Search

The use of Digital Twins in the industry has become a growing trend in recent years, allowing to improve the lifecycle of any process by taking advantage of the relationship between the physical and the virtual world. Existing literature formulates several challenges for building Digital Twins, as well as some proposals for overcoming them. However, in the vast majority of the cases, the architectures and technologies presented are strongly bounded to the domain where the Digital Twins are applied. This article proposes the FIWARE Ecosystem, combining its catalog of components and its Smart Data Models, as a solution for the development of any Digital Twin. We also provide a use case to showcase how to use FIWARE for building Digital Twins through a complete example of a Parking Digital Twin. We conclude that the FIWARE Ecosystem constitutes a real reference option for developing DTs in any domain.

Thanks to network slicing, mobile networks can now support multiple and diverse services, each requiring different key performance indicators (KPIs). In this new scenario, it is critical to allocate network and computing resources efficiently and in such a way that all KPIs targeted by a service are met. Accounting for all sorts of KPIs (e.g., availability and reliability, besides the more traditional throughput and latency) is an aspect that has been scarcely addressed so far and that requires tailored models and solution strategies. We address this issue by proposing a novel methodology and resource orchestration scheme, named OKpi, which provides high-quality decisions on VNF (Virtual Network Function) placement and data routing, including the selection of radio points of attachment. Importantly, OKpi has polynomial computational complexity and accounts for all KPIs required by each service, and for any resource available from the fog to the cloud. We prove several properties of OKpi and demonstrate that it performs very closely to the optimum under real-world scenarios. We also implement OKpi in a testbed supporting a robot-based, smart factory service, and we present some field tests that further confirm the ability of OKpi to make high-quality decisions. This work was supported by the EU Commission through the 5Growth Project under Agreement 856709.

Artificial Neural networks (ANNs) are widely used to solve classification problems for many machine learning applications. When errors occur in the computational units of an ANN implementation due to for example radiation effects, the result of an arithmetic operation can be changed, and therefore, the predicted classification class may be erroneously affected. This is not acceptable when ANNs are used in many safety-critical applications, because the incorrect classification may result in a system failure. Existing error-tolerant techniques usually rely on physically replicating parts of the ANN implementation or incurring in a significant computation overhead. Therefore, efficient protection schemes are needed for ANNs that are run on a processor and used in resource-limited platforms. A technique referred to as Selective Neuron Re-Computation (SNRC), is proposed in this paper. As per the ANN structure and algorithmic properties, SNRC can identify the cases in which the errors have no impact on the outcome; therefore, errors only need to be handled by re-computation when the classification result is detected as unreliable. Compared with existing temporal redundancy-based protection schemes, SNRC saves more than 60 percent of the re-computation (more than 90 percent in many cases) overhead to achieve complete error protection as assessed over a wide range of datasets. Different activation functions are also evaluated. This research was supported by the National Science Foundation Grants CCF-1953961 and 1812467, by the ACHILLES project PID2019-104207RB-I00 and the Go2Edge network RED2018-102585-T funded by the Spanish Ministry of Science and Innovation and by the Madrid Community research project TAPIR-CM P2018/TCS-4496. Publicado

This paper is the first demonstration of multiplexed wideband data transmission in the millimeter-wave range using rectangular-coordinate orthogonal multiplexing (ROM) antennas. This spatial wireless multiplex communication method can be applied at several hundred GHz for further improvements in the data rate because much wider bandwidth is available and this multiplexing method does not require any signal processing. The multiplexing is achieved through the spatial eigenmodes of a novel antenna based on a rectangular coordinate system and magic-T which eliminates the need for computational signal processing efforts. The aperture distributions of these spatial eigenmodes are designed to have different polarities to avoid crosstalk and operate over a wide bandwidth range. We demonstrate their performance with four eigenmodes, achieving crosstalk between modes below -37.8 dB over a 14.6% relative bandwidth (57-66 GHz). We have introduced these antennas on a photonics-enabled real-time wireless data transmission, transmitting over two channels simultaneously, without any signal processing at the transmitter (multiplex) or the receiver (demultiplex). The two multiplexed channels show a total data rate up to 9.0 Gbps at most (5.875 Gbps and 3.125 Gbps for each channel) limited by the bandwidth of the low noise amplifiers at the receiver. The measured bit error rate (BER) is below the forward error correction (FEC) limit. This work was supported in part by SEI Group CSR Foundation and the Murata Science Foundation.

Speech intelligibility can be affected by multiple factors, such as noisy environments, channel distortions or physiological issues. In this work, we deal with the problem of automatic prediction of the speech intelligibility level in this latter case. Starting from our previous work, a non-intrusive system based on LSTM networks with attention mechanism designed for this task, we present two main contributions. In the first one, it is proposed the use of per-frame modulation spectrograms as input features, instead of compact representations derived from them that discard important temporal information. In the second one, two different strategies for the combination of per-frame acoustic log-mel and modulation spectrograms into the LSTM framework are explored: at decision level or late fusion and at utterance level or Weighted-Pooling (WP) fusion. The proposed models are evaluated with the UA-Speech database that contains dysarthric speech with different degrees of severity. On the one hand, results show that attentional LSTM networks are able to adequately modeling the modulation spectrograms sequences producing similar classification rates as in the case of log-mel spectrograms. On the other hand, both combination strategies, late and WP fusion, outperform the single-feature systems, suggesting that per-frame log-mel and modulation spectrograms carry complementary information for the task of speech intelligibility prediction, than can be effectively exploited by the LSTM-based architectures, being the system with the WP fusion strategy and Attention-Pooling the one that achieves best results. The work leading to these results has been partly supported by the Spanish Government-MinECo under Projects TEC2017-84395-P and TEC2017-84593-C2-1-R. Publicado

The Count-Min sketch is the most popular data structure for flow size estimation, a basic measurement task required in many networks. Typically the number of potential flows is large, eliminating the possibility to maintain a counter per flow within memory of high access rate. The Count-Min sketch is probabilistic and relies on mapping each flow to multiple counters through hashing. This implies potential estimation error such that the size of a flow is overestimated when all flow counters are shared with other flows with observed traffic. Although the error in the estimation can be probabilistically bounded, many applications can benefit from accurate flow size estimation and the guarantee to completely avoid overestimation. We describe a design of the Count-Min sketch with accurate estimations whenever the number of flows with observed traffic follows a known bound, regardless of the identity of these particular flows. We make use of a concept of Bloom filters that avoid false positives and indicate the limitations of existing Bloom filter designs towards accurate size estimation. We suggest new Bloom filter constructions that allow scalability with the support for a larger number of flows and explain how these can imply the unique guarantee of accurate flow size estimation in the well known Count-Min sketch. Ori Rottenstreich was partially supported by the German-Israeli Foundation for Scientic Research and Development (GIF), by the Gordon Fund for System Engineering as well as by the Technion Hiroshi Fujiwara Cyber Security Research Center and the Israel National Cyber Directorate. Pedro Reviriego would like to acknowledge the sup-port of the ACHILLES project PID2019-104207RB-I00 and the Go2Edge network RED2018-102585-T funded by the Spanish Ministry of Science and Innovation and of the Madrid Community research project TAPIR-CM grant no. P2018/TCS-4496.

We present finite-blocklength achievability bounds for the unsourced A-channel. In this multiple-access channel, users noiselessly transmit codewords picked from a common codebook with entries generated from a $q$-ary alphabet. At each channel use, the receiver observes the set of different transmitted symbols but not their multiplicity. We show that the A-channel finds applications in unsourced random-access (URA) and group testing. Leveraging the insights provided by the finite-blocklength bounds and the connection between URA and non-adaptive group testing through the A-channel, we propose improved decoding methods for state-of-the-art A-channel codes and we showcase how A-channel codes provide a new class of structured group testing matrices. The developed bounds allow to evaluate the achievable error probabilities of group testing matrices based on random A-channel codes for arbitrary numbers of tests, items and defectives. We show that such a construction asymptotically achieves the optimal number of tests. In addition, every efficiently decodable A-channel code can be used to construct a group testing matrix with sub-linear recovery time., 11 pages, 4 figures, extended version of the paper presented at the 58th Annual Allerton Conference on Communication, Control, and Computing (2022)

A new method for example-dependent cost (EDC) classification is proposed. The method constitutes an extension of a recently introduced training algorithm for neural networks. The surrogate cost function is an estimate of the Bayesian risk, where the estimates of the conditional probabilities for each class are defined in terms of a 1-D Parzen window estimator of the output of (discriminative) neural networks. This probability density is modeled with the objective of allowing an easy minimization of a sampled version of the Bayes risk. The conditional probabilities included in the definition of the risk are not explicitly estimated, but the risk is minimized by a gradient-descent algorithm. The proposed method has been evaluated using linear classifiers and neural networks, with both shallow (a single hidden layer) and deep (multiple hidden layers) architectures. The experimental results show the potential and flexibility of the proposed method, which can handle EDC classification under imbalanced data situations that commonly appear in this kind of problems. This work has been partly supported by grants CASI-CAM-CM (S2013/ICE-2845, Madrid C/ FEDER, EUSF) and MacroADOBE (TEC2015-67719-P, MINECO/FEDER, UE).

The arrival of 5G paves the way for the deployment of the so-called Industry 4.0, which is a new paradigm devoted to the digital transformation of manufacturing and factory production. Because of the resources required to perform this transformation, the importance of field trials and experimentation cannot be overstated, both to support the design of novel methodologies and to validate these designs. In this article, we leverage the 5G EVE end-to-end open platform to design and validate a novel operation approach for automated guided vehicles (AGVs). This use case consists of the placement of the intelligence that controls the AGV in a remote entity. This movement could improve and simplify the operation of industrial processes. The customizability of the 5G validation platform proves fundamental to evaluate the solution under different deployment architectures and to assess its performance under hazardous radio conditions. Our results demonstrate the ability of 5G to handle latency-constrained use cases with superior performance compared to the current state-of-the-art mobile technology. This work was partly funded by the European Commission under the European Union's Horizon 2020 program, grant agreement number 815074 (5G EVE project). The article solely reflects the views of the authors. The Commission is not responsible for the contents of this article or any use made thereof. The authors would like to thank Ignacio Berberana and Neftali Gonzalez from the 5TONIC Laboratory for their support in setting up the Spanish site.

In Press/En prensa In this article, we develop a modeling framework to describe the uplink behavior of radio access in a sliced cell, including most features of the standard 3GPP multiple access procedures. Our model allows evaluating throughput and latency of each slice, as a function of cell parameters, when resources are in part dedicated to individual slices and in part shared. The availability of an accurate model is extremely important for the automated run time management of the cell and for the correct setting of its parameters. Indeed, our model considers most details of the behavior of sliced 5G cells, including Access Class Barring (ACB) and Random Access CHannel (RACH) procedures, preamble decoding, Random Access Response (RAR), and Radio Resource Control (RRC) procedures. To cope with a number of slices devoted to serve various co-deployed tenants, we derive a multi-class queueing model of the network processor. We then present (i) an accurate and computationally efficient technique to derive the performance measures of interest using continuous-time Markov chains, which scales up to a few slices only, and (ii) tight performance bounds, which are useful to tackle the case of more than a fistful of slices. We prove the accuracy of the model by comparison against a detailed simulator. Eventually, with our performance evaluation study, we show that our model is very effective in providing insight and guidelines for allocation and management of resources in cells hosting slices for services with different characteristics and performance requirements, such as machine type communications and human type communications. This work is partially supported by the Region of Madrid through the TAPIR-CM project (S2018/TCS-4496), by a Ramon y Cajal grant (ref: RYC-2014-16285) from the Spanish Ministry of Economy and Competitiveness, and by HOME (Hierarchical Open Manufacturing Europe) project supported by the Regione Piemonte, Italia (framework program PORFESR 14/20). This work was supported by the EU Commission through the 5GROWTH project (grant agreement no. 856709).

Frailty is a prevalent condition among Canadians; over one million are diagnosed as medically frail, and in the next ten years this number will double. Information and telecommunication technologies can provide a low-cost method for managing frailty more proactively. This study aims to examine the range and extent of information and telecommunication technologies for managing frailty in older adults, their technology readiness level, the evidence, and the associated outcomes. A systematic literature review was conducted. Four databases were searched for studies: Medline, EMBASE, CINAHL, and Web of Science. In total, we included 19 studies (out of 9,930) for the data abstraction. Overall, our findings indicate that (1) the proposed frailty phenotype is the most common ground truth to be used for assessing frailty; (2) the most common uses of information and telecommunication technologies for managing frailty are detection, and monitoring and detection, while interventional studies on frailty are very rare; (3) the five main types of information and telecommunication technologies for managing frailty in older adults are information and telecommunication technology-based platforms, smartphones, telemonitoring (home monitoring), wearable sensors and devices (commercial off-the-shelf), and multimedia formats for online access; (4) the technology readiness level of information and telecommunication technologies for managing frailty in older adults is the “Technology Demonstration” level, i.e., not yet ready to be operated in an actual operating environment; and (5) the level of evidence is still low for information and telecommunication technology studies that manage frailty in older adults. In conclusion, information and telecommunication technologies for managing frailty in the older adult population are not yet ready to be full-fledged technologies for this purpose.INGENIERÍA•ISSN impreso: 1794-4953 • ISSN online: 2619-6581 • Vol. 15 (1) • DOI: https://doi.org/10.18041/avances.v15i1 • pp 171-179 (2018), La fragilidad es una condición prevalente entre los canadienses; más de un millón son diagnosticados como médicamente frágiles, y en los próximos diez años esta cifra se duplicará. Las tecnologías de la información y las telecomunicaciones pueden proporcionar un método de bajo coste para gestionar la fragilidad de forma más proactiva. Este estudio tiene como objetivo examinar la gama y el alcance de las tecnologías de la información y las telecomunicaciones para la gestión de la fragilidad en los adultos mayores, su nivel de preparación tecnológica, la evidencia y los resultados asociados. Se realizó una revisión sistemática de la literatura. Se buscaron estudios en cuatro bases de datos: Medline, EMBASE, CINAHL y Web of Science. En total, se incluyeron 19 estudios (de 9.930) para la extracción de datos. En general, nuestros resultados indican que (1) el fenotipo de fragilidad propuesto es la verdad básica más comúnmente utilizada para evaluar la fragilidad; (2) los usos más comunes de las tecnologías de la información y las telecomunicaciones para gestionar la fragilidad son la detección, y la monitorización y detección, mientras que los estudios de intervención sobre la fragilidad son muy raros; (3) los cinco tipos principales de tecnologías de la información y las telecomunicaciones para la gestión de la fragilidad en los adultos mayores son las plataformas basadas en la tecnología de la información y las telecomunicaciones, los teléfonos inteligentes, la telemonitorización (seguimiento en el hogar), los sensores y dispositivos portátiles (comerciales), y los formatos multimedia para el acceso en línea; (4) el nivel de preparación tecnológica de las tecnologías de la información y las telecomunicaciones para la gestión de la fragilidad en los adultos mayores es el nivel de "Demostración de Tecnología", es decir. e., (5) el nivel de evidencia es todavía bajo para los estudios de las tecnologías de la información y las telecomunicaciones que gestionan la fragilidad en los adultos mayores. En conclusión, las tecnologías de la información y las telecomunicaciones para gestionar la fragilidad en la población de adultos mayores aún no están listas para ser tecnologías de pleno derecho para este fin.INGENIERÍA-ISSN impreso: 1794-4953 - ISSN online: 2619-6581 - Vol. 15 (1) - DOI: https://doi.org/10.18041/avances.v15i1 - pp 171-179 (2018), 1Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada. 2Glenrose Rehabilitation Research, Innovation & Technology (GRRIT) Hub, Glenrose Rehabilitation Hospital, Edmonton, AB, Canada. 3Faculty of Applied Health Sciences, University of Waterloo, Waterloo, ON, Canada. 4School of Medicine and Health Sciences, Universidad del Rosario, Bogota, Colombia, [Received September 27, 2020; Revised November 14, 2020; Accepted November 15, 2020]

In Press / En Prensa The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complexrelationship between computing and radio dynamics make vRAN resource control particularly daunting. We present vrAIn, a resourceorchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data(traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithmbased on an actor-critic neural network structure and a classifier to map contexts into resource control decisions.We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over aproduction RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods;(ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning,vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To ourknowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-freesolution that does not need to assume any particular platform or context. This work was partially supported by the European Commission through Grant No. 856709 (5Growth) and Grant No. 856950 (5G-TOURS); by Science Foundation Ireland (SFI) through Grant No. 17/CDA/4760; and AEI/FEDER through project AIM under Grant No. TEC2016-76465-C2-1-R. Furthermore, the work is closely related to the EU project DAEMON (Grant No. 101017109).

Speech 'in-the-wild' is a handicap for speaker recognition systems due to the variability induced by real-life conditions, such as environmental noise and the emotional state of the speaker. Taking advantage of the principles of representation learning, we aim to design a recurrent denoising autoencoder that extracts robust speaker embeddings from noisy spectrograms to perform speaker identification. The end-to-end proposed architecture uses a feedback loop to encode information regarding the speaker into low-dimensional representations extracted by a spectrogram denoising autoencoder. We employ data augmentation techniques by additively corrupting clean speech with real-life environmental noise in a database containing real stressed speech. Our study presents that the joint optimization of both the denoiser and speaker identification modules outperforms independent optimization of both components under stress and noise distortions as well as hand-crafted features., Published on Monday 10th of May 2021 in Neural Computing and Applications, Springer

Network slicing will permit offering to vertical customers tailored end-to-end logical networks in an on-demand fashion, on top of a common telecom infrastructure, achieving a Slices-as-a-Service (SlaaS) business model. This is possible due to the progressive introduction of network softwarization techniques, such as programmability and virtualization, into existing operational networks, enabling dynamic and flexible provision of slices. Those vertical customers could require the control not only of the network functions composing the end-to-end service, but also of the connectivity among them, e.g., for influencing the paths for steering traffic among function instances. However, this can be problematic since decisions from one vertical customer can collide with decisions from others. One aspect not yet sufficiently investigated is how to permit vertical customers to jointly control the service functions and the underlay connectivity, in such a way that could operate the allocated slice as if it was actually a dedicated network entirely for them. This paper explores some architectural proposition in this respect illustrated with some potential use cases and it provides an example of the provision of SlaaS for a vertical customer. This work has been partly funded by the European Commission through the projects 5G-PPP H2020 5GROWTH (Grant Agreement No 856709), EU-TW 5G-DIVE (Grant Agreement No 859881) and by the Spanish AURORAS (RTI2018-099178-B-I00) project. This information reflects the consortia views, but neither the consortia nor the European Commission are liable for any use that may be done of the information contained therein.

In Press / En prensa Massive access for Internet-of-Things (IoT) in beyond 5G networks represents a daunting challenge for conventional bandwidth-limited technologies. Millimeter-wave technologies (mmWave)— which provide large chunks of bandwidth at the cost of more complex wireless processors in harsher radio environments— is a promising alternative to accommodate massive IoT but its cost and power requirements are an obstacle for wide adoption in practice. In this context, meta-materials arise as a key innovation enabler to address this challenge by Re-configurable Intelligent Surfaces (RISs). In this paper we take on the challenge and study a beyond 5G scenario consisting of a multi-antenna base station (BS) serving a large set of single-antenna user equipments (UEs) with the aid of RISs to cope with non-line-of-sight paths. Specifically, we build a mathematical framework to jointly optimize the precoding strategy of the BS and the RIS parameters in order to minimize the system sum mean squared error (SMSE). This novel approach reveals convenient properties used to design two algorithms, RISMA and Lo-RISMA, which are able to either find simple and efficient solutions to our problem (the former) or accommodate practical constraints with low-resolution RISs (the latter). Numerical results show that our algorithms outperform conventional benchmarks that do not employ RIS (even with low-resolution meta-surfaces) with gains that span from 20% to 120% in sum rate performance. The work of P. Mursia is supported by Marie Skłodowska-Curie actions (MSCA-ITN-ETN 722788 SPOTLIGHT). The work of V. Sciancalepore, A. Garcia-Saavedra and X. Costa-Perez has been partially funded by the EU H2020 project 5GROWTH (grant agreement no. 856709).

Sound Event Detection is a task with a rising relevance over the recent years in the field of audio signal processing, due to the creation of specific datasets such as Google AudioSet or DESED (Domestic Environment Sound Event Detection) and the introduction of competitive evaluations like the DCASE Challenge (Detection and Classification of Acoustic Scenes and Events). The different categories of acoustic events can present diverse temporal and spectral characteristics. However, most approaches use a fixed time-frequency resolution to represent the audio segments. This work proposes a multi-resolution analysis for feature extraction in Sound Event Detection, hypothesizing that different resolutions can be more adequate for the detection of different sound event categories, and that combining the information provided by multiple resolutions could improve the performance of Sound Event Detection systems. Experiments are carried out over the DESED dataset in the context of the DCASE 2020 Challenge, concluding that the combination of up to 5 resolutions allows a neural network-based system to obtain better results than single-resolution models in terms of event-based F1-score in every event category and in terms of PSDS (Polyphonic Sound Detection Score). Furthermore, we analyze the impact of score thresholding in the computation of F1-score results, finding that the standard value of 0.5 is suboptimal and proposing an alternative strategy based in the use of a specific threshold for each event category, which obtains further improvements in performance, This work was supported in part by the Project Deep Speech for Forensics and Security (DSForSec) under Grant RTI2018-098091-B-I00, in part by the Ministry of Science, Innovation and Universities of Spain, and in part by the European Regional Development Fund (ERDF)

The landing point of a saccade defines the new fixation region, the new region of interest. We asked whether it was possible to predict the saccade landing point early in this very fast eye movement. This work proposes a new algorithm based on LSTM networks and a fine-grained loss function for saccade landing point prediction in real-world scenarios. Predicting the landing point is a critical milestone toward reducing the problems caused by display-update latency in gaze-contingent systems that make real-time changes in the display based on eye tracking. Saccadic eye movements are some of the fastest human neuro-motor activities with angular velocities of up to 1,000°/s. We present a comprehensive analysis of the performance of our method using a database with almost 220,000 saccades from 75 participants captured during natural viewing of videos. We include a comparison with state-of-the-art saccade landing point prediction algorithms. The results obtained using our proposed method outperformed existing approaches with improvements of up to 50% error reduction. Finally, we analyzed some factors that affected prediction errors including duration, length, age, and user intrinsic characteristics., This work was supported in part by the Project BIBECA through MINECO/FEDER under Grant RTI2018-101248-B-100, in part by the Jose Castillejo Program through MINECO under Grant CAS17/00117, and in part by the National Institutes of Health (NIH) under Grant P30EY003790 and Grant R21EY023724.

This paper presents a metasurface based multiple-input multiple-output (MIMO) antenna with a wideband operation for millimeter-wave 5G communication systems. The antenna system consists of four elements placed with a 90 degree shift in order to achieve a compact MIMO system while a 2×2 non-uniform metasurface (total four elements) is placed at the back of the MIMO configuration to improve the radiation characteristics of it. The overall size of the MIMO antenna is 24×24 mm 2 while the operational bandwidth of the proposed antenna system ranges from 23.5-29.4 GHz. The peak gain achieved by the proposed MIMO antenna is almost 7dB which is further improved up to 10.44 dB by employing a 2×2 metasurface. The total efficiency is also observed more than 80% across the operating band. Apart from this, the MIMO performance metrics such as envelope correlation coefficient (ECC), diversity gain (DG), and channel capacity loss (CCL) are analyzed which demonstrate good characteristics. All the simulations of the proposed design are carried out in computer simulation technology (CST) software, and measured results reveal good agreement with the simulated one which make it a potential contender for the upcoming 5G communication systems. This work was supported in part by the Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant Agreement No 801538, and in part by the the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (MCIU/AEI/FEDER,UE) under Grant RTI2018-095499-B-C31.

The Industry 4.0 paradigm conceives a cyber-physical supporting framework for the manufacturing processes in smart factories. In this context, solutions concerning the wired communications at the field-level have been reported which utilize either fieldbuses, which exhibit a huge distance range but a reduced data rate in a bus topology, or Ethernet-based technologies, which provide an increased data rate but reduced distance in a ring topology. To overcome this shortage, we propose the use of orthogonal frequency division multiplexing (OFDM) to significantly increase the achievable data rates over large distances in industrial bus systems. Also, we establish a novel methodology to compute the signal-to-noise ratio between arbitrary pairs of nodes, which in turn allows to compute the communication capacity. Our wideband system was validated by connecting up to 32 nodes in the distance range 100 m–1 km. Compared to fieldbuses, the results of the proposal exhibit an amazing improvement in data rate of about fifty times for 100m distance and more than ten times for 0.5 km. Moreover, with respect to Ethernet-based solutions, the results show a relevant improvement in the data rate of around five times for 100m distance, but Ethernet-based systems cannot go beyond this distance, to which our proposal is not limited. This work was supported in part by the Elektromobilität mit Redundanter Intelligenter Kommunikations-Architektur (ERIKA) Project, in part by the Bundesministerium für Wirtschaft und Energie, Germany, in part by the Spanish National Project Hybrid TERrEstrial/Satellite Air Interface for 5G and Beyond-Areas of Difficult Access (TERESA-ADA) (TEC2017-90093-C3-2-R) through Ministerio de Economía y Competitividad (MINECO)/Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE), and in part by the CORE project Cognitive Cohesive Networks of Distributed Units for Active and Passive Space Applications (COHESAT) through the Fonds National de la Recherche (FNR), Luxembourg.

The ability to detect elephant flows in the forwarding device itself, i.e., a switch, facilitates the deployment of new advanced applications such as load-balancing, per-flow QoS management, etc. Sketches and Space Saving summarization techniques are used for elephant flow detection. However, their memory and computing requirements force the cooperation of an external controller device, due to the scarce resources of current programmable switches. To overcome this limitation, we adapt Sketch and Space Saving elephant flow detection techniques to operate with instant notification and sampled traffic. We evaluate the performance of the resulting techniques with three real traffic traces. The use of sampling allows the identification of a large share of the total traffic corresponding to the elephant flows with a low memory footprint and a reduction of the computing requirements in two orders of magnitude compared to unsampled versions. In turn, we observe a slight increase in the number of false positives and the number of flow notifications. The work of Alberto García-Martínez and Marcelo Bagnulo was supported by the TRUE5G Project ('Evolución hacia redes y servicios auto-gestionados para el 5G del futuro') by the Spanish National Research Agency under Grant PID2019-108713RB-C52/AEI/10.13039/501100011033.

As networks grow larger in size and complexity, their monitoring is becoming an increasing challenge because of the required polling performance and also due to heterogeneity of devices. As it turns out, SNMP (Simple Network Management Protocol) is by far the most popular monitoring protocol. However, due to the increase in the number of network devices, it becomes necessary to employ multiple SNMP managers, which is not cost-effective due to the hardware requirements. Additionally, the different proprietary SNMP implementations require custom configuration very often, as new devices are being incorporated into the network. Therefore, current SNMP managers not only require capabilities for large-scale monitoring but also a high degree of flexibility and programmability. In response, we propose an SNMP manager architecture with a flexible multi-threaded architecture, which effectively reduces the hardware resources necessary to poll the increasing number of SNMP agents. In addition, it features a scripting component to deal with the different data representations caused by proprietary implementations. Our experience has shown that SNMP agents can have high variability in their response times. Actually, our findings show a strong correlation between high response times and CPU load. As a solution, we propose and analyze novel adaptive polling algorithms that decrease the load on agents' CPUs while keeping the desired polling rate for fast agents. Finally, we present several real-world use cases where we show the benefits of the polling algorithms and the scripting component, by means of extensive measurement campaigns, This work was supported by Ayudas para la formación de doctores en empresas, Doctorados Industriales, under Grant DI-16-08979

Near-lossless compression is a generalization of lossless compression, where the codec user is able to set the maximum absolute difference (the error tolerance) between the values of an original pixel and the decoded one. This enables higher compression ratios, while still allowing the control of the bounds of the quantization errors in the space domain. This feature makes them attractive for applications where a high degree of certainty is required. The JPEG-LS lossless and near-lossless image compression standard combines a good compression ratio with a low computational complexity, which makes it very suitable for scenarios with strong restrictions, common in embedded systems. However, our analysis shows great coding efficiency improvement potential, especially for lower entropy distributions, more common in near-lossless. In this work, we propose enhancements to the JPEG-LS standard, aimed at improving its coding efficiency at a low computational overhead, particularly for hardware implementations. The main contribution is a low complexity and efficient coder, based on Tabled Asymmetric Numeral Systems (tANS), well suited for a wide range of entropy sources and with simple hardware implementation. This coder enables further optimizations, resulting in great compression ratio improvements. When targeting photographic images, the proposed system is capable of achieving, in mean, 1.6%, 6%, and 37.6% better compression for error tolerances of 0, 1, and 10, respectively. Additional improvements are achieved increasing the context size and image tiling, obtaining 2.3% lower bpp for lossless compression. Our results also show that our proposal compares favorably against state-of-the-art codecs like JPEG-XL and WebP, particularly in near-lossless, where it achieves higher compression ratios with a faster coding speed, This work was supported in part by the Spanish Research Agency through the Project AgileMon under Grant AEI PID2019-104451RB-C21

Vehicular networks use Decentralized Congestion Control (DCC) mechanisms to operate effectively, but this mechanism may introduce queuing delays. Freshness of Cooperative Awareness Messages (CAMs) is critical for their usefulness. In this letter we explore how the presence of other types of traffic additional to CAMs, even with lower priorities, has an impact on the freshness of CAM messages due to DCC queuing. Finally, we propose Generate-on-Time (GoT), which is a simple mechanism that reduces DCC queuing delays for CAM messages without introducing any downside in other performance metrics.

Cooperative spectrum sensing has proved to be an effective method to improve the detection performance in cognitive radio systems. This work focuses on centralized cooperative schemes based on the soft fusion of the energy measurements at the cognitive radios (CRs). In these systems, the likelihood ratio test (LRT) is the optimal detection rule, but the sufficient statistic depends on the local signal-to-noise ratio (SNR) at the CRs, which are unknown in most practical cases. Therefore, the detection problem becomes a composite hypothesis test. The generalized LRT is the most popular approach in those cases. Unfortunately, in mobile environments, its performance is well below the LRT because the local energies are measured under varying SNRs. In this work, we present a new algorithm that jointly estimates the instantaneous SNRs and detects the presence of primary signals. Due to its adaptive nature, the algorithm is well suited for mobile scenarios where the local SNRs are time-varying. Simulation results show that its detection performance is close to the LRT in realistic conditions. This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with European Commission [European Regional Development Fund (ERDF)], under Grant TEC2017-86921-C2-1-R and Grant TEC2017-86921-C2-2-R (CAIMAN) and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICO-CM).

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, The lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches thus need to incorporate the specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an eigen-patches’ reconstruction method based on the principal component analysis eigen-transformation of local image patches. The structure of the iris is exploited by building a patch-position-dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded the high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators that were used to carry out biometric verification and identification experiments. The experimental results show that the proposed method significantly outperforms both the bilinear and bicubic interpolations at a very low resolution. The performance of a number of comparators attains an impressive equal error rate as low as 5% and a Top-1 accuracy of 77%–84% when considering the iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matching, This work was supported by the EU COST Action under Grant IC1106. The work of F. Alonso-Fernandez and J. Bigun was supported in part by the Swedish Research Council, in part by the Swedish Innovation Agency, and in part by the Swedish Knowledge Foundation through the CAISR/SIDUS-AIR projects. The work of J. Fierrez was supported by the Spanish MINECO/FEDER through the CogniMetrics Project under Grant TEC2015-70627-R. The authors acknowledge the Halmstad University Library for its support with the open access fees

Special Issue: Enabling Technologies for Future Mobile and Edge Networks and Enabling AI Technologies for Internet of Energy. With the advent of increasingly diverse services, applications, and use cases, the 5G mobility environment is expected to be highly heterogeneous. The distributed mobility management (DMM) is emerging as a promising approach for 5G, which provides decentralized handover and traffic management at the edge of the network. However, in order to meet the disparate mobility needs, the DMM process requires flexible design considerations. This paper presents a novel mobile node (MN) centered, software-defined networking (SDN)–based flexible mobility management architecture, named adaptive multimode mobility management (A3M), which can adaptively operate in multiple modes. The proposed A3M architecture incorporates a novel handover mode selection phase among the traditional handover phases during which a suitable mode of handover operation is evaluated. This makes the A3M handover process adaptable to the varying mobility requirements of the MN and enables it to provide differentiated handover management for MNs with different mobility profiles. The performance analysis of A3M shows that it offers optimal handover performance in terms of primary handover performance metrics such as session disruption delay, packet losses, and signaling costs, compared to the popular network-based DMM approaches. Publicado

This article belongs to the Special Issue Optical Technologies Applied to Cultural Heritage. The virtual modification of the appearance of an object using lighting technologies has become very important in recent years, since the projection of light on an object allows us to alter its appearance in a virtual and reversible way. Considering the limitation of non-contact when analysing a work of art, these optical techniques have been used in fields of restoration of cultural heritage, allowing us to visualize the work as it was conceived by its author, after a process of acquisition and treatment of the image. Furthermore, the technique of altering the appearance of objects through the projection of light has been used in projects with artistic or even educational purposes. This review has treated the main studies of light projection as a technique to alter the appearance of objects, emphasizing the calibration methods used in each study, taking into account the importance of a correct calibration between devices to carry out this technology. In addition, since the described technique consists of projecting light, and one of the applications is related to cultural heritage, those studies that carry out the design and optimization of lighting systems will be described for a correct appreciation of the works of art, without altering its state of conservation This work has been funded by project number RTI2018-097633-A-I00 of the Ministry of Science and Innovation of Spain, entitled 'Photonic restoration applied to cultural heritage: Application to Dali's painting: Two Figures.'

[EN] Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks. However, recent works have pointed out that the outputs provided by these models are not well -calibrated, seriously limiting their use in critical decision scenarios. In this work, we propose to use a decoupled Bayesian stage, implemented with a Bayesian Neural Network (BNN), to map the uncalibrated probabilities provided by a DNN to calibrated ones, consistently improving calibration. Our results evi-dence that incorporating uncertainty provides more reliable probabilistic models, a critical condition for achieving good calibration. We report a generous collection of experimental results using high -accuracy DNNs in standardized image classification benchmarks, showing the good performance, flexibil-ity and robust behaviour of our approach with respect to several state-of-the-art calibration methods. Code for reproducibility is provided. (C) 2020 Elsevier B.V. All rights reserved., We gratefully acknowledge the feedback provided by Emilio Granell and Enrique Vidal on an earlier manuscript. The authors thank the EU-FEDER Comunitat Valenciana 2014-2020 grant IDIFE-DER/2018/025. We also acknowledge the support of NVIDIA by providing two GPU Titan XP from their grant program and Mario Parreno for providing the logits of the ADIENCE and VGGFACE2 models. Juan Maronas is supported by grant FPI-UPV. Daniel Ramos is supported by the Spanish Ministry of Science, Innovation and Universities via grant RTI2018-098091-B-I00.

This article presents a simple, cost-effective system for complex measurements of the field radiated by an antenna. In particular, the proposed architecture is based on an array of antenna probes connected to a set of phase difference detectors and a power detector. Phase difference measurements are processed to recover the phase of the antenna under test. The main novelty of the proposed system is that it does not require regular sampling, making it suitable for in situ antenna measurements. The influence of parameters, such as the number of antennas composing the array and hence the number of phase difference detectors) and the positioning errors, is analyzed. The proposed architecture, implemented with off-the-shelf hardware, has been assessed for antenna measurement and diagnostics. A discussion of the issues related to the implementation of the system has been included, as well as the proposed solutions to improve its accuracy. Finally, diagnostics and radiation pattern measurement results are compared with those calculated with other phaseless techniques., This work was supported in part by the Ministerio de Educación–Gobierno de España under Grant FPU15/06341, in part by the Ministerio de Ciencia, Innovación y Universidades–Gobierno de España under Project RTI2018-095825-B-I00 (“MilliHand”), in part by the Government of the Principality of Asturias (PCTI) and European Union (FEDER) under Grant IDI/2018/000191, in part by the Instituto Universitario de Investigación Industrial de Asturias (IUTA) under Project SV-19-GIJON-1-17 (“RadioUAV”), and in part by the University of Oviedo under Project PAPI-18-GR-2010-0015.

Future 4G wireless communication systems include, in their capabilities portfolio, emergency-specific needs, such as data support, broadband communication, and extremely high reliability. An emergency situation can be addressed with undoubtedly more chances of success if augmented information is enabled within the public safety communication novel capabilities. In this article, for a fully augmented information provision based on broadband transmission, a user-end (UE) communication-capabilities enhancement is addressed by deploying multiple antennas without compromising the portability and light weight of first-responder equipment. With this aim, we propose the design of a 4.9-GHz conformal antenna array at the rescuer side (integrated in a helmet) and evaluate its performance in terms of relative data rate gain. The conformal array design is based on traditional patch antennas that consider the need for deployment over an ellipsoidal surface. The antenna array is simulated and then built, and several parameter characterizations (bandwidth, radiation pattern, reflection coefficient, and MC) and measurements are undertaken to ensure the suitability of the design. Furthermore, an analysis of the specific absorption rate (SAR) is performed to guarantee that the exposure to electromagnetic fields is below the standardized levels. The authors would like to thank Prof. Eva Rajo-Iglesias from Universidad Carlos III de Madrid. This work has been partly funded by the Spanish Government through projects CIES (RTC-2015-4213-7), MIMOTEX (TEC2014-61776-EXP) and TERESAADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE)

This article presents a system for 5G networks that makes it possible to meet the diverse needs of vertical industries simultaneously sharing the same physical infrastructure. Orchestration, network slicing, edge computing, and federation are key technologies enabling industry verticals to have their own virtual networks, which might require aggregating transport networking and computing fabric, from the edge up to the core and cloud. Three novel building blocks are defined to meet these challenges in an automated manner: a vertical slicer as the entry point to create services and request slices, a service orchestrator to manage the services and decide their placement and allocation of required resources, and a mobile transport and computing platform virtualizing infrastructure networking and computing resources in an integrated manner. An experimental evaluation of the developed system shows its feasibility and confirms some of the benefits expected.

In hybrid analog-digital schemes, proposed to reduce the number of RF chains especially at millimeter waves, the precoding at the transmitter and the combining at the receiver are split into digital and analog parts. We analyze the sensitivity of hybrid schemes to phase noise and channel estimation errors and we compare them to a full-digital approach. The scheme adopted for the analog part employs fixed phase shifters, then the digital part is optimized by a singular-value decomposition. We derive analytical expressions for the interference and the SNR degradation arising from the imperfect decomposition due to phase noise and the channel estimation error, for typical millimter-wave massive MIMO channels. In particular we show that when the channel estimation is made in the beam-space, this hybrid scheme is more robust to the phase noise and to the channel estimation errors than a full-digital approach.

Modern computer vision techniques have to deal with vast amounts of visual data, which implies a computational effort that has often to be accomplished in broad and challenging scenarios. The interest in efficiently solving these image and video applications has led researchers to develop methods to expertly drive the corresponding processing to conspicuous regions that either depend on the context or are based on specific requirements. In this paper, we propose a general hierarchical probabilistic framework, independent of the application scenario, and relied on the most outstanding psychological studies about attention and eye movements which support that guidance is not based directly on the information provided by early visual processes but on a contextual representation that arose from them. The approach defines the task of context-driven visual attention as a mixture of latent sub-tasks, which are, in turn, modeled as a combination of specific distributions associated to low-, mid-, and high-level spatio-temporal features. Learning from fixations gathered from human observers, we incorporate an intermediate level between feature extraction and visual attention estimation that enables to obtain comprehensively guiding representations. The experiments show how our proposal successfully learns particularly adapted hierarchical explanations of visual attention in diverse video genres, outperforming several leading models in the literature. This work has been partially funded by the Spanish Ministry of Education, Culture and Sports FPU Grant FPU13/04904, and the National Grants TEC2014-53390-P, TEC2014-61729-EXP and TEC2017-84395-P of the Spanish Ministry of Economy and Competitiveness. Publicado

Before a dense Wi-Fi network is deployed, Wi-Fi providers must be careful with the performance promises they made in their way to win a bidding process. After such deployment takes place, Wi-Fi-network owners—such as public institutions—must verify that the QoS agreements are being fulfilled. We have merged both needs into a low-cost measurement system, a report of measurements at diverse scenarios and a performance prediction tool. The measurement system allows measuring the actual goodput that a set of users are receiving, and it has been used in a number of schools on a national scale. From this experience, we report measurements for different scenarios and diverse factors—which may result of interest to practitioners by themselves. Finally, we translate all the learned lessons to a freely-available capacity-planning tool for forecasting performance given a set of input parameters such as frequency, signal strength and number of users—and so, useful for estimating the cost of future deployments, This work was partially funded by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under the project TRAFICA (MINECO/FEDER TEC2015-69417-C2-1-R) and by Naudit High Performance Computing and Networking under the project ESCUELAS CONECTADAS (Convenios 2018 y 2019 ‘‘Analisis de trafico y supercomputación de sobremesa’’, art. 83)

The fifth-generation (5G) of cellular communications is expected to be deployed in the next years to support a wide range of services with different demands of peak data rates, latency and quality of experience (QoE). In this work, we propose a novel approach for radio network dimensioning (RND), named as Heuristic RND (HRND), which uses real open data in the network dimensioning process. This procedure, named as NetDataDrilling, provides the dimensioning target area by means of network data selection and visualization from the existing infrastructure. Moreover, the proposed NetDimensioning heuristic considers the necessary parameters of numerology and bandwidth parts (BWP) supported by New Radio (NR) to provide a balanced network design mediating among the requirements of coverage, capacity, QoE and cost. The proposed HRND is based on the novel quality of experience (QoE) parameter ζ by probabilistically characterizing the 5G radio resource control (rrc) states to ensure the availability of peak data rates for the MNO's required percentage of the time. The simulation results show the fulfilment of QoE and load balancing parameters with significant cost savings compared to the conventional RND methodology. This work was supported by the Spanish National Project TERESA-ADA (MINECO/AEI/R, UE), under Grant TEC2017-90093-C3-2-R.

This paper considers passive detection of a cyclostationary signal in two multiple-input multiple-output (MIMO) channels. The passive detection system consists of an illuminator of opportunity (IO), a reference array, and a surveillance array, each equipped with multiple antennas. As common transmission signals of the IO are cyclostationary, our goal is to detect the presence of cyclostationarity at the surveillance array, given observations from both channels. To this end, we analyze the existence of optimal invariant tests, and we derive an alternative and more insightful expression for a previously proposed generalized likelihood ratio test (GLRT). Since we show that neither the uniformly most powerful invariant test (UMPIT) nor the locally most powerful invariant test (LMPIT) exist, we propose an LMPIT-inspired detector that is given by a function of the cyclic cross-power spectral density. We show that the LMPIT-inspired detector outperforms the GLRT, and both detectors outperform state-of-the-art techniques. S. Horstmann and P. J. Schreier were supported by the German Research Foundation (DFG) under grant SCHR 1384/6-1. The work of D. Ramírez was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under grant TEC2017-86921-C2-2-R (CAIMAN), by The Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM), and by the German Research Foundation (DFG) under grant RA 2662/2-1. Publicado

Human eye movements while driving reveal that visual attention largely depends on the context in which it occurs. Furthermore, an autonomous vehicle which performs this function would be more reliable if its outputs were understandable. Capsule Networks have been presented as a great opportunity to explore new horizons in the Computer Vision field, due to their capability to structure and relate latent information. In this article, we present a hierarchical approach for the prediction of eye fixations in autonomous driving scenarios. Context-driven visual attention can be modeled by considering different conditions which, in turn, are represented as combinations of several spatio-temporal features. With the aim of learning these conditions, we have built an encoder-decoder network which merges visual features' information using a global-local definition of capsules. Two types of capsules are distinguished: representational capsules for features and discriminative capsules for conditions. The latter and the use of eye fixations recorded with wearable eye tracking glasses allow the model to learn both to predict contextual conditions and to estimate visual attention, by means of a multi-task loss function. Experiments show how our approach is able to express either frame-level (global) or pixel-wise (local) relationships between features and contextual conditions, allowing for interpretability while maintaining or improving the performance of black-box related systems in the literature. Indeed, our proposal offers an improvement of 29% in terms of information gain with respect to the best performance reported in the literature. The authors would like to thank the authors from DR(eye)VE Project [49] for the support provided during this work, as well as the Multimedia Processing Group from the Universidad Carlos III de Madrid for their entire personal and academic implication.

This work presents a generalization of classical factor analysis (FA). Each of M channels carries measurements that share factors with all other channels, but also contains factors that are unique to the channel. Furthermore, each channel carries an additive noise whose covariance is diagonal, as is usual in factor analysis, but is otherwise unknown. This leads to a problem of multi-channel factor analysis with a specially structured covariance model consisting of shared low-rank components, unique low-rank components, and diagonal components. Under a multivariate normal model for the factors and the noises, a maximum likelihood (ML) method is presented for identifying the covariance model, thereby recovering the loading matrices and factors for the shared and unique components in each of the M multiple-input multipleoutput (MIMO) channels. The method consists of a three-step cyclic alternating optimization, which can be framed as a block minorization-maximization (BMM) algorithm. Interestingly, the three steps have closed-form solutions and the convergence of the algorithm to a stationary point is ensured. Numerical results demonstrate the performance of the proposed algorithm and its application to passive radar. The work of D. Ramírez was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under grant TEC2017-86921-C2-2-R (CAIMAN), and by The Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM). The work of I. Santamaría and S. Van Vaerenbergh was supported by Ministerio de Ciencia, Innovación y Universidades and AEI/FEDER funds of the E.U., under grant TEC2016-75067-C4-4-R (CARMEN). The work of L. Scharf was supported in part by the National Science Foundation under grant CCF-1712788. Publicado

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., In this study, we focus on the egocentric segmentation of arms to improve self-perception in Augmented Virtuality (AV). The main contributions of this work are: $i$ ) a comprehensive survey of segmentation algorithms for AV; $ii$ ) an Egocentric Arm Segmentation Dataset (EgoArm), composed of more than 10, 000 images, demographically inclusive (variations of skin color, and gender), and open for research purposes. We also provide all details required for the automated generation of groundtruth and semi-synthetic images; $iii$ ) the proposal of a deep learning network to segment arms in AV; $iv$ ) a detailed quantitative and qualitative evaluation to showcase the usefulness of the deep network and EgoArm dataset, reporting results on different real egocentric hand datasets, including GTEA Gaze+, EDSH, EgoHands, Ego Youtube Hands, THU-Read, TEgO, FPAB, and Ego Gesture, which allow for direct comparisons with existing approaches using color or depth. Results confirm the suitability of the EgoArm dataset for this task, achieving improvements up to 40% with respect to the baseline network, depending on the particular dataset. Results also suggest that, while approaches based on color or depth can work under controlled conditions (lack of occlusion, uniform lighting, only objects of interest in the near range, controlled background, etc.), deep learning is more robust in real AV applications

The irruption of the cell-free (CF) massive multiple-input multiple-output (MIMO) network topology has meant taking one step further the concept of massive MIMO as a means to provide uniform service in large coverage areas. A key property of massive MIMO networks is channel hardening, by which the channel becomes deterministic when the number of antennas grows large enough relative to the number of serviced users, easing the signal processing and boosting the performance of simple precoders. However, in CF massive MIMO, the fulfillment of this condition depends on several aspects that are not considered in classical massive MIMO systems. In this work, we address the presence of channel hardening in both CF massive MIMO and the recently appeared user-centric (UC) approach, under a spatially correlated Ricean fading channel using distributed and cooperative precoding and combining schemes and different power control strategies for both the downlink (DL) and uplink (UL) segments. We show that the line-of-sight (LOS) component, spatially correlated antennas and UC schemes have an impact on how the channel hardens. In addition, we examine the existent gap between the estimated achievable rate and the true network performance when channel hardening is compromised. Exact closed-form expressions for both the hardening metric and achievable DL/UL rates are given as well. This work was supported in part by the Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional (AEI/FEDER, UE), Ministerio de Economía y Competitividad (MINECO), Spain, through the project TERESA under Grant TEC2017-90093-C3-2-R and Grant TEC2017-90093-C3-3-R, and in part by the Spanish CDTI PID through the project OPALL5G: Optimization of Small Cells Performance in 5G NR.

Visible light communications (VLC) are proposed for increasing the spectral efficiency and the number of devices served in indoor environments, while providing illumination through light emitting diodes (LED). For VLC, each optical access point (AP) provides a small and confined area of coverage. Since several sources of light are usually deployed in overlapping fashion in order to provide satisfactory illumination, VLC are limited by inter-cell interference. Moreover, transmission from a specific optical AP can be blocked by the elements of the scenario. On the other hand, radio-frequency (RF) systems such as WiFi are usually available in most of the indoor scenarios. In this work, we first propose a dynamic cell formation method for grouping the optical APs in multiple optical cells that cover a footprint each minimizing the inter-cell interference. After that, we use transmission based on blind interference alignment (BIA) in each optical cell. Considering the coexistence with RF systems based on orthogonal frequency division multiplexing (OFDM), a load balancing algorithm is proposed for managing the resources of the resulting hybrid VLC/RF network and determining the user association to each system. However, the complexity of this optimization problem is excessively high for practical VLC/RF networks. In order to obtain a suboptimal but tractable solution, we propose a decentralized optimization method based on Lagrangian multipliers. Simulation results show that the proposed scheme outperforms other approaches for user grouping and managing the resources of hybrid VLC/RF networks. This work was supported in part by the Spanish National Project TERESA-ADA under Grant TEC2017-90093-C3-2-R and Grant MINECO/AEI/FEDER, UE, and in part by the research project GEOVEOLUZ-CM-UC3M funded by the call Programa de apoyo a la realización de proyectos interdisciplinares de I+D para jóvenes investigadores de la Universidad Carlos III de Madrid 2019-2020 under the frame of the Convenio Plurianual Comunidad de Madrid, Universidad Carlos III de Madrid

Leading zero count (LZC) is a fundamental building block in floating-point arithmetic and data sketches. These applications are increasingly being implemented on field-programmable gate arrays (FPGAs), however, existing architectures for LZC target application-specific integrated circuits and to the best of our knowledge specific LZC implementations tailored to FPGA structures have not been presented. In this letter, the implementation of LZC on Xilinx FPGA is considered and it is shown that by carefully adapting the LZC design to the FPGA structure, more efficient implementations can be obtained. In more detail, LZC designs for different bit widths are presented and evaluated. The results show that significant reductions in the FPGA resources needed are obtained that reach 33% lookup tables (LUTs) saving for 32-bit vectors and 20% LUTs saving for 64-bit vectors. The work of Pedro Reviriego was supported in part by the ACHILLES Project funded by the Spanish Ministry of Science and Innovation under Grant PID2019-104207RB-I00; and in part by the Madrid Government (Comunidad de Madrid-Spain) through the Multiannual Agreement with Universidad Carlos III de Madrid (UC3M) in the line of Excellence of University Professors under Grant EPUC3M21 in the Context of the V Plan Regional de Investigación Científica e Innovación Tecnológica (V PRICIT). Publicado

Circular polarized (CP) antennas are well suited for long-distance transmission attainment. In order to be adaptable for beyond 5G communication, a detailed and systematic investigation of their important conventional features is required for expected enhancements. The existing designs employing millimeter wave, microwave, and ultra-wideband (UWB) frequencies form the elementary platform for future studies. The 3.4–3.8 GHz frequency band has been identified as a worthy candidate for 5G communications because of spectrum availability. This band comes under UWB frequencies (3.1–10.6 GHz). In this survey, a review of CP antennas in the selected areas to improve the understanding of early-stage researchers specially experienced antenna designers has presented for the first time as best of our knowledge. Design implementations involving size, axial ratio, efficiency, and gain improvements are covered in detail. Besides that, various design approaches to realize CP antennas including (a) printed CP antennas based on parasitic or slotted elements, (b) dielectric resonator CP antennas, (c) reconfigurable CP antennas, (d) substrate integrated waveguide CP antennas, (e) fractal CP antennas, (f) hybrid techniques CP antennas, and (g) 3D printing CP antennas with single and multiple feeding structures have investigated and analyzed. The aim of this work is to provide necessary guidance for the selection of CP antenna geometries in terms of the required dimensions, available bandwidth, gain, and useful materials for the integration and realization in future communication systems. This project has received funding from Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant 801538. Also, this work was partially supported by RTI2018-095499-B-C31, Funded by Ministerio de Ciencia,Innovación y Universidades, Gobierno de España (MCIU/AEI/FEDER,UE).

RAN virtualization will become a key technology for the last mile of next-generation mobile networks driven by initiatives such as the O-RAN alliance. However, due to the computing fluctuations inherent to wireless dynamics and resource contention in shared computing infrastructure, the price to migrate from dedicated to shared platforms may be too high. Indeed, we show in this paper that the baseline architecture of a base station��s distributed unit (DU) collapses upon moments of deficit in computing capacity. Recent solutions to accelerate some signal processing tasks certainly help but do not tackle the core problem: a DU pipeline that requires predictable computing to provide carrier-grade reliability. We present Nuberu, a novel pipeline architecture for 4G/5G DUs specifically engineered for non-deterministic computing platforms. Our design has one key objective to attain reliability: to guarantee a minimum set of signals that preserve synchronization between the DU and its users during computing capacity shortages and, provided this, maximize network throughput. To this end, we use techniques such as tight deadline control, jitter-absorbing buffers, predictive HARQ, and congestion control. Using an experimental prototype, we show that Nuberu attains >95% of the theoretical spectrum efficiency in hostile environments, where state-of-art approaches lose connectivity, and at least 80% resource savings, This work has been supported by the European Commission through Grant No. 101017109 (DAEMON project) and Grant No. 101015956 (Hexa-X project), and the CERCA Programme/Generalitat de Catalunya.

Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir Interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the Extended Virtual Sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed. This work was supported by the European Commission through the H2020 5G-TRANSFORMER project (Project ID 761536). The work of Christian Vitale was also supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for Euro-pean Programmes, Coordination, and Development.

As a response to the future challenges that power systems will have to face, a gradual growth of present High Voltage Direct Current (HVDC) links is expected, leading to an HVDC supergrid integrating different configurations of HVDC stations. Such stations may suffer contingencies affecting just one pole (asymmetrical contingencies). The effects of these contingencies on an HVDC grid are very dependent on the configuration of the HVDC stations and their earthing system. Furthermore, when symmetrical monopolar stations exist in the DC grid, asymmetrical contingencies will also affect its healthy pole. For that reason, this paper focuses on the influence of the earthing system resistance of symmetrical monopolar stations on the performance of a heterogeneous HVDC grid during asymmetrical operation. The impact on the protection system is also investigated. The analysis concludes that an inappropriate earthing resistance magnitude may lead to a poor performance of heterogenous HVDC grids during asymmetrical operation. In addition, the study also indicates that the decisive factors for the selection of the grounding impedance of a symmetrical monopolar station are the asymmetrical operation of the whole grid and its contribution to pole-to-ground fault currents.

The telecom industry is moving from a "horizontal" service delivery model, where services are defined independent of their consumers, toward a "vertical" delivery model, where the provided services are tailored to specific industry sectors and verticals. In order to enable this transition, an end-to-end comprehensive 5G architecture is needed, with capabilities to support the use cases of the different vertical industries. A key feature of this architecture is the implementation of network slicing over a single infrastructure to provision highly heterogeneous vertical services, as well as a network slicing management system capable of handling simultaneous slices. On top of the network slicing technology, functionality needs to be devised to deploy the slices required by the different vertical players and provide them with a suitable interface to manage their slice. In this article, we design a 5G mobile network architecture to support vertical industries. The proposed architecture builds on ongoing standardization efforts at 3GPP and ETSI, and incorporates additional modules to provide enhanced MANO and control functionality as well as artificial-intelligence-based data analytics. On top of these modules, a service layer is provided to offer vertical players an easyto- use interface to manage their services. This work was supported by the H2020 5G-TOURS European project (Grant Agreement No. 856950).

Pilot-pouring superimposed training (PPST) is a novel channel estimation technique specially designed for cyclic block filtered multi-tone (CB-FMT), where the pilot symbols are poured into the subcarriers taking advantage of the power left unused by the data symbols. Hence, since this technique is based on superimposed training (ST) principles, the data rate is not reduced, unlike the pilot symbol assisted modulation (PSAM). Besides, it exploits a weighted average at the receiver side that is capable of minimizing the mean squared error (MSE) of the channel estimation, and then enhancing the performance of the system. However, the existing proposal on PPST is limited to the minimization of the MSE to improve channel estimation for a given power allocation factor, without solving the joint optimization of channel estimation and data detection procedures. With this aim, this work addresses the whole problem to reach the best performance for both tasks, thus taking into account also the power allocation factor in the opt where the pilot symbols are poured into the subcarriers taking advantage of the power left unused by the data symbols. Hence, since this technique is based on superimposed training (ST) principles, the data rate is not reduced, unlike the pilot symbol assisted modulation (PSAM). Besides, it exploits a weighted average at the receiver side that is capable of minimizing the mean squared error (MSE) of the channel estimation, and then enhancing the performance of the system. However, the existing proposal on PPST is limited to the minimization of the MSE to improve channel estimation for a given power allocation factor, without solving the joint optimization of channel estimation and data detection procedures. With this aim, this work addresses the whole problem to reach the best performance for both tasks, thus taking into account also the power allocation factor in the optimization process, where the spectral efficiency must be maximized through the signal-to-interference plus noise ratio (SINR). Two optimization approaches are proposed, where the first one, referred as pilot-pouring optimization (PPO), is focused on performance at the expense of a high complexity, while the second one, denoted as low-complexity PPO (LPPO), is able to trade-off between performance and execution time. Numerical results are provided in order to show the validity of our proposal, where the different optimization problems are compared in terms of SINR and execution time. This work was supported by Spanish National Projects TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE), and IRENE-EARTH (PID2020-115323RB-C33 / AEI / 10.13039/501100011033). The work of A. M. Tonello was supported by the Chair of Excellence Program of the Universidad Carlos III de Madrid.

Background: Surgeons need to train and certify their technical skills. This is usually done with the intervention of experts who monitor and assess trainees. Nevertheless, this is a time-consuming task that is subject to variations among evaluators. In recent decades, subjectivity has been significantly reduced through 1) the introduction of standard curricula, such as the Fundamentals of Laparoscopic Surgery (FLS) program, which measures students’ performance in specific exercises, and 2) rubrics, which are widely accepted in the literature and serve to provide feedback about the overall technical skills of the trainees. Although these two elements reduce subjectivity, they do not, however, eliminate the figure of the expert evaluator, and so the process remains time consuming. Objectives: The objective of this work is to automate those parts of the work of the expert evaluator that the technology can measure objectively, using sensors to collect evidence, and visualizations to provide feedback. We designed and developed 1) a cost-effective IoT (Internet of Things) learning environment for the training and assessment of surgical technical skills and 2) visualizations supported by the literature on visual learning analytics (VLA) to provide feedback about the exercises (in real time) and overall performance (at the end of the training) of the trainee. Methods: A hybrid approach was followed based on previous research for the design of the sensor based IoT learning environment. Previous studies were used as the basis for getting best practices on the tracking of surgical instruments and on the detection of the force applied to the tissue, with a focus on reducing the costs of data collection. The monitoring of the specific exercises required the design of sensors and collection mechanisms from scratch as there is little existing research on this subject. Moreover, it was necessary to design the overall architecture to collect, process, synchronize and communicate the data coming from the different sensors to provide high-level information relevant to the end user. The information to be presented was already validated by the literature and the focus was on how to visualize this information and the optimal time for its presentation to end users. The visualizations were validated with 18 VLA experts assessing the technical aspects of the visualizations and 4 medical experts assessing their functional aspects. Results: This IoT learning environment amplifies the evaluation mechanisms already validated by the literature, allowing automatic data collection. First, it uses IoT sensors to automatically correct two of the exercises defined in the FLS (peg transfer and precision cutting), providing real-time visualizations. Second it monitors the movement of the surgical instruments and the force applied to the tissues during the exercise, computing 6 of the high-level indicators used by expert evaluators in their rubrics (efficiency, economy of movement, hand tremor, depth perception, bimanual dexterity, and respect for tissue), providing feedback about the technical skills of the trainee using a radar chart with these six indicators at the end of the training (summative visualizations). Conclusions: The proposed IoT learning environment is a promising and cost-effective alternative to help in the training and assessment of surgical technical skills. The system shows the trainees’ progress and presents new indicators about the correctness of each specific exercise through real-time visualizations, as well as their general technical skills through summative visualizations, aligned with the 6 more frequent indicators in standardized scales. Early results suggest that although both types of visualizations are useful, it is necessary to reduce the cognitive load of the graphs presented in real time during training. Nevertheless, an additional evaluation is needed to confirm these results. This work was supported in part by the FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación, through the Smartlet and H2O Learn projects under Grant TIN2017-85179-C3-1-R and PID2020-112584RB-C31, by the Ministerio de Economía y Competitividad through the SimLAP project (RTC-2014-2811-1), and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307 and under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M21), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation), a project which is co-funded by the European Structural Funds (FSE and FEDER). Partial support has also been received from the European Commission through Erasmus + Capacity Building in the Field of Higher Education projects, more specifically through projects LALA, InnovaT, and PROF-XXI (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP) and through other Erasmus+ projects EUCare (2021-1-FR01-KA220-VET-000024860) and POEM-SET (2021-FR01-KA220-HED-000032171). This publication reflects the views only of the authors and funders cannot be held responsible for any use which may be made of the information contained therein. The authors also appreciate the work by Julio Villena-Román and the experts who participated in the evaluation of the IoT learning environment.

MOOCs (massive open online courses) have a built-in forum where learners can share experiences as well as ask questions and get answers. Nevertheless, the work of the learners in the MOOC forum is usually not taken into account when calculating their grade in the course, due to the difficulty of automating the calculation of that grade in a context with a very large number of learners. In some situations, discussion forums might even be the only available evidence to grade learners. In other situations, forum interactions could serve as a complement for calculating the grade in addition to traditional summative assessment activities. This paper proposes an algorithm to automatically calculate learners&rsquo, grades in the MOOC forum, considering both the quantitative dimension and the relevance in their contributions. In addition, the algorithm has been implemented within a web application, providing instructors with a visual and a numerical representation of the grade for each learner. An exploratory analysis is carried out to assess the algorithm and the tool with a MOOC on programming, obtaining a moderate positive correlation between the forum grades provided by the algorithm and the grades obtained through the summative assessment activities. Nevertheless, the complementary analysis conducted indicates that this correlation may not be enough to use the forum grades as predictors of the grades obtained through summative assessment activities.