Security protocols suite for machine-to-machine systems

Nowadays, the great diffusion of advanced devices, such as smart-phones, has shown that there is a growing trend to rely on new technologies to generate and/or support progress; the society is clearly ready to trust on next-generation communication systems to face today’s concerns on economic and social fields. The reason for this sociological change is represented by the fact that the technologies have been open to all users, even if the latter do not necessarily have a specific knowledge in this field, and therefore the introduction of new user-friendly applications has now appeared as a business opportunity and a key factor to increase the general cohesion among all citizens. Within the actors of this technological evolution, wireless machine-to-machine (M2M) networks are becoming of great importance. These wireless networks are made up of interconnected low-power devices that are able to provide a great variety of services with little or even no user intervention. Examples of these services can be fleet management, fire detection, utilities consumption (water and energy distribution, etc.) or patients monitoring. However, since any arising technology goes together with its security threats, which have to be faced, further studies are necessary to secure wireless M2M technology. In this context, main threats are those related to attacks to the services availability and to the privacy of both the subscribers’ and the services providers’ data. Taking into account the often limited resources of the M2M devices at the hardware level, ensuring the availability and privacy requirements in the range of M2M applications while minimizing the waste of valuable resources is even more challenging. Based on the above facts, this Ph. D. thesis is aimed at providing efficient security solutions for wireless M2M networks that effectively reduce energy consumption of the network while not affecting the overall security services of the system. With this goal, we first propose a coherent taxonomy of M2M network that allows us to identify which security topics deserve special attention and which entities or specific services are particularly threatened. Second, we define an efficient, secure-data aggregation scheme that is able to increase the network lifetime by optimizing the energy consumption of the devices. Third, we propose a novel physical authenticator or frame checker that minimizes the communication costs in wireless channels and that successfully faces exhaustion attacks. Fourth, we study specific aspects of typical key management schemes to provide a novel protocol which ensures the distribution of secret keys for all the cryptographic methods used in this system. Fifth, we describe the collaboration with the WAVE2M community in order to define a proper frame format actually able to support the necessary security services, including the ones that we have already proposed; WAVE2M was funded to promote the global use of an emerging wireless communication technology for ultra-low and long-range services. And finally sixth, we provide with an accurate analysis of privacy solutions that actually fit M2M-networks services’ requirements. All the analyses along this thesis are corroborated by simulations that confirm significant improvements in terms of efficiency while supporting the necessary security requirements for M2M networks

A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics

Mesoscopic simulations of hydrocarbon flow in source shales are challenging, in part due to the heterogeneous shale pores with sizes ranging from a few nanometers to a few micrometers. Additionally, the sub-continuum fluid-fluid and fluid-solid interactions in nano- to micro-scale shale pores, which are physically and chemically sophisticated, must be captured. To address those challenges, we present a GPU-accelerated package for simulation of flow in nano- to micro-pore networks with a many-body dissipative particle dynamics (mDPD) mesoscale model. Based on a fully distributed parallel paradigm, the code offloads all intensive workloads on GPUs. Other advancements, such as smart particle packing and no-slip boundary condition in complex pore geometries, are also implemented for the construction and the simulation of the realistic shale pores from 3D nanometer-resolution stack images. Our code is validated for accuracy and compared against the CPU counterpart for speedup. In our benchmark tests, the code delivers nearly perfect strong scaling and weak scaling (with up to 512 million particles) on up to 512 K20X GPUs on Oak Ridge National Laboratory's (ORNL) Titan supercomputer. Moreover, a single-GPU benchmark on ORNL's SummitDev and IBM's AC922 suggests that the host-to-device NVLink can boost performance over PCIe by a remarkable 40\%. Lastly, we demonstrate, through a flow simulation in realistic shale pores, that the CPU counterpart requires 840 Power9 cores to rival the performance delivered by our package with four V100 GPUs on ORNL's Summit architecture. This simulation package enables quick-turnaround and high-throughput mesoscopic numerical simulations for investigating complex flow phenomena in nano- to micro-porous rocks with realistic pore geometries

EFFECTS OF NEUROMODULATION ON NEUROVASCULAR COUPLING

The communication between neurons within neural circuits relies on neurotransmitters (glutamate, γ-aminobutyric acid (GABA)) and neuromodulators (acetylcholine, dopamine, serotonin, etc.). However, despite sharing similar molecular elements, neurotransmitters and neuromodulators are distinct classes of molecules and mediate different aspects of neural activity and metabolism. Neurotransmitters on one hand are responsible for synaptic signal transmission (classical transmission) while neuromodulators exert their functions by mediating different postsynaptic events that result in changes to the balance between excitation and inhibition. Neuromodulation, while essential to nervous system function, has been significantly more difficult to study than neurotransmission. This is principally due to the fact that effects elicited by neuromodulators are usually of slow onset, long lasting, and are not simply excitation or inhibition. In contrast to the effects of neurotransmitters, neuromodulators enable neurons to be more flexible in their ability to encode different sorts of information (e.g. sensory information) on a variety of time scales. However, it is important to appreciate that one of the challenges in the study of neuromodulation is to understand the extent to which neuromodulators’ actions are coordinated at all levels of brain function. That is, from the cellular and metabolic level to network and cognitive control. Therefore, understanding the molecules that mediate brain networks interactions is essential to understanding the brain dynamic, and also helps to put the cellular and molecular processes in perspective. Functional magnetic resonance imaging (fMRI) is a technique that allows access to various cellular and metabolic aspects of network communication that are difficult to access when studying one neuron at the time. Its non-invasiveness nature allows the comparison of data and hypotheses of the primate brain to that of the human brain. Hence, understanding the effects of neuromodulation on local microcircuits is needed. Furthermore, given the massive projections of the neuromodulatory diffuse ascending systems, fMRI combined with pharmacological and neurophysiological methods may provide true insight into their organization and dynamics. However, little is known about how to interpret the effects of neuromodulation in fMRI and neurophysiological data, for instance, how to disentangle blood oxygenation level dependent (BOLD) signal changes relating to cognitive changes (presumably neuromodulatory influences) from stimulus-driven or perceptual effects. The purpose of this dissertation is to understand the causal relationship between neural activity and hemodynamic responses under the influence of neuromodulation. To this end we present the results of six studies. In the first study, we aimed to establish a mass-spectrometry-based technique to uncover the distribution of different metabolites, neurotransmitters and neuromodulators in the macaque brain. We simultaneously measured the concentrations of these biomolecules in brain and in blood. In a second study, we developed a multimodal approach consisting of fMRI (BOLD and cerebral blood flow or CBF), electrophysiological recording with a laminar probe and pharmacology to assess the effects of neuromodulation on neurovascular coupling. We developed a pharmacological injection delivery system using pressure-operated pumps to reliably apply drugs either systemically or intracortically in the NMR scanner. In our third study, we systemically injected lactate and pyruvate to explore whether the plasma concentration of either of these metabolites affects the BOLD responses. This is important given that both metabolites are in a metabolic equilibrium; if this equilibrium is disrupted, changes in the NAD and NADH concentrations would elicit changes in the CBF. In a fourth study, we explored the influence of dopaminergic (DAergic) neuromodulation in the BOLD, CBF and neurophysiological activity. Here we found that DAergic neuromodulation dissociated the BOLD responses from the underlying neural activity. Interestingly, the changes in the neural activity were tightly coupled to the effects seen in the CBF responses. In a subsequent study, we explored whether the effects of dopamine (DA) on the electrophysiological responses are cortical layer dependent and whether specific patterns of neural activity can be used to infer the effects of neuromodulation on the neural activity. This is important, given that different types of neural activity provide independent information about the amplitude and dynamics from BOLD responses, and studies have shown that these bands originate from different cortical layers. What this study revealed, is that local field potentials (LFPs) in the midrange frequencies can indeed provide indications about the sustained effects of neuromodulation on cortical sensory processing. Given the results from the previous study, in our sixth study, we aimed at understanding how different cortical layers may process incoming and outgoing information in the different LFP bands. These findings provide evidence that neuromodulation has profound effects on neurovascular coupling. By changing the excitation-inhibition balance of neural circuits, neuromodulators not only mediate the neural activity, but also adjust the metabolic demands. Therefore, understanding how the different types of neuromodulators affect the BOLD response is essential for an effective interpretation of fMRI-data, not only in tasks involving attentional and reward-related processes, but also for future diagnostic use of fMRI, since many psychiatric disorders are the result of alterations in neuromodulatory systems.Die Kommunikation zwischen den Neuronen innerhalb neuronalen Schaltkreise beruht auf Neurotransmitter (Glutamat, γ-Aminobuttersäure (GABA)) und Neuromodulatoren (Acetylcholin, Dopamin, Serotonin, etc.). Neurotransmitter und Neuromodulatoren sind jedoch unterschiedliche Klassen von Molekülen und verschiedenen Aspekte der neuronalen Aktivität und den Stoffwechsel vermitteln. Neurotransmitters sind einerseits verantwortlich für die synaptische Signalübertragung (klassische Übertragung), während ihre Funktionen ausüben, Neuromodulatoren durch verschiedene postsynaptischen Ereignisse zu vermitteln, die in Änderungen an der Balance zwischen Erregung und Hemmung führen. Neuromodulation , während wesentlich Funktion des Nervensystems hat sich als Neurotransmission wesentlich schwieriger gewesen, zu studieren. Dies ist hauptsächlich auf die Tatsache zurückzuführen, die durch Neuromodulatoren sind in der Regel von langsamen Beginn, langlebig, und sind nicht einfach Anregung oder Hemmung ausgelöst beeinflusst. Im Gegensatz zu den Wirkungen von Neurotransmittern, Neuromodulatoren ermöglichen Neuronen flexibler zu sein in ihrer Fähigkeit, verschiedene Arten von Informationen (beispielsweise sensorische Informationen) auf einer Vielzahl von Zeitskalen zu kodieren. Im Gegensatz zu den Wirkungen von Neurotransmittern, Neuromodulatoren ermöglichen Neuronen flexibler zu sein in ihrer Fähigkeit, verschiedene Arten von Informationen (beispielsweise sensorische Informationen) auf einer Vielzahl von Zeitskalen zu kodieren. Im Gegensatz zu den Wirkungen von Neurotransmittern, Neuromodulatoren ermöglichen Neuronen flexibler zu sein in ihrer Fähigkeit, verschiedene Arten von Informationen (beispielsweise sensorische Informationen) auf einer Vielzahl von Zeitskalen zu kodieren. Jedoch ist es wichtig, dass eine der Herausforderungen bei der Untersuchung von Neuromodulations zu schätzen ist, das Ausmaß, in dem Neuromodulatoren Aktionen koordiniert sind auf allen Ebenen der Gehirnfunktion zu verstehen. Das heißt, von der zellulären und metabolischen Ebene zu vernetzen und kognitive Kontrolle. Daher die Moleküle zu verstehen, die Gehirn Netzwerke Interaktionen vermitteln ist wesentlich für das Verständnis des Gehirns dynamisch, und hilft auch, die zellulären und molekularen Prozesse in Perspektive zu setzen. Funktionellen Kernspintomographie (fMRI) ist eine Technik, die Zugang zu verschiedenen zellulären und metabolischen Aspekte der Netzwerk-Kommunikation ermöglicht, die schwer zugänglich sind, wenn zu der Zeit eines Neurons zu studieren. Seine nicht-Invasivität Natur ermöglicht den Vergleich von Daten und Hypothesen des Primatengehirn zu der des menschlichen Gehirns. Somit wurde das Verständnis der Auswirkungen der Neuromodulation auf lokale Mikro benötigt. Darüber hinaus sind die massiven Projektionen der neuromodulatorischen diffuse Aufstiegsanlagen gegeben, kombiniert fMRI mit pharmakologischen und neurophysiologischen Methoden wahren Einblick in ihre Organisation und Dynamik liefern. Allerdings ist nur wenig darüber bekannt, wie die Auswirkungen der Neuromodulations in fMRI und neurophysiologische Daten zu interpretieren, zum Beispiel, wie Blutoxydation pegelabhängig (BOLD) Signaländerungen in Bezug auf kognitive Veränderungen (vermutlich neuromodulatorischen Einflüsse) von Stimulus-driven oder Wahrnehmungseffekte zu entwirren. Der Zweck dieser Arbeit ist es, die kausale Beziehung zwischen neuronaler Aktivität und hämodynamischen Reaktionen unter dem Einfluss der Neuromodulations zu verstehen. Zu diesem Zweck stellen wir die Ergebnisse von sechs Studien. In der ersten Studie wollten wir eine auf Massenspektrometrie basierende Technik einzurichten, um die Verteilung von verschiedenen Metaboliten, Neurotransmittern und Neuromodulatoren in Makakengehirn aufzudeckenWir maßen gleichzeitig die Konzentrationen dieser Biomoleküle im Gehirn und im Blut. In einer zweiten Studie entwickelten wir einen multimodalen Ansatz, bestehend aus fMRI (BOLD und zerebralen Blutflusses oder CBF), elektrophysiologische Aufzeichnung mit einer laminaren Sonde und Pharmakologie, die Auswirkungen der Neuromodulation auf neurovaskulären Kopplung zu beurteilen. Wir entwickelten eine pharmakologische Injektionsverabreichungssystem druckbetriebenen Pumpen mit zuverlässiger Medikamente gelten entweder systemisch oder intrakortikale im NMR-Scanner. In unserer dritten Studie injizierten wir systemisch Laktat und Pyruvat zu untersuchen, ob die Plasmakonzentration von entweder dieser Metaboliten die BOLD-Antworten beeinflusst. Dies ist wichtig, dass beide gegeben Metaboliten in einem Stoffwechselgleichgewicht sind; wenn dieses Gleichgewicht gestört ist, Veränderungen in den NAD und NADH-Konzentrationen würden Veränderungen in der CBF entlocken. In einer vierten Studie untersuchten wir den Einfluss von dopaminergen (DA-erge) -Neuromodulation im BOLD, CBF und neurophysiologische Aktivität. Hier fanden wir, dass DAerge -Neuromodulation die BOLD-Antworten von der zugrunde liegenden neuronalen Aktivität distanzierte. Interessanterweise waren verbunden, um die Veränderungen in der neuronalen Aktivität eng auf die in den CBF Reaktionen gesehen Wirkungen. In einer nachfolgenden Studie untersuchten wir, ob die Wirkungen von Dopamin (DA) auf die elektrophysiologischen Reaktionen sind Rindenschicht abhängig, und ob bestimmte Muster der neuronalen Aktivität verwendet werden kann, die Wirkungen von Neuromodulations auf die neurale Aktivität zu schließen. Dies ist wichtig, da verschiedene Arten von neuralen Aktivität liefern unabhängige Informationen über die Amplitude und die Dynamik von BOLD-Antworten, und Studien haben gezeigt, dass diese Bands aus verschiedenen kortikalen Schichten stammen. Was diese Studie ergab, dass lokale Feldpotentiale (LFP) in den mittleren Frequenzen in der Tat Hinweise über die nachhaltige Wirkung der Neuromodulation auf die kortikale sensorische Verarbeitung zur Verfügung stellen kann. In Anbetracht der Ergebnisse der früheren Studie, in unserer sechsten Studie wollten wir auf das Verständnis, wie die verschiedenen kortikalen Schichten verarbeiten kann ein- und ausgehenden Informationen in den verschiedenen LFP-Bands. Diese Ergebnisse belegen, dass -Neuromodulation profunde Auswirkungen auf die neurovaskulären Kopplung hat. Durch die Veränderung der Erregungs Hemmung Gleichgewicht neuronaler Schaltkreise vermitteln Neuromodulatoren nicht nur die neurale Aktivität, sondern auch die metabolischen Anforderungen anzupassen. Daher verstehen, wie die verschiedenen Arten von Neuromodulatoren beeinflussen die BOLD-Antwort für eine effektive Interpretation von fMRI-Daten notwendig ist, nicht nur in Aufgaben attentional und Belohnung bezogenen Prozessen mit, sondern auch für zukünftige diagnostische Verwendung von fMRI, da viele psychiatrische Störungen sind das Ergebnis von Veränderungen in neuromodulatorischen Systemen.La comunicación de las neuronas en los circuitos neuronales depende de los neurotransmisores (glutamato, acido γ-amino-butírico o GABA) y los neuromoduladores (acetilcolina, dopamina, serotonina, etc.). Sin embargo, tanto neurotransmisores como neuromoduladores son diferentes clases de moléculas y median diferentes aspectos de la actividad neuronal y del metabolismo, a pesar de compartir elementos moleculares muy similares. Los neurotransmisores, por una lado, son responsables de la transmisión sináptica de la información mientras que los neuromoduladores median diferentes eventos pos-sinápticos que resultan en cambios en el balance de la excitación e inhibición. La influencia de la neuromodulación es esencial para la función del sistema nerviosos, sin embargo es más difícil de estudiar que neurotransmisión. Esto se debe a que los efectos de los neuromoduladores suelen ser de un inicio lento, de larga duración, y no reflejan excitación o inhibición. En contraste a los efectos de los neurotransmisores, los neuromoduladores permiten que las neuronas sean más flexibles en su habilidad de codificar diferentes tipos de información (por ejemplo, información sensorial) en varias escalas temporales. Sin embargo, es importante darse cuenta que uno de objetivos primordiales en el estudio de neuromodulación es el de entender el grado en que la acción de los neuromoduladores está coordinada a todos los niveles de la función cerebral. Es decir, desde los aspectos celulares y metabólicos hasta los niveles de redes neuronales y control cognitivo. Por lo tanto, comprender los forma en la que diferentes moléculas median la interacción entre redes neuronal es esencial para el entendimiento de la dinámica cerebral, y también nos ayudara a comprender los procesos celulares y moleculares asociados a la percepción. La resonancia magnética funcional (fMRI, por sus siglas en inglés) es una técnica que permite acceder a varios aspectos celulares y metabólicos de la comunicación entre redes neuronales que suele ser de difícil acceso. Al mismo tiempo y debido que la fMRI es de naturaleza no invasiva, también permite comparar resultados e hipótesis entre humanos y primates. Por lo tanto, entender los efectos de la neuromodulación en la actividad de los circuitos neuronales es de alta relevancia. Dado que las proyecciones anatómicas de los sistemas de neuromoduladores, el uso de fMRI en combinación con farmacología y neurofisiología puede incrementar nuestro conocimiento sobre la estructura y dinámica de los sistemas de neuromoduladores. Sin embargo, poco se sabe sobre cómo interpretar los efectos de neuromodulation usando fMRI y neurofisiología, por ejemplo, como diferenciar los cambios en la señal BOLD que están relacionados a diferentes estados cognitivos (presumiblemente reflejando la influencia de neuromodulation). El propósito de esta disertación es la de comprender la relación causal que existe entre la actividad neural y la respuesta hemodinámica bajo la influencia de neuromodulación. Para tal fin presentamos los resultados de seis estudios que fueron producto de esta disertacion. En el primer estudio, desarrollamos una técnica basada en espectrometría de masa para detectar y medir la concentración de diferente metabolitos, neurotransmisores y neuromoduladores en el cerebro de primates. Dicha cuantificación se desarrollo simultáneamente tanto in sangre y cerebro. En un segundo estudio, utilizamos varias técnicas de fMRI (BOLD y flujo cerebral sanguíneo, CBF por sus siglas en ingles), registros electrofisiológicos con electrodos laminares y farmacología para acceder a los efectos de neuromodulation en el acople neurovascular. Para este fin, desarrollamos un sistema de inyecciones, basada en cambios de presión, para aplicar substancias sistémicamente o intracorticalmente dentro de un escáner de resonancia magnética. En nuestro tercer estudio, comparamos los efectos de lactato y piruvato para explorar como el desequilibrio metabólico de estas dos substancias afecta la respuesta BOLD. Esto es de gran importancia ya que ambas substancias metabólicas usualmente están en equilibrio. Sin embargo, cuando dicho equilibrio es interrumpido, los procesos metabólicos que acontecen en la mitocondria afectan las concentraciones de NAD y NADH causado cambios en el CBF. En un cuarto estudio, exploramos los efectos de las modulación dopaminergica (DAergic) en las señales BOLD, CBF y en la actividad neuronal. Encontramos que la modulación DAergic disocia las respuesta BOLD de la respuesta neuronal. Interesalmente, los cambios que observamos en la actividad de las neuronas estaba altamente acoplados a los efectos que observamos en la señal de CBF. En un estudio subsecuente, exploramos si los efectos de dopamina en la actividad neuronal es diferentes en las distintas capas de la corteza cerebral. Al mismo tiempo y ya que los neuromoduladores afectan la actividad de circuitos neuronales, exploramos si dichos efectos pueden usados como marcadores de la influencia de la neuromodulación . Esto es importante, ya que diferentes tipos de actividad neuronal brinda información sobre la amplitud y dinámica de la repuesta BOLD, y estudies han demostrado que estas bandas se originan de diferentes capas cortical. Este estudio revelo, que los potenciales de capo (LFPs, por sus siglas en ingles) en frecuencias intermedias puede ser indicativos sobre los efectos de neuromodulation en el procesamiento cortical. Dado los resultados en el estudio previo, en un sexto estudio, nos enfocamos a entender que tan diferentes las capas de la corteza procesan información entrante y saliente en diferentes frecuencias de los LFPs. Estos descubrimientos demuestran que los efectos de los neuromoduladores tiene una fuerte influencia en el acople neurovascular. Los neuromoduladores cambian el balance de excitación e inhibición de los circuitos neuronal, pero también median las demandas metabólicas. De esta manera, entender cómo interpretar los efectos de los neuromoduladores en la respuesta BOLD es esencial para una interpretación veraz y efectiva de los datos generados con fMRI. Estos resultados, no solo nos permiten comprender los procesos que están relacionados a la atención o de varios procesos cognitivos, sino que a su vez, nos permite comprender la señal de fMRI para su futuro uso en la medicina diagnostica, ya que muchas enfermedades psiquiátricas están asociadas a trastornos en el sistemas neuromoduladores

Adaptive dynamical networks

It is a fundamental challenge to understand how the function of a network is related to its structural organization. Adaptive dynamical networks represent a broad class of systems that can change their connectivity over time depending on their dynamical state. The most important feature of such systems is that their function depends on their structure and vice versa. While the properties of static networks have been extensively investigated in the past, the study of adaptive networks is much more challenging. Moreover, adaptive dynamical networks are of tremendous importance for various application fields, in particular, for the models for neuronal synaptic plasticity, adaptive networks in chemical, epidemic, biological, transport, and social systems, to name a few. In this review, we provide a detailed description of adaptive dynamical networks, show their applications in various areas of research, highlight their dynamical features and describe the arising dynamical phenomena, and give an overview of the available mathematical methods developed for understanding adaptive dynamical networks

Towards unconventional computing through simulated evolution: Control of nonlinear media by a learning classifier system

We propose that the behavior of nonlinear media can be controlled automatically through evolutionary learning. By extension, forms of unconventional computing (viz., massively parallel nonlinear computers) can be realized by such an approach. In this initial study a light-sensitive subexcitable Belousov-Zhabotinsky reaction in which archeckerboard image, composed of cells of varying light intensity projected onto the surface of a thin silica gel impregnated with a catalyst and indicator, is controlled using a learning classifier system. Pulses of wave fragments are injected into the checkerboard grid, resulting in rich spatiotemporal behavior, and a learning classifier system is shown to be able to direct the fragments to an arbitrary position through dynamic control of the light intensity within each cell in both simulated and real chemical systems. Similarly, a learning classifier system is shown to be able to control the electrical stimulation of cultured neuronal networks so that they display elementary learning. Results indicate that the learned stimulation protocols identify seemingly fundamental properittes of in vitro neuronal networks. Use of another learning scheme presented in the literature confirms that such fundamental behavioral characteristics of a given network must be considered in training experiments. © 2008 Massachusetts Institute of Technology

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Investigating epileptiform activity associated with slow wave sleep

PhD ThesisThe characteristic EEG trait of patients with nocturnal idiopathic epilepsies during childhood is the spike and wave discharge. Cognitive dysfunction is prevalent among these patients and is thought to be linked to disturbances in memory consolidation processes that normally occur during slow wave sleep. Several genetic mutations of nicotinic receptor subunits have been linked to these disorders. However, there is little known about the underlying mechanisms or the spatiotemporal characteristics of this epileptiform activity within the neocortex. This thesis presents a rat in vitro model of the epileptiform activity synonymous with nocturnal childhood epilepsies, that allows for pharmacological manipulation of receptor subunits linked to these disorders. The application of DTC [10 M], a non-selective, competitive nicotinic acetylcholine receptor antagonist, to an in vitro model of the cortical delta rhythm induced two individual forms of paroxysm events - wave discharges and the conventional spike and wave discharges. Pharmacological manipulation of this model suggest that the epileptiform activity is mediated by excitatory currents which is consistent with the use of glutamate antagonists as anticonvulsants. A blanket blockade of inhibition by a GABAA antagonist resulted in severe discharges, hence hugely increasing excitatory response. Only partial disinhibition is suggested to be required to generate epileptiform activity as nicotinic acetylcholine receptors and 5-HT3 receptors are located on dendrite targeting interneurons. Mapping of unit activity revealed the di erence between the two paroxysm events was recruitment of super cial layers with simultaneous paroxysm events in delta frequency-generating Layer V pyramidal cells. It is proposed that the hyperexcitability responsible for the generation of the spike component of a spike and wave discharge is mediated by the lack of excitatory tone in 5-HT3 and nicotinic acetylecholine receptor expressing inhibitory interneuron subtypes. The disinhibition, spike generation and disruption of interplay between deep and super cial layers of the neocortex is thought to be associated with synaptic plastic changes

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications