Neuromorphic engineering — A paradigm shift for future IM technologies

Vanarse, Anup; Osseiran, Adam; Rassau, Alexander

doi:10.1109/mim.2019.8674627

Cited by 25 publications

(16 citation statements)

References 18 publications

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“…Biologically plausible models of image capture and processing seek to bridge the physical gap between sensing and computation. In doing so, advantages in comparison to traditional cameraprocessor approaches manifest in the form of heightened parallelism, small chip size and low power dissipation, and high dynamic range [10], [11]. To achieve this standard of neuromorphic hardware implementation, there are several barriers to overcome.…”

Section: Retinomorphic Processingmentioning

confidence: 99%

Nonlinear retinal response modeling for future neuromorphic instrumentation

Eshraghian

Baek

Levi³

et al. 2020

IEEE Instrum. Meas. Mag.

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Section: Retinomorphic Processingmentioning

confidence: 99%

Nonlinear retinal response modeling for future neuromorphic instrumentation

Eshraghian

Baek

Levi³

et al. 2020

IEEE Instrum. Meas. Mag.

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“…The lowest average mean square error of prediction was 3.33%, obtained by a particle swarm optimization method based on the bacterial chemotaxis backpropagation (BP) method. A study describing the extension of neuromorphic methods for artificial olfaction is presented in [38]. The neuromorphic engineering aims to overcome data processing challenges and reduce the output latency.…”

Section: Related Workmentioning

confidence: 99%

Data Classification Methodology for Electronic Noses Using Uniform Manifold Approximation and Projection and Extreme Learning Machine

et al. 2021

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The classification and use of robust methodologies in sensor array applications of electronic noses (ENs) remain an open problem. Among the several steps used in the developed methodologies, data preprocessing improves the classification accuracy of this type of sensor. Data preprocessing methods, such as data transformation and data reduction, enable the treatment of data with anomalies, such as outliers and features, that do not provide quality information; in addition, they reduce the dimensionality of the data, thereby facilitating the tasks of a machine learning classifier. To help solve this problem, in this study, a machine learning methodology is introduced to improve signal processing and develop methodologies for classification when an EN is used. The proposed methodology involves a normalization stage to scale the data from the sensors, using both the well-known min−max approach and the more recent mean-centered unitary group scaling (MCUGS). Next, a manifold learning algorithm for data reduction is applied using uniform manifold approximation and projection (UMAP). The dimensionality of the data at the input of the classification machine is reduced, and an extreme learning machine (ELM) is used as a machine learning classifier algorithm. To validate the EN classification methodology, three datasets of ENs were used. The first dataset was composed of 3600 measurements of 6 volatile organic compounds performed by employing 16 metal-oxide gas sensors. The second dataset was composed of 235 measurements of 3 different qualities of wine, namely, high, average, and low, as evaluated by using an EN sensor array composed of 6 different sensors. The third dataset was composed of 309 measurements of 3 different gases obtained by using an EN sensor array of 2 sensors. A 5-fold cross-validation approach was used to evaluate the proposed methodology. A test set consisting of 25% of the data was used to validate the methodology with unseen data. The results showed a fully correct average classification accuracy of 1 when the MCUGS, UMAP, and ELM methods were used. Finally, the effect of changing the number of target dimensions on the reduction of the number of data was determined based on the highest average classification accuracy.

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“…UtilizationUse of event-based cameras for tactile sensing applications provides a much higher sampling rate as well as significant reduction of power consumption. Neuromorphic vision sensors have become significantly popular recently and introduce a paradigm in computer vision applications for instrumentation and measurements [29,30]. The Dynamic Vision Sensor (DVS) that is used in this paper is one of the well-known neuromorphic cameras.…”

Section: Neuromorphic Vision-based Tactile Sensormentioning

confidence: 99%

Dynamic-Vision-Based Force Measurements Using Convolutional Recurrent Neural Networks

Naeini

Makris

Gan

et al. 2020

Sensors

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In this paper, a novel dynamic Vision-Based Measurement method is proposed to measure contact force independent of the object sizes. A neuromorphic camera (Dynamic Vision Sensor) is utilizused to observe intensity changes within the silicone membrane where the object is in contact. Three deep Long Short-Term Memory neural networks combined with convolutional layers are developed and implemented to estimate the contact force from intensity changes over time. Thirty-five experiments are conducted using three objects with different sizes to validate the proposed approach. We demonstrate that the networks with memory gates are robust against variable contact sizes as the networks learn object sizes in the early stage of a grasp. Moreover, spatial and temporal features enable the sensor to estimate the contact force every 10 ms accurately. The results are promising with Mean Squared Error of less than 0.1 N for grasping and holding contact force using leave-one-out cross-validation method.

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Neuromorphic engineering — A paradigm shift for future IM technologies

Cited by 25 publications

References 18 publications

Nonlinear retinal response modeling for future neuromorphic instrumentation

Nonlinear retinal response modeling for future neuromorphic instrumentation

Data Classification Methodology for Electronic Noses Using Uniform Manifold Approximation and Projection and Extreme Learning Machine

Dynamic-Vision-Based Force Measurements Using Convolutional Recurrent Neural Networks

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