (QoS). We
evaluate DESiRED in a realistic P4-based test environment running an MPEG-DASH
service. Our findings demonstrate up to a 90x reduction in video stall and a
42x increase in high-resolution video playback quality when the target delay is
adjusted dynamically by DESiRED.Comment: Preprint (Computer Networks under review
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