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6G and beyond networks will merge communication and computation capabilities in order to adapt to changes. As they will consist of many sensors gathering information from its environment, new schemes for managing these large amounts of data are needed. For this purpose, we review Over the Air (OTA) computing in the context of estimation and detection. For distributed scenarios, such as a Wireless Sensor Network, it has been proven that a separation theorem does not necessarily hold, whereas analog schemes may outperform digital designs. We outline existing gaps in the literature, evincing that current state of the art requires a theoretical framework based on analog and hybrid digital-analog schemes that will boost the evolution of OTA computing. Furthermore, we motivate the development of 3D networks based on OTA schemes, where satellites function as sensors. We discuss its integration within the satellite segment, delineate current challenges and present a variety of use cases that benefit from OTA computing in 3D networks. © 2022 IEEE.

Exploring the possibility of using machines to achieve body gesture-based activity recognition, and even emotion understanding is a promising topic and drives this research. To facilitate research on this topic with computer vision methods, this thesis makes related contributions via four stages: regular body gesture recognition, micro-gesture dataset and analysis, 3D body gesture transfer and generation, and specific applications. For regular human gestures, two analysis methods are proposed that aim at temporal segmentation and recognition tasks. The first work proposes a novel temporal hierarchical dictionary for hidden Markov model transition with deep neural networks. Then, the second work extends the proposed temporal hierarchical dictionary to a more robust online segmentation and recognition of gesture dynamics. Next, we explore the possibility of emotion understanding from human gestures. In the field of psychology, a specific group of body gestures, called micro-gestures (MGs), are used to interpret the inner feelings of humans. To fill the gap in the research of spontaneous emotional gestures, we collect the first spontaneous MG dataset. A comprehensive analysis of MGs is then conducted, leading to interesting insights. Body gestures transfer and generation is another main research direction in this thesis. We try to achieve the 3D human body gesture transfer that can endow target 3D human models with desired MGs. Then, we research how to learn the disentanglement of 3D human pose and shape in an unsupervised setting. Furthermore, we research the generation of animated 3D sequences of a target human body model by directly taking the driving sequences as inputs. Lastly, we present an application for collaborative learning with gesture analysis in the education field. Specifically, we present an interdisciplinary work that introduces an explainable AI prototype for collaborative learning that seeks to provide interpretable insights with machine learning-based models. In summary, we illustrate the contributions of the work and conclude the advantages and limitations of the current work. Potential future work plans are also discussed. Tiivistelmä Ihmisillä on synnynnäinen kyky välittää ja ymmärtää monipuolista tietoa kehonliikkeiden avulla. Tällainen viestintä on läsnä lähes kaikkialla arjen elämässä. Tässä tutkimuksessa tarkastelemme koneen opettamista tunnistamaan toimia ja jopa ymmärtämään tunteita kehon eleiden perusteella. Väitöskirjatutkimuksessa tarkastelemme aihetta konenäkömenetelmillä ja jaamme tulokset neljään kategoriaan: tavanomaisten kehon eleiden tunnistus, mikroeleiden tietoaineisto ja analyysi, kehon eleiden siirtäminen kolmiulotteiseen malliin ja tuottaminen sillä sekä erityiset sovellukset. Tavanomaisten eleiden analyysia varten ehdotamme kahta menetelmää ajalliseen segmentointiin ja tunnistustoimintoihin. Ensimmäisessä työssä ehdotamme uutta, syviä neuroverkostoja hyödyntävää ajallis-hierarkkista sanastoa Markovin piilomallin siirtymille. Toisessa työssä laajennamme ehdotettua ajallis-hierarkkista sanastoa tehokkaammalla verkkopohjaisella segmentoinnilla ja eledynamiikan tunnistamisella. Viitekehys perustuu tila-ajalliseen tarkkaavaisuusverkostoon. Se hyödyntää Lien ryhmien monimuotoisia esityksiä ja oppii kuviot iteratiivisesti. Seuraavaksi tutkimme tunteiden ymmärtämistä ihmiseleistä. Psykologiassa kutsutaan mikroeleiksi tietynlaisten, tunteita ilmentävien ruumiineleiden ryhmää. Mikroeleet, kuten nenän koskettaminen, ovat hienovaraisia, spontaaneja ruumiineleitä, jotka voivat tahattomasti välittää tietoa piilotetuista tunteista. Spontaanien tunne-eleiden tutkimuksen aukon täyttämiseksi kokoamme ensimmäisen spontaaneihin mikroeleisiin keskittyvän tietoaineiston. Seuraavaksi suoritamme mikroeleiden kattavan analyysin, joka johtaa mielenkiintoisiin tuloksiin. Väitöskirjan toinen tärkeä tutkimussuunta on kehon eleiden siirtäminen ja tuottaminen. Yritämme siirtää eleitä kolmiulotteiseen ihmiskehon malliin mahdollistaaksemme haluttujen mikroeleiden tuottamisen. Tämän jälkeen tutkimme koneen opettamista erottamaan kolmiulotteiset asennot ja muodot valvomattomassa ympäristössä. Lisäksi tutkimme animoitujen kolmiulotteisten sekvenssien tuottamista ihmiskehon mallilla käyttämällä ajojaksoja suorina syötteinä. Lopuksi esittelemme eleiden analysointia hyödyntävän yhteistoiminnallisen oppimisen koulutussovelluksen. Tarkemmin sanottuna tarkastelemme poikkitieteellistä työtä, jossa luomme yhteistoiminnalliseen oppimiseen soveltuvan tekoälyn prototyypin, jonka tarkoitus on tuottaa ymmärrettävää tietoa koneoppimiseen perustuvien mallien avulla. Tiivistelmäosiossa havainnollistamme työn tuloksia ja pohdimme nykyisen tutkimuksen etuja ja rajoituksia. Lisäksi tarkastelemme mahdollisia jatkotutkimussuunnitelmia, kuten luotettavien tunnemallien hyödyntämistä ihmiseleiden analysoinnissa sekä 3D-teknologian yhdistämistä affektiiviseen laskentaan.

Understanding the mechanisms underlying cell-surface interaction is of fundamental importance for the rational design of scaffolds aiming at tissue engineering, tissue repair and neural regeneration applications. Here, we examined patterns of neuroblastoma cells cultured in three-dimensional polymeric scaffolds obtained by two-photon lithography. Because of the intrinsic resolution of the technique, the micrometric cylinders composing the scaffold have a lateral step size of ~200 nm, a surface roughness of around 20 nm, and large values of fractal dimension approaching 2.7. We found that cells in the scaffold assemble into separate groups with many elements per group. After cell wiring, we found that resulting networks exhibit high clustering, small path lengths, and small-world characteristics. These values of the topological characteristics of the network can potentially enhance the quality, quantity and density of information transported in the network compared to equivalent random graphs of the same size. This is one of the first direct observations of cells developing into 3D small-world networks in an artificial matrix.

Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-to-end packet headers. However, this information is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management, for preventing network intrusion, and for anomaly detection. 3D networks offer multiple routes that can guarantee different levels of QoS. Therefore, service classification and separation are essential to guarantee the required QoS level to each traffic sub-flow through the appropriate network trunk. In this paper, a federated feature selection and feature reduction learning scheme is proposed to classify network traffic in a semi-supervised cooperative manner. The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.

Implementation of tubular endothelial cell networks is a prerequisite for 3D tissue engineering of constructs with clinically relevant size as nourishment of cells is challenged by the diffusion limit. In vitro generation of 3D networks is often achieved under conditions using serum containing cell culture medium and/or animal derived matrices. Here, 3D endothelial cell networks were generated by using human umbilical vein endothelial cells (HUVECs) in combination with human adipose tissue derived stromal cells (hASCs) employing human collagen I as hydrogel and decellularized porcine small intestinal submucosa as starter matrix. Matrigel/rat tail collagen I hydrogel was used as control. Resulting constructs were cultivated either in serum-free medium or in endothelial growth medium-2 serving as control. Endothelial cell networks were quantified, tested for lumen formation, and interaction of HUVECs and hASCs. Tube diameter was slightly larger in constructs containing human collagen I compared to Matrigel/rat tail collagen I constructs under serum-free conditions. All other network parameters were mostly similar. Thereby, the feasibility of generating 3D endothelial cell networks under serum-free culture conditions in human collagen I as hydrogel was demonstrated. In summary, the presented achievements pave the way for the generation of clinical applicable constructs.

Hydrogen gas (H2) is being intensively proposed as a next-generation clean energy owing to the depletion of fossil fuels. Electrochemical water splitting is one of the most promising processes for hydrogen production. Furthermore, many efforts focusing on electrochemical water splitting have been made to develop low-cost, electrochemically active, and stable catalysts for efficient hydrogen production. MoS2 has emerged as an attractive material for developing catalysts for the hydrogen evolution reaction (HER). Hence, in this study, we design hierarchically ordinated two-dimensional (2D) MoS2 nanosheets on three-dimensional (3D) reduced graphene oxide (rGO) (H-2D/3D-MoS2-rGO) aerogel structures as a new class of electrocatalysts for the HER. We use the one-pot hydrothermal synthesis route for developing high-performance electroactive materials for the HER. The as-prepared H-2D/3D-MoS2-rGO contains a unique 3D hierarchical structure providing large surface areas owing to the 3D porous networks of rGO and more active sites owing to the many edge sites in the MoS2 nanosheets. In addition, the H-2D/3D-MoS2-rGO structure exhibits remarkable electrochemical properties during the HER. It shows a lower overpotential than pure MoS2 and excellent electrochemical stability owing to the large number of active sites (highly exposed edge sites) and high electrical conductivity from the rGO structure.

Nowadays, mobile communication networks are an essential part of our lives. They are present in both our personal and professional lives. The current networks are not prepared to support the population's future needs, such as having a reliable connection anytime and anywhere. The project shows an empirical design accompanied by a simulation and emulation platform for 3D networks based on UAVs and nanosatellites using functional virtualization of split B. In this regard, a completely virtualized test system has been created to attain the main objective of this study, i.e. to verify the results of previous theoretical studies on the virtualization of this split. Through a sample of the orbital arcs and the use of the experimental evaluation platform designed here, it has been possible to promisingly estimate the virtualization behaviour of this functional split. This study provides a new experimental tool in research on functional splits and verifies the operation of a split B in a virtualized 3D network scenario. Hoy en día, las redes de comunicación móvil son una parte esencial de nuestras vidas. Están presentes tanto en nuestra vida personal como profesional. Las redes actuales no están preparadas para soportar las necesidades futuras de la población, como tener una conexión confiable en cualquier momento y lugar. El proyecto muestra un diseño empírico acompañado de una plataforma de simulación y emulación para redes 3D basada en UAVs y nanosatélites utilizando virtualización funcional de split B. En este sentido, se ha creado un sistema de prueba completamente virtualizado para lograr el objetivo principal de este estudio, es decir verificar los resultados de estudios teóricos previos sobre la virtualización de esta escisión. A través de una muestra de los arcos orbitales y el uso de la plataforma de evaluación experimental diseñada aquí, ha sido posible estimar de manera prometedora el comportamiento de virtualización de esta división funcional. Este estudio proporciona una nueva herramienta experimental en la investigación de splits funcionales y verifica el funcionamiento de un split B en un escenario de red 3D virtualizada. Actualment, les xarxes de comunicacions mòbils són una part essencial de la nostra vida. Estan presents tant en la nostra vida personal com professional. Les xarxes actuals no estan preparades per donar suport a les necessitats futures de la població, com ara tenir una connexió fiable en qualsevol moment i en qualsevol lloc. El projecte mostra un disseny empíric acompanyat d’una plataforma de simulació i emulació per a xarxes 3D basades en UAV i nanosatèl·lits que utilitzen la virtualització funcional del split B. En aquest sentit, s’ha creat un sistema de proves completament virtualitzat per assolir l’objectiu principal d’aquest estudi, és a dir, per verificar els resultats d’estudis teòrics previs sobre la virtualització d’aquest split. Mitjançant una mostra dels arcs orbitals i l’ús de la plataforma d’avaluació experimental dissenyada aquí, s’ha pogut estimar prometedorament el comportament de virtualització d’aquest split funcional. Aquest estudi proporciona una nova eina experimental en la investigació dels splits funcionals i verifica el funcionament d’un split B en un escenari de xarxa 3D virtualitzada.

Objective. The goal of this work is to develop and characterize an innovative experimental framework to design interconnected (i.e. modular) heterogeneous (cortical-hippocampal) neuronal cultures with a three-dimensional (3D) connectivity and to record their electrophysiological activity using micro-electrode arrays (MEAs). Approach. A two-compartment polymeric mask for the segregation of different neuronal populations (cortex and hippocampus) was coupled to the MEA surface. Glass microbeads were used as a scaffold to mimic the 3D brain micro-architecture. Main results. We built a fully functional heterogeneous 3D neuronal network. From an electrophysiological point of view, we found that the heterogeneity induces a global increase of the activity rate, while the 3D connectivity modulates the duration and the organization of the bursting activity. Significance. In vivo, studies of network dynamics and interactions between neuronal populations are often time-consuming, low-throughput, complex, and suffer from reproducibility. On the other hand, most of the commonly used in vitro brain models are too simplified and thus far from the in vivo situation. The achieved results demonstrate the feasibility to build a more realistic and controllable experimental in vitro model of interconnected brain regions on-a-chip whose applications may have impacts on the study of neurological disorders that impair the connectivity between brain areas (e.g. Parkinson disease).

The synthesis of flexible photonic materials, by sustainable and scalable methods, is proved to be a difficult task for the materials science and industrial communities. Alongside, the modern society has also grown a strong dependence on polymeric materials, demanding superpolymers that combine functionality and cost with superior after‐use properties. The path for accomplishing this aim is made possible by mimicking nature through the merging of self‐ordered nanostructures and a commodity thermoplastic, resorting to basic fabrication infrastructure. This work presents the development of a flexible material that exhibits tunable structural color due to its 3D polyethylene based nanonetwork. These nanonetworks are hydrophobic, and change color depending on the refractive index of the material filling their voids. This developed flexible metamaterial is projected to open opportunities for the fabrication of economically affordable (around 0.008 € cm−2) and solvent‐free photonic nanostructures with multipurpose applications such as sensing, energy saving, clothing, and photovoltaics, among others., M.M.‐G. acknowledges the financial support from the project TONALITy ERC POC665634 and INFANTE 201550E072. O.C.‐C. acknowledges fruitful discussion with Prof. G. Armelles. R.S. acknowledges the European Commission through the H2020‐MSCA‐IF project TONSOPS (Grant No. 706094). O.C.‐C. acknowledges financial support from Ramon y Cajal research grant (MINECO). The authors acknowledge the service from the X‐SEM Laboratory at IMN, and funding from MINECO under projectCSIC13‐4E‐1794 with support from EU (FEDER, FSE).

Item does not contain fulltext Currently, large-scale networks derived from dissociated neurons growing and developing in vitro on extracellular micro-transducer devices are the gold-standard experimental model to study basic neurophysiological mechanisms involved in the formation and maintenance of neuronal cell assemblies. However, in vitro studies have been limited to the recording of the electrophysiological activity generated by bi-dimensional (2D) neural networks. Nonetheless, given the intricate relationship between structure and dynamics, a significant improvement is necessary to investigate the formation and the developing dynamics of three-dimensional (3D) networks. In this work, a novel experimental platform in which 3D hippocampal or cortical networks are coupled to planar Micro-Electrode Arrays (MEAs) is presented. 3D networks are realized by seeding neurons in a scaffold constituted of glass microbeads (30-40 microm in diameter) on which neurons are able to grow and form complex interconnected 3D assemblies. In this way, it is possible to design engineered 3D networks made up of 5-8 layers with an expected final cell density. The increasing complexity in the morphological organization of the 3D assembly induces an enhancement of the electrophysiological patterns displayed by this type of networks. Compared with the standard 2D networks, where highly stereotyped bursting activity emerges, the 3D structure alters the bursting activity in terms of duration and frequency, as well as it allows observation of more random spiking activity. In this sense, the developed 3D model more closely resembles in vivo neural networks.