Braccio Robotic Arm
TinkerKit Braccio is a fully operational robotic arm that can be controlled by Arduino micro-controller. Braccio robotic arm can be assembled in several ways for multiple tasks. Braccio can also support various objects on the end-effector which makes it versatile in its functions.
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Voice Activated Robotic Arm
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Smart Robot Car
In this project, we will use the Elegoo Smart Robot Car. The Smart Car has features like Ultrasonic sensor for collision avoidance, Optical sensors for line following navigation, Bluetooth connection for Smart phone remote control operation, and an Infrared Remote Control.
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Short Range Radar
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Breadboard Power Supply
This Breadboard Power Supply will power breadboard projects to either 3.3Volts or 5Volts regulated. For input power, the BBPS uses either a DC barrel jack as a primary input or stripped wires on the secondary input terminals. The BBPS can take 6 to 30VDC power from a DC wall wart and outputs 5V or 3.3V regulated voltage. The user can select the desired voltage using a slide switch.
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Dual-Polarity-Variable Power Supply
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DC to DC Boost Converter
There are many different switching voltage regulator topologies like buck, boost, buck-boost, and chuck. However, this project covers the design and construction of a boost converter only. This converter is capable of boosting a 12V input voltage to a variable 12 to 24V output voltage (potentiometer controlled). The boost converter steps-up the input voltage by storing the electrical energy in an inductor and then releasing this energy to the load at a higher voltage level.
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Deep Learning | Part 1 | Classification
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Deep Learning | Part 2 | Transfer Learning
The purpose of a transfer learning is to fine-tune a pre-trained Convolutional Neural Network that will be used to perform classification on a new problem. According Mathworks, "AlexNet has been trained on over a million images and can classify images into 1000 object categories (such as keyboard, coffee mug, pencil, and many animals)." However, if we are looking for a classification accuracy over 99% on given objects, or if our image set is outside of the CNN, then we have to perform transfer learning. Transfer learning is commonly used in deep learning applications that we can take a pretrained network and use it as a starting point to learn new tasks. Fine-tuning a network with transfer learning is usually much faster and easier than training a network with randomly initialized weights from scratch. We can quickly transfer the learned features onto a new task using a smaller number of training images (aka ImageDataStore).
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HARDWARE DESCRIPTION LANGUAGE (VHDL)
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HARDWARE DESCRIPTION LANGUAGE (Verilog)
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