With the advent of Sixth Generation (6G) telecommunication systems already envisioned, increased effort is made to further develop current communication technologies, so they can be incorporated together with the novel ones, to deliver uninterrupted and satisfactory service for any application in every location on the ground, underwater, in the air, or in space. One such technology is Cognitive Radio (CR) which has received much attention due to its potential for increase of utilization, especially in the bands below 6 GHz. The main enabler for CR is spectrum sensing because it provides the opportunity for dynamic assessment of the radio environment to identify unused channels. This functionality has been the object of many research works for that very reason. In spite of this, the provision of accurate and fast spectrum characterization in time, frequency and space has proven to be a non-trivial task. This paper presents a detailed review of probabilistic spectrum sensing methods classified by the feature they extract from the received signal samples, to provide accurate detection of the primary user (PU) signal. The main design characteristics (such as probability of detection, robustness to noise and fading, signal/noise model assumptions, and computational complexity), strengths and weaknesses for each type are also summarized. Based on current concepts for 6G networks and applications, a framework for human-centric cognition-based wireless access is presented, which specifies the role of spectrum sensing-based CR in future networks.

Augmented, mixed and virtual reality are changing the way people interact and communicate. Five dimensional communications and services, integrating information from all human senses are expected to emerge, together with holographic communications (HC), providing a truly immersive experience. HC presents a lot of challenges in terms of data gathering and transmission, demanding Artificial Intelligence empowered communication technologies such as 5G. The goal of the paper is to present a model of a context-aware holographic architecture for real time communication based on semantic knowledge extraction. This architecture will require analyzing, combining and developing methods and algorithms for: 3D human body model acquisition; semantic knowledge extraction with deep neural networks to predict human behaviour; analysis of biometric modalities; context-aware optimization of network resource allocation for the purpose of creating a multi-party, from-capturing-to-rendering HC framework. We illustrate its practical deployment in a scenario that can open new opportunities in user experience and business model innovation.

Holographic-type communication (HTC) permits new levels of engagement between remote users. It is anticipated that it will give a very immersive experience while enhancing the sense of spatial co-presence. In addition to the newly revealed advantages, however, stringent system requirements are imposed, such as multi-sensory and multi-dimensional data capture and reproduction, ultra-lightweight processing, ultra-low-latency transmission, realistic avatar embodiment conveying gestures and facial expressions, support for an arbitrary number of participants, etc. In this paper, we review the current limitations to the HTC system implementation and systemize the main challenges into a few major groups. Furthermore, we propose a conceptual framework for the realization of an HTC system that will guarantee the desired low-latency transmission, lightweight processing, and ease of scalability, all accompanied with a higher level of realism in human body appearance and dynamics.

In this paper some challenges in the design and realization of an effective Ambient Assisted Living (AAL) system are discussed. Solutions to meet those challenges are proposed. Example of the practical implementation of the architecture of an AAL system - “eWall for Active Long Living” (eWALL) and the related context-aware services are presented.

Holographic-type communication (HTC) is on the verge of revolutionizing current communication paradigms. It will seamlessly connect people from distant locations through a fully immersive experience that will engage all five senses: sight, hearing, touch, smell, and taste. However, the practical realization and widespread adoption of HTC impose significant demands on current networks and end-user devices. While recent studies have primarily focused on discussing the HTC challenges and potential research directions, this paper takes a further step by evaluating the capabilities of wireless networks to meet HTC requirements. Specifically, it highlights the limitations of the fifth-generation (5G) networks by identifying HTC-related Key Performance Indicators (KPIs) and explores the potential of the sixth-generation (6G) networks. Moreover, it not only proposes potential research approaches for HTC enhancement but also analyzes their impact on the challenges in HTC implementation. Finally, the paper questions the ubiquitous potential of 6G, suggesting that a coordinated approach leveraging artificial intelligence (AI) for jointly optimizing user sites and communication networks is the most promising strategy, rather than solely relying on the capabilities of specific network generations.

Current methods of assessing pain depend almost entirely on verbal report such as clinical interview or questionnaires of the patients. Pain being a symptom that can neither be felt nor seen, poses a major problem for the medical personnel involved in pain management since there are no accurate objective measures to establish the extent of pain the patient is suffering from when using a remote assistive medical system. The verbal report grading pain has obvious discrepancies, especially when it comes to children or people with limited ability to communicate (i.e. the mute, mentally impaired, or patients having assisted breathing). When designing a medical assistive system measuring pain in an efficient way is of great importance. In this paper we proposes an algorithm for both automatic pain recognition (i.e. pain/no pain presence in human) and continuous pain intensity estimation based on facial expression analysis. To locate specific landmarks in the face we used Supervised Descent Method (SDM) and then extract feature vectors using Scale Invariant Feature Transform (SIFT). For the recognition task we build a classier based on Support Vector Machines (SVM) and for the continuous pain intensity estimation task we trained linear regressor. The experiments with patients with shoulder pain show very good recognition rate (more than 95.7%). For the pain intensity estimation we reached an average Mean Squared Error of 1.28 and Correlation coefficient of 0.59. The recorded results demonstrate performance that exceeds state-of-the-art results on a standard data set.