Interoperable services based on activity monitoring in ambient assisted living environments

Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human’s activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users’ comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceiling- mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption

An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects

The Internet of Things (IoT) paradigm is establishing itself as a technology to improve data acquisition and information management in the construction field. It is consolidating as an emerging technology in all phases of the life cycle of projects and specifically in the execution phase of a construction project. One of the fundamental tasks in this phase is related to Health and Safety Management since the accident rate in this sector is very high compared to other phases or even sectors. For example, one of the most critical risks is falling objects due to the peculiarities of the construction process. Therefore, the integration of both technology and safety expert knowledge in this task is a key issue including ubiquitous computing, real-time decision capacity and expert knowledge management from risks with imprecise data. Starting from this vision, the goal of this paper is to introduce an IoT infrastructure integrated with JFML, an open-source library for Fuzzy Logic Systems according to the IEEE Std 1855-2016, to support imprecise experts’ decision making in facing the risk of falling objects. The system advises the worker of the risk level of accidents in real-time employing a smart wristband. The proposed IoT infrastructure has been tested in three different scenarios involving habitual working situations and characterized by different levels of falling objects risk. As assessed by an expert panel, the proposed system shows suitable results.This research was funded by University of Naples Federico II through the Finanziamento della Ricerca di Ateneo (FRA) 2020 (CUP: E69C20000380005) and has been partially supported by the ”Programa de ayuda para Estancias Breves en Centros de Investigación de Calidad” of the University of Málaga and the research project BIA2016-79270-P, the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund-ERDF (Fondo Europeo de Desarrollo Regional-FEDER) under project PGC2018-096156-B-I00 Recuperación y Descripción de Imágenes mediante Lenguaje Natural usando Técnicas de Aprendizaje Profundo y Computación Flexible and the Andalusian Government under Grant P18-RT-2248

A distributed architecture for fuzzy logic systems and its application in human activity recognition

Fuzzy Logic Systems (FLS) have the full potential in handling imprecise and uncertain data due to the inherent advantages of the Fuzzy Inference System (FIS). Traditionally, fuzzy logic systems are linked to specific hardware or software systems. The literature review reveals that dispersed and distributed architectures of FLS are in high demand due to their capability to handle the complexities of fuzzy logic computations. However, the absence of best practices and standard methodologies prevents widespread adoption. As a result, some specific requirements, such as web communications and Service-Oriented Architecture (SOA), which can be found in many modern systems, are rarely adapted for FLSs. Sharing FLSs accessibility as web services (called Fuzzy-as-a-Service alias FaaS), in which the service is developed independently from a specific client platform, allows for autonomy, openness, load balancing, efficient resource allocation and eventually cost-effective, particularly for computationally intense FLSs. The proposed novel architectural solution (FaaS) is a web-based service that distributes the main services for FLS on more than one client and servers nodes that can reach multiple users. By extending the IEEE-1855 (2016) standard in terms of system definition and data exchange, this research offers a standard solution for building FaaS as a novel method of implementing fuzzy logic systems by means of a cloud-based collecting, processing, and examining data over the web. Recent advances in standardising Fuzzy Mark-up Language (IEEE 1855-2016) and its associated software libraries (such as JFML and Simpful) have made this achievable. Two different cloud service providers and software libraries (Amazon Web Services using JFML as a java-based library and Azure Web Services using Simpful as a python-based library) are exploited to realise the FaaS on the cloud. As a case study to establish the efficacy of the proposed FaaS, Human Activity Recognition (HAR) that plays a pivotal role in monitoring the health status of the Persons Under Observation (PUO)has been taken under consideration. In order to monitor the data related to HAR and physiological data, which are imprecise and uncertain in nature, various previous researchers have developed a good number of machine learning tools. However, such monitoring systems suffer from certain limitations due to the nature and amount of data being analysed. A number of experiments are carried out in order to showcase and evaluate FaaS performance in different HAR scenarios. The first scenario has been a real-time walking/running detection. Secondly, a fall detection system via FaaS is designed based on IEEE 1855-2016 and JFML. In view, the pandemic caused due to COVID-19, the third application dealt with developing a system to determine the health status of individuals by remotely monitoring their Oxygen saturation and heartbeat rate using wearable sensors. Finally, a performance comparison between a stand-alone fuzzy system and a FaaS solution for fall detection is performed on two different cloud services, namely AWS and Azure. Research findings exhibit that while the proposed algorithm can keep the same accuracy as a stand-alone fuzzy system (90%), it can significantly improve the processing time, e.g., reducing the processing time for 10K data samples from 179 to 45 seconds (78% improvement). Towards the end of this PhD project, the new IEEE 1855 extension is taken as a proposal into the consideration of the IEEE standards committee and is currently in the process of final approval in 2023

Expressive Quantum Perceptrons for Quantum Neuromorphic Computing

Quantum neuromorphic computing (QNC) is a sub-field of quantum machine learning (QML) that capitalizes on inherent system dynamics. As a result, QNC can run on contemporary, noisy quantum hardware and is poised to realize challenging algorithms in the near term. One key issue in QNC is the characterization of the requisite dynamics for ensuring expressive quantum neuromorphic computation. We address this issue by proposing a building block for QNC architectures, what we call quantum perceptrons (QPs). Our proposed QPs compute based on the analog dynamics of interacting qubits with tunable coupling constants. We show that QPs are, with restricted resources, a quantum equivalent to the classical perceptron, a simple mathematical model for a neuron that is the building block of various machine learning architectures. Moreover, we show that QPs are theoretically capable of producing any unitary operation. Thus, QPs are computationally more expressive than their classical counterparts. As a result, QNC architectures built our of QPs are, theoretically, universal. We introduce a technique for mitigating barren plateaus in QPs called entanglement thinning. We demonstrate QPs' effectiveness by applying them to numerous QML problems, including calculating the inner products between quantum states, energy measurements, and time-reversal. Finally, we discuss potential implementations of QPs and how they can be used to build more complex QNC architectures

From cyber-physical convergence to digital twins : a review on edge computing use case designs

AUTHOR CONTRIBUTIONS : The authors contributed equally in this work. All authors have read and agreed to the published version of the manuscript.As a result of the new telecommunication ecosystem landscape, wireless communication has become an interdisciplinary field whose future is shaped by several interacting dimensions. These interacting dimensions, which form the cyber–physical convergence, closely link the technological perspective to its social, economic, and cognitive sciences counterparts. Beyond the current operational framework of the Internet of Things (IoT), network devices will be equipped with capabilities for learning, thinking, and understanding so that they can autonomously make decisions and take appropriate actions. Through this autonomous operation, wireless networking will be ushered into a paradigm that is primarily inspired by the efficient and effective use of (i) AI strategies, (ii) big data analytics, as well as (iii) cognition. This is the Cognitive Internet of People Processes Data and Things (CIoPPD&T), which can be defined in terms of the cyber–physical convergence. In this article, through the discussion of how the cyber–physical convergence and the interacting dynamics of the socio-technical ecosystem are enablers of digital twins (DTs), the network DT (NDT) is discussed in the context of 6G networks. Then, the design and realization of edge computing-based NDTs are discussed, which culminate with the vehicle-to-edge (V2E) use cases.The Sentech Chair in Broadband Wireless and Multimedia Communication (BWMC) at the University of Pretoria.https://www.mdpi.com/journal/applsciam2024Electrical, Electronic and Computer EngineeringSDG-09: Industry, innovation and infrastructur

CROSS-BORDER DATA TRANSFER REGULATION: A COMPARATIVE STUDY OF CHINA AND EUROPE

With the so-called Industry 4.0 revolution ongoing, end-to-end digitalisation of all assets and integration into a digital ecosystem led the world to the unprecedented increases in connectivity and global flows. Cross-border data flow has become the cornerstone of the cross-border economy, especially for digital products. Without cross-border data flow, there will be no transactions. As a result, governments have started updating the data-related policies, such as restrictive measures for data cross-border flows or rules to mandate local data storage. Against this background, this study focuses on emerging research topics, starting with contemporary public policies on the cross-border data transfer. The objective is to examine whether the policymakers from both regions could better achieve their goals of promoting digital economy by establishing a mutual understanding with the industrial entities, while maintaining the balance between the protection of personal information and the innovation in digital markets. For that purpose, this research explores the historical development of data transfer regulatory measures in China, the EU and the U.S., studied the specific challenges they are encountering in the data globalisation era. Part I studied the evolvement of the CBDT rules. It is pointed out that the CBDT regulation is a technology-led phenomenon yet not novel. It is an emerging threat to privacy posed by the development of technology, thus attracted the scrutiny from the public and the authorities. The CBDT regulation reflects the enforcement of national jurisdiction in the cyberspace, which does not enjoy an indisputable general consensus in the contemporary international law. The rulemaking of CBDT cannot avoid the controversial debate over the legitimacy of state supervision of the network. CBDT regulation is originated from the protection of personal data in the EU, yet the disagreement with regard to its philosophy is derived from the conflict of different legislative values, that is, different legislators have different understandings of the freedom of free flow of information and the right to personal information. The author also questioned the rationale of the EU data transfer rules by discussing the target validity of the current rules, that is, the target validity for data protection. Part II compared the EU and China\u2019s data protection laws as well as the CBDT rules respectively. Challenges that CBDT restriction measures might face are listed, since the data transborder transmission is not a legislative measure by nature. In the process of rulemaking and implementation existed dual pressures from domestic and abroad, categorised as technological, international legislative and theoretical challenges. Theoretically, Cyberspace does not have a boundary similar to a physical space, the theoretical premise that the EU CBDT rules ignored is that the state must control the transborder transmission of data by setting the borders. Thus, for China, two aspects must be addressed: is there an independent cyberspace law, and where is the boundary between the virtual and real world. International legislative challenges arise from the oversea data access of the U.S. government. The EU CBDT framework has limited impact when facing such data access under the cover of FISA and CLOUD Act of the U.S. Particularly, this dissertation discussed the potentials for a free flow of data transfer mechanism between the EU and China. It is worth exploring the possibility for a region-based bilateral collaboration, such as a free trade zone in China, to seek for the EU Commission\u2019s recognition of adequate level of protection of personal information. For general data-intensive entities, binding corporate rules and standard contractual clauses are still the preferrable approaches. Part III examines the data protection implementation and data transfer compliance in the context of the HEART project. By analysing the use-cases the HEART deployed, as well as the architecture that it proposed, Chapter 6 studies the privacy-enhancing measures from both the organisational and technical perspectives. Specifically, the data classification system and dynamic data security assessments are proposed. Chapter 7 studied the use case of federated recommender system within the HEART platform and its potentials for the promotion of GDPR compliance. The recommender system is thoroughly analysed under the requirements of the GDPR, including the fundamental data processing principles and threat assessment within the data processing

Proceeding Of Mechanical Engineering Research Day 2015 (MERD’15)

This Open Access e-Proceeding contains 74 selected papers from the Mechanical Engineering Research Day 2015 (MERD’15) event, which is held in Kampus Teknologi, Universiti Teknikal Malaysia Melaka (UTeM) - Melaka, Malaysia, on 31 March 2015. The theme chosen for this event is ‘Pioneering Future Discovery’. The response for MERD’15 is overwhelming as the technical committees have received more than 90 papers from various areas of mechanical engineering. From the total number of submissions, the technical committees have selected 74 papers to be included in this proceeding. The selected papers are grouped into 12 categories: Advanced Materials Processing; Automotive Engineering; Computational Modeling and Analysis & CAD/CAE; Energy Management & Fuels and Lubricants; Hydraulics and Pneumatics & Mechanical Control; Mechanical Design and Optimization; Noise, Vibration and Harshness; Non-Destructive Testing & Structural Mechanics; Surface Engineering and Coatings; Others Related Topic. With the large number of submissions from the researchers in other faculties, the event has achieved its main objective which is to bring together educators, researchers and practitioners to share their findings and perhaps sustaining the research culture in the university. The topics of MERD’15 are based on a combination of advanced research methodologies, application technologies and review approaches. As the editor-in-chief, we would like to express our gratitude to the editorial board members for their tireless effort in compiling and reviewing the selected papers for this proceeding. We would also like to extend our great appreciation to the members of the Publication Committee and Secretariat for their excellent cooperation in preparing the proceedings of MERD’15

Navigation coopérative de véhicules autonomes basée sur la communication V2X dans un réseau de 5ème génération

In today’s world, road transport is essential to our daily routines and business activities. However, the exponential growth in the number of vehicles has led to problems such as traffic congestion and road accidents. Vehicular communication presents an innovative solution, envisaging a future where vehicles communicate with each other, the road infrastructure, and even the road itself, sharing real-time data to optimize traffic flow and enhance safety. This thesis focuses on 5G and Beyond 5G (B5G) technologies, which promise to revolutionize Vehicle-to-Everything (V2X) communication. With the emergence of millimeter-wave (mmWave) communication, high-speed, low-latency data transmission is essential for vehicular networks. However, mmWave communication faces problems with signal attenuation and interference. Our research focuses on solving these problems using a deep learning-based approach. Three significant contributions are proposed. First, we introduce a classical optimization technique, the simulated annealing algorithm, to improve beam alignment in 5G vehicular networks. This reduces latency and improves data transmission between millimeter-wave base stations and vehicles. Our second contribution is a new approach involving a hybrid deep-learning model that predicts optimal beam angles. Combining a 1D CNN and a BiLSTM improves th accuracy of the prediction and reduces errors. This approach eliminates time-consuming computations and iterations critical to the success of B5G vehicular networks. The third contribution introduces a BiLSTM-based model to select the optimal beam pair angles at the mmWave base station (mmBS) and the moving vehicle side. This approach improves the reliability of data transmission while minimizing the error probabilities and overheads during beam search. This research contributes to advancing vehicular communications, offering innovative solutions for 5G and B5G networks. We aim to enhance the efficiency, reduce the latency, and improve the reliability of communications for connected vehicles. This thesis explores beam alignment through classical and deep learning techniques and presents solutions for the challenges of millimeter-wave vehicular networks. Our research provides the foundation for the next generation of vehicular communication and its vital role in making road transport safer and more efficient

Light Field Methods for the Visual Inspection of Transparent Objects

Transparent objects play crucial roles in humans’ everyday life, must meet high quality requirements and therefore must be visually inspected. Developing automated visual inspection systems for complex-shaped transparent objects still represents a challenging task. As a solution, this book introduces light field methods for all main components of a visual inspection system: a novel light field sensor, suitable processing methods and a light field illumination approach

Structure and Causality in Understanding Complex Systems

A central goal of science and engineering is to understand the causal structure of complex computational, physical, and social systems. Inferring this causal structure without performing experiments, however, is often extremely challenging. This thesis develops new mathematical approaches for exploiting the structure underlying many types of data to reveal insights about the causal relationships governing complex systems. The work consists of four aims, each of which leverages structure and causal modeling to understand a different type of system. In the first aim, we develop an algorithm based on the sparse Bayesian learning (SBL) framework for exploiting sparse and temporal structure in order to more efficiently collect data from time-varying high-dimensional systems. In the second aim, we develop a framework for explaining the operation of black-box machine learning classifiers using a causal model of how the data and classifier output are generated. In the third aim, we analyze a class of algorithms that use low-dimensional structure to infer causal interactions in coupled dynamical systems. In the final aim, we use surveys of the public and AI practitioners to model attitudes toward artificial intelligence adoption and governance, and employ the model to answer policy-relevant questions about AI governance.Ph.D