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.

This research aimed to develop a high accuracy machine learning algorithm that can diagnose cardiovascular diseases from the stream of data from multiple body surface potential mapping devices equipped with 102 textile electrodes. The algorithm is based on the 1D convolutional neural network, trained on the comparable real-life data gathered from the FLUKE ECG simulator connected to the resistance-based human phantom. The developed neural network achieved an accuracy of 99.91% on the test data. Additionally, an additional algorithm was developed that can use the neural network to analyse the data streamed from the medical device and notice the medical staff about dangerous heart rhythms detected by the system.

This article presents concurrent associative memories with synaptic delays useful for processing sequences of real vectors. Associative memories with synaptic delays were introduced by the authors for symbolic sequential inputs and demonstrated several advantages over other sequential memories. They were easy to organize and train. It was demonstrated that they were more robust than long short-term memories in recognition of damaged sequences. The associative memories can be applied in combination with deep neural networks to solve such symbol grounding problems, such as speech recognition, and support sequential memories triggered by sensory inputs. Several practical considerations for developed memories were discussed and illustrated. A continuous speech database was used to compare the developed method with LSTM memories. Tests demonstrated that the developed approach is more robust in recognition of speech sequences, particularly when the test sequences are damaged.

In this paper, we introduce a new concept of associative memories in which synaptic connections of the self-organizing neural network learn time delays between input sequence elements. Synaptic connections represent both the synaptic weights and expected delays between the network inputs. This property of synaptic connections facilitates recognition of time sequences and provides context-based associations between sequence elements. Characteristics of time delays are learned and are updated each time an input sequence is presented. There are no separate learning and testing modes typically used in other neural networks, as the network starts to predict the next input element as soon as there is no expected input signal. The network generates output signals useful for associative recall and prediction. These output signals depend on the presented input context and the knowledge stored in the graph. Such a mode of operation is preferred for the organization of episodic memories used to store the observed episodes and to recall them if a sufficient context is provided. The associative sequential recall is useful for the operation of working memory in a cognitive agent. Test results demonstrate that the network correctly recognizes the input sequences with variable delays and that it is more efficient than other recently developed sequential memory networks based on associative neurons.

In the original publication, Acknowledgments was published incorrectly. The correct Acknowledgments is provided in this correction.

This article introduces methods for modeling compound granules used in algorithms which could successfully construct a mosaic from the images coming from an endoscope capsule. In order to apply the algorithm, combined images must have a common area where the correspondence of points is determined. That allows to determine the transformation parameters to compensate movement of the capsule that occurs between moments when the mosaic images were acquired. The developed algorithm for images from the capsule endoscopy has proved to be faster and comparably accurate as commercial GDB-ICP algorithm.