Cross modal emotion Embedding model for Emotion analysis AI

Is it really possible to extract Real emotion Systematically?

            Yes, Many research works carried out to identify the real emotion and embed the emotion with the physiological signals to correlate the outcome. Such a model utilize various form of data source such as audio data, physiological signals, face expressions, unique face landmarks, gestures and body languages etc.

              Automatic extraction of emotion feature have the capability to effectively interact with the computer. Over the decades, optimum research is carried out to improvise the prediction accuracy, reducing the processing delay for robustness of Human-computer interface (HCI)

               Mono modal and Multi modal are the types of scenarios utilized for feature extraction prominently explore the uniqueness of the inputs. Deep learning algorithms give promising results on detection process. perhaps, Cross model approach combines the benefit of multi-modal and Mono-modal extraction process.

        

                Cross modal approach enables to read the combinational options to determine the exact emotion. using embedded systems. the real time EEG data also used. EEG determines the brain patterns, Audio is highly recommended input for emotion extraction.

Such Computing process is called Affective Computing  in Psychology. Some of the tools used are 

• Python, 
• Kiros, 
• CrowdEmotion and measuring tool such as 
• SAM, 
• emoji model etc.

The below architecture is the sample Emotion detection process using Affective Computing.

Architecture of affective computing sample Model using CNN [1] 

References

[1] J. Han, Z. Zhang, Z. Ren and B. Schuller, "EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings," in IEEE Transactions on Affective Computing, vol. 12, no. 3, pp. 553-564, 1 July-Sept. 2021, doi: 10.1109/TAFFC.2019.2928297.

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Prediction of Unknown room data using Virtual sensors modeling through Machine learning | Python projects in chennai

Prediction of Unknown Location data using Virtual sensors modeling through Machine learning

What is Virtual Sensors?

Virtual sensors are created with the help of physical sensors that determine the information of the particular location using integrated embedded applications. These sensors physically record and fetch the recorded data for analysis and prediction of the invisible room

Is Virtual Sensors replace Physical sensors?

Virtual sensors are not the complete replacement of Physical sensors but it could replace few Physical sensors, so that it get the data from the existing physical sensors to create and model a new Virtual data

How Unknown Parameters are calculated?

Using machine learning algorithms, The existing physical sensors record the room parameters and create the training model that evaluate the time series data of  virtual data. The model is developed, Trained in such as way if the training data is acquired from the real time existing environment, using time series prediction, (future prediction model) the proposed Virtual data is being acquired and hence the Unknown parameters of the location is determined.

Example :Application to Determine the Unknown room Parameter

Consider Room A, Room B and Room C available in the Particular Building.

Room A and B is connected with Physical sensors Namely 

1. Illuminance Sensor

2. Temperature sensor

3. Humidity Sensor

• Room A and B data is considered as the Physical data and Training data for the Machine Learning Model
• Create a Time Series NARX model or any other Time Series regression Algorithm using Machine Learning ToolBox (Python or MATLAB)
• At the end of the prediction state , The time State t+1 determine the Room C data (Which is unknown)
• Each sensors are tuned like this and Predicted with t+1, 2t+1, 3t+1,4t+1 etc.....
• Accumulated results at the time series data act as the Virtual Data and the Entire analysis Set up act as the Virtual Sensor

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FPGA for Machine learning Applications

FPGA for Machine learning Applications

The merging growth in information handling technology that enables the developers to understand the activity, evaluate the pattern and detect the anomalies using Machine Learning algorithms in almost all industries. We need high speed processing platforms at the other end to handle these data generated features and ensemble various other platforms too. 

Figure. AI Pre-processor Accelerator

 The FPGA are High speed, configurable platforms that can adopt the fast twisted operation speeds within the single integrated SOC. Many semiconductor manufacturing industries are focused on developing the artificial intelligence AI sensors that stacked inside the SOC modules itself to help the ML developers utilize such IP cores for real time predictions.

 These companies also incorporate references, design protocols, neural network IP cores, Software development tools and customized design services within the single SOC platforms. These FPGAs are high in performance, low power(starting from 1mW to 1W), flexible architecture and 5.5.mm package available [1] (ref. Lattice semiconductor)

The integrated FPGA accelerators helpful in developing lots of Smart applications enabled with machine learning frameworks. Where the data is larger, the device could take over the processing capability using the in-built accelerators. These devices are nowadays hardware adoptable and software adoptable too. evaluation of Tensor flow modules for training are accessed with the integrated environment itself.

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References

[1] https://www.latticesemi.com/en/Solutions/Solutions/SolutionsDetails02/sensAI