This paper presents the first edge computing framework for RTI systems combining intermediate sensor fusion with model quantization. Traditional RTI approaches [1] rely on centralized architectures (450 ms latency), while our key innovations enable 73% faster edge processing (120 ms) through hybrid ResNet architecture compressed via 8-bit quantization and intermediate fusion of RTI attenuation maps and RGB features. Our work introduces an on-device processing pipeline implemented on low-power IoT devices---including Jetson Nano, Raspberry Pi, and ESP32---that enables real-time inference without cloud-based computation.

This paper presents a system that fuses radio tomography data with RGB camera information for enhanced multi-person detection and tracking in indoor environments. Experiments were conducted in an irregularly shaped room with four subjects. Due to its limited resolution, radio tomography initially represented all individuals as a single entity. By incorporating RGB camera data, we were able to accurately identify and track each person individually. Our findings underscore the significance of integrating data from both modalities for improved detection and tracking performance in indoor surveillance and security systems.

Artificial Intelligence offers cost-effective solutions to improve business processes and ensure more satisfying customer service. The advantage of solutions based on artificial intelligence is the possibility of using the API with mobile or stationary applications and cloud services. The research presented here aims to develop a deep learning model using cross-modal techniques on the example of a multi-tasking network. The main task is to use computer vision on IoT devices using sensors for customer service. Additionally, the solution will be based on distributed systems. Finally, the method of building the multi-tasking model, which will be designed to determine the person in the image and their emotional state, will be verified.

The research aims to develop and optimize a deep learning model for faster inference using multi-task learning for embedded systems. Experiments using face photos and sound in the form of a spectrogram were prepared to verify the model's performance in recognizing a person and their emotional state. Research has shown that in IoT devices, the inference is faster when a multi-tasking model is used compared to a system based on several models, each responsible for inferring one thing.

The study aims to develop a measurement system for acquiring ultrasonic transmission tomography data. Along building the automated measurement system we test the algorithm which use incorporation of image compression into the process of reconstruction what speed up computation and simplifies regularization of the solution to the inverse problem of tomography. The reconstructive algorithm used in transmission tomography is based on the Discrete Cosine Transformation method. The algorithm applies image compression techniques for each block of the image separately, according to the conducted test such a solution seems to be much more robust than classical methods used for ultrasonic tomography. Presented work is a part of the research that aim for applying ultrasonic technology for analyse and visualise technological processes in distributed systems and in the next step for development of wearable equipment for monitoring in medicine.

Location systems and their daily use in industry and individual users have become an inseparable part of our everyday functioning. However, often they are used in the so-called intelligent buildings. Unfortunately, just like location methods operating outside buildings are precise enough to determine our location (e.g. GPS systems for drivers), the situation of locating people inside the building is different, especially when such people cannot be equipped with additional devices (e.g. a transmitter signal). Nevertheless, it is essential in communication routes, large clusters of people, such as airports, railway stations or office spaces. At the same time, determine the number of people in space and the development of services that do not require direct contact (automatic patient/client registration) in times of a possible pandemic.