Reconfigurable intelligent surface (RIS) is a recent low-cost and energy-efficient technology with potential applicability for future wireless communications. Performance gains achieved by employing RIS directly depend on accurate channel estimation (CE). It is common in the literature to assume channel reciprocity since it minimizes channel feedback, simplifies the beamforming design, and reduces the overall latency. However, in practice, due to hardware limitations at the RIS and transceivers, the channel non-reciprocity may occur naturally, so such behavior needs to be considered. In this paper, we focus on the CE problem in a non-reciprocal RIS-assisted multiple-input multiple-output (MIMO) wireless communication system. Making use of a novel closed-loop three-phase protocol for non-reciprocal CE estimation, we propose a two-stage fourth-order Tucker decomposition-based CE algorithm. In contrast to classical time-division duplexing (TDD) and frequency-division duplexing (FDD) approaches the proposed method concentrates all the processing burden for CE on the base station (BS) side, thereby freeing hardware-limited user terminal (UT) from this task. Our simulation results show that the proposed method has satisfactory performance in terms of CE accuracy compared to benchmark FDD LS-based and tensor-based techniques.

A key step of any statistical multivariate analysis concerns the choice of variables in line with the main objectives of the study. Usually, the available procedures to face this problem are restricted to a-posteriori statistical analysis, using Bayesian approaches or stepwise selection procedures. The main objective of the present paper is to revisit a framework where the a-priori choice of variables makes sense under specific conditions and to propose a factor analysis model particularly adapted to structured quantitative big data. We have associated our complete sample of variables to a mixture of two bipolar Watson distributions defined on the n-sphere, W ( μ i , ξ i ) , i = 1 , 2 , where μ i is a direction parameter and ξ i is a concentration parameter. The likelihood estimates of the direction parameter μ i is just the first principal component associated of a PCA of cluster i. The identification of the mixture of Watson distribution was obtained by cluster analysis, namely a previous hierarchical cluster analysis followed by a k-means partition of the global sample of variables. These multivariate data were explained by an alternative factor analysis model potentially delivering directly interpretable solutions without the need of rotations procedures. The loadings of this factorial model were obtained by regression. The final results concerning communalities of the 16 variables showed that for a great part of them unit variance was quite well explained by the factorial model.

Intelligent reconfigurable surface IRS are becoming an attractive component of cellular networks due to their ability to shape the propagation environment and thereby improve coverage. While IRS nodes incorporate a great number of phase-shifting elements and a controller entity, the phase shifts are typically determined by the cellular base station (BS) due to its computational capability. Since controlling a large number of phase shifts may become prohibitive in practice, it is important to reduce the control overhead between the BS and the IRS controller. To this end, in this paper, we propose a low-rank modeling approach for the IRS phase shifts. The key idea is to represent the IRS phase shift vector using a low-rank tensor approximation model, where each rank-one component is modeled as the Kronecker product of a predefined number of factors of smaller sizes, obtained via tensor decomposition algorithms. We show that the proposed low-rank models drastically reduce the required feedback requirements associated with the BS-IRS control links. Our simulation results indicate that the proposed method is especially attractive in scenarios with a strong line of sight component, in which case nearly the same spectral efficiency is reached as in the cases with near-optimal phase shifts, but with significantly lower feedback overhead.

Reconfigurable intelligent surface is a potential technology component of future wireless networks due to its capability of shaping the wireless environment. The promising MIMO systems in terms of extended coverage and enhanced capacity are, however, critically dependent on the accuracy of the channel state information. However, traditional channel estimation schemes are not applicable in RIS-assisted MIMO networks, since passive RISs typically lack the signal processing capabilities that are assumed by channel estimation algorithms. This becomes most problematic when physical imperfections or electronic impairments affect the RIS due to its exposition to different environmental effects or caused by hardware limitations from the circuitry. While these real-world effects are typically ignored in the literature, in this paper we propose efficient channel estimation schemes for RIS-assisted MIMO systems taking different imperfections into account. Specifically, we propose two sets of tensor-based algorithms, based on the parallel factor analysis decomposition schemes. First, by assuming a long-term model in which the RIS imperfections, modeled as unknown phase shifts, are static within the channel coherence time we formulate an iterative alternating least squares (ALS)-based algorithm for the joint estimation of the communication channels and the unknown phase deviations. Next, we develop the short-term imperfection model, which allows both amplitude and phase RIS imperfections to be non-static with respect to the channel coherence time. We propose two iterative ALS-based and closed-form higher order singular value decomposition-based algorithms for the joint estimation of the channels and the unknown impairments. Moreover, we analyze the identifiability and computational complexity of the proposed algorithms and study the effects of various imperfections on the channel estimation quality.

Reconfigurable intelligent surface (RIS) is a candidate technology for future wireless networks. It is known that the promised gains of RIS-assisted communications depend on the channel estimation performance. When the RIS is affected by imperfections, the associated phase shift responses present a non-ideal behavior, which translates into unknown, and possibly time-varying, phase deviations. Such perturbations can be caused by physical, electronic, or environmentalrelated conditions. In this scenario, traditional channel estimation schemes may fail to provide sufficiently accurate channel estimates. In this work, considering a time-varying RIS imperfection model, we propose an efficient and low-complexity tensor-based method to estimate the involved communication channels under unknown phase-shift responses. The proposed algorithm relies on a tensor modeling of the received signals and has a closed-form solution based on the higher order singular value decomposition. Simulation results show the effectiveness of our proposed solution in terms of estimation accuracy and computational complexity compared to the benchmark method.

In this paper, we propose a rank-one tensor modeling approach that yields a compact representation of the optimum intelligent reconfigurable surface (IRS) phase-shift vector for reducing the feedback overhead. The main idea consists of factorizing the IRS phase-shift vector as a Kronecker product of smaller vectors, namely factors. The proposed phase-shift model allows the network to trade-off between achievable data rate and feedback reduction by controling the factorization parameters. Our simulations show that the proposed phase-shift factorization drastically reduces the feedback overhead, while improving the data rate in some scenarios, compared to the state-of-the-art schemes.

Reconfigurable intelligent surface (RIS) is a candidate technology for future wireless networks. It enables to shape the wireless environment to reach massive connectivity and enhanced data rate. The promising gains of RIS-assisted networks are, however, strongly depends on the accuracy of the channel state information. Due to the passive nature of the RIS elements, channel estimation may become challenging. This becomes most evident when physical imperfections or electronic impairments affect the RIS due to its exposition to different environmental effects or caused by hardware limitations from the circuitry. In this paper, we propose an efficient and low-complexity tensor-based channel estimation approach in RIS-assisted networks taking different imperfections into account. By assuming a short-term model in which the RIS imperfections behavior, modeled as unknown amplitude and phase shifts deviations, is non-static with respect to the channel coherence time, we formulate a closed-form higher order singular value decomposition based algorithm for the joint estimation of the involved channels and the unknown impairments. Furthermore, the identifiability and computational complexity of the proposed algorithm are analyzed, and we study the effect of different imperfections on the channel estimation quality. Simulation results demonstrate the effectiveness of our proposed tensor-based algorithm in terms of the estimation accuracy and computational complexity compared to competing tensor-based iterative alternating solutions.

Intelligent reflecting surface (IRS) is a promising technology for beyond of the wireless communications. In fully passive IRS-assisted systems, channel estimation is challenging and should be carried out only at the base station or at the terminals since the elements of the IRS are incapable of processing signals. In this letter, we formulate a tensor-based semi-blind receiver that solves the joint channel and symbol estimation problem in an IRS-assisted multi-user multiple-input multiple-output system. The proposed approach relies on a generalized PARATUCK tensor model of the signals reflected by the IRS, based on a two-stage closed-form semi-blind receiver using Khatri-Rao and Kronecker factorizations. Simulation results demonstrate the superior performance of the proposed semi-blind receiver, in terms of the normalized mean squared error and symbol error rate, as well as a lower computational complexity, compared to recently proposed parallel factor analysis-based receivers.

Recorrendo ao método da mineração de dados e confecção de redes semânticas, este artigo objetiva oferecer um “instantâneo” ou “radiografia” preliminar das pesquisas que têm pautado os estudos recentes de cinema e audiovisual no Brasil. Para tal, elegemos como estudo de caso o mais importante evento científico na área de cinema e audiovisual da América do Sul, o encontro anual da Sociedade Brasileira para os Estudos de Cinema e Audiovisual (Socine). Nosso trabalho de mineração de dados e confecção de redes semânticas se dá a partir de um olhar sobre os trabalhos apresentados nos encontros anuais da Socine, com a verificação das programações completas, contendo títulos e resumos dos encontros Socine de 2013 a 2017.

In wireless communications, the propagation environment is not always favorable and may have several adverse conditions for data transmission and reception.The improper propagation conditions can be mitigated by the operator or by the current signal processing techniques.This classical paradigm can be rethought with the emerging concept of intelligent reflecting surface (IRS).IRS is a cost-effective, power-efficient and low hardware complexity solution capable of make wireless propagation environments more favorable by controlling in a software-defined way the amplitude and phase of the incident signal in order to improve the signal-to-noise ratio or mitigate interference at the intended receiver.However, in practice, the IRS operation are subject to inevitable impairments and practical issues, such as hardware limitations, blockages, and channel estimation errors that directly affects the system performance.In this paper, we study an IRS-assisted wireless communication system operating under one practical hardware model, which blockages at the IRS reflecting units are taken into account.Then, we investigate the impact of these real-world impairments on the system performance under practical channel estimation.The performance of an IRS-assisted communication under such practical constraints are evaluated in terms of the spectral efficiency (SE), normalized mean square error (NMSE) and symbol error rate (SER).

Introduction: About a year and a half after the declaration of the COVID-19 pandemic, almost the entire planet has been affected by SARS-CoV-2 coronavirus and its variants, with serious public health consequences and other repercussions not yet thoroughly evaluated or foreseen in terms of economic, financial and social disruption throughout communities. Therefore, it is of utmost importance to understand the geography of the evolution of successive pandemic waves. Particularly in European countries, where, in recent decades, more advanced models for cohesion and competitiveness of a whole with more than 400 million inhabitants have been achieved, with ambitious challenges for horizon 2030 regarding this vast territory’s economic, social, and environmental sustainability. Objective: The main objective of this research is to describe the multivariate trajectories of COVID-19 incidence, mortality, hospital admissions, ICU admissions and testing, over three successive waves, covering all European Union (EU) countries with more than two million inhabitants, over 14-days periods before May 4 2020, until February 22 2021. Methods: This research includes 22 European countries representing about 98.8% of the EU population, described by six epidemiological variables over 43 time periods from the ECDC database: the 14-day notification rate Biometrics & Biostatistics International Journal Research Article Open Access of new cases reported for 100,000 inhabitants; the 14-day notification rate of reported deaths per one million inhabitants; the mean and the rate for 100,000 population of hospital occupancy and ICU occupancy; the testing rate per 100,000 population; and the 14-days percentage of test positivity An exploratory data analysis of each epidemiological variable identified a typology of countries profiles evolution. Multivariate exploratory statistical methods, namely a 3-way data analysis (double principal components and rank principal components analyses), were applied with software R version 4.1.0. Results: The multivariate evolution profile of the COVID-19 pandemic in the EU over the studied period highlighted 3 phases: the first phase over 24 time periods, with a relatively low COVID-19 incidence, hitting only part of EU countries; a second phase at the beginning of the second wave, when COVID-19 spread to most countries, with a higher impact on national health systems; lastly, a third phase coincident with the peak of the second wave and the onset of the third wave, a particularly reactive phase from the public authorities, with intensified testing of the population. These results are clear from the principal component analysis of the centres of gravity of the 43 time periods (interstructure). The multivariate statistical analysis of the global dataset of all countries over the 43 time periods additionally provides the main factorial representation of the trajectories of COVID-19 for each country in direct comparison with the global average ranked values reached by the six epidemiological variables over the whole period under study (intrastructure). These trajectories make it possible to identify different country profiles throughout the successive pandemic waves and counter-cyclical behaviours, partly explained by the insufficient harmonisation of public policies to tackle the pandemic within the EU.

Phase-noise is a system impairment caused by the mismatch between the oscillators at the transmitter and the receiver. In OFDM systems, this induces inter-carrier-interference (ICI) by rotating the transmitted symbols. Thus it can cause severe system performance degradation. To reduce its effects, the phase-noise must be estimated or compensated. In this work, we propose a two-stage tensor-based receiver for a joint channel, phase-noise (PN), and data estimation in MIMO-OFDM systems. In the first stage, we show that the received signal at the pilot subcarriers can be modeled as a third-order PARAFAC tensor. Based on this model, we propose two algorithms for channel and phase-noise estimation at the pilot subcarriers. The first algorithm, based on the BALS (Bilinear Alternating Least Squares), is an iterative algorithm that estimates the channel gains and the phase-noise impairments. The second is a closed-form algorithm based on the LS-KRF (Least Squares - Khatri-Rao Factorization) that estimates the channel gains and the phase-noise terms through multiple rank-one factorizations. Both algorithms achieve similar performance, but in terms of computational complexity, we show that the LS-KRF becomes more attractive than the BALS as the number of receive antennas is increased. The second stage consists of data estimation, for which we propose a ZF (Zero-Forcing) receiver that capitalizes on the PARATuck tensor structure of the received signal at the data subcarriers using the Selective Kronecker Product (SKP) operator. Our numerical simulations show that the proposed receiver achieves an improved performance compared to the state-of-art receivers in terms of symbol error rate (SER) and normalized mean square error (NMSE) of the estimated channel and phase-noise matrices.

E-REDES concluded in June 2020 an advanced asset management project, called Analytics 4 Assets (A4A), leveraging data and analytical models to enhance health index and probability of failure calculations. The A4A project was delivered in 8 months with a multidisciplinary team with more than 70 people, from 4 different companies: E-REDES (DSO), DGU (EDP Group Digital Global Unit), LABELEC (EDP Group laboratory) and Accenture. The project scope has eight main building blocks (mVP – minimum viable products) because it was implemented as Digital Boost: 2 Dashboards designed in MS Power Bi; 2 Analytical models (Probability of Failure Models for HV Circuit Breakers, HV Overhead Lines sections) and 3 Health Index models for the same assets and for Power Transformers (based on Common Network Asset Indices Methodology), mostly developed in python and pyspark, using Azure Databricks; 1 Data Lake. This project is a breakthrough because the developed models are applied to all the assets in scope (∼750 Power Transformers, ∼2.000 Circuit Breakers, ∼2.600 Overhead Line sections) with automated data sources allowing significant improvements in asset management decisions. Furthermore, it contributes to democratization of decision information across the organization and leverages a data-driven culture.

In this work, we propose a two-stage tensor-based receiver for joint channel, phase-noise (PN), and data estimation in MIMO-OFDM systems. First, we cast the received signal at the pilot subcarriers as a third-order PARAFAC model. Based on this model, we propose a closed-form algorithm based on the LS-KRF (Least Squares - Khatri-Rao Factorization) that estimates the channel gains and the phase-noise terms through multiple rank-one factorizations. From the estimated channel, the second stage of the receiver consists of data estimation based on a ZF (Zero-Forcing) receiver that capitalizes on the tensor structure of the received signal at the data subcarriers via a Selective Kronecker Product (SKP) approach. Our numerical simulations show that the proposed receiver achieves an improved performance compared to the state-of-art receivers.

Lipids play a critical role in the skin as components of the epidermal barrier and as signaling and antimicrobial molecules. Atopic dermatitis in dogs is associated with changes in the lipid composition of the skin, but whether these precede or follow the onset of dermatitis is unclear. We applied rapid lipid-profiling mass spectrometry to skin and blood of 30 control and 30 atopic dogs. Marked differences in lipid profiles were observed between control, nonlesional, and lesional skin. The lipid composition of blood from control and atopic dogs was different, indicating systemic changes in lipid metabolism. Female and male dogs differed in the degree of changes in the skin and blood lipid profiles. Treatment with oclacitinib or lokivetmab ameliorated the skin condition and caused changes in skin and blood lipids. A set of lipid features of the skin was selected as a biomarker that classified samples as control or atopic dermatitis with 95% accuracy, whereas blood lipids discriminated between control and atopic dogs with 90% accuracy. These data suggest that canine atopic dermatitis is a systemic disease and support the use of rapid lipid profiling to identify novel biomarkers.

Intelligent reflecting surface (IRS) has emerged as a promising technology to enhance wireless communications by smartly shaping the radio propagation environment with reduced hardware and energy costs.In this paper, we integrate the IRS to a multiple-input single-output (MISO) fifth generation (5G) system via the joint optimization of the IRS reflecting coefficients and the transmit beamforming at the base station (BS) to maximize the spectral efficiency in an urban micro (UMi) propagation environment.Simulation results indicate that IRS successfully enhances the performance of the wireless network in terms of spectral and energy efficiencies compared with traditional transmit beamforming and relay-assisted systems.

The proof-of-concept of a robust and extensible disaggregated network element management using a SONiC-compliant Go-based software agent implementation of OpenConfig gNMI optical streaming telemetry, is demonstrated and evaluated in a multivendor testbed.

—In this paper, we propose a two-stage tensor-based semi-blind receiver for joint channel, phase noise (PN) and symbol estimation for frequency-selective MIMO systems in the presence of PN impairments. In the first stage, the frequency-selective MIMO channel is directly estimated through a tensor-based alternating least squares (ALS) algorithm that fits a PARAFAC model to the noisy received signal. In the second stage, the closed-form least squares Khatri-Rao factorization (LS-KRF) approach is used to extract the PN components at both the transmitter and receiver to be compensated for symbol detection. The proposed receiver has a satisfactory performance compared to the state-of-the-art receivers in terms of symbol error rate (SER). On the other hand, it provides high accuracy individual estimates for the channel and PN, presenting thus a clear advantage over the aforementioned receivers.

In computer courses, logical reasoning is essential for understanding the structure of a computer system and for problem solving. The challenge is to provide a teaching and learning environment that enables students to understand this new paradigm of reasoning. Programming competitions are commonly considered educational methodologies capable of instigating students' critical thinking and curiosity, as well as developing teamwork skills, creative thinking and self-guided learning. The programming marathon is one of the most widespread programming competitions. This activity enables students to solve computational problems through programming skills. In general, competition problems are presented to students by complex problem statements that require reading comprehension. Students must then to implement the algorithm to solve the proposed problem using a high-level programming language. However, this competition approach requires programming skills that are still distant from beginner students who are developing their logical reasoning. For this reason, participation in competitions is generally restricted to experienced students. In order to anticipate student participation in these activities, a programming competition approach based on the algorithm interpretation is presented. Problems are presented to students through executable programs that running from terminal. The algorithm interpretation requires the execution of different input data to generate the program outputs. The available programs have the least possible interaction with the user, which requires the student to investigate the operation of the implemented algorithm. After interpreting the algorithm, students should construct the pseudocode of it. The developed pseudocode is then submitted to the virtual learning environment for evaluation by the teacher. If the algorithm is equivalent to the one implemented by the executable program, the pseudocode is considered correct. The proposed approach was applied in the laboratory discipline for the first year of the Computer Science course. The results showed that the activity was considered attractive by the students. Finally, it was observed that the proposed approach was able to increase students' awareness of the importance of testing the programs.