A Multiprocessor Platform Based on FPGA Technology Targeted for a Driver Vigilance Monitoring Device

Medical devices processing images or audio or executing complex AI algorithms are able to run more efficiently and meet real time requirements if the parallelism in those algorithms is exploited. In this research a methodology is proposed to exploit the flexibility and short design cycle of FPGAs (Field Programmable Gate Arrays) in order to achieve this target. Hardware/software co-design and dynamic partitioning allow the optimization of the multiprocessor platform design parameters and software code targeting each core to meet real time constraints. This is practically demonstrated by building a real life driver vigilance monitoring system based on visual cues extraction and evaluation. The application drives the whole design process to prove its effectiveness. An algorithm was built to achieve the goal of detecting the eye state of the driver (open or closed) and it is applied on captured consecutive frames to evaluate the vigilance state of the driver. Vigilance state is measured depending on duration of eye closure. This video processing application is then targeted to run on a multi-core FPGA based processing platform using the proposed methodology. Results obtained were very good using the Grimace Face Database and when operating the system on one’s face. On operating the device, a false positive of eye closure must take place two consecutive times in order to get an alarm, which decreases the probability of failure. The timing analysis applied proved the importance of using the concept of parallelism to achieve performance constraints. FPGA technology proved to be a very powerful prototyping tool for complex multiprocessor systems design. The flexible FPGA technology coupled with hardware/software co-design provided means to explore the design space and reach decisions that satisfy the design constraints with minimum time investment and cost

Electronic systems for the restoration of the sense of touch in upper limb prosthetics

In the last few years, research on active prosthetics for upper limbs focused on improving the human functionalities and the control. New methods have been proposed for measuring the user muscle activity and translating it into the prosthesis control commands. Developing the feed-forward interface so that the prosthesis better follows the intention of the user is an important step towards improving the quality of life of people with limb amputation. However, prosthesis users can neither feel if something or someone is touching them over the prosthesis and nor perceive the temperature or roughness of objects. Prosthesis users are helped by looking at an object, but they cannot detect anything otherwise. Their sight gives them most information. Therefore, to foster the prosthesis embodiment and utility, it is necessary to have a prosthetic system that not only responds to the control signals provided by the user, but also transmits back to the user the information about the current state of the prosthesis. This thesis presents an electronic skin system to close the loop in prostheses towards the restoration of the sense of touch in prosthesis users. The proposed electronic skin system inlcudes an advanced distributed sensing (electronic skin), a system for (i) signal conditioning, (ii) data acquisition, and (iii) data processing, and a stimulation system. The idea is to integrate all these components into a myoelectric prosthesis. Embedding the electronic system and the sensing materials is a critical issue on the way of development of new prostheses. In particular, processing the data, originated from the electronic skin, into low- or high-level information is the key issue to be addressed by the embedded electronic system. Recently, it has been proved that the Machine Learning is a promising approach in processing tactile sensors information. Many studies have been shown the Machine Learning eectiveness in the classication of input touch modalities.More specically, this thesis is focused on the stimulation system, allowing the communication of a mechanical interaction from the electronic skin to prosthesis users, and the dedicated implementation of algorithms for processing tactile data originating from the electronic skin. On system level, the thesis provides design of the experimental setup, experimental protocol, and of algorithms to process tactile data. On architectural level, the thesis proposes a design ow for the implementation of digital circuits for both FPGA and integrated circuits, and techniques for the power management of embedded systems for Machine Learning algorithms

Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces

Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen Geräten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur für Menschen mit neurologischen Verletzungen entwickelt, sondern auch für ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfänglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser Bemühungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein großes Potenzial für eine Vielzahl von Anwendungen, auch für weniger stark eingeschränkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hängt jedoch auch von der Verfügbarkeit zuverlässiger BCI-Hardware ab, die den Einsatz in der realen Welt gewährleistet. Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was Flexibilität und Effizienz bei der EEG-Signalverarbeitung gewährleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewährleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschließlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller Mobilität. Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die Flexibilität des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die für verschiedene BCI-Anwendungen erforderlich ist. Darüber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung für mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht. Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte Leistungsfähigkeit und Ausstattung für ein mobiles BCI. Es erfüllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg für eine schnelle Übertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf für die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard für BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application. The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors. The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies. Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

Distributed Sensing and Stimulation Systems Towards Sense of Touch Restoration in Prosthetics

Modern prostheses aim at restoring the functional and aesthetic characteristics of the lost limb. To foster prosthesis embodiment and functionality, it is necessary to restitute both volitional control and sensory feedback. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing high-fidelity spatial information. To provide this type of feedback in prosthetics, it is necessary to sense tactile information from artificial skin placed on the prosthesis and transmit tactile feedback above the amputation in order to map the interaction between the prosthesis and the environment. This thesis proposes the integration of distributed sensing systems (e-skin) to acquire tactile sensation, and non-invasive multichannel electrotactile feedback and virtual reality to deliver high-bandwidth information to the user. Its core focus addresses the development and testing of close-loop sensory feedback human-machine interface, based on the latest distributed sensing and stimulation techniques for restoring the sense of touch in prosthetics. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives and the used methodology and contributions; as well as three studies distributed over stimulation system level and sensing system level. The first study presents the development of close-loop compensatory tracking system to evaluate the usability and effectiveness of electrotactile sensory feedback in enabling real-time close-loop control in prosthetics. It examines and compares the subject\u2019s adaptive performance and tolerance to random latencies while performing the dynamic control task (i.e. position control) and simultaneously receiving either visual feedback or electrotactile feedback for communicating the momentary tracking error. Moreover, it reported the minimum time delay needed for an abrupt impairment of users\u2019 performance. The experimental results have shown that electrotactile feedback performance is less prone to changes with longer delays. However, visual feedback drops faster than electrotactile with increased time delays. This is a good indication for the effectiveness of electrotactile feedback in enabling close- loop control in prosthetics, since some delays are inevitable. The second study describes the development of a novel non-invasive compact multichannel interface for electrotactile feedback, containing 24 pads electrode matrix, with fully programmable stimulation unit, that investigates the ability of able-bodied human subjects to localize the electrotactile stimulus delivered through the electrode matrix. Furthermore, it designed a novel dual parameter -modulation (interleaved frequency and intensity) and compared it to conventional stimulation (same frequency for all pads). In addition and for the first time, it compared the electrotactile stimulation to mechanical stimulation. More, it exposes the integration of virtual prosthesis with the developed system in order to achieve better user experience and object manipulation through mapping the acquired real-time collected tactile data and feedback it simultaneously to the user. The experimental results demonstrated that the proposed interleaved coding substantially improved the spatial localization compared to same-frequency stimulation. Furthermore, it showed that same-frequency stimulation was equivalent to mechanical stimulation, whereas the performance with dual-parameter modulation was significantly better. The third study presents the realization of a novel, flexible, screen- printed e-skin based on P(VDF-TrFE) piezoelectric polymers, that would cover the fingertips and the palm of the prosthetic hand (particularly the Michelangelo hand by Ottobock) and an assistive sensorized glove for stroke patients. Moreover, it developed a new validation methodology to examine the sensors behavior while being solicited. The characterization results showed compatibility between the expected (modeled) behavior of the electrical response of each sensor to measured mechanical (normal) force at the skin surface, which in turn proved the combination of both fabrication and assembly processes was successful. This paves the way to define a practical, simplified and reproducible characterization protocol for e-skin patches In conclusion, by adopting innovative methodologies in sensing and stimulation systems, this thesis advances the overall development of close-loop sensory feedback human-machine interface used for restoration of sense of touch in prosthetics. Moreover, this research could lead to high-bandwidth high-fidelity transmission of tactile information for modern dexterous prostheses that could ameliorate the end user experience and facilitate it acceptance in the daily life

Computing with noise in spiking neural networks

Trial-to-trial variability is an ubiquitous characteristic in neural firing patterns and is often regarded as a side-effect of intrinsic noise. Increasing evidence indicates that this variability is a signature of network computation. The computational role of noise is not yet clear and existing frameworks use abstract models for stochastic computation. In this work, we use networks of spiking neurons to perform stochastic inference by sam- pling. We provide a novel analytical description of the neural response function with an unprecedented range of validity. This description enables an implementation of spiking networks in simulations to sample from Boltzmann distributions. We show the robust- ness of these networks to parameter variations and highlight the substantial advantages of short-term plasticity in our framework. We demonstrate accelerated inference on neu- romorphic hardware with a speed-up of 10^4 compared to biological networks, regardless of network size. We further explore the role of noise as a computational component in our sampling networks and identify the functional equivalence between synaptic connec- tions and mutually shared noise. Based on this, we implement interconnected sampling ensembles which exploit their activity as noise resource to maintain a stochastic firing regime

A gaze-contingent framework for perceptually-enabled applications in healthcare

Patient safety and quality of care remain the focus of the smart operating room of the future. Some of the most influential factors with a detrimental effect are related to suboptimal communication among the staff, poor flow of information, staff workload and fatigue, ergonomics and sterility in the operating room. While technological developments constantly transform the operating room layout and the interaction between surgical staff and machinery, a vast array of opportunities arise for the design of systems and approaches, that can enhance patient safety and improve workflow and efficiency. The aim of this research is to develop a real-time gaze-contingent framework towards a "smart" operating suite, that will enhance operator's ergonomics by allowing perceptually-enabled, touchless and natural interaction with the environment. The main feature of the proposed framework is the ability to acquire and utilise the plethora of information provided by the human visual system to allow touchless interaction with medical devices in the operating room. In this thesis, a gaze-guided robotic scrub nurse, a gaze-controlled robotised flexible endoscope and a gaze-guided assistive robotic system are proposed. Firstly, the gaze-guided robotic scrub nurse is presented; surgical teams performed a simulated surgical task with the assistance of a robot scrub nurse, which complements the human scrub nurse in delivery of surgical instruments, following gaze selection by the surgeon. Then, the gaze-controlled robotised flexible endoscope is introduced; experienced endoscopists and novice users performed a simulated examination of the upper gastrointestinal tract using predominately their natural gaze. Finally, a gaze-guided assistive robotic system is presented, which aims to facilitate activities of daily living. The results of this work provide valuable insights into the feasibility of integrating the developed gaze-contingent framework into clinical practice without significant workflow disruptions.Open Acces

Augmentation of Brain Function: Facts, Fiction and Controversy. Volume III: From Clinical Applications to Ethical Issues and Futuristic Ideas

The final volume in this tripartite series on Brain Augmentation is entitled “From Clinical Applications to Ethical Issues and Futuristic Ideas”. Many of the articles within this volume deal with translational efforts taking the results of experiments on laboratory animals and applying them to humans. In many cases, these interventions are intended to help people with disabilities in such a way so as to either restore or extend brain function. Traditionally, therapies in brain augmentation have included electrical and pharmacological techniques. In contrast, some of the techniques discussed in this volume add specificity by targeting select neural populations. This approach opens the door to where and how to promote the best interventions. Along the way, results have empowered the medical profession by expanding their understanding of brain function. Articles in this volume relate novel clinical solutions for a host of neurological and psychiatric conditions such as stroke, Parkinson’s disease, Huntington’s disease, epilepsy, dementia, Alzheimer’s disease, autism spectrum disorders (ASD), traumatic brain injury, and disorders of consciousness. In disease, symptoms and signs denote a departure from normal function. Brain augmentation has now been used to target both the core symptoms that provide specificity in the diagnosis of a disease, as well as other constitutional symptoms that may greatly handicap the individual. The volume provides a report on the use of repetitive transcranial magnetic stimulation (rTMS) in ASD with reported improvements of core deficits (i.e., executive functions). TMS in this regard departs from the present-day trend towards symptomatic treatment that leaves unaltered the root cause of the condition. In diseases, such as schizophrenia, brain augmentation approaches hold promise to avoid lengthy pharmacological interventions that are usually riddled with side effects or those with limiting returns as in the case of Parkinson’s disease. Brain stimulation can also be used to treat auditory verbal hallucination, visuospatial (hemispatial) neglect, and pain in patients suffering from multiple sclerosis. The brain acts as a telecommunication transceiver wherein different bandwidth of frequencies (brainwave oscillations) transmit information. Their baseline levels correlate with certain behavioral states. The proper integration of brain oscillations provides for the phenomenon of binding and central coherence. Brain augmentation may foster the normalization of brain oscillations in nervous system disorders. These techniques hold the promise of being applied remotely (under the supervision of medical personnel), thus overcoming the obstacle of travel in order to obtain healthcare. At present, traditional thinking would argue the possibility of synergism among different modalities of brain augmentation as a way of increasing their overall effectiveness and improving therapeutic selectivity. Thinking outside of the box would also provide for the implementation of brain-to-brain interfaces where techniques, proper to artificial intelligence, could allow us to surpass the limits of natural selection or enable communications between several individual brains sharing memories, or even a global brain capable of self-organization. Not all brains are created equal. Brain stimulation studies suggest large individual variability in response that may affect overall recovery/treatment, or modify desired effects of a given intervention. The subject’s age, gender, hormonal levels may affect an individual’s cortical excitability. In addition, this volume discusses the role of social interactions in the operations of augmenting technologies. Finally, augmenting methods could be applied to modulate consciousness, even though its neural mechanisms are poorly understood. Finally, this volume should be taken as a debate on social, moral and ethical issues on neurotechnologies. Brain enhancement may transform the individual into someone or something else. These techniques bypass the usual routes of accommodation to environmental exigencies that exalted our personal fortitude: learning, exercising, and diet. This will allow humans to preselect desired characteristics and realize consequent rewards without having to overcome adversity through more laborious means. The concern is that humans may be playing God, and the possibility of an expanding gap in social equity where brain enhancements may be selectively available to the wealthier individuals. These issues are discussed by a number of articles in this volume. Also discussed are the relationship between the diminishment and enhancement following the application of brain-augmenting technologies, the problem of “mind control” with BMI technologies, free will the duty to use cognitive enhancers in high-responsibility professions, determining the population of people in need of brain enhancement, informed public policy, cognitive biases, and the hype caused by the development of brain- augmenting approaches

Innovative Technologies and Services for Smart Cities

A smart city is a modern technology-driven urban area which uses sensing devices, information, and communication technology connected to the internet of things (IoTs) for the optimum and efficient utilization of infrastructures and services with the goal of improving the living conditions of citizens. Increasing populations, lower budgets, limited resources, and compatibility of the upgraded technologies are some of the few problems affecting the implementation of smart cities. Hence, there is continuous advancement regarding technologies for the implementation of smart cities. The aim of this Special Issue is to report on the design and development of integrated/smart sensors, a universal interfacing platform, along with the IoT framework, extending it to next-generation communication networks for monitoring parameters of interest with the goal of achieving smart cities. The proposed universal interfacing platform with the IoT framework will solve many challenging issues and significantly boost the growth of IoT-related applications, not just in the environmental monitoring domain but in the other key areas, such as smart home, assistive technology for the elderly care, smart city with smart waste management, smart E-metering, smart water supply, intelligent traffic control, smart grid, remote healthcare applications, etc., signifying benefits for all countries