Ansys Real Time Physics Based Radar Simulation – An Enabler for Machine Learning in the Context of Autonomous Driving

In recent years, machine vision algorithms have played an influential role as core technologies in several practical applications, such as surveillance, autonomous driving, and object recognition/localization. However, as almost all such algorithms are applicable to clear weather conditions, their performance is severely affected by any atmospheric turbidity. Several image visibility restoration algorithms have been proposed to address this issue, and they have proven to be a highly efficient solution. This paper proposes a novel method to recover clear images from degraded ones. To this end, the proposed algorithm uses a supervised machine learning-based technique to estimate the pixel-wise extinction coefficients of the transmission medium and a novel compensation scheme to rectify the post-dehazing false enlargement of white objects. Also, a corresponding hardware accelerator implemented on a Field Programmable Gate Array chip is in order for facilitating real-time processing, a critical requirement of practical camera-based systems. Experimental results on both synthetic and real image datasets verified the proposed method’s superiority over existing benchmark approaches. Furthermore, the hardware synthesis results revealed that the accelerator exhibits a processing rate of nearly 271.67 Mpixel/s, enabling it to process 4K videos at 30.7 frames per second in real time.

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Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications

ACM Transactions on Embedded Computing Systems ◽

10.1145/3477026 ◽

2021 ◽

Vol 20 (5s) ◽

pp. 1-26

Author(s):

Yeli Feng ◽

Daniel Jun Xian Ng ◽

Arvind Easwaran

Keyword(s):

Machine Learning ◽

Real Time ◽

State Of The Art ◽

Autonomous Driving ◽

The State ◽

Theory And Practice ◽

Data Sets ◽

Variational Autoencoder ◽

Distribution Shifts ◽

Better Than

Uncertainties in machine learning are a significant roadblock for its application in safety-critical cyber-physical systems (CPS). One source of uncertainty arises from distribution shifts in the input data between training and test scenarios. Detecting such distribution shifts in real-time is an emerging approach to address the challenge. The high dimensional input space in CPS applications involving imaging adds extra difficulty to the task. Generative learning models are widely adopted for the task, namely out-of-distribution (OoD) detection. To improve the state-of-the-art, we studied existing proposals from both machine learning and CPS fields. In the latter, safety monitoring in real-time for autonomous driving agents has been a focus. Exploiting the spatiotemporal correlation of motion in videos, we can robustly detect hazardous motion around autonomous driving agents. Inspired by the latest advances in the Variational Autoencoder (VAE) theory and practice, we tapped into the prior knowledge in data to further boost OoD detection’s robustness. Comparison studies over nuScenes and Synthia data sets show our methods significantly improve detection capabilities of OoD factors unique to driving scenarios, 42% better than state-of-the-art approaches. Our model also generalized near-perfectly, 97% better than the state-of-the-art across the real-world and simulation driving data sets experimented. Finally, we customized one proposed method into a twin-encoder model that can be deployed to resource limited embedded devices for real-time OoD detection. Its execution time was reduced over four times in low-precision 8-bit integer inference, while detection capability is comparable to its corresponding floating-point model.

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Machine-learning-based Real-Time Multi-object Recognition Method for Urban Autonomous Driving

Journal of Institute of Control Robotics and Systems ◽

10.5302/j.icros.2020.20.0045 ◽

2020 ◽

Vol 26 (6) ◽

pp. 499-505

Author(s):

Sang-Bae Park ◽

Jung-Ha Kim

Keyword(s):

Machine Learning ◽

Object Recognition ◽

Real Time ◽

Autonomous Driving ◽

Recognition Method

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A Machine Learning Based Strategy for Predicting the Sagittal Angles in real-time for Actuating an Active Knee-Ankle Prosthesis

10.1055/s-0040-1717439 ◽

2020 ◽

Author(s):

S Dey ◽

T Yoshida ◽

AF Schilling

Keyword(s):

Machine Learning ◽

Real Time ◽

Ankle Prosthesis

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Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽

10.32964/tj18.11.679 ◽

2019 ◽

Vol 18 (11) ◽

pp. 679-689

Author(s):

CYDNEY RECHTIN ◽

CHITTA RANJAN ◽

ANTHONY LEWIS ◽

BETH ANN ZARKO

Keyword(s):

Machine Learning ◽

Real Time ◽

Predictive Analytics ◽

Paper Industry ◽

Quality Parameters ◽

Fast Change ◽

The Face ◽

Model Tuning ◽

Reducing Costs ◽

Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

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Enhancing functionality of real time systems through machine learning

10.2514/6.1993-4662 ◽

1993 ◽

Author(s):

Z. Chen

Keyword(s):

Machine Learning ◽

Real Time ◽

Real Time Systems ◽

Time Systems

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Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

10.21437/interspeech.2019-1593 ◽

2019 ◽

Author(s):

Hironori Takemoto ◽

Tsubasa Goto ◽

Yuya Hagihara ◽

Sayaka Hamanaka ◽

Tatsuya Kitamura ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Machine Learning Method ◽

Learning Method ◽

Contour Extraction

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Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽

10.3390/s21144736 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4736

Author(s):

Sk. Tanzir Mehedi ◽

Adnan Anwar ◽

Ziaur Rahman ◽

Kawsar Ahmed

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Intrusion Detection ◽

Real Time ◽

Transfer Learning ◽

Security Requirements ◽

Detection Accuracy ◽

Area Network ◽

Complex Data ◽

Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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