I am a computer engineer with experience in academia, the software industry and the financial sector. Throughout my professional career, I have held various positions, including infrastructure support technician, software developer, database administrator, and information security specialist. My doctoral research has focused on the characterization and classification of fruits through the application of artificial intelligence techniques, digital image processing and computer vision. I have developed AI tools for fruit identification and classification, having worked with oranges and apples.
Welcome to my site!
Here I try to publish what I do, that includes: notes, investigations, works, code. I hope this is useful to you, and if it is, please let me know.
- Researchgate -> https://www.researchgate.net/profile/Juan-Miranda-6/
- Scholar.Google -> https://scholar.google.com/citations?hl=es&user=B2f3BHYAAAA
- ORCID 0000-0001-5912-9704 -> https://orcid.org/0000-0001-5912-9704/
- SCOPUS ID 57947116100 -> https://www.scopus.com/authid/detail.uri?authorId=57947116100
- Site -> https://juancarlosmiranda.github.io/
- LinkedIn -> https://www.linkedin.com/in/juan-carlos-miranda-py/
- X.com -> https://twitter.com/mirandajuancar/
Some notes.
Resources | Description |
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Azure Kinect camera setup (automated scripts for Linux and Windows) | Ready-to-use installation scripts (https://github.com/juancarlosmiranda/azure_kinect_notes. |
ResearchGate - Azure Kinect camera information resources | Question open in ResearchGate about Azure Kinect DK camera. |
Currently maintaining software packages at (Pypi.org)[https://pypi.org].
Package | Description | |
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AK_ACQS Azure Kinect Acquisition System (https://github.com/GRAP-UdL-AT/ak_acquisition_system) | AK_ACQS is a software solution for data acquisition in fruit orchards using a sensor system boarded on a terrestrial vehicle. It allows the coordination of computers and sensors through the sending of remote commands via a GUI. At the same time, it adds an abstraction layer on library stack of each sensor, facilitating its integration. This software solution is supported by a local area network (LAN), which connects computers and sensors from different manufacturers ( cameras of different technologies, GNSS receiver) for in-field fruit yield testing. |
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AK_SM_RECORDER - Azure Kinect Standalone Mode (https://pypi.org/project/ak-sm-recorder/) | A simple GUI recorder based on Python to manage Azure Kinect camera devices in a standalone mode. |
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AK_FRAEX - Azure Kinect Frame Extractor (https://pypi.org/project/ak-frame-extractor/) | AK_FRAEX is a desktop tool created for post-processing tasks after field acquisition. It enables the extraction of information from videos recorded in MKV format with the Azure Kinect camera. Through a GUI, the user can configure initial parameters to extract frames and automatically create the necessary metadata for a set of images. |
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AK_SW_BENCHMARKER - Azure Kinect Size Estimation & Weight Prediction Benchmarker (https://pypi.org/project/ak-sw-benchmarker/) | Python based GUI tool for fruit size estimation and weight prediction. |
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AK_VIDEO_ANALYSER - Azure Kinect Video Analyser (https://pypi.org/project/ak-video-analyser/) | Python based GUI tool for fruit size estimation and weight prediction from videos. |
Currently maintaining software packages at (Pypi.org)[https://pypi.org].
Resource | Description |
---|---|
AK_FRAEX - Azure Kinect Frame Extractor demo videos | Video samples recorded in the field using the Azure Kinect DK. These videos are part of the https://pypi.org/project/ak-frame-extractor/ software to demonstrate the use of frame extraction tasks. |