AY 2018 GT Topic Assignments の履歴(No.8)

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• AY 2018 GT Topic Assignments へ行く。
  • 1 (2018-04-11 (水) 02:05:21)
  • 2 (2018-04-11 (水) 04:07:41)
  • 3 (2018-05-01 (火) 10:22:41)
  • 4 (2018-05-15 (火) 06:48:22)
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  • 6 (2018-05-17 (木) 02:37:42)
  • 7 (2018-05-30 (水) 08:23:09)
  • 8 (2018-06-04 (月) 10:16:58)
  • 9 (2018-06-22 (金) 03:44:10)

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NASH Project - http://adaptive.u-aizu.ac.jp/aslint/index.php?NASH&aname(ab7b22b9,super,full,nouserselect){†};

1. Hardware Acceleration of Character Recognition with Deep Neural-Network†

s1230014 B4 Yoshiki Tanaka

• Reference

2. Design of a Clustered-Neuron Core based on LIF Spiking Neuron Model†

s1230161 B4 Shinji Hironaka

• Reference

3. Performance Study of a Neuro-inspired Architecture in Hardware†

• Reference

Smart Drone/Veicule Project: GT/MT Topics†

Create an experimental environment using open source Autoware platform that realizes automatic operation.

Tasks:

• Development of a compact vehicle on which various sensors can be placed
• Creation of ROS module connecting sensor and Autoware,
• Electronic control of the produced vehicle

Reference:

• https://autoware.ai/
• compe_2018.compressed.pdf

• Kalman Filter
• PID control

Using arithmetic operations using Spiking Neural Network (SNN) Perform the following control.

• Brushless motor control
• Servo control
• Kalman Filter
• PID control

Reference:

• Neuro-Inspired Spike-Based PID
• DC brashless motor controllor on FPGA

Although the camera is the most information quantity device among the sensors, the throughput also increases. Make algorithms with as little processing time and processing time as possible.

• SVO: Posture estimation using camera; https://ieeexplore.ieee.org/document/7782863/
• SLAM: Simultaneous execution of self location estimation and cartography; https://128.84.21.199/abs/1610.06475
• TSCDP: Time Space Continuous Dynamic Programming; https://ci.nii.ac.jp/naid/110009713447

References:

• SVO
• ORB-SLAM