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The inertial navigation domain is considered a very classical one. It aims to provide us with navigation solutions (position, velocity, and orientation) using (low-graded) inertial sensors and accurate (low frequency) sensors, like the GPS receiver. A question has arisen about how powerful Deep Learning tools can boost this classical domain.
Imagine you wake up in the morning and check your navigation app, only to discover that “it is searching for a network”. Imagine that while you are driving, the navigation app suddenly stops working. Can you still navigate?
In recent years, e-scooters have become an integral part of the urban landscape and its habitants’ daily lives. The micro-mobility revolution happens too fast for regulation to keep up and poses an opportunity for technological innovation. Municipalities all over the world are limiting the permitted area for rental e-scooter operation, limiting the maximum riding speed, and managing parking space by squeezing whole fleets into magical rectangles.
Graph convolutional network (GCN) is an absolute game-changer in the deep learning domain.
Transfer learning is the process of using skills and knowledge, that have been learned in one situation to solve a different, related problem.
Class imbalance is a common issue where the distribution of examples within a dataset is skewed or biased.
Exploring a new paper that aims to explain DNN behaviors.
It is very common to use the F1 measure for binary classification. This is known as the Harmonic Mean. However, a more generic F-beta score criterion might better evaluate model performance. So, what about F2, F3, and F-beta?
One of the main problems of autonomous vehicles is navigating in a GPS-denied environment. In this post, we focus on car positioning inside tunnels and present state-of-the-art accuracy in inertial navigation. The novel approach was developed by ALMA engineers combining deep learning, inertial sensors, and classical signal processing methods.
Deep Reinforcement learning has been a rising field in the last few years. A good approach to start with is the value-based method, where the state (or state-action) values are learned. In this post, a comprehensive review is provided where we focus on Q-learning and its extensions.
Kalman Filter (KF) is widely used for vehicle navigation tasks, and in particular for vehicle trajectory smoothing. One of the problems associated while applying the KF for navigation tasks is the modeling of the vehicle trajectory.
The random forest model is considered one of the promising ML ensemble models that recently became highly popular. In this post, we review the last trends of the random forest.
The Kalman filter is one of the most influential ideas used in Engineering, Economics, and Computer Science for real-time applications. This year we mention 60 years for the novel publication.
COVID-19 has affected the worldwide economy, politics, education, tourism, and actually EVERYTHING. Many academic papers address trend prediction in various fields due to COVID-19, with the power of Artificial Intelligence.
A fundamental problem in geometry was solved using a Deep Neural Network (DNN). We learned a geometric property from examples in the supervised learning approach. As the simplest geometric object is a curve, we focused on learning the length of planar curves. For this reason, the fundamental length axioms were reconstructed and the ArcLengthNet was established.
In this post, we deal with exploding and Vanishing Gradient in Time Series and in particular in Recurrent Neural Networks (RNN) by Truncated BackPropagation Through Time and Gradient Clipping.
The reinforcement learning field is used in many robotics problems and has a unique mechanism, where rewards should be accumulated through actions. But, what about the time between these actions?
This post reviews the latest innovations of TCN-based solutions. We first present a case study of motion detection and briefly review the TCN architecture and its advantages over conventional approaches such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Then, we introduce several novels using TCN, including improving traffic prediction, sound event localization & detection, and probabilistic forecasting.
A short review of cutting-edge deep learning-based solutions for inertial navigation.
Online Deep Learning (ODL) and Hedge Back-Propagation
Online learning is an ML method in which data is available in sequential order, and we use it in order to predict future data at each time step. Online Deep Learning is very challenging as it cannot use back-propagation.
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