Online Learning for Multimodal Data Fusion With Application to Object Recognition

Shahrampour, Shahin; Noshad, Mohammad; Ding, Jie; Tarokh, Vahid

doi:10.1109/tcsii.2017.2754141

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…In recent years, online learning has been used in various fields to help improve system performance. Shahrampour et al [31] used online learning in object recognition to help identify the current object through historical information from other modes. Sakulkar and Krishnamachari [32] proposed two online learning algorithms to help them solve the power allocation problem modelled as a Markov decision process.…”

Section: Related Workmentioning

confidence: 99%

Private Computing Offloading in Edge Cloud via Collaborative Online Learning

Wang

Yang

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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Computing offloading based on mobile edge computing (MEC) for mobile devices (MDs) has received great attentions in recent years. Strategy selection is an extremely important part of computing offloading, so how to make an optimal decision quickly and accurately during the computing offloading is a difficult point. Furthermore, MDs are likely to leak personal privacy when interacting with edge cloud, and there is also an issue about commercial privacy leakage between different cloud service suppliers. In this paper, we propose the privacy-protected edge cloud computing offloading (EPCO) algorithm based on online learning to improve the efficiency of computing offloading while ensuring the privacy of system users. Simultaneously, EPCO also supports different MDs customize their privacy level. We prove that adding privacy protection mechanism is almost no effect on the convergence of the algorithm. The simulation results validate our conclusion using a real-world dataset.

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Section: Related Workmentioning

confidence: 99%

Private Computing Offloading in Edge Cloud via Collaborative Online Learning

Wang

Yang

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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“…Multimodal perception is an important research topic in robotics. Fusing multiple sources of information provides excellent advantages to tackle perception challenges [36]- [38], and has been extensively used in multiple domains with excellent results [39]- [41]. Recent trends consider the problem of integrating multiple haptic or tactile-based sources of information.…”

Section: B Tactile-based Object Recognition Via Multiple Physical Pro...mentioning

confidence: 99%

Bayesian and Neural Inference on LSTM-Based Object Recognition From Tactile and Kinesthetic Information

Pastor

García‐González

Gandarias

et al. 2021

IEEE Robot. Autom. Lett.

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Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encountered in the sense of touch (e.g., tactile and kinesthetic sensations). This letter focuses on multimodal object recognition and proposes analytical and data-driven methodologies to fuse tactile-and kinesthetic-based classification results. The procedure is as follows: a three-finger actuated gripper with an integrated high-resolution tactile sensor performs squeeze-andrelease Exploratory Procedures (EPs). The tactile images and kinesthetic information acquired using angular sensors on the finger joints constitute the time-series datasets of interest. Each temporal dataset is fed to a Long Short-term Memory (LSTM) Neural Network, which is trained to classify in-hand objects. The LSTMs provide an estimation of the posterior probability of each object given the corresponding measurements, which after fusion allows to estimate the object through Bayesian and Neural inference approaches. An experiment with 36-classes is carried out to evaluate and compare the performance of the fused, tactile, and kinesthetic perception systems. The results show that the Bayesian-based classifiers improves capabilities for object recognition and outperforms the Neural-based approach.

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“…To efficiently and safely interact with the external environment or the user, these devices require various perceptual abilities. Tactile sensing is one of them and the tactile sensor has been one of the indispensable components for intelligent devices to achieve cognition and interaction for the environment and has been applied in robotics grasping [1,2], health monitoring [3,4], feature recognition [5][6][7][8][9], interactive input [10], etc. The sensing mechanisms of existed * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

A liquid lens-based optical sensor for tactile sensing

Yang¹,

Fu²,

Cao³

et al. 2022

Smart Mater. Struct.

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Tactile sensing plays a crucial role in robot manipulation, robot interaction, and health monitoring. Because of high sensitivity, simple structure, and superior interference immunity, optical tactile sensors based on optical imaging or optical conduction have been one of the most active research. Herein, a novel liquid lens-based optical sensor (LLOS) is presented. Different with existed optical tactile sensors, the main body of the proposed sensor belongs to a variable-focus optical lens with a liquid-membrane structure, and its focal length is changed with the contact force, thereby changing the propagation direction of light and affecting the perceived light intensity of the photosensitive element. By conducting some testing experiments, the LLOS demonstrates fast response (about 0.021s), stable dynamic response characteristics, and good linearity (R-squared is about 0.99), repeated measurement accuracy(<0.006V), and measurement accuracy (< 0.2N). Hence, the LLOS provides a new and promising method to measure tactile and has potential application in robotics nondestructive grasping and interactive input devices.

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Online Learning for Multimodal Data Fusion With Application to Object Recognition

Cited by 12 publications

References 26 publications

Private Computing Offloading in Edge Cloud via Collaborative Online Learning

Private Computing Offloading in Edge Cloud via Collaborative Online Learning

Bayesian and Neural Inference on LSTM-Based Object Recognition From Tactile and Kinesthetic Information

A liquid lens-based optical sensor for tactile sensing

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