Achieving Neuroplasticity in Artificial Neural Networks through Smart Cities

Hybrid computational model depicts the contribution of non-significant lobes of human brain during the perception of emotional stimuli

Emotions are synchronizing responses of human brain while executing cognitive tasks. Earlier studies had revealed strong correlation between specific lobes of the brain to different types of emotional valence. In the current study, a comprehensive three-dimensional mapping of human brain for executing emotion specific tasks had been formulated. A hybrid computational machine learning model customized from Custom Weight Allocation Model (CWAM) and defined as Custom Rank Allocation Model (CRAM). This regression-based hybrid computational model computes the allocated tasks to different lobes of the brain during their respective executive stage. Event Related Potentials (ERP) were obtained with significant effect at P1, P2, P3, N170, N2, and N4. These ERPs were configured at Pz, Cz, F3, and T8 regions of the brain with maximal responses; while regions like Cz, C4 and F4 were also found to make effective contributions to elevate the responses of the brain, and thus these regions were configured as augmented source regions of the brain. In another circumstance of frequent -deviant - equal (FDE) presentation of the emotional stimuli, it was observed that the brain channels C3, C4, P3, P4, O1, O2, and Oz were contributing their emotional quotient to the overall response of the brain regions; whereas, the interaction effect was found presentable at O2, Oz, P3, P4, T8 and C3 regions of brain. The proposed computational model had identified the potential neural pathways during the execution of emotional task.

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change.

A Neural Network Model for Color Element Data Analysis for Urban Spatial Environment

In this paper, a CNN model for color element data analysis of the urban spatial environment is constructed through an in-depth study of color element data analysis. This paper investigates a high-order structure formed by a few nodes; it proposes a motif-based graph autoencoder MODEL, combining redefined first- and second-order similarities and perfectly integrating motif structure and autoencoder. The algorithm first proposes an efficient graph transformation method to add the influence of central nodes. It then offers a primary awareness mechanism to aggregate the information of noncentral neighbors. Cen GCN_D and Cen GCN_E outperform the latest algorithms in node classification, link prediction, node clustering, and network visualization. As the number of network layers increases, the advantages of these two variants become progressively more prominent. This paper uses a support vector machine to implement classification validation based on CNN. The experimental results show that when 450 images are randomly selected as training data, the classification accuracy obtained by using the features of different CNN output layers is distributed between 91.4% and 95.2%. When the training set of the experiment reaches more than 300, the accuracy can exceed 90%, and the experimental results corresponding to different training sets a more stable trend. Finally, the trained classifier model is obtained in this thesis, which achieves the purpose of fast classification prediction based on CNN for color element data analysis of urban spatial environments.

An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application

As smart cities (SCs) emerge, the Internet of Things (IoT) is able to simplify more sophisticated and ubiquitous applications employed within these cities. In this regard, we investigate seven predominant sectors including the environment, public transport, utilities, street lighting, waste management, public safety, and smart parking that have a great effect on SC development. Our findings show that for the environment sector, cleaner air and water systems connected to IoT-driven sensors are used to detect the amount of CO2, sulfur oxides, and nitrogen to monitor air quality and to detect water leakage and pH levels. For public transport, IoT systems help traffic management and prevent train delays, for the utilities sector IoT systems are used for reducing overall bills and related costs as well as electricity consumption management. For the street-lighting sector, IoT systems are used for better control of streetlamps and saving energy associated with urban street lighting. For waste management, IoT systems for waste collection and gathering of data regarding the level of waste in the container are effective. In addition, for public safety these systems are important in order to prevent vehicle theft and smartphone loss and to enhance public safety. Finally, IoT systems are effective in reducing congestion in cities and helping drivers to find vacant parking spots using intelligent smart parking.

Unpacking the '15-Minute City' via 6G, IoT, and Digital Twins: Towards a New Narrative for Increasing Urban Efficiency, Resilience, and Sustainability

The ‘15-minute city’ concept is emerging as a potent urban regeneration model in post-pandemic cities, offering new vantage points on liveability and urban health. While the concept is primarily geared towards rethinking urban morphologies, it can be furthered via the adoption of Smart Cities network technologies to provide tailored pathways to respond to contextualised challenges through the advent of data mining and processing to better inform urban decision-making processes. We argue that the ‘15-minute city’ concept can value-add from Smart City network technologies in particular through Digital Twins, Internet of Things (IoT), and 6G. The data gathered by these technologies, and processed via Machine Learning techniques, can unveil new patterns to understand the characteristics of urban fabrics. Collectively, those dimensions, unpacked to support the ‘15-minute city’ concept, can provide new opportunities to redefine agendas to better respond to economic and societal needs as well as align more closely with environmental commitments, including the United Nations’ Sustainable Development Goal 11 and the New Urban Agenda. This perspective paper presents new sets of opportunities for cities arguing that these new connectivities should be explored now so that appropriate protocols can be devised and so that urban agendas can be recalibrated to prepare for upcoming technology advances, opening new pathways for urban regeneration and resilience crafting.

Safety and Risk Analysis of Autonomous Vehicles Using Computer Vision and Neural Networks

The autonomous vehicle (AVs) market is expanding at a rapid pace due to the advancement of information, communication, and sensor technology applications, offering a broad range of opportunities in terms of energy efficiency and addressing climate change concerns and safety. With regard to this last point, the rate of reduction in accidents is considerable when switching safety control tasks to machines from humans, which can be noted as having significantly slower response rates. This paper explores this thematic by focusing on the safety of AVs by thorough analysis of previously collected AV crash statistics and further discusses possible solutions for achieving increased autonomous vehicle safety. To achieve this, this technical paper develops a dynamic run-time safe assessment system, using the standard autonomous drive system (ADS), which is developed and simulated in case studies further in the paper. OpenCV methods for lane detection are developed and applied as robust control frameworks, which introduces the factor of vehicle crash predictability for the ego vehicle. The developed system is made to predict possible crashes by using a combination of machine learning and neural network methods, providing useful information for response mechanisms in risk scenarios. In addition, this paper explores the operational design domain (ODD) of the AV’s system and provides possible solutions to extend the domain in order to render vehicle operationality, even in safe mode. Additionally, three case studies are explored to supplement a discussion on the implementation of algorithms aimed at increasing curved lane detection ability and introducing trajectory predictability of neighbouring vehicles for an ego vehicle, resulting in lower collisions and increasing the safety of the AV overall. This paper thus explores the technical development of autonomous vehicles and is aimed at researchers and practitioners engaging in the conceptualisation, design, and implementation of safer AV systems focusing on lane detection and expanding AV safe state domains and vehicle trajectory predictability.

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

How does a (Smart) Age-Friendly Ecosystem Look in a Post-Pandemic Society?

COVID-19 has impacted not only the health of citizens, but also the various factors that make up our society, living environments, and ecosystems. This pandemic has shown that future living will need to be agile and flexible to adapt to the various changes in needs of societal populations. Digital technology has played an integral role during COVID-19, assisting various sectors of the community, and demonstrating that smart cities can provide opportunities to respond to many future societal challenges. In the decades ahead, the rise in aging populations will be one of these challenges, and one in which the needs and requirements between demographic cohorts will vary greatly. Although we need to create future smart age-friendly ecosystems to meet these needs, technology still does not feature in the WHO eight domains of an age-friendly city. This paper extends upon Marston and van Hoof's 'Smart Age-friendly Ecosystem' (SAfE) framework, and explores how digital technology, design hacking, and research approaches can be used to understand a smart age-friendly ecosystem in a post-pandemic society. By exploring a series of case studies and using real-life scenarios from the standpoint of COVID-19, we propose the 'Concept of Age-friendly Smart Ecologies (CASE)' framework. We provide an insight into a myriad of contemporary multi-disciplinary research, which are capable to initiate discussions and bring various actors together with a positive impact on future planning and development of age-friendly ecosystems. The strengths and limitations of this framework are outlined, with advantages evident in the opportunity for towns, regions/counties, provinces, and states to take an agile approach and work together in adopting and implement improvements for the greater benefits of residents and citizens.

Smart City-Ranking of Major Australian Cities to Achieve a Smarter Future

A Smart City is a solution to the problems caused by increasing urbanization. Australia has demonstrated a strong determination for the development of Smart Cities. However, the country has experienced uneven growth in its urban development. The purpose of this study is to compare and identify the smartness of major Australian cities to the level of development in multi-dimensions. Eventually, the research introduces the openings to make cities smarter by identifying the focused priority areas. To ensure comprehensive coverage of all aspects of the smart city’s performance, 90 indicators were selected to represent 26 factors and six components. The results of the assessment endorse the impacts of recent government actions taken in different urban areas towards building smarter cities. The research has pointed out the areas of deficiencies for underperforming major cities in Australia. Following the results, appropriate recommendations for Australian cities are provided to improve the city’s smartness.

On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management

As the Coronavirus (COVID-19) expands its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. The placing of entire cities in ‘lockdown’ directly affects urban economies on a multi-lateral level, including from social and economic standpoints. This is being emphasised as the outbreak gains ground in other countries, leading towards a global health emergency, and as global collaboration is sought in numerous quarters. However, while effective protocols in regard to the sharing of health data is emphasised, urban data, on the other hand, specifically relating to urban health and safe city concepts, is still viewed from a nationalist perspective as solely benefiting a nation’s economy and its economic and political influence. This perspective paper, written one month after detection and during the outbreak, surveys the virus outbreak from an urban standpoint and advances how smart city networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same.

Mobile Computing for Disaster Emergency Management: Empirical Requirements Analysis for a Cooperative Crowdsourced System for Emergency Management Operation

In large-scale civil emergencies such as floods, earthquakes, and extreme weather conditions, extended geographic areas and a great number of people may be affected by the unfortunate events. The wireless internet and the widespread diffusion of smart-phones and mobile devices make it possible to introduce new systems for emergency management. These systems could improve the efficiency of the interventions by transferring information between affected areas and a central decision support system. Information on the state of the infrastructures, on people displacement, and on every other important and urgent issue can be gathered in the disaster area. The central system can manage all the received information and communicate decisions back to people and also facilitate the exchange of information for different people that are still in the disaster area. This paper presents a requirement analysis for these kinds of systems. The presented analysis allows better tailoring of the features of these systems with the aim to meet the real need of emergency management operators and citizens.

The Emergence of Voluntary Citizen Networks to Circumvent Urban Health Data Sharing Restrictions During Pandemics

COVID-19 has impacted the global landscape well beyond initial estimates, impacting on both societal and economic fronts. Immediate responses by corporations and governments were geared toward building knowledge so that accurate and efficient programs could be devised toward curbing the impacts of the pandemic on society. However, one aspect to this was noted as to the limited availability of data sharing across platforms, systems, and jurisdictions, leading to limited datasets, hence, rendering inaccurate predictions that can be used to contain and limit the virus outbreak. In view of required immediate actions, volunteered geographic information (VGI) and citizen science concept have emerged, where people voluntarily share location and health status data to circumvent data sharing restrictions imposed upon corporations and governments. This is leading to more accurate predictions and supporting an emergence of alternative tools. This chapter explores this dimension and outlines how people, previously aggressively resisting data sharing, do so willingly in times of emergencies.

The Forceful Reevaluation of Cash-Based Transactions by COVID-19 and Its Opportunities to Transition to Cashless Systems in Digital Urban Networks

Cash-based transactions have been losing in popularity due to the high cost associated to maintaining the cash system and due to its numerous risks. Through the advent of the fourth industrial revolution, it was believed that a rapid transition to a more flexible, secure, and open cashless system would be accelerated. However, numerous hurdles in regard to legislation, protocols, and resistance to change were noted, rendering a slow adoption rate. However, the advent of COVID-19 pandemic and the noted potential contamination of bank notes and coinage revealed a steep support toward cashless payments and supported an immediate need to amend legislations and protocols to maintain the efficiency and stability of banking systems while catering for the shift in user demand. This chapter surveys the shift created by the COVID-19 pandemic from cash-based to cashless systems in cities and unveils how payment giants and information and communication technology corporations have been active in providing the underlaying foundation that would be critical in supporting the transition.

Sustainable Cities: A Reflection on Potentialities and Limits based on Existing Eco-Districts in Europe

The need for more sustainable cities has become a primary objective of urban strategies. The urgency for a radical transition towards sustainability in a long term-vision has brought with it several new concepts, such as smart urbanism, and models, such as smart city, eco-city, sustainable neighborhood, eco-district, etc. While these terms are fascinating and visionary, they often lack a clear definition both in terms of theoretical insight and empirical evidence. In this light, this contribution aims at defining a conceptual framework through which to further substantiate the blurred concept of eco-district and sustainable neighborhood. It does so by reviewing the concepts of smart urbanism and sustainable neighborhood/eco-districts in the literature, including also references to other well-known sustainability-oriented models of urban development. It then explores whether several indicators, emerging from the analysis of exemplary case studies of sustainable neighborhoods in Europe, can be used to clearly identify the characteristics of a sustainable approach at the district scale. The analysis, built on a review of existing literature, allows for both the clarification of several issues related to these fields of inquiry, as well as for the identification of the potential bridges to link these issues.

AI-Based Physical and Virtual Platform with 5-Layered Architecture for Sustainable Smart Energy City Development

To build sustainable smart energy cities (SECs) around the world, many countries are now combining customized services and businesses within their energy infrastructure and urban environments. Such changes could then promote the development of platforms that ultimately provide benefits for citizens such as convenience, safety, and cost savings. Currently, the development of technologies for SECs focuses on independent products and unit technology. However, this is problematic, as it may not be possible to develop sustainable cities if there is a lack of connectivity between various elements within the SEC. To solve such problems, this paper presents an AI-based physical and virtual platform using a 5-layer architecture to develop a sustainable smart energy city (SSEC). The architecture employs both a top-down and bottom-up approach and the links between each energy element in the SSEC can readily be analyzed. The economic analysis based on return on investment (ROI) is carried out by comparing the economic benefits before and after the application of this system. Deploying the proposed platform will enable the speedy development and application of new services for SSECs and will provide SSECs with measures to ensure sustainable development, such as rapid urban development, and cost reductions.