Current sensor-gateway-cloud solutions for machine learning (ML) life cycle development require extensive coding, hardware, and software expertise. We present ST AIoT Craft, the first no-code/low-code online platform for training, deploying, and managing distributed artificial intelligence of things (AIoT) and big data. The framework provides user-interface dashboards and web applications for sensor data logging and management, dataset preprocessing and visualization, automatic tinyML for in-sensor processors and microcontrollers, and end-to-end Internet of things (IoT) system setup and monitoring. The tools allow developers to manage intelligent IoT devices, gateways, and cloud infrastructure directly from a web browser. ST AIoT Craft is accessible at https://staiotcraft.st.com/.

In this paper we propose a method to encode/decode multiview textures and associated depth maps together in a single bitstream. Encoded bits of depth maps have been merged into that of associated textures in an innovative way with very minimal loss in PSNR of textures, 0.1-0.2 dB for high delay and 0.2-1 dB for low delay applications. Benchmarking results in terms of compression efficiency and quality on HD sequences have been reported to explore the viability of using this technique for Free-view point Video (FVV) or 3DV use cases.

In this paper we describe an image-based rendering pipeline for interactive real-time Free Viewpoint Television (FTV) on embedded systems. Description of the processing steps and optimizations implemented targeting the hardware acceleration of a commercial programmable Graphics Processing Unit (GPU) is given. As a result, real-time view synthesis at 70 fps in XGA resolution has bee achieved. Restrictions and modifications introduced to support the application on OpenGL ES 2.0 based GPUs for embedded systems have also been discussed.

In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates of payoff. A novel interpretation of classifier and an extension of the accuracy concept are presented. The probabilistic approach is aimed at increasing XCS learning capabilities and tendency to evolve accurate, maximally general classifiers, especially when uncertainty affects the environment or the reward function. We show that BXCS can approximate optimal solutions in stochastic environments with a high level of uncertainty.