A Deep Learning-Based Soil Moisture Estimation in Conus Region Using Cygnss Delay Doppler Maps

Nabi, M M; Senyurek, Volkan; Gürbüz, Ali Cafer; Kurum, Mehmet

doi:10.1109/igarss46834.2022.9883916

Cited by 4 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Introducing more intelligence and using localization sensors such as Global Navigation Satellite Systems (GNSS) [ 108 ] or global positioning systems (GPS), AGVs get information about their positions and then can localize themselves in known and unknown environments. As wireless physical layer technologies can generally adapt to the wireless environment, their combination with reconfigurable surfaces and deep learning approaches can open up new paths to secure 6G vehicular-aided heterogeneous networks [ 109 , 110 ].…”

Section: Discussionmentioning

confidence: 99%

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Islam¹,

Nabi²,

Ball³

2022

Sensors

View full text Add to dashboard Cite

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

show abstract

Section: Discussionmentioning

confidence: 99%

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Islam¹,

Nabi²,

Ball³

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The recent development of intelligent vehicles and transportation systems has paved the way for solving the problems of detecting and tracking objects around a vehicle. 10 The invention of artificial intelligence (AI), computer vision, machine learning (ML), deep learning (DL), 11 and sensor technology has enabled it to achieve high performance even in severe weather conditions and off-road environments. 12 Recently, RL, one type of ML algorithm, has been used for autonomous driving optimization strategies.…”

Section: Introductionmentioning

confidence: 99%

Evaluate the performance of attention DDPG-based adaptive cruise control system in all weather conditions

Islam,

Ebu,

Rahman

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

View full text Add to dashboard Cite

Adaptive Cruise Control (ACC) is one of the common advanced driving features that aims to assist the driver from fatigue. A lot of models for ACC are available, such as Proportional, Integral, and Derivative (PID), Model Predictive Control (MPC), and reinforcement learning (RL). Recently a deep RL technique called deep deterministic policy gradient (DDPG) has been widely used for velocity control. DDPG is a mixture of deep learning (DL) and RL method, that uses neural networks (NN) to estimate its action. In this work, we utilize an attention DDPG model. The attention mechanism in DDPG increases the overall effectiveness of the model by reducing focus on the less important features. The network structure consists of one hidden layer with 30 neurons, which has been deployed in our previous work. The reward function has been designed to concentrate on overall safety and comfort. In this paper, we introduce a new criterion of weather conditions, which has not been used before in the literature. We trained our model with a publicly available dataset. For testing, we created simulated sensor data created in the Mississippi State University Autonomous Vehicular Simulator (MAVS). We introduced various scenarios within the simulation environment with differing weather conditions to assess the performance of the ACC model. In many cases, the sensors are weather-sensitive, which can affect the performance of the ACC system. The primary objective of this study is to evaluate the performance of the Attention DDPG-based ACC model under varying weather conditions. The results show that the agent can maintain safety across a range of weather conditions.

show abstract

“…In computer vision, deep learning has been widely used to solve a variety of issues, including detection, localization, estimation, and classification. [10][11][12][13] Several machine learning (ML) and deep learning algorithms have been developed to categorize fish species. For instance, Jager et al 14 employed AlexNet architecture for feature extraction and multiclass SVM for classification, whereas hierarchical features and support vector machine (SVM) are used for fish classification.…”

Section: Introductionmentioning

confidence: 99%

An enhanced YOLOv5 model for fish species recognition from underwater environments

Shah

Alaba

Nabi³

et al. 2023

Ocean Sensing and Monitoring XV

View full text Add to dashboard Cite

Species recognition is an important aspect of video based surveys, which support stock assessments, inspecting the ecosystem, handling production management, and protecting endangered species. It is a challenging task to implement fish species detection algorithms in underwater environments. In this work, we introduce the YOLOv5 model for the recognition of fish species that can be implemented as an object detection model for analyzing multiple fishes in a single image. Moreover, we have modified the depth scale of different layers in the backbone of the YOLOv5 model to obtain improved results on fish species recognition. In addition, we have implemented a transformer block in the backbone network and introduced a class balance loss function to obtain enhanced performance. It can perform fish species recognition as an object detection approach by classifying each of the fish species in addition to localizing for the estimation of the position and size of the fish in an image. Experiments are conducted on the fine-grained and large-scale reef fish dataset that we have obtained from the Gulf of Mexico -the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results demonstrate that an enhanced YOLOv5 model can yield better detection results in comparison to YOLOv5 for underwater fish species recognition.

show abstract

A Deep Learning-Based Soil Moisture Estimation in Conus Region Using Cygnss Delay Doppler Maps

Cited by 4 publications

References 13 publications

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Evaluate the performance of attention DDPG-based adaptive cruise control system in all weather conditions

An enhanced YOLOv5 model for fish species recognition from underwater environments

Contact Info

Product

Resources

About