281 research outputs found
Multi-task Learning for Radar Signal Characterisation
Radio signal recognition is a crucial task in both civilian and military
applications, as accurate and timely identification of unknown signals is an
essential part of spectrum management and electronic warfare. The majority of
research in this field has focused on applying deep learning for modulation
classification, leaving the task of signal characterisation as an understudied
area. This paper addresses this gap by presenting an approach for tackling
radar signal classification and characterisation as a multi-task learning (MTL)
problem. We propose the IQ Signal Transformer (IQST) among several reference
architectures that allow for simultaneous optimisation of multiple regression
and classification tasks. We demonstrate the performance of our proposed MTL
model on a synthetic radar dataset, while also providing a first-of-its-kind
benchmark for radar signal characterisation.Comment: 5 pages, 3 figure
Unobtrusive hand gesture recognition using ultra-wide band radar and deep learning
Hand function after stroke injuries is not regained rapidly and requires physical rehabilitation for at least 6 months. Due to the heavy burden on the healthcare system, assisted rehabilitation is prescribed for a limited time, whereas so-called home rehabilitation is offered. It is therefore essential to develop robust solutions that facilitate monitoring while preserving the privacy of patients in a home-based setting. To meet these expectations, an unobtrusive solution based on radar sensing and deep learning is proposed. The multi-input multi-output convolutional eXtra trees (MIMO-CxT) is a new deep hybrid model used for hand gesture recognition (HGR) with impulse-radio ultra-wide band (IR-UWB) radars. It consists of a lightweight architecture based on a multi-input convolutional neural network (CNN) used in a hybrid configuration with extremely randomized trees (ETs). The model takes data from multiple sensors as input and processes them separately. The outputs of the CNN branches are concatenated before the prediction is made by the ETs. Moreover, the model uses depthwise separable convolution layers, which reduce computational cost and learning time while maintaining high performance. The model is evaluated on a publicly available dataset of gestures collected by three IR-UWB radars and achieved an average accuracy of 98.86%
Deep Learning Network for Classifying Target of Same Shape using RCS Time Series
The main intension of this work is to find the warhead and decoy classification and identification. Classification of radar target is one of the utmost imperatives and hardest practical problems in finding out the missile. Detection of target in the pool of decoys and debris is one of the major radas technologies widely used in practice. In this study we mainly focus on the radar target recognition in different shapes like cone, cylinder and sphere based on radar cross section (RCS). RCS is a critical element of the radar signature that is used in this work to identify the target. The concept is to focus on new technique of ML for analyzing the input data and to attain a better accuracy. Machine learning has had a significant impact on the entire industry as a result of its high computational competency for target prediction with precise data analysis. We investigated various machine learning classifiers methods to categorize available radar target data. This chapter summarizes conventional and deep learning technique used for classification of radar target
EarthPT: a foundation model for Earth Observation
We introduce EarthPT -- an Earth Observation (EO) pretrained transformer.
EarthPT is a 700 million parameter decoding transformer foundation model
trained in an autoregressive self-supervised manner and developed specifically
with EO use-cases in mind. We demonstrate that EarthPT is an effective
forecaster that can accurately predict future pixel-level surface reflectances
across the 400-2300 nm range well into the future. For example, forecasts of
the evolution of the Normalised Difference Vegetation Index (NDVI) have a
typical error of approximately 0.05 (over a natural range of -1 -> 1) at the
pixel level over a five month test set horizon, out-performing simple
phase-folded models based on historical averaging. We also demonstrate that
embeddings learnt by EarthPT hold semantically meaningful information and could
be exploited for downstream tasks such as highly granular, dynamic land use
classification. Excitingly, we note that the abundance of EO data provides us
with -- in theory -- quadrillions of training tokens. Therefore, if we assume
that EarthPT follows neural scaling laws akin to those derived for Large
Language Models (LLMs), there is currently no data-imposed limit to scaling
EarthPT and other similar `Large Observation Models.'Comment: 7 pages, 4 figures, submitted to NeurIPS CCAI worksho
PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions
Hypercomplex neural networks have proven to reduce the overall number of parameters while ensuring valuable performance by leveraging the properties of Clifford algebras. Recently, hypercomplex linear layers have been further improved by involving efficient parameterized Kronecker products. In this article, we define the parameterization of hypercomplex convolutional layers and introduce the family of parameterized hypercomplex neural networks (PHNNs) that are lightweight and efficient large-scale models. Our method grasps the convolution rules and the filter organization directly from data without requiring a rigidly predefined domain structure to follow. PHNNs are flexible to operate in any user-defined or tuned domain, from 1-D to nD regardless of whether the algebra rules are preset. Such a malleability allows processing multidimensional inputs in their natural domain without annexing further dimensions, as done, instead, in quaternion neural networks (QNNs) for 3-D inputs like color images. As a result, the proposed family of PHNNs operates with 1/n free parameters as regards its analog in the real domain. We demonstrate the versatility of this approach to multiple domains of application by performing experiments on various image datasets and audio datasets in which our method outperforms real and quaternion-valued counterparts
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