scholarly journals Generalization Ability of Deep Learning Algorithms Trained using SEM Data for Objects Classification

2021 ◽  
Author(s):  
Yasmina Zaky ◽  
nicolas fortino ◽  
Benoit Miramond ◽  
Jean-Yves Dauvignac
Keyword(s):  
Wide Band ◽  
Expansion Method ◽  
Observation Angle ◽  
Data Types ◽  
Single Observation ◽  
Aspect Angle ◽  
Learning Techniques ◽  
Frequency Domains ◽  
Receiving Antenna ◽  
Robust Feature Extraction

This paper proposes a workflow to efficiently determine the material of spherical objects and the location of the receiving antenna relative to their position in bi-static measurements using supervised learning techniques. From a single observation, we compare classification performances resulting from the application of several classifiers on different data types: the Ultra-Wide Band scattered field in time and frequency domains and pre-processed data from the Singularity Expansion Method (SEM). Indeed, the resonances extracted using the SEM are aspect independent and therefore, are used to discriminate the objects. As for the residues, they depend upon the aspect angle and can hence be exploited to determine the observation angle. We construct 3 datasets to assess which one yields the highest accuracy while using the simplest and fastest classifiers. Hence, 80% of each dataset is used for training and the remaining 20% are used for testing. In a further step, we test with sphere sizes and data with several noisy levels that were not in the training datasets. Although SEM is noise sensitive, associating a robust feature extraction technique with a simple but reliable classifier is promising, particularly when generalizing to data not included in the training set.

scholarly journals Generalization Ability of Deep Learning Algorithms Trained using SEM Data for Objects Classification

2021 ◽  
Author(s):  
Yasmina Zaky ◽  
nicolas fortino ◽  
Benoit Miramond ◽  
Jean-Yves Dauvignac
Keyword(s):  
Wide Band ◽  
Expansion Method ◽  
Observation Angle ◽  
Data Types ◽  
Single Observation ◽  
Aspect Angle ◽  
Learning Techniques ◽  
Frequency Domains ◽  
Receiving Antenna ◽  
Robust Feature Extraction

This paper proposes a workflow to efficiently determine the material of spherical objects and the location of the receiving antenna relative to their position in bi-static measurements using supervised learning techniques. From a single observation, we compare classification performances resulting from the application of several classifiers on different data types: the Ultra-Wide Band scattered field in time and frequency domains and pre-processed data from the Singularity Expansion Method (SEM). Indeed, the resonances extracted using the SEM are aspect independent and therefore, are used to discriminate the objects. As for the residues, they depend upon the aspect angle and can hence be exploited to determine the observation angle. We construct 3 datasets to assess which one yields the highest accuracy while using the simplest and fastest classifiers. Hence, 80% of each dataset is used for training and the remaining 20% are used for testing. In a further step, we test with sphere sizes and data with several noisy levels that were not in the training datasets. Although SEM is noise sensitive, associating a robust feature extraction technique with a simple but reliable classifier is promising, particularly when generalizing to data not included in the training set.

scholarly journals Convolutional Neural Network Computation for Autonomous Vehicle

2020 ◽  
pp. 368-372
Author(s):  
Aires Da Conceicao ◽  
Sheshang D. Degadwala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Autonomous Vehicle ◽  
Human Interaction ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Robust Feature Extraction

Self-driving vehicle is a vehicle that can drive by itself it means without human interaction. This system shows how the computer can learn and the over the art of driving using machine learning techniques. This technique includes line lane tracker, robust feature extraction and convolutional neural network.

scholarly journals Deep Learning in Mining Biological Data

2021 ◽  
Author(s):  
Mufti Mahmud ◽  
M. Shamim Kaiser ◽  
T. Martin McGinnity ◽  
Amir Hussain
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Meta Analysis ◽  
Biological Data ◽  
Machine Learning Techniques ◽  
Data Types ◽  
Multimodal Data ◽  
Data Intensive ◽  
Learning Techniques ◽  
Access Data

AbstractRecent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

scholarly journals Modeling of range-Doppler images of multicopters, formed by radar with continuous frequency-modulated signal.

2021 ◽  
Vol 2021 (3) ◽  
Author(s):  
I.F. Kupryashkin ◽  
◽  
N.V. Sokolik ◽  
◽  
◽  
...  
Keyword(s):  
Doppler Frequency ◽  
Wide Band ◽  
Observation Angle ◽  
Detection Range ◽  
Doppler Image ◽  
Proposed Model ◽  
Flight Parameters ◽  
Continuous Frequency ◽  
Cae System ◽  
Complex Valued

The paper presents an approach to modeling of a range-Doppler image of a multicopter, formed in a radar with a wide-band continuous signal. A feature of the approach is taking into account the observation angle, spatial orientation and the current angle of rotation of each of the multicopter propellers, based on wide-band all-angle estimates of their complex-valued reflection coefficients on horizontally and vertically polarized signals, formed using the microwave devices CAE system. The results of simulation of the range-Doppler image of the DJI Phantom 4 multicopter propeller system based on the actual recording of its flight parameters loggeded with the onboard autopilot are presented. Using the proposed model it was defined that the feature of range-Doppler images formed with wide-band continuous radar is the components frequency shifting not only on the Doppler frequency coordinate, but the slant detection range coordinate too.

scholarly journals Retrieval of cloud spherical albedo from top-of-atmosphere reflectance measurements performed at a single observation angle

2006 ◽  
Vol 6 (2) ◽  
pp. 2175-2188
Author(s):  
A. Kokhanovsky ◽  
B. Mayer ◽  
W. von Hoyningen-Huene
Keyword(s):  
Solar Light ◽  
Observation Angle ◽  
Analytical Relationship ◽  
Reflection Function ◽  
Single Observation ◽  
Modis Data ◽  
The Relationship ◽  
Reflectance Measurements

Abstract. The paper is devoted to the derivation of the simple analytical relationship between the cloud spherical albedo and the cloud reflection function. The relationship obtained can be used for the derivation of the spherical albedo from backscattered solar light measurements performed by radiometers on geostationary and polar orbiting satellites. The example of the application of the technique to MODIS data is shown.

scholarly journals Deep learning in cancer diagnosis, prognosis and treatment selection

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Khoa A. Tran ◽  
Olga Kondrashova ◽  
Andrew Bradley ◽  
Elizabeth D. Williams ◽  
John V. Pearson ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Diagnosis ◽  
Cancer Biology ◽  
Large Data ◽  
Data Sets ◽  
Precision Oncology ◽  
Data Types ◽  
Early Results ◽  
Learning Techniques ◽  
Rich Data

AbstractDeep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.

Sign in / Sign up

Export Citation Format

Share Document