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Van-Tam NGUYEN: Projects

 

Ongoing Projects

Paris AIoT

Along with 18 other faculty members from 3 departments, we have launched the Paris AIoT initiative, a collaboration between Telecom Paris and member industrial companies to educate and train world-class researchers and PhDs to enable truly ubiquitous sensing, computing and communication with fully distributed AI and embedded intelligence. The Paris AIoT emphasizes application-oriented and system-oriented research, its areas of interest include software and hardware at all levels of the system stack offering innovative and pioneering solutions on the convergence of AI and IoT. It represents a real strategic opportunity for university-industry-government research partnerships in France and Europe in the key areas of personalized health, autonomous driving, 6G, global mentoring and personalized project-based education. It will forge close relationships with industry leaders to facilitate rapid technology transfer and provide an environment for research into core issues of future generations of AIoT systems as well as a platform to develop personalized and project-based training programs for MSc and PhD students. The Paris AIoT consists of MSc and PhD students, post-doctoral researchers and professors engaged in a pre-competitive research program that is continuously evolving according to the interests of faculty members and our industrial partners. Our program will build on the unique strengths of both university and industry to enhance the productivity and competitiveness of both. Research areas of interest include:

1.       AI for Training Data Efficiency (Augmentation and Frugality) - Adequate Development of Training Data;

2.       AI Defined Network (Centralized, Hybrid and Distributed) and Distributed Intelligence;

3.       Energy and Memory Efficient AI and Algorithms for IoT devices;

4.       Energy Efficient and Hardware Accelerator, Smart Sensors and Low-Power Cognitive Connectivity;

5.       AI, Algorithms and Encryption for Privacy and Security.

 

AI-N2N

 

 

Future 6G networks with their massive connectivity will elevate connected objects capabilities to new levels as well as expand intelligence into new devices, deployments, and industries. 

Our goal is to make AI advancements inherently synergistic with future 6G networks  in order to improve system performance and efficiency. With the proliferation of connected devices and the role of on-device intelligence becoming ever more important, the transformation of AI into fully distributed intelligence will be one of the keys to realize the full potential of future 6G networks. Fully distributing AI from cloud down to end-user devices will realize better system efficiency, enhanced privacy and security, improved performance, reduced latency, and new levels of personalization. 

The end-user devices will be designed to sense, learn, reason, interpret and act intelligently - all with optimal interaction with the cloud by sharing insights, but not raw data. With an AI-native, holistic, end to end approach, the system will further be able to support continuous improvements through self-learning, where both sides of the AI-native air interface — the network and device - can adapt to their surroundings dynamically and optimize operations based on what they experience. Distributed AI will drive the core and RAN (radio access network) with intelligent network operation to provide enhanced QoS and QoE, better efficiency, simplified deployment, and improved security while reducing energy consumption, CO2 emissions and operational costs. 

The underlying enabling capability of on-device AI is radio awareness, through environmental and contextual sensing that can reduce overheads and latency. Through radio awareness, the 6G system can support enhanced device experiences, improve system performance and improve security and privacy.

 

AIoT based Neural Decoding and Neurofeedback for Cognitive Training Acceleration

Attention is a very important factor in cognitive efficiency. It allows us to notice and select a subset of information from all that is available so that we can process that information. Our attention system is needed for almost everything we do - whether it's learning, memorizing, perceiving, communicating, or solving problems, etc. it is also important for the regulation of our own emotions. Attention guides the allocation of processing resources. Efficient resource allocation means rapid availability of information for superior mental processing. Moreover, attentional guidance is crucial in tasks that require the coordination of several cognitive operations by providing the appropriate resources. All of these functions in information processing present attention as a major source of cognitive efficiency. Finally, sustained attention and rapid changes between various cognitive operations are strongly correlated with intelligence.

Working memory, another very important component of the cognitive base, emerged due to mental activities requiring the availability of several pieces of information in a limited amount of time. Such activities link multiple pieces of information together in a complex pattern. It is essential for the mental activities that are believed to be the basis of intelligence.

Because attention and working memory show a substantial relationship with intelligence, and thus a strong correlation with academic and professional success, improving attention and working memory is particularly relevant. Moreover, neuroplasticity is a remarkable feature of the brain. Indeed, neurons are able to adapt quickly to the demands imposed on them. By developing new neural networks and strengthening important connections, a cognitive training program can measurably and sustainably improve brain activity. It can also trigger the birth of new neurons. This is why the last two decades have seen an impressive effort towards the design and implementation of cognitive training programs especially with new technologies to improve general cognitive ability and slow its decline in the elderly.

In this project, we will not design a new computerized cognitive training technique, but rather build a revolutionary concept based on millennium and well-known training techniques, such as mindfulness for attention training and the method Loci or memory palaces for memory training. We will collect appropriate physiological signals, including EEG and ECG, the design of adequate tiny machine learning with the ability to implement it on wearable devices and pattern recognition algorithms to extract relevant features and finally the design of a neurofeedback system to accelerate the training of attention, working memory and emotional intelligence by improving efficiency and reducing training time.

 

Funded Projects

 

 

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