Low-Complexity Fixed-Point Convolutional Neural Networks For Automatic Target Recognition

Dbouk, Hassan; Geng, Hanfei; Vineyard, Craig Michael; Shanbhag, Naresh R.

doi:10.1109/icassp40776.2020.9054094

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Despite this, the CNNs usually devised for automotive applications are not as deep as those employed in computer vision. This difference is mainly due to the fact that: a) the information provided by range-azimuth or range-Doppler maps are not as rich as traditional RGB images; b) the employed inference procedure has to be as fast as possible [126]. These ideas are exemplified by the CNN proposed in ref.…”

Section: Autonomous Drivingmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

DAVOLI¹,

Guerzoni²,

Vitetta³

2021

Preprint

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<p>Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.</p>

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Section: Autonomous Drivingmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

DAVOLI¹,

Guerzoni²,

Vitetta³

2021

Preprint

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“…CNNs require a massive amount of parameters and operations to generate a single inference, making them unsuitable for latency-and energy-constrained applications such as SAR-TR. To reduce the cost of implementing these networks, Dbouk et al [110] developed a set of compact network architectures, which achieves an overall 984 times reduction in terms of storage requirements and 71 times reduction in terms of computational complexity compared to state-of-the-art CNNs for automatic target recognition. To achieve good performance with a small number of parameters, Huang et al [111] proposed a lightweight two-stream CNN to extract multilevel features.…”

Section: Remote Object Detection and Recognitionmentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

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