Vu Pham
Innovator

Vu Pham

Berlin, Germany

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Vupham

Vu P. added photos to project VeggieBox - your kitchen assistant for healthy meals

Medium 28ab7de2 6313 440d 9aa0 7d1f2332e070

VeggieBox - your kitchen assistant for healthy meals

See the document in the link for more information.

2 Technology

2.1. Vision
At the core of VeggieBox is Intel Movidius NCS running Deep Learning models for object detection on the low-powered device. It will take as input an image captured by the camera, analyze and detect the ingredients. Using state-of-the-art Deep Learning models, the device is able to recognize multiple ingredients at once. The system will then query our recipe database and suggest those that are best matched to the given ingredients. It will then show a step-by-step guide on how to prepare the meal on its touch screen. Text-to-speech and simple voice command engines will provide more comfort when your hands are busy with chopping and picking.

2.2. Energy Saving
Thanks to the Movidius NCS, low powered home devices can now leverage Deep Learning. Our design of VeggieBox further builds up on this advantage so that it consumes as least power as possible. The device always run in idle mode, in particular the vision eye and other background services are not running all the time. The infrared sensor however can detect human interactions with the kitchen table, or other interaction close to the device, henceforth activate the device. Such an infrared sensor mounted on Andruino or Raspberry Pi consumes little energy.
Operating in real time, the nature of the problem allows the detection engine works at low frame rate. Overall, this leads to low power consumption for the camera, video processing chip, and the neural stick altogether.

2.3. Connected Search & Personalization
VeggieBox itself is an standalone device but has great potential to be a front-end device whose centralized servers process instantly recipe searches and collect meal photos. Backend technology uses NodeJS with ExpressJS for serving and ElasticSearch for database and searching.

2.4. Narration and Voice Interaction
State-of-the-art text-to-speech models will be used to transform instructions into voice, helping users to prepare their meal in the most natural and convenient way. On the other hands, models for speech recognition will be employed to detect simple voice commands from users. The touch screen will always be available for interaction with the device, while voice-based interaction will further enhance user's experience.

2.5. Timeline
- Q3 2017: Proposal and Feasibility Study
- Q4 2017: Finish VBox version 1, finalized hardware engineering and fundamental software features: Machine Learning models and recipe database.
- Q2 2018: Case design and advanced features: text-to-speech, voice command, features for social networks.

Medium vupham

Vu P. created project VeggieBox - your kitchen assistant for healthy meals

Medium 28ab7de2 6313 440d 9aa0 7d1f2332e070

VeggieBox - your kitchen assistant for healthy meals

See the document in the link for more information.

2 Technology

2.1. Vision At the core of VeggieBox is Intel Movidius NCS running Deep Learning models for object detection on the low-powered device. It will take as input an image captured by the camera, analyze and detect the ingredients. Using state-of-the-art Deep Learning models, the device is able to recognize multiple ingredients at once. The system will then query our recipe database and suggest those that are best matched to the given ingredients. It will then show a step-by-step guide on how to prepare the meal on its touch screen. Text-to-speech and simple voice command engines will provide more comfort when your hands are busy with chopping and picking.

2.2. Energy Saving Thanks to the Movidius NCS, low powered home devices can now leverage Deep Learning. Our design of VeggieBox further builds up on this advantage so that it consumes as least power as possible. The device always run in idle mode, in particular the vision eye and other background services are not running all the time. The infrared sensor however can detect human interactions with the kitchen table, or other interaction close to the device, henceforth activate the device. Such an infrared sensor mounted on Andruino or Raspberry Pi consumes little energy. Operating in real time, the nature of the problem allows the detection engine works at low frame rate. Overall, this leads to low power consumption for the camera, video processing chip, and the neural stick altogether.

2.3. Connected Search & Personalization VeggieBox itself is an standalone device but has great potential to be a front-end device whose centralized servers process instantly recipe searches and collect meal photos. Backend technology uses NodeJS with ExpressJS for serving and ElasticSearch for database and searching.

2.4. Narration and Voice Interaction State-of-the-art text-to-speech models will be used to transform instructions into voice, helping users to prepare their meal in the most natural and convenient way. On the other hands, models for speech recognition will be employed to detect simple voice commands from users. The touch screen will always be available for interaction with the device, while voice-based interaction will further enhance user's experience.

2.5. Timeline - Q3 2017: Proposal and Feasibility Study - Q4 2017: Finish VBox version 1, finalized hardware engineering and fundamental software features: Machine Learning models and recipe database. - Q2 2018: Case design and advanced features: text-to-speech, voice command, features for social networks.

Vupham

Vu P. added photos to project GridRNN in tensorflow

Medium cdaf6c53 a59a 47c7 8708 db3f279c55e7

GridRNN in tensorflow

he GridRNN implementation in tensorflow is generic, in the sense that it supports multiple dimensions with various settings for input/output dimensions, priority dimensions and non-recurrent dimensions. The type of recurrent cell can also be selected among LSTM, GRU or vanilla RNN.

Here we collect some examples that demonstrate GridRNN, which will be added over time. The current list of examples include:

- char-rnn: 2GridLSTM for character-level language modeling.

Medium vupham

Vu P. created project GridRNN in tensorflow

Medium cdaf6c53 a59a 47c7 8708 db3f279c55e7

GridRNN in tensorflow

he GridRNN implementation in tensorflow is generic, in the sense that it supports multiple dimensions with various settings for input/output dimensions, priority dimensions and non-recurrent dimensions. The type of recurrent cell can also be selected among LSTM, GRU or vanilla RNN.

Here we collect some examples that demonstrate GridRNN, which will be added over time. The current list of examples include:

  • char-rnn: 2GridLSTM for character-level language modeling.

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