This paper presents the Chips JU project EECONE, a European initiative dedicated to reducing electronic waste. We outline the project’s overarching goals and highlight ongoing work focused on integrating circularity principles into the design phase of electronics. Our research aims to develop methods and tools that enable designers to assess environmental impacts and embed circular economy strategies from the outset. We also examine the data and metrics essential for effective ecodesign and demonstrate early results from the EECONE ecodesign platform through real world case studies. These findings illustrate the potential of design stage interventions to drive environmental sustainability across the electronics value chain.

The industrial landscape is undergoing a transformative shift towards Industry 5.0, a paradigm characterized by the convergence of sustainability, digital autonomy, and human-centric design. This article focuses on the adoption, enhancement, and implementation of AI-driven hardware, tools, methodologies, and semiconductor technologies in this progression. We present here a comprehensive strategy from the AIMS5.0 project with the objective of connecting academic developments with practical industrial use, fostering a harmonious relationship between humans and machines to improve efficiency, spur innovation, and enhance adaptability. Hence we show here our global vision, and examples of how the creation of AI-based industrial solutions is supported by novel AI-tool chains, advancements in hardware, and tools supporting human aspects.

ShapeFuture will drive innovation in fundamental Electronic Components and Systems (ECS) that are essential for robust, powerful, fail-operational and integrated perception, cognition, AI-enabled decision making, resilient automation and computing, as well as communications, for highly automated vehicles. The overarching vision of ShapeFuture is to bring ECS Innovation to the heart of Europe’s Mobility Transformation, thereby elevating sovereignty by perfecting programmable ECS solutions for intelligent, safe, connected, and highly automated vehicles. In this paper, we detail not only the vision and mission of the ShapeFuture project, but we also showcase the results achieved during the first year.

The rapid evolution of industrial artificial intelligence (AI) has given rise to a diverse landscape of developers and users across various sectors. Understanding their personas—including their backgrounds, expertise, and work environments—is crucial for fostering innovation and ensuring compliance with AI regulations. This study presents insights from a persona-based questionnaire aimed at mapping the industrial AI ecosystem and informing the development of a user-centred self-assessment compliance tool. Through a structured survey of 61 participants from European AI research projects, the study identifies key attributes of AI professionals, their industry affiliations, and their approaches to AI adoption and compliance. Findings reveal gaps in awareness and application of AI guidelines, with half of the respondents uncertain whether their organizations follow formal AI standards. Moreover, AI familiarity influences risk perception, with experienced users more likely to identify algorithmic and resource-related challenges. The study integrates the Quantitative Effect and Technology Acceptance Model (QETAM) to assess chatbot acceptance for AI compliance support, ensuring the proposed tool aligns with user needs.

Effective energy management in households is critical to achieving overall energy efficiency and sustainability goals. This study introduces a novel approach to predicting short-term energy consumption for households using federated learning (FL) models. The approach achieves short-term energy consumption predictions (i.e. for the next 10 minutes) by analyzing local data, such as the current watt consumption and activated devices. The key innovation in this approach is the use of privacy-preserving machine learning techniques, ensuring that personal data is never shared during the training process. The models are used to predict potential spikes in energy demand, allowing for proactive management. In addition, a local Large Language Model (LLM) is integrated to generate personalized recommendations for users, aimed at avoiding predicted consumption spikes and promoting energy-efficient behavior. This approach not only preserves user privacy but also enhances user engagement by providing actionable insights based on local consumption patterns.

The comprehension of the underwater environment is recently being accelerated by technological advances in sensors, robotics and Artificial Intelligence (AI). At the forefront of this evolution, lies the Autonomous Underwater Vehicle (AUV), a sophisticated ocean exploration tool that is capable of performing underwater mapping, leveraging data obtained by onboard sensors. AUVs can navigate autonomously in unknown environments without any human interaction, while their level of autonomy is tightly linked to their path planning strategy. In this study, we perform a comparative analysis of a Deep Reinforcement Learning (DRL) method utilising two neural network models, a Linear Model (LM) that consists only of linear layers, and a Convolutional Model (CM) that consists of convolution layers for feature extraction that are merged with linear layers. Our evaluation focuses on assessing the performance of the proposed models for generating optimal paths in 3D underwater environments based on path length and obstacle avoidance. Through comprehensive simulations, we showcase the efficiency of our solution and present a comprehensive framework tailored for solving path planning problems in 3D complex underwater settings.

To encourage the uptake of AI in industrial use cases, tools are required to support the engineering process throughout the life cycle of the AI application that is central to the use cases. Providing guidance on using AI in industrial setups is vital for creating trustworthy, reliable, and ethically compliant AI-based solutions. The related standardization landscape and available guideline repositories are large, scattered over the web, change rapidly, and are hard to keep up with. This limits the access and ease of use of these standards and guidelines. To address these limitations, we propose developing a self-assessment tool, empowered through AI algorithms and models such as Large Language Models, to improve the process of accessing and benefiting from those standards and guidelines. This self-assessment tool will support various user groups while engineering their applications by identifying the most applicable guidelines according to the individual attributes of the specific user group. We argue that modeling specific attributes and mapping appropriate controls for self-assessments could be achieved by applying AI-based technologies. This paper outlines our ongoing efforts concerning the suggested supporting tools, offering a human-centric methodology. Additionally, we present initial results demonstrating how the needs of a particular user group can be accurately modeled. The results of this study will be used for applications that are deploying AI in an industrial setting with the objective of enabling the two most important goals of Industry 5.0, which are the well-being of workers at the center of the production process, and sustainable and resilient industries.

Traffic light detection stands as a pivotal challenge in the realm of connected and automated driving systems and intelligent traffic management. Traffic light detection lies in the real-time and accurate recognition of traffic lights and their states ’go’, ’warning’, ’stop’, ’goLeft’, ’warningLeft’, ’stopLeft’, even under diverse environmental conditions. This paper introduces a synthetic approach for intelligent traffic light detection and classification, namely FL-TLDC, that synergizes federated learning with Fast Region-based Convolutional Neural Networks (Faster R-CNN). The proposed collaborative and decentralised approach allows the collective improvement of traffic light detection algorithms without the need to share sensitive or proprietary data. Simulation results demonstrate that the proposed analysis achieves a high detection performance and provides fast responses.

Artificial Intelligence (AI) advances are pushing the boundaries across research domains with AI-driven solutions in healthcare claiming a significant share. A key objective of these studies concerns the timely prediction of various pathological conditions. Sepsis is a life-threatening syndrome and one of the main causes of death in intensive care unit (ICU) patients. As it becomes a major health problem worldwide, sepsis early prediction could assist healthcare professionals towards making informed clinical decisions, and thereby, significantly reducing the sepsis' morbidity and mortality. A notable body of literature involving the use of AI for sepsis prediction exists. However, to the best of our knowledge, only a handful of studies focus on performing a systematic review of the AI enabled solutions for sepsis prediction in ICUs. In this context, the present paper aims to identify knowledge gaps, stimulate interest and yield motivations for future research. Moreover, to discuss ethical and explainability aspects and associated challenges. The literature search was conducted between February 2023 and April 2023 and considered eligible articles published within the last five years.

The industrial world is rapidly moving towards its $5^{\text {th}}$ industrial revolution (a.k.a. “Industry5.0”). Industry5.0 is reflected on the digital sovereignty in comprehensively sustainable production, through adopting, extending and implementing $A I$-enabled hardware, as well as $A I$ tools & methods and semiconductors technology across the whole industrial value chain. It is expected that in doing so, manufacturing costs will be decreased, while at the same time, product quality will be increased through AI-enabled innovation, time-to-market will be shortened and user acceptance of versatile technology offerings will be achieved and global supply chains stabilized. The above will, in turn, foster a sustainable development, in an economical, ecological and societal sense and act as enablers for the Green Deal. This paper accordingly discusses on the fundamental research areas relevant to further advancing the digitalized industry, its research and development achievements so far, as well as the challenges confronted, in order to boost industrial competitiveness through interdisciplinary innovations, establishing sustainable value chains and therefore contribute to the Digital Sovereignty.

Highly automated driving continues to attract massive research efforts at a global scale, leading to developments that pave the way towards the future of mobility. Despite the innumerable innovations that have already penetrated our lives, there are still several challenges to overcome in this area. Indicatively, highly automated driving decisions are often associated with undertaking partially unknown risks, which are critical for fail-safe and fail-operational components and systems of modern vehicles, operating at alternative Levels of Autonomy (LoA). As such, novel functionality is required to a-priori assess those risks and propose decisions that affect vehicular behavior, in a cognitive (knowledge-based) manner. This paper proposes an in-vehicle cognitive management functionality that dynamically suggests the most appropriate LoA, by (a) incorporating in the decision-making process the "a priori risk assessment" associated with every possible decision, by applying the Failure Mode and Effect Analysis (FMEA) to model the identified risks of each candidate LoA, (b) predicting at a 1st level the most suitable LoA by means of Bayesian networks, (c) further enhancing predictions at a 2nd level, by using neural networks, namely a Binary Classification model and a MultiClass Classification model and (d) storing and exploiting in the future all associated decisions through experience creation (3rd cognition level). A simulation scenario is used to validate the effectiveness of the proposed functionality. Results showcase that the method can a priori, quickly, and effectively lead to optimum LoA decisions with minimum risk, contributing further to safer roads.

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3.

Although today’s advanced biomedical technology provides unsurpassed power in diagnosis, monitoring, and treatment, interpretation of vast streams of information generated by this technology often poses excessive demands on the cognitive skills of health-care personnel (nurses, doctors, etc). These problems are most severe in critical care environments such as Intensive Care Units (ICUs) where many events are life-threatening and thus require immediate attention and the implementation of definitive corrective actions. Septic syndrome is one of the most common pathogens of ICUs. Early diagnosis of this condition is complicated by nonspecific clinical signs and symptoms and the fact that not all infections lead to sepsis and to progression into septic shock. On this basis, the use of Cognitive Clinical Management Systems (CCMSs) is receiving increasing interest in recent years, enabling increased precision of diagnostics and novel clinical decision making solutions. Such systems operate on the basis of collecting information from various sources, intelligently processing it, integrating knowledge and experience, and finally, taking the most appropriate clinical decisions. Following the above, the ambition of the present study is to create a CCMS architecture for direct patient use with real-time monitoring, namely I-CSSP, being able to support intelligent septic shock predictions in ICUs.

The ceaseless evolution of Information and Communication Technologies (ICT) is reflected on their migration towards the Future Internet (FI) era, which is characterized, among others, by powerful and complex network infrastructures, and innovative applications, services and content. An area of applications that finds prosperous ground in the FI era lies in the world of road transportation and intelligent vehicles. In particular, recent and future ICT findings are envisaged to contribute to the enhancement of transportation efficiency at various levels, such as traffic, parking, safety and emergency management. In this context, the goal of this paper is to introduce an Intelligent Management Functionality (IMF) that enables vehicles to operate each time in the best available Driving Style (DS) by responding quickly to changing driving environment situations and driver’s preferences. Such a functionality aims to operate on the basis of collecting information from various sources, intelligently processing it, integrating knowledge and experience and, finally, selecting the optimal DS. Knowledge is obtained through the exploitation of Bayesian networking principles and Naive-Bayes modeling. Indicative simulation results showcase the effectiveness of the proposed system.

The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the extent of explaining the internal system mechanics in human terms. Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable to increase user trust and improve the acceptance of recommendations. In this study, we focus on a context-aware recommendation system for energy efficiency and develop a mechanism for explainable and persuasive recommendations, which are personalized to user preferences and habits. The persuasive facts either emphasize on the economical saving prospects (Econ) or on a positive ecological impact (Eco) and explanations provide the reason for recommending an energy saving action. Based on a study conducted using a Telegram bot, different scenarios have been validated with actual data and human feedback. Current results show a total increase of 19% on the recommendation acceptance ratio when both economical and ecological persuasive facts are employed. This revolutionary approach on recommendation systems, demonstrates how intelligent recommendations can effectively encourage energy saving behavior.

In the world of transportation, the ceaseless evolution of Information and Communication Technologies (ICT) is reflected on their migration towards In-Vehicle-Infotainment (IVI) systems, which are characterized by innovative applications and services. Such systems aim to support drivers/passengers with a varying set of functions, enhancing the quality of driving through entertainment. The goal of this study is to introduce a novel IVI cognitive functionality that automatically and dynamically proposes the optimal music genre to the drivers/users when they want to make a certain journey with their owned (or shared) vehicles. The proposed recommender functionality utilizes in an automated manner (i) drivers/users’ profile data and current situation, (ii) drivers/users’ personal preferences, (iii) external environment information obtained from sensor measurements, and (iv) previous knowledge and experience. Knowledge is obtained through the exploitation of Bayesian networking principles in combination with a practical implementation of the Naive-Bayes model. Indicative simulation results showcase the efficiency of the proposed infotainment functionality, in terms of accuracy and speed of convergence, in proactively identifying the optimal music genre and accordingly notifying the drivers/users.

Autonomous driving has the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI controlled vehicle demonstrators) achieved until year 2.

This Paper presents an Internet of Things (IoT) based framework to monitor ECG for biometric recognition and acceleration for fall detection. To this end, an-IoT based Remote Elderly Monitoring System (REMS) platform is described. REMS consists of a Shimmer3TM device transmitting physiological signal wirelessly to a nearby gateway which routes the data to a remote IoT-platform, able to accommodate dynamically changing configurations. The Shimmer firmware has been modified to send data based on the compressive sensing theory in order to ameliorate energy consumption in addition of real data, and the analysis and processing are done locally on a heterogeneous multicore edge device in order to solve latency issues related to cloud reliance. Subsequently the framework has been designed to handle the different parameter settings and multiple scenarios in a user-friendly way. Furthermore, it allows the user to monitor physiological data and acquire some feedback related to their analysis. Depending on a scenario (energy save, secure communication) the system can be configured manually or automatically to monitor ECG or acceleration data and displays them, it can also identify the subject based on ECG recognition and detect fall if it occurs.

Among the actual trends that will affect society in the coming years, autonomous driving stands out as having the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches also due to missing reliable environment perception and sensor fusion. PRYSTINE will realize Fail-operational Urban Surround perceptION (FUSION) which is based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. In this paper, we detail the vision of the PRYSTINE project and we showcase the results achieved during the first year.

Among the actual trends that will affect society in the coming years, autonomous driving stands out as having the potential to disruptively change the automotive industry as we know it today. As a consequence, this will also highly impact the semiconductor industry and open new market opportunities, since semiconductors play an indispensable role as enablers for automated vehicles. Fully automated driving has been identified as one major enabler to master the Grand Societal Challenges of safe, clean, and efficient mobility. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches also due to missing reliable environment perception and sensor fusion. PRYSTINE, which was the highest ranked ECSEL project proposal in 2017, will realize Fail-operational Urban Surround perceptION (FUSION) which is based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. Furthermore, PRYSTINE will strengthen and extend traditional core competencies of the European industry, research organizations, and universities in smart mobility and in particular in the electronic component and systems and cyber-physical systems domain.

Economic growth in Europe has been, strongly associated with urbanization, overwhelming cities with vehicles. This renders mobility inside cities problematic, since it is often associated with large waste of time in traffie congestions, environmental pollution and accidents. Cities struggle to invent and deploy “smart” solutions in the domain of urban mobility, so as to offer innovative services to citizens and visitors and improve the overall quality of life. In this context, the paper discusses on the fundamental challenges that cities face when trying to become smarter, focusing on the particular area of smart buildings and the management of multi-core, smart building architectures and presents some key research achievements and relevant research challenges.

The increasing need for mobility has revealed inefficiencies related to transportation, such as congestion, accidents and environmental pollution. An innovative solution to achieve more efficient and safer mobility is to attribute to vehicles embedded intelligence functionality. This paper introduces a framework for providing intelligence to vehicles. The framework operates by collecting contextual, profile, and policy information from the surrounding vehicles, traffic lights and road signs, processes it intelligently, integrating knowledge and experience, and visually informs the driver proactively for forthcoming emergencies, based on a set of warning functions. In order to reach the required driver-assisting decisions, a heuristic algorithm is used, based on rules-of-thumb. Extensive simulation results showcase the algorithm's advantages compared to current ADAS solutions in terms of response times, simplicity and scalability, since it acts proactively thus reducing response times, while it utilizes existing 3G/4G/5G infrastructures.

The Future Internet (FI) era dictates the deployment of high complexity systems having a variety of properties such as adaptivity, self configuration and self optimization. This paper discusses on the design and deployment of an autonomic management framework, targeting real-time optimization of operational properties in a smart building cognitive environment. The architecture of an autonomous smart building system is described, focusing on diverse requirement satisfaction, including response time, energy efficiency and users' satisfaction. The main systems' components are introduced along with a indicative operation scenario that demonstrates multi - criticality issues explored. The scope of the paper is a) to point out challenges in building an autonomic smart building environment to support multi-critical applications and b) to indicate efficient solutions for dealing with them, promoting self-configuration and self-optimisation properties.

It is of utmost importance to design and develop Intelligent Transport Systems (ITS) that can be efficient, reliable and cost effective. A valid manner to realize this is to attribute vehicles with embedded intelligence functionalities, so as to provide novel traffic, safety and emergency management solutions. This paper presents such a framework, which operates on the basis of collecting information from various sources through vehicular sensor networks, intelligently processing it, integrating knowledge and experience coming from the past and, finally, issuing directives to the driver for globally protecting the vehicle and proactively alerting the driver for forthcoming emergencies. The overall approach is presented in detail, whereas a novel heuristic is proposed for the algorithmic process towards reaching decisions. Indicative simulation results showcase its efficiency.

The internet and Social Media have been playing a vital role in almost everyone's communication and interactions. The same holds true for a company's two-way communication with its consumers. This tremendous flow of information can drastically increase any company's exposure to its consumers and shoppers. Consequently, it can decisively affect consumers' opinion about products and services. Molloy College in Rockville Centre, New York is the home of GiF, the first worldwide, holistic ICT-based approach to managing the Big Data issue in Social Media Marketing. GlobaliFusion (GiF) aims at bringing together entrepreneurs and companies of all sizes with their consumers by aggregating insights from Social Media and online publications in order to translate them into return-on-investment (ROI) positive marketing strategies and to accelerate their growth, applying technologically innovative and efficient marketing practices. Over the last five years there has been a tremendous shift of investments by marketing departments of major corporations, focusing on Social Media and digital marketing solutions versus traditional media. Despite the abundance of Social Media marketing solutions, there is no concrete framework on how to actually listen to people interacting in Social Media and to use these insights to perform and monitor Integrated Marketing campaigns Furthermore, monitoring Social Media campaigns' impact on public opinion and decisions through Social Media channels, and assisting companies accelerate their growth accordingly with novel integrated tools and strategies has been an unexplored field. GiF is striving to be established as the global leading platform for increasing the intelligence of companies through social media management and through a.

Information and communication technologies (ICTs) have long been attracting research interest, which is reflected in the design and development of powerful and complex network infrastructures, advanced applications/services, efficient power management, and extensions in the business model. A field of applications where ICTs find prosperous ground is transportation, in the sense of utilizing ITC findings in developing intelligent transportation systems (ITSs), i.e., systems for increasing transportation efficiency. This is motivated by the fact that transportation is associated with several drawbacks, e.g., with regard to continuously increasing traffic congestion in large cities worldwide. A transportation alternative to address congestion is the concept of car pooling, i.e., sharing a vehicle toward a common destination, based on a priori agreements. The goal of this paper is to present novel management functionality for dynamic ride matching, within a car-pooling context. The functionality uses previous knowledge in proposing valid car-pooling matches. Knowledge is obtained through the exploitation of Bayesian networking concepts, specifically the Naïve-based model. Simulation results showcase the effectiveness of the proposed functionality, the advantage of which lies in the fact that the reliability of the knowledge-based selection decisions is higher. This means that there is higher probability of satisfying the drivers' and passengers' preferences through the selected matches.

This paper details the current work status of the ETSI Reconfigurable Radio Systems (RRS) Technical Committee (TC) and gives an outlook on the future evolution. In particular, Software Defined Radio (SDR) related study results are presented with a focus on SDR architectures for Mobile Devices (MD), such as mobile phones, etc., as well as for Reconfigurable Base Stations (RBS). For MDs, a novel architecture is presented enabling the usage of SDR principles in a mass market context. Cognitive Radio (CR) principles within ETSI RRS are concentrated on two topics, a Cognitive Pilot Channel (CPC) proposal and a Functional Architecture (FA) for Management and Control of Reconfigurable Radio Systems, including Dynamic Self-Organising Planning and Management, Dynamic Spectrum Management, Joint Radio Resource Management, etc. Finally, study results are indicated which are targeting a SDR/CR security framework

The future of telecommunications is depicted on the convergence of mobile systems and IP networks, towards a global communications system, operating over a common access infrastructure, namely the "Beyond the 3rd Generation (B3G) wireless access infrastructure". The goal of this vision is the ubiquitous delivery of a great variety of innovative services, via a multitude of Radio Access Technologies (RATs). To achieve this, it is mandatory to identify and embrace the requirements for support of heterogeneity in wireless technologies. Framed within such context is the efficient management of each distinct RAT's resources (e.g. spectrum, codes, power levels etc) within the heterogeneous environment. Accordingly, this paper considers the Universal Mobile Telecommunications System (UMTS) as one of the most common operating RATs in B3G environments, and focuses on the efficient management of the total spectrum assigned to UMTS Network Providers (NPs). Specifically, the method proposed herewith, called Demand Allocation into Multiple UMTS Carriers (DAMUC), aims at finding the optimum partitioning of the demand to the multiple available, wideband UMTS carrier frequencies owned by a single NP. The method investigates certain demand allocation policies and selects the most appropriate one in terms of minimizing the total downlink power consumption in the network. Validation and consolidation can be ensured through indicative results from the application of DAMUC to a simulated network.

Adaptive networks are envisaged to play a significant part in the future, where the time and space variations in the traffic pattern will necessitate the ability to continuously amend the Radio Access Technologies' (RATs') operating parameters. Reconfiguration of communications systems is a facilitator towards this convergence and enables the dynamic adaptation and optimization of the access characteristics. However, such far ranging optimization concept involves many different mechanisms and work areas. Each of these areas provides an answer to a different optimization problem; Dynamic Network Planning and Management (DNPM) provides a load and demand driven optimization of the radio planning of multiple different networks within a given area. Advanced Spectrum Management (ASM) enables short term use of spectrum for services with higher demand. Finally Joint Radio Resource Management (JRRM) coordinates different access schemes and facilitates a more centralized approach to allocation of radio resource. Each of the schemes optimizes spectrum and radio resource usage on a different time scale. ARRM deals with the rather short term allocation, ASM with more medium term spectrum assignments while DNPM assumes time scales up to the range of weeks or months. Consequently, there is need of combining all working areas in the form of a Functional Architecture (FA), where each module represents a concept, aiming at forming part of the global end-to-end reconfigurability architecture. This paper includes a detailed analysis of the Reconfigurability FA, along with a description of the functionality of each of the modules included therein.

The continuous introduction of applications and services for wireless communications is depicted on the convergence of mobile systems and IP networks, towards a global system, operating over a common access infrastructure, namely the "Beyond the 3rd Generation (B3G) wireless access infrastructure". In such context, the Universal Mobile Telecommunications System (UMTS) will constitute one of the most common operating Radio Access Technologies (RATs), where Network Providers (NPs) will need to manage their specific resources (spectrum), so as to deliver services efficiently and cost-effectively. This paper deals with such issues, by presenting functionality for managing UMTS spectrum in B3G environments. The Demand Allocation into Multiple UMTS Carriers (DAMUC) method proposed herein results in finding the optimum partitioning of the demand to the multiple available, UMTS carrier frequencies (carriers) owned by a single NP. The method investigates certain demand allocation possibilities and selects the most appropriate one in terms of partitioning the demand as per balancing the uplink load (uplink loading factors) between the carriers. Moreover, results from the application of the method in a simulated network are also contained.

The migration of telecommunications towards the B3G era is characterized by the convergence of mobile communication systems and IP networks, in order to better adapt to the - continuously increasing - user demands. Reconfigurability is a key concept facilitating such convergence. This paper emphasizes on the potential benefits reconfigurability may hold far operators and manufacturers, and also presents some business models describing its penetration into the global market. Moreover, the introduction of such concept can change the way wireless networks are designed. In this context, the paper also discusses on some strategies for the dynamic planning and management of reconfigurable network segments, including some simulations and indicative results.

The evolution of wireless communications over the last years has resulted in a clear trend towards beyond third generation (B3G) systems, which support the integration and coexistence of multiple, diverse radio access technologies (RATs) in a common composite radio environment. The reconfigurability concept has been developed for the facilitation of B3G Systems. Reconfigurability supports the B3G concept by providing technologies that enable equipment (terminals and network elements) to dynamically select and adapt to the most appropriate RAT. Negotiation and selection of the most appropriate reconfigurations is a capability required by the Reconfigurability concept. This paper presents the integration of negotiation and selection functionality in a management and control system for reconfigurable equipment (MCS-RE) in B3G environments. The negotiation and selection process and the related interfaces are elaborated.