Rapid advances in industrialisation and informatisation methods have spurred tremendous progress in developing the next generation of manufacturing technology. Today, we are on the cusp of the Fourth Industrial Revolution. In 2013, amongst one of 10 ‘Future Projects’ identified by the German government as part of its High-Tech Strategy 2020 Action Plan, the Industry 4.0 project is considered to be a major endeavour for Germany to establish itself as a leader of integrated industry. In 2014, China’s State Council unveiled their ten-year national plan, Made-in-China 2025, which was designed to transform China from the world’s workshop into a world manufacturing power. Made-in-China 2025 is an initiative to comprehensively upgrade China’s industry including the manufacturing sector. In Industry 4.0 and Made-in-China 2025, many applications require a combination of recently emerging new technologies, which is giving rise to the emergence of Industry 4.0. Such technologies originate from different disciplines ...

Industry 4.0: state of the art and future trends

OFDMA femtocells have been pointed out by the industry as a good solution not only to overcome the indoor coverage problem but also to deal with the growth of traffic within macrocells. However, the deployment of a new femtocell layer may have an undesired impact on the performance of the macrocell layer. The allocation of spectrum resources and the avoidance of electromagnetic interference are some of the more urgent challenges that operators face before femtocells become widely deployed. In this article a coverage and interference analysis based on a realistic OFDMA macro/femtocell scenario is provided, as well as some guidelines on how the spectrum allocation and interference mitigation problems can be approached in these networks. Special attention is paid to the use of self-configuration and self-optimization techniques for the avoidance of interference.

OFDMA femtocells: A roadmap on interference avoidance

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

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A survey of MRI-based medical image analysis for brain tumor studies

China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”

Nowadays, numerous journals and conferences have published articles related to context-aware systems, indicating many researchers' interest. Therefore, the goal of this paper is to review the works that were published in journals, suggest a new classification framework of context-aware systems, and explore each feature of classification framework. This paper is based on a literature review of context-aware systems from 2000 to 2007 using a keyword index and article title search. The classification framework is developed based on the architecture of context-aware systems, which consists of the following five layers: concept and research layer, network layer, middleware layer, application layer and user infrastructure layer. The articles are categorized based on the classification framework. This paper allows researchers to extract several lessons learned that are important for the implementation of context-aware systems.

Context-aware systems

Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of VANETs over the past few years. VANET consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. This innovation in wireless communication has been envisaged to improve road safety and motor traffic efficiency in near future through the development of Intelligent Transport Systems (ITS). Hence, government, auto-mobile industries and academia are heavily partnering through several ongoing research projects to establish standards for VANETs. The typical set of VANET application areas, such as vehicle collision warning and traffic information dissemination have made VANET an interested field of wireless communication. This paper provides an overview on current research state, challenges, potentials of VANETs as well the way forward to achieving the long awaited ITS.

Vehicular ad hoc networks (VANETs): Current state, challenges, potentials and way forward

Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of vehicular ad-hoc networks (VANETs) over the past few years. A vehicular network consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. This innovation in wireless communication has been envisaged to improve road safety and motor traffic efficiency in near future through the development of intelligent transportation system (ITS). Hence, governments, auto-mobile industries and academia are heavily partnering through several ongoing research projects to establish standards for VANETs. The typical set of VANET application areas, such as vehicle collision warning and traffic information dissemination have made VANET an interesting field of mobile wireless communication. This paper provides an overview on current research state, challenges, potentials of VANETs as well as the ways forward to achieving the long awaited ITS.

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Advances in vehicular ad-hoc networks (VANETs): Challenges and road-map for future development

Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse human life patterns in an IoT environment, lifelogging personal data contains huge uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, lifelogging physical activity (LPA) is taken as a target to explore how to improve the validity of lifelogging data in an IoT enabled healthcare system. A rule-based adaptive LPA validation (LPAV) model, LPAV-IoT, is proposed for eliminating irregular uncertainties (IUs) and estimating data reliability in IoT healthcare environments. A methodology specifying four layers and three modules in LPAV-IoT is presented for analyzing key factors impacting validity of LPA. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on a personalized healthcare platform myhealthavatar connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of IU and adaptively indicating the reliability of LPA data on certain condition of IoT environments.

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Lifelogging Data Validation Model for Internet of Things Enabled Personalized Healthcare

Over the last two years, GSM and UMTS femtocell access points have been proposed as a solution to the poor indoor coverage problem experienced in certain areas. Research on these devices has shown that femtocells will not only increase indoor system coverage, but also system capacity. Femtocells will allow new services and business models to be offered to indoor users. Almost parallely, the WiMAX standard has emerged as a potential candidate technology for the future wireless networks. WiMAX femtocells are currently under development and will therefore play an important role in the world of indoor broadband wireless access. However, several aspects of this new technology, such as the access method and interference avoidance techniques play a crucial role in the amount of interference caused to co-channel deployed macrocells. This paper provides a framework for the study of WiMAX macro-femtocell hybrid scenarios. An in-depth description of the necessary radio coverage prediction and system-level simulation for this kind of scenarios is introduced. Simulations and numerical results for two different types of access methods (public and private) in the downlink are also presented.

Access methods to WiMAX femtocells: A downlink system-level case study

Internet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75 MHz spectrum in the 5.9 GHz band to support vehicular communication. The authors studied the application of cognitive radio (CR) technology to IoVs to increase the spectra opportunities for vehicular communication, especially when the allocated 75 MHz is not enough due to high demands because of the increasing number of connected vehicles as already foreseen in the near era of IoVs. They proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum on the highways. They also developed a novel cooperative three-state spectrum sensing and allocation model which makes CR vehicular secondary units aware of additional spectrum resources opportunities on their current and future positions, and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing, and provide vehicles with additional spectrum opportunities.

Enjie Liu

Papers

Vehicular ad hoc networks (VANETs): Current state, challenges, potentials and way forward

Advances in vehicular ad-hoc networks (VANETs): Challenges and road-map for future development

Lifelogging Data Validation Model for Internet of Things Enabled Personalized Healthcare

Access methods to WiMAX femtocells: A downlink system-level case study

Cognitive radio-enabled Internet of Vehicles: a cooperative spectrum sensing and allocation for vehicular communication