Towards Haptic Microrobotic Intracellular Injection

This paper proposes a system providing the operator with an intuitive method for controlling a micromanipulator during intracellular injection. A low-cost haptic device is utilised and 3D position-to-position kinematic mapping allows the operator to control the micropipette using a similar method to handheld needle insertion. The workspaces of the haptic device and micromanipulator are analysed and the importance of appropriate scaling to positioning resolution and tracking performance is investigated. The control issues integral to achieving adequate control of the micromanipulator using the Phantom Omni haptic device are addressed. Aside from offering an intuitive method for controlling the micropipette, this work lays the foundation for real-time haptic assistance in the cell injection task.

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LOW COST REAL TIME ROCKET TELEMETRY SYSTEM DESIGN

10.26678/abcm.cobem2019.cob2019-1585 ◽

2019 ◽

Author(s):

Gabriel de Almeida Souza ◽

Larissa Barbosa ◽

Glênio Ramalho ◽

Alexandre Zuquete Guarato

Keyword(s):

System Design ◽

Real Time ◽

Low Cost ◽

Telemetry System

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Low-Cost High-Speed Techniques for Real-Time Simulation of Power Electronic Systems

10.21236/ada485330 ◽

2007 ◽

Author(s):

R. E. Crosbie ◽

J. J. Zenor ◽

R. Bednar ◽

D. Word ◽

N. G. Hingorani

Keyword(s):

Real Time ◽

High Speed ◽

Low Cost ◽

Electronic Systems ◽

Power Electronic ◽

Real Time Simulation ◽

Time Simulation

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Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

Mobile Information Systems ◽

10.1155/2019/4570808 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Yong He ◽

Hong Zeng ◽

Yangyang Fan ◽

Shuaisheng Ji ◽

Jianjian Wu

Keyword(s):

Deep Learning ◽

Integrated Pest Management ◽

Pest Management ◽

Real Time ◽

Oilseed Rape ◽

Data Augmentation ◽

Low Cost ◽

Response Speed ◽

Original Model ◽

Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

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Real-Time Low-Cost Drift Compensation for Chemical Sensors Using a Deep Neural Network with Hadamard Transform and Additive Layers

IEEE Sensors Journal ◽

10.1109/jsen.2021.3084220 ◽

2021 ◽

pp. 1-1

Author(s):

Diaa Badawi ◽

Agamyrat Agambayev ◽

Sule Ozev ◽

A. Enis Cetin

Keyword(s):

Neural Network ◽

Real Time ◽

Chemical Sensors ◽

Deep Neural Network ◽

Low Cost ◽

Hadamard Transform ◽

Drift Compensation

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A Low-Cost, Mobile Real-Time Kinematic Geolocation Service for Engineering and Research Applications

HardwareX ◽

10.1016/j.ohx.2021.e00203 ◽

2021 ◽

pp. e00203

Author(s):

André Broekman ◽

Petrus Johannes Gräbe

Keyword(s):

Real Time ◽

Low Cost ◽

Real Time Kinematic

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Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

Applied Sciences ◽

10.3390/app11114940 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4940

Author(s):

Jinsoo Kim ◽

Jeongho Cho

Keyword(s):

Embedded System ◽

Real Time ◽

Action Recognition ◽

Processing Speed ◽

Recognition Accuracy ◽

Low Cost ◽

Human Action Recognition ◽

Human Action ◽

Video Data ◽

Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

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