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Smart gas sensor arrays powered by artificial intelligence

Journal of Semiconductors 2019年第11期40卷 3-12页

作者： Zhesi Chen Zhuo Chen Zhilong Song Wenhao Ye Zhiyong Fan HKUST-Shenzhen Research Institute Shenzhen 518057China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology(HKUST)Hong KongChina

Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision,hearing,touch,smell,and taste.A gas sensor array(GSA),also known as electronic nose,is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas *** intelligence(AI)applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA.A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring,gas leakage detection,food and beverage production and storage,and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability,low-powerconsumption and *** is exciting to envisage robots equipped with a"nose"acting as family doctor who will guard every family member's health and keep their home *** this review,we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems,exploring their potential applications in disease diagnosis,environmental monitoring,and explosive *** also discuss the key limitations of gas sensor units and their possible ***,we present the outlook of GSAs over the horizon of smart homes and cities.

关键词： mobile robots gas sensor array electronic nose artificial intelligence environmental monitoring disease diagnosis

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3D vision for Precision Dairy Farming

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IFAC-PapersOnLine 2019年第30期52卷 312-317页

作者： Niall O’ Mahony Sean Campbell Anderson Carvalho Lenka Krpalkova Daniel Riordan Joseph Walsh IMaR Research Centre Institute of Technology Tralee Tralee Co. Kerry Ireland

3D vision systems will play an important role in next-generation dairy farming due to the sensing capabilities they provide in the automation of animal husbandry tasks such as the monitoring, herding, feeding, milking and bedding of animals. This paper will review 3D computer vision systems and techniques that are and may be implemented in Precision Dairy Farming. This review will include evaluations of the applicability of Time of Flight and Streoscopic vision systems to agricultural applications as well as a breakdown of the categories of computer vision algorithms which are being explored in a variety of use cases. These use cases range from robotic platforms such as milking robots and autonomous vehicles which must interact closely and safely with animals to intelligent systems which can identify dairy cattle and detect deviations in health indicators such as Body Condition Score and Locomotion Score. Upon analysis of each use case, it is apparent that systems which can operate in unconstrained environments and adapt to variations in herd characteristics, weather conditions, farmyard layout and different scenarios in animal-robot interaction are required. Considering this requirement, this paper proposes the application of techniques arising from the emerging field of research in Artificial Intelligence that is Geometric Deep Learning.

关键词： Automation Robotics in Agriculture Sensing Automation in Animal Farming

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Artificial intelligence for robotics : build intelligent robots that perform human tasks using AI...

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2018年

作者： Govers Francis X.

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Towards Event-driven Object Detection with Off-the-shelf Deep Learning

Towards Event-driven Object Detection with Off-the-shelf Dee...

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25th IEEE/RSJ International Conference on intelligent robots and Systems (IROS)

作者： Iacono, Massimiliano Weber, Stefan Glover, Arren Bartolozzi, Chiara Ist Italiano Tecnol ICub Facil Genoa Italy

ISBN: (纸本)9781538680940

Event cameras are an emerging technology in computer vision, offering extremely low latency and bandwidth, as well as a high temporal resolution and dynamic range. Inherent data compression is achieved as pixel data is only produced by contrast changes at the edges of moving objects. However, current trends in state-of-the-art visual algorithms rely on deep-learning with networks designed to process colour and intensity information contained in dense arrays, but are notoriously computationally heavy. While the combination of these visual technologies could lead to fast, efficient, and accurate detection and recognition algorithms, it is uncertain whether the compressed event-camera data actually contain the required information for these techniques to discriminate between objects and a cluttered background. This paper presents a pilot study in which off-the-shelf deep-learning is applied to visual events for object detection on the iCub robotic platform, and analyses the impact of temporal integration of the event data. We also present a novel pipeline that bootstraps event-based dataset annotation from mature frame-based algorithms, in order to more quickly generate the required datasets.

关键词： Cameras Robot vision systems Visualization Training Object detection

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Efficient Absolute Orientation Revisited

Efficient Absolute Orientation Revisited

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25th IEEE/RSJ International Conference on intelligent robots and Systems (IROS)

作者： Lourakis, Manolis Terzakis, George Fdn Res & Technol Hellas Inst Comp Sci Iraklion Greece Univ Portsmouth Fac Technol Portsmouth Hants England

ISBN: (纸本)9781538680940

Absolute orientation estimation is the determination of the similarity transformation between two sets of corresponding 3D points, a task arising frequently in computer vision and robotics. We have recently proposed an absolute orientation algorithm based on the Fast Optimal Attitude Matrix (FOAM) algorithm from astronautics and demonstrated that it is more efficient computationally compared to widely-used approaches involving costly eigen- and singular-value matrix decompositions. In this work, we compare our FOAM-based solution with several more algorithms derived from attitude estimation techniques and show that further computational savings are possible by employing an algorithm grounded on the Optimal Linear Attitude Estimator (OLAE) method.

关键词： Quaternions Estimation Symmetric matrices Eigenvalues and eigenfunctions Matrix decomposition Covariance matrices computer vision

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Scenario-based simulation of intelligent driving functions using neural networks 9

Scenario-based simulation of intelligent driving functions u...

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9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Kaur, Parampreet Sobti, Rajeev Lovely Profess Univ Sch Comp Sci Engn Jalandhar India

ISBN: (纸本)9781538644300

The modern technological era is witnessing a significant advancement in AI related fields such as machine learning, mobile robots and autonomous vehicles. The success of such systems is immensely dependent on computer vision algorithms. The entirely software enabled intelligent vehicles will soon be hitting the roads in the coming decade. Such self-controlled mobile robots may still be vulnerable to accidents or crashes. Therefore, the industry requires some state of the art techniques to substantiate the safety and protection of its passengers as well as other road users. Simulation based testing methods have been in use from a long time, but the newer smart vehicle innovations require better versions of simulation techniques. We thus provide an improved mechanism for simulation testing to validate safe navigation of cars in variety of traffic scenarios. Neural network approach integrated with agent based modelling is described in this paper.

关键词： Agent based modelling intelligent driving SAE autonomy levels simulation ADAS

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CAMAL: Context-aware multi-scale attention framework for lightweight visual place recognition

arXiv

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arXiv 2019年

作者： Khaliq, Ahmad Ehsan, Shoaib Milford, Michael McDonald-Maier, Klaus Embedded and Intelligent System Lab in School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Australian Centre for Robotic Vision and School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art performances for Visual Place Recognition (VPR). Their prestigious generalization power has played a vital role in identifying persistent image regions under changing conditions and viewpoints. However, against the computation intensive D-CNNs based VPR algorithms, lightweight VPR techniques are preferred for resource-constraints mobile robots. This paper presents a lightweight CNN-based VPR technique that captures multilayer context-aware attentions robust under changing environment and viewpoints. Evaluation of challenging benchmark datasets reveals better performance at low memory and resources utilization over state-of-the-art contemporary VPR methodologies. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Artificial Intelligence for Robotics : Build intelligent robots That Perform Human Tasks Using AI techniques

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2018年

作者： Govers, Francis X. Packt Publishing, Limited

Bring a new degree of interconnectivity to your world by building your own intelligent robots Key Features Leverage fundamentals of AI and robotics Work through use cases to implement various machine learning algorithms Explore Natural Language Processing (NLP) concepts for efficient decision making in robots Book Description Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills. As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks. By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence. What you will learn Get started with robotics and artificial intelligence Apply simulation techniques to give your robot an artificial personality Understand object recognition using neural networks and supervised learning techniques Pick up objects using genetic algorithms for manipulation Teach your robot to listen using NLP via an expert system Use machine learning and computer vision to teach your robot how to avoid obstacles Understand path planning, decision trees, and search algorithms in order to enhance your robot Who this book is for If you have basic knowledge about robotics and want to build or enhance your existing robot's intelligence, then Artificial Intelligence for Robotics is for you. This book is also for enthusiasts who want to gain know

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Teaching robots to Predict Human Motion

Teaching Robots to Predict Human Motion

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25th IEEE/RSJ International Conference on intelligent robots and Systems (IROS)

作者： Gui, Liang-Yan Zhang, Kevin Wang, Yu-Xiong Liang, Xiaodan Moura, Jose M. F. Veloso, Manuela Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9781538680940

Teaching a robot to predict and mimic how a human moves or acts in the near future by observing a series of historical human movements is a crucial first step in human-robot interaction and collaboration. In this paper, we instrument a robot with such a prediction ability by leveraging recent deep learning and computer vision techniques. First, our system takes images from the robot camera as input to produce the corresponding human skeleton based on real-time human pose estimation obtained with the OpenPose library. Then, conditioning on this historical sequence, the robot forecasts plausible motion through a motion predictor, generating a corresponding demonstration. Because of a lack of high-level fidelity validation, existing forecasting algorithms suffer from error accumulation and inaccurate prediction. Inspired by generative adversarial networks (GANs), we introduce a global discriminator that examines whether the predicted sequence is smooth and realistic. Our resulting motion GAN model achieves superior prediction performance to state-of-the-art approaches when evaluated on the standard H3.6M dataset. Based on this motion GAN model, the robot demonstrates its ability to replay the predicted motion in a human-like manner when interacting with a person.

关键词： robots Gallium nitride Generative adversarial networks Predictive models Cameras Skeleton Decoding

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Scenario-based simulation of intelligent driving functions using neural networks

Scenario-based simulation of intelligent driving functions u...

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International Conference on Computing, Communication and Networking Technologies

作者： Parampreet Kaur Rajeev Sobti School of Computer Science Engineering Lovely Professional University Jalandhar India

关键词： Agent based modelling intelligent driving SAE autonomy levels Simulation ADAS

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