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in Architecture & Planning
Periodicals

Simplification and repair of polygonal models using volumetric techniques

Two important tools for manipulating polygonal models are simplification and repair and we present voxel-based methods for performing both of these tasks. We describe a method for converting polygonal models to a volumetric representation in a way that handles models with holes, double walls, and intersecting parts. This allows us to perform polygon model repair simply by converting a model to and from the volumetric domain. We also describe a new topology-altering simplification method that is based on 3D morphological operators. Visually unimportant features such as tubes and holes may be eliminated from a model by the open and close morphological operators. Our simplification approach accepts polygonal models as input, scan converts these to create a volumetric description, performs topology modification, and then converts the results back to polygons. We then apply a topology-preserving polygon simplification technique to produce a final model. Our simplification method produces results that are everywhere manifold.

Architecture & Planning• 2003• Institute of Electrical and Electronics Engineers
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A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0

In the era of Industry 4.0, manufacturing and production systems were revolutionized by increasing operational efficiency and developing and implementing new business models, services, and products. Concretely, the milestone set for Industry 4.0 was to improve the sustainability and efficiency of production systems. By extension, the emphasis was focused on both the digitization and the digitalization of systems, providing room for further improvement. However, the current technological evolution is more system/machine-oriented, rather than human-oriented. Thus, several countries have begun orchestrating initiatives towards the design and development of the human-centric aspect of technologies, systems, and services, which has been coined as Industry 5.0. The impact of Industry 5.0 will extend to societal transformation, which eventually leads to the generation of a new society, the Society 5.0. The developments will be focused on the social and human-centric aspect of the tools and technologies introduced under the framework of Industry 4.0. Therefore, sustainability and human well-being will be at the heart of what comes next, the Industry 5.0, as a subset of Society 5.0. Industry 5.0 will build on the foundations laid during Industry 4.0 by emphasizing human-centered, resilient, and sustainable design. Consequently, the authors in this research work, through a critical literature review, aim to provide adequate reasoning for considering Industry 5.0 as a framework for enabling the coexistence of industry and emerging societal trends and needs. The contribution of this research work extends to the provision of a framework to facilitate the transition from Industry 4.0 to Society 5.0.

Architecture & Planning• 2022• Multidisciplinary Digital Publishing Institute
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Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing

Wireless communication at the terahertz (THz) frequency bands (0.1–10 THz) is viewed as one of the cornerstones of tomorrow’s 6G wireless systems. Owing to the large amount of available bandwidth, if properly deployed, THz frequencies can potentially provide significant wireless capacity performance gains and enable high-resolution environment sensing. However, operating a wireless system at high-frequency bands such as THz is limited by a highly uncertain and dynamic channel. Effectively, these channel limitations lead to unreliable intermittent links as a result of an inherently short communication range, and a high susceptibility to blockage and molecular absorption. Consequently, such impediments could disrupt the THz band’s promise ofhigh-rate communications and high-resolution sensingcapabilities. In this context, this paper panoramically examines the steps needed to efficiently and reliably deploy and operate next-generation THz wireless systems that will synergistically support a fellowship of communication and sensing services. For this purpose, we first set the stage by describing the fundamentals of the THz frequency band. Based on these fundamentals, we characterize and comprehensively investigateseven unique defining features of THz wireless systems: 1) Quasi-opticality of the band, 2) THz-tailored wireless architectures, 3) Synergy with lower frequency bands, 4) Joint sensing and communication systems, 5) PHY-layer procedures, 6) Spectrum access techniques, and 7) Real-time network optimization. These seven defining features allow us to shed light on how to re-engineer wireless systems as we know them today so as to make them ready to support THz bands and their unique environments. On the one hand, THz systems benefit from their quasi-opticality andcan turn every communication challenge into a sensing opportunity, thus contributing to a new generation ofversatile wireless systemsthat can perform multiple functions beyond basic communications. On the other hand, THz systems can capitalize on the role of intelligent surfaces, lower frequency bands, and machine learning (ML) tools to guarantee a robust system performance. We conclude our exposition by presenting the key THz 6G use cases along with their associated major challenges and open problems. Ultimately, the goal of this article is to chart a forward-looking roadmap that exposes the necessary solutions and milestones for enabling THz frequencies to realize their potential as a game changer for next-generation wireless systems.

Architecture & Planning• 2022• Institute of Electrical and Electronics Engineers
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A Survey of Security in Software Defined Networks

The proposition of increased innovation in network applications and reduced cost for network operators has won over the networking world to the vision of software-defined networking (SDN). With the excitement of holistic visibility across the network and the ability to program network devices, developers have rushed to present a range of new SDN-compliant hardware, software, and services. However, amidst this frenzy of activity, one key element has only recently entered the debate: Network Security. In this paper, security in SDN is surveyed presenting both the research community and industry advances in this area. The challenges to securing the network from the persistent attacker are discussed, and the holistic approach to the security architecture that is required for SDN is described. Future research directions that will be key to providing network security in SDN are identified.

Architecture & Planning• 2015• Institute of Electrical and Electronics Engineers
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Trifluoroacetate induced small-grained CsPbBr3 perovskite films result in efficient and stable light-emitting devices

Abstract Quantum efficiencies of organic-inorganic hybrid lead halide perovskite light-emitting devices (LEDs) have increased significantly, but poor device operational stability still impedes their further development and application. All-inorganic perovskites show better stability than the hybrid counterparts, but the performance of their respective films used in LEDs is limited by the large perovskite grain sizes, which lowers the radiative recombination probability and results in grain boundary related trap states. We realize smooth and pinhole-free, small-grained inorganic perovskite films with improved photoluminescence quantum yield by introducing trifluoroacetate anions to effectively passivate surface defects and control the crystal growth. As a result, efficient green LEDs based on inorganic perovskite films achieve a high current efficiency of 32.0 cd A −1 corresponding to an external quantum efficiency of 10.5%. More importantly, our all-inorganic perovskite LEDs demonstrate a record operational lifetime, with a half-lifetime of over 250 h at an initial luminance of 100 cd m −2 .

Architecture & Planning• 2019• Nature Portfolio
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Molecular Imaging of Hypoxia

Hypoxia, a condition of insufficient O2 to support metabolism, occurs when the vascular supply is interrupted, as in stroke or myocardial infarction, or when a tumor outgrows its vascular supply. When otherwise healthy tissues lose their O2 supply acutely, the cells usually die, whereas when cells gradually become hypoxic, they adapt by up-regulating the production of numerous proteins that promote their survival. These proteins slow the rate of growth, switch the mitochondria to glycolysis, stimulate growth of new vasculature, inhibit apoptosis, and promote metastatic spread. The consequence of these changes is that patients with hypoxic tumors invariably experience poor outcome to treatment. This has led the molecular imaging community to develop assays for hypoxia in patients, including regional measurements from O2 electrodes placed under CT guidance, several nuclear medicine approaches with imaging agents that accumulate with an inverse relationship to O2, MRI methods that measure either oxygenation directly or lactate production as a consequence of hypoxia, and optical methods with NIR and bioluminescence. The advantages and disadvantages of these approaches are reviewed, along with the individual strategies for validating different imaging methods. Ultimately the proof of value is in the clinical performance to predict outcome, select an appropriate cohort of patients to benefit from a hypoxia-directed treatment, or plan radiation fields that result in better local control. Hypoxia imaging in support of molecular medicine has become an important success story over the last decade and provides a model and some important lessons for development of new molecular imaging probes or techniques.

Architecture & Planning• 2008• Society of Nuclear Medicine and Molecular Imaging
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Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Security Services Using Blockchains: A State of the Art Survey

This paper surveys blockchain-based approaches for several security services. These services include authentication, confidentiality, privacy and access control list, data and resource provenance, and integrity assurance. All these services are critical for the current distributed applications, especially due to the large amount of data being processed over the networks and the use of cloud computing. Authentication ensures that the user is who he/she claims to be. Confidentiality guarantees that data cannot be read by unauthorized users. Privacy provides the users the ability to control who can access their data. Provenance allows an efficient tracking of the data and resources along with their ownership and utilization over the network. Integrity helps in verifying that the data has not been modified or altered. These services are currently managed by centralized controllers, for example, a certificate authority. Therefore, the services are prone to attacks on the centralized controller. On the other hand, blockchain is a secured and distributed ledger that can help resolve many of the problems with centralization. The objectives of this paper are to give insights on the use of security services for current applications, to highlight the state of the art techniques that are currently used to provide these services, to describe their challenges, and to discuss how the blockchain technology can resolve these challenges. Further, several blockchain-based approaches providing such security services are compared thoroughly. Challenges associated with using blockchain-based security services are also discussed to spur further research in this area.

Architecture & Planning• 2018• Institute of Electrical and Electronics Engineers
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Perspectives of quantum annealing: methods and implementations

Quantum annealing is a computing paradigm that has the ambitious goal of efficiently solving large-scale combinatorial optimization problems of practical importance. However, many challenges have yet to be overcome before this goal can be reached. This perspectives article first gives a brief introduction to the concept of quantum annealing, and then highlights new pathways that may clear the way towards feasible and large scale quantum annealing. Moreover, since this field of research is to a strong degree driven by a synergy between experiment and theory, we discuss both in this work. An important focus in this article is on future perspectives, which complements other review articles, and which we hope will motivate further research.

Architecture & Planning• 2020• IOP Publishing
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A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning

Digital twin is a significant way to achieve smart manufacturing, and provides a new paradigm for fault diagnosis. Traditional data-based fault diagnosis methods mostly assume that the training data and test data are following the same distribution and can acquire sufficient data to train a reliable diagnosis model, which is unrealistic in the dynamic changing production process. In this paper, we present a two-phase digital-twin-assisted fault diagnosis method using deep transfer learning (DFDD), which realizes fault diagnosis both in the development and maintenance phases. At first, the potential problems that are not considered at design time can be discovered through front running the ultra-high-fidelity model in the virtual space, while a deep neural network (DNN)-based diagnosis model will be fully trained. In the second phase, the previously trained diagnosis model can be migrated from the virtual space to physical space using deep transfer learning for real-time monitoring and predictive maintenance. This ensures the accuracy of the diagnosis as well as avoids wasting time and knowledge. A case study about fault diagnosis using DFDD in a car body-side production line is presented. The results show the superiority and feasibility of our proposed method.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
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Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture

The smart management of freshwater for precision irrigation in agriculture is essential for increasing crop yield and decreasing costs, while contributing to environmental sustainability. The intense use of technologies offers a means for providing the exact amount of water needed by plants. The Internet of Things (IoT) is the natural choice for smart water management applications, even though the integration of different technologies required for making it work seamlessly in practice is still not fully accomplished. The SWAMP project develops an IoT-based smart water management platform for precision irrigation in agriculture with a hands-on approach based on four pilots in Brazil and Europe. This paper presents the SWAMP architecture, platform, and system deployments that highlight the replicability of the platform, and, as scalability is a major concern for IoT applications, it includes a performance analysis of FIWARE components used in the Platform. Results show that it is able to provide adequate performance for the SWAMP pilots, but requires specially designed configurations and the re-engineering of some components to provide higher scalability using less computational resources.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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An Ontology for Strongly Sustainable Business Models

Business is increasingly employing sustainability practices, aiming to improve environmental and social responsibility while maintaining and improving profitability. For many organizations, profit-oriented business models are a major constraint impeding progress in sustainability. A formally defined ontology, a model definition, for profit-oriented business models has been employed globally for several years. However, no equivalent ontology is available in research or practice that enables the description of strongly sustainable business models, as validated by ecological economics and derived from natural, social, and system sciences. We present a framework of strongly sustainable business model propositions and principles as findings from a transdisciplinary review of the literature. A comparative analysis was performed between the framework and the Osterwalder profit-oriented ontology for business models. We introduce an ontology that enables the description of successful strongly sustainable business models that resolves weaknesses and includes functionally necessary relationships.

Architecture & Planning• 2015• SAGE Publishing
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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.

Architecture & Planning• 2018• IEEE Computer Society
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Renewable Energy in the Sustainable Development of Electrical Power Sector: A Review

The electrical power sector plays an important role in the economic growth and development of every country around the world. Total global demand for electric energy is growing both in developed and developing economies. The commitment to the decarbonization of economies, which would mean replacing fossil fuels with renewable energy sources (RES) as well as the electrification of transport and heating as a means to tackle global warming and dangerous climate change, would lead to a surge in electricity consumption worldwide. Hence, it appears reasonable that the electric power sector should embed the principles of sustainable development into its functioning and operation. In addition, events such as the recent European gas crisis that have emerged as a result of the massive deployment of renewables need to be studied and prevented. This review aims at assessing the role of the renewable energy in the sustainable development of the electrical power sector, focusing on the energy providers and consumers represented both by businesses and households that are gradually becoming prosumers on the market of electric energy. Furthermore, it also focuses on the impact of renewables on the utility side and their benefits for the grid. In addition, it identifies the major factors of the sustainable development of the electrical power sector.

Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
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A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network

In this study, a new technique is proposed to forecast short-term electrical load. Load forecasting is an integral part of power system planning and operation. Precise forecasting of load is essential for unit commitment, capacity planning, network augmentation and demand side management. Load forecasting can be generally categorized into three classes such as short-term, midterm and long-term. Short-term forecasting is usually done to predict load for next few hours to few weeks. In the literature, various methodologies such as regression analysis, machine learning approaches, deep learning methods and artificial intelligence systems have been used for short-term load forecasting. However, existing techniques may not always provide higher accuracy in short-term load forecasting. To overcome this challenge, a new approach is proposed in this paper for short-term load forecasting. The developed method is based on the integration of convolutional neural network (CNN) and long short-term memory (LSTM) network. The method is applied to Bangladesh power system to provide short-term forecasting of electrical load. Also, the effectiveness of the proposed technique is validated by comparing the forecasting errors with that of some existing approaches such as long short-term memory network, radial basis function network and extreme gradient boosting algorithm. It is found that the proposed strategy results in higher precision and accuracy in short-term load forecasting.

Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
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INet: Convolutional Networks for Biomedical Image Segmentation

Encoder-decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may discard spatial information, and therefore low-level semantics are lost. Feature fusion methods can mitigate these problems but feature maps of different scales cannot be easily fused because downand upsampling change the spatial resolution of feature map. To address these issues, we propose INet, which enlarges receptive fields by increasing the kernel sizes of convolutional layers in steps (e.g., from 3 × 3 to 7 × 7 and then 15 × 15) instead of downsampling. Inspired by an Inception module, INet extracts features by kernels of different sizes through concatenating the output feature maps of all preceding convolutional layers. We also find that the large kernel makes the network feasible for biomedical image segmentation. In addition, INet uses two overlapping max-poolings, i.e., max-poolings with stride 1, to extract the sharpest features. Fixed-size and fixed-channel feature maps enable INet to concatenate feature maps and add multiple shortcuts across layers. In this way, INet can recover low-level semantics by concatenating the feature maps of all preceding layers and expedite the training by adding multiple shortcuts. Because INet has additional residual shortcuts, we compare INet with a UNet system that also has residual shortcuts (ResUNet). To confirm INet as a backbone architecture for biomedical image segmentation, we implement dense connections on INet (called DenseINet) and compare it to a DenseUNet system with residual shortcuts (ResDenseUNet). INet and DenseINet require 16.9% and 37.6% fewer parameters than ResUNet and ResDenseUNet, respectively. In comparison with six encoder- decoder approaches using nine public datasets, INet and DenseINet demonstrate efficient improvements in biomedical image segmentation. INet outperforms DeepLabV3, which implementing atrous convolution instead of downsampling to increase receptive fields. INet also outperforms two recent methods (named HRNet and MS-NAS) that maintain high-resolution representations and repeatedly exchange the information across resolutions.

Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
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A review of the use of convolutional neural networks in agriculture

Abstract Deep learning (DL) constitutes a modern technique for image processing, with large potential. Having been successfully applied in various areas, it has recently also entered the domain of agriculture. In the current paper, a survey was conducted of research efforts that employ convolutional neural networks (CNN), which constitute a specific class of DL, applied to various agricultural and food production challenges. The paper examines agricultural problems under study, models employed, sources of data used and the overall precision achieved according to the performance metrics used by the authors. Convolutional neural networks are compared with other existing techniques, and the advantages and disadvantages of using CNN in agriculture are listed. Moreover, the future potential of this technique is discussed, together with the authors’ personal experiences after employing CNN to approximate a problem of identifying missing vegetation from a sugar cane plantation in Costa Rica. The overall findings indicate that CNN constitutes a promising technique with high performance in terms of precision and classification accuracy, outperforming existing commonly used image-processing techniques. However, the success of each CNN model is highly dependent on the quality of the data set used.

Architecture & Planning• 2018• Cambridge University Press
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Additive Manufacturing: A Comprehensive Review

Additive manufacturing has revolutionized manufacturing across a spectrum of industries by enabling the production of complex geometries with unparalleled customization and reduced waste. Beginning as a rapid prototyping tool, additive manufacturing has matured into a comprehensive manufacturing solution, embracing a wide range of materials, such as polymers, metals, ceramics, and composites. This paper delves into the workflow of additive manufacturing, encompassing design, modeling, slicing, printing, and post-processing. Various additive manufacturing technologies are explored, including material extrusion, VAT polymerization, material jetting, binder jetting, selective laser sintering, selective laser melting, direct metal laser sintering, electron beam melting, multi-jet fusion, direct energy deposition, carbon fiber reinforced, laminated object manufacturing, and more, discussing their principles, advantages, disadvantages, material compatibilities, applications, and developing trends. Additionally, the future of additive manufacturing is projected, highlighting potential advancements in 3D bioprinting, 3D food printing, large-scale 3D printing, 4D printing, and AI-based additive manufacturing. This comprehensive survey aims to underscore the transformative impact of additive manufacturing on global manufacturing, emphasizing ongoing challenges and the promising horizon of innovations that could further elevate its role in the manufacturing revolution.

Architecture & Planning• 2024• Multidisciplinary Digital Publishing Institute
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COVID-19 Lockdown: Housing Built Environment’s Effects on Mental Health

Since the World Health Organization (WHO) declared the coronavirus infectious disease 2019 (COVID-19) outbreak a pandemic on 11 March, severe lockdown measures have been adopted by the Italian Government. For over two months of stay-at-home orders, houses became the only place where people slept, ate, worked, practiced sports, and socialized. As consolidated evidence exists on housing as a determinant of health, it is of great interest to explore the impact that COVID-19 response-related lockdown measures have had on mental health and well-being. We conducted a large web-based survey on 8177 students from a university institute in Milan, Northern Italy, one of the regions most heavily hit by the pandemic in Europe. As emerged from our analysis, poor housing is associated with increased risk of depressive symptoms during lockdown. In particular, living in apartments <60 m2 with poor views and scarce indoor quality is associated with, respectively, 1.31 (95% CI: 1046–1637), 1.368 (95% CI: 1166–1605), and 2.253 (95% CI: 1918–2647) times the risk of moderate–severe and severe depressive symptoms. Subjects reporting worsened working performance from home were over four times more likely to also report depression (OR = 4.28, 95% CI: 3713–4924). Housing design strategies should focus on larger and more livable living spaces facing green areas. We argue that a strengthened multi-interdisciplinary approach, involving urban planning, public mental health, environmental health, epidemiology, and sociology, is needed to investigate the effects of the built environment on mental health, so as to inform welfare and housing policies centered on population well-being.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Integrating Personality Structure, Personality Process, and Personality Development

In this target article, we argue that personality processes, personality structure, and personality development have to be understood and investigated in integrated ways in order to provide comprehensive responses to the key questions of personality psychology. The psychological processes and mechanisms that explain concrete behaviour in concrete situations should provide explanation for patterns of variation across situations and individuals, for development over time as well as for structures observed in intra–individual and inter–individual differences. Personality structures, defined as patterns of covariation in behaviour, including thoughts and feelings, are results of those processes in transaction with situational affordances and regularities. It cannot be presupposed that processes are organized in ways that directly correspond to the observed structure. Rather, it is an empirical question whether shared sets of processes are uniquely involved in shaping correlated behaviours, but not uncorrelated behaviours (what we term ‘correspondence’ throughout this paper), or whether more complex interactions of processes give rise to population–level patterns of covariation (termed ‘emergence’). The paper is organized in three parts, with part I providing the main arguments, part II reviewing some of the past approaches at (partial) integration, and part III outlining conclusions of how future personality psychology should progress towards complete integration. Working definitions for the central terms are provided in the appendix. Copyright © 2017 European Association of Personality Psychology

Architecture & Planning• 2017• SAGE Publishing
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A Blockchain-Based Smart Contract System for Healthcare Management

Blockchain is evolving to be a secure and reliable platform for secure data sharing in application areas such as the financial sector, supply chain management, food industry, energy sector, internet of things and healthcare. In this paper, we review existing literature and applications available for the healthcare system using blockchain technology. Besides, this work also proposes multiple workflows involved in the healthcare ecosystem using blockchain technology for better data management. Different medical workflows have been designed and implemented using the ethereum blockchain platform which involves complex medical procedures like surgery and clinical trials. This also includes accessing and managing a large amount of medical data. Within the implementation of the workflows of the medical smart contract system for healthcare management, the associated cost has been estimated for this system in terms of a feasibility study which has been comprehensively presented in this paper. This work would facilitate multiple stakeholders who are involved within the medical system to deliver better healthcare services and optimize cost.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Functional Roles of Alpha-Band Phase Synchronization in Local and Large-Scale Cortical Networks

Alpha-frequency band (8-14 Hz) oscillations are among the most salient phenomena in human electroencephalography (EEG) recordings and yet their functional roles have remained unclear. Much of research on alpha oscillations in human EEG has focused on peri-stimulus amplitude dynamics, which phenomenologically support an idea of alpha oscillations being negatively correlated with local cortical excitability and having a role in the suppression of task-irrelevant neuronal processing. This kind of an inhibitory role for alpha oscillations is also supported by several functional magnetic resonance imaging and trans-cranial magnetic stimulation studies. Nevertheless, investigations of local and inter-areal alpha phase dynamics suggest that the alpha-frequency band rhythmicity may play a role also in active task-relevant neuronal processing. These data imply that inter-areal alpha phase synchronization could support attentional, executive, and contextual functions. In this review, we outline evidence supporting different views on the roles of alpha oscillations in cortical networks and unresolved issues that should be addressed to resolve or reconcile these apparently contrasting hypotheses.

Architecture & Planning• 2011• Frontiers Media
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