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Dr. Marko Beko
Universidade Lusofona, Lisboa, Portugal

Basic Info


Research Keywords & Expertise

0 Cognitive Radio
0 Machine Learning
0 Signal Processing
0 sensor networks
0 Source Localization

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sensor networks
Source Localization
Cognitive Radio
Machine Learning
PAPR reduction
Signal Processing
Wireless communications and networking

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Short Biography

MARKO BEKO was born in Belgrade, Serbia, in November 1977. He received the Ph.D. degree in electrical and computer engineering from the Instituto Superior Técnico (IST), Universidade de Lisboa, Portugal, in 2008. He received the title of the Professor with Aggregation/Habilitation of electrical and computer engineering from the Universidade Nova de Lisboa, Lisbon, in 2018. Currently, he is an Associate Professor at IST. He has published 60 journal papers, 85 conference papers, 3 book chapters, and 1 book. He holds 8 patents (granted and pending) in USA and Portugal. His current research interests lie in the area of signal processing for wireless communications. He serves as an Associate Editor for the IEEE Open Journal of the Communications Society and Journal on Physical Communication (Elsevier). He is the winner of the 2008 IBM Portugal Scientific Award. According to the methodology proposed by Stanford University, he was among the most influential researchers in the world in 2019 where he joined the top 1 percent of scientists whose work is most cited by other colleagues in the field of Information and Communication Technologies, sub-area Networks and Telecommunications. He is one of the founders of Koala Tech.

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Review
Published: 13 July 2021 in Electronics
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Telecommunications have grown to be a pillar to a functional society and the urge for reliable and high throughput systems has become the main objective of researchers and engineers. State-of-the-art work considers massive Multiple-Input Multiple-Output (massive MIMO) as the key technology for 5G and beyond. Large spatial multiplexing and diversity gains are some of the major benefits together with an improved energy efficiency. Current works mostly assume the application of well-established techniques in a massive MIMO scenario, although there are still open challenges regarding hardware and computational complexities and energy efficiency. Fully digital, analog, and hybrid structures are analyzed and a multi-layer massive MIMO transmission technique is detailed. The purpose of this article is to describe the most acknowledged transmission techniques for massive MIMO systems and to analyze some of the most promising ones and identify existing problems and limitations.

ACS Style

David Borges; Paulo Montezuma; Rui Dinis; Marko Beko. Massive MIMO Techniques for 5G and Beyond—Opportunities and Challenges. Electronics 2021, 10, 1667 .

AMA Style

David Borges, Paulo Montezuma, Rui Dinis, Marko Beko. Massive MIMO Techniques for 5G and Beyond—Opportunities and Challenges. Electronics. 2021; 10 (14):1667.

Chicago/Turabian Style

David Borges; Paulo Montezuma; Rui Dinis; Marko Beko. 2021. "Massive MIMO Techniques for 5G and Beyond—Opportunities and Challenges." Electronics 10, no. 14: 1667.

Journal article
Published: 25 June 2021 in Journal of Sensor and Actuator Networks
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The present work proposed a low-cost portable device as an enabling technology for agriculture using multispectral imaging and machine learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed a good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.

ACS Style

Gilson Helfer; Jorge Barbosa; Douglas Alves; Adilson da Costa; Marko Beko; Valderi Leithardt. Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology. Journal of Sensor and Actuator Networks 2021, 10, 40 .

AMA Style

Gilson Helfer, Jorge Barbosa, Douglas Alves, Adilson da Costa, Marko Beko, Valderi Leithardt. Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology. Journal of Sensor and Actuator Networks. 2021; 10 (3):40.

Chicago/Turabian Style

Gilson Helfer; Jorge Barbosa; Douglas Alves; Adilson da Costa; Marko Beko; Valderi Leithardt. 2021. "Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology." Journal of Sensor and Actuator Networks 10, no. 3: 40.

Journal article
Published: 23 June 2021 in Digital Signal Processing
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Basis functions of the Discrete Hermite transform (DHT) can be formed as the set of eigenvectors of a symmetric tridiagonal matrix which commutes with the centered discrete Fourier transform matrix. In this paper, we consider the optimization of the associated time-axis scaling factor, which is of crucial importance in the DHT-based applications. The parameter optimization is performed to enhance the signal representation by matching the smallest possible number of basis functions with the time-domain signal waveform, therefore minimizing the number of Hermite coefficients with significant or non-zero values. Such highly concentrated signal representation is particularly amenable for signal compression, filtering, and denoising, while the coefficients of the optimized transform can be exploited as features in signal classification and other machine learning applications. The proposed parameter optimization approach is verified on numerical examples, including the experiments with QRS complexes, specific segments of electrocardiogram (ECG) signals being particularly important in biomedical applications.

ACS Style

Miloš Brajović; Irena Orović; Marko Beko; Srdjan Stanković. Parameter Optimization of Orthogonal Discrete Hermite Transform Formed Using Eigenvectors of a Symmetric Tridiagonal Matrix. Digital Signal Processing 2021, 117, 103140 .

AMA Style

Miloš Brajović, Irena Orović, Marko Beko, Srdjan Stanković. Parameter Optimization of Orthogonal Discrete Hermite Transform Formed Using Eigenvectors of a Symmetric Tridiagonal Matrix. Digital Signal Processing. 2021; 117 ():103140.

Chicago/Turabian Style

Miloš Brajović; Irena Orović; Marko Beko; Srdjan Stanković. 2021. "Parameter Optimization of Orthogonal Discrete Hermite Transform Formed Using Eigenvectors of a Symmetric Tridiagonal Matrix." Digital Signal Processing 117, no. : 103140.

Journal article
Published: 16 June 2021 in IEEE Access
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This work addresses the problem of unmanned aerial vehicle (UAV) navigation in indoor environments. Due to unavailability of satellite signals, the proposed algorithm takes advantage of terrestrial radio measurements between the UAV and a set of stationary reference points, from which it extracts range information, as well as odometry by means of inertial sensors, such as accelerometer. On the one hand, based on maximum a posteriori (MAP) criterion, the range information and accumulated knowledge throughout the UAV’s movement are employed to derive a generalized trust region sub-problem (GTRS), that is solved exactly via bisection procedure. On the other hand, by using the UAV’s transform in relation to the world, another position estimation is obtained by employing odometry. Finally, the two position estimates are combined through a Kalman filter (KF) to enhance the positioning accuracy and obtain the final UAV’s position estimation. The UAV is then navigated to a desired destination, by simply calculating the velocity components in the shortest path. Our results show that the proposed algorithm is robust to various model parameters for high precision (HP) UAV sensors, achieving reasonably good positioning accuracy. Besides, the results corroborate that the proposed algorithm is suitable for real-time applications, consuming (on average) only 21 ms to estimate the UAV position.

ACS Style

J. P. Matos-Carvalho; Ricardo Santos; Slavisa Tomic; Marko Beko.. GTRS-based Algorithm for UAV Navigation in Indoor Environments Employing Range Measurements and Odometry. IEEE Access 2021, 9, 1 -1.

AMA Style

J. P. Matos-Carvalho, Ricardo Santos, Slavisa Tomic, Marko Beko.. GTRS-based Algorithm for UAV Navigation in Indoor Environments Employing Range Measurements and Odometry. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

J. P. Matos-Carvalho; Ricardo Santos; Slavisa Tomic; Marko Beko.. 2021. "GTRS-based Algorithm for UAV Navigation in Indoor Environments Employing Range Measurements and Odometry." IEEE Access 9, no. : 1-1.

Journal article
Published: 09 June 2021 in IEEE Access
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Wireless communication systems are being considered for medical applications to facilitate the doctors’ operation and the quality of the medical procedures. A demonstrative example of this is the catheterization laboratory (CathLab), where it is desirable to replace the existent wired connections by wireless alternatives. However, there are some challenging requirements that need to be fulfilled by the wireless link, especially for intra-vascular ultra-sound (IVUS) systems, since the images acquired by the catheter should be transmitted with very high data rate and low latency, together with the highest possible amplification efficiency, to increase the battery life. The communication requirements can be achieved with latest the Wi-Fi standard IEEE 802.11ax (Wi-Fi 6). However, since Wi-Fi is based on orthogonal frequency division multiplexing (OFDM) waveforms, the transmitted signals present high envelope fluctuations, leading to amplification difficulties due to the nonlinear distortion effects and low energy efficiency. In this paper, we present an innovative amplification scheme named quantized digital amplification (QDA). It is shown that the QDA allows a quasi-linear amplification of IEEE 802.11ax signals while maintaining a very high energy efficiency. To demonstrate this, a QDA prototype and a set of performance results, regarding both the linearity of the transmitted signals and the energy efficiency, are presented.

ACS Style

Pedro Viegas; Hugo Serra; Joao Guerreiro; Ricardo Madeira; David Borges; Rui Dinis; Paulo Montezuma; Joao Pedro Oliveira; Luis M. Campos; Marko Beko. A Novel Highly-Efficient Amplification Scheme for Wireless Communications in a CathLab Environment. IEEE Access 2021, 9, 87520 -87530.

AMA Style

Pedro Viegas, Hugo Serra, Joao Guerreiro, Ricardo Madeira, David Borges, Rui Dinis, Paulo Montezuma, Joao Pedro Oliveira, Luis M. Campos, Marko Beko. A Novel Highly-Efficient Amplification Scheme for Wireless Communications in a CathLab Environment. IEEE Access. 2021; 9 ():87520-87530.

Chicago/Turabian Style

Pedro Viegas; Hugo Serra; Joao Guerreiro; Ricardo Madeira; David Borges; Rui Dinis; Paulo Montezuma; Joao Pedro Oliveira; Luis M. Campos; Marko Beko. 2021. "A Novel Highly-Efficient Amplification Scheme for Wireless Communications in a CathLab Environment." IEEE Access 9, no. : 87520-87530.

Journal article
Published: 23 April 2021 in Sensors
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In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks’ practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. The proposed algorithm is, by its nature, scalable and robust to node and link failures. The paper presents a detailed discussion and analysis of the algorithm’s characteristics, including the effects of denoising, the possibility of organizing coordinated actions, and the convergence rate improvement induced by the consensus scheme. The results of extensive simulations demonstrate the high effectiveness of the proposed algorithm, and that its behavior is close to the centralized scheme even in the case of sparse neighbor-based inter-node communication.

ACS Style

Dejan Dašić; Nemanja Ilić; Miljan Vučetić; Miroslav Perić; Marko Beko; Miloš Stanković. Distributed Spectrum Management in Cognitive Radio Networks by Consensus-Based Reinforcement Learning. Sensors 2021, 21, 2970 .

AMA Style

Dejan Dašić, Nemanja Ilić, Miljan Vučetić, Miroslav Perić, Marko Beko, Miloš Stanković. Distributed Spectrum Management in Cognitive Radio Networks by Consensus-Based Reinforcement Learning. Sensors. 2021; 21 (9):2970.

Chicago/Turabian Style

Dejan Dašić; Nemanja Ilić; Miljan Vučetić; Miroslav Perić; Marko Beko; Miloš Stanković. 2021. "Distributed Spectrum Management in Cognitive Radio Networks by Consensus-Based Reinforcement Learning." Sensors 21, no. 9: 2970.

Journal article
Published: 22 April 2021 in Journal of Sensor and Actuator Networks
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The localization of an acoustic source has attracted much attention in the scientific community, having been applied in several different real-life applications. At the same time, the use of neural networks in the acoustic source localization problem is not common; hence, this work aims to show their potential use for this field of application. As such, the present work proposes a deep feed-forward neural network for solving the acoustic source localization problem based on energy measurements. Several network typologies are trained with ideal noise-free conditions, which simplifies the usual heavy training process where a low mean squared error is obtained. The networks are implemented, simulated, and compared with conventional algorithms, namely, deterministic and metaheuristic methods, and our results indicate improved performance when noise is added to the measurements. Therefore, the current developed scheme opens up a new horizon for energy-based acoustic localization, a field where machine learning algorithms have not been applied in the past.

ACS Style

Sérgio Correia; Slavisa Tomic; Marko Beko. A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization. Journal of Sensor and Actuator Networks 2021, 10, 29 .

AMA Style

Sérgio Correia, Slavisa Tomic, Marko Beko. A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization. Journal of Sensor and Actuator Networks. 2021; 10 (2):29.

Chicago/Turabian Style

Sérgio Correia; Slavisa Tomic; Marko Beko. 2021. "A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization." Journal of Sensor and Actuator Networks 10, no. 2: 29.

Erratum
Published: 29 March 2021 in Computers
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The third affiliation of the paper should have been completed in our original article

ACS Style

Sérgio Correia; João Fé; Slavisa Tomic; Marko Beko. Erratum: Correia, S.D., et al. Development of a Test-Bench for Evaluating the Embedded Implementation of the Improved Elephant Herding Optimization Algorithm Applied to Energy-Based Acoustic Localization. Computers 2020, 9, 87. Computers 2021, 10, 41 .

AMA Style

Sérgio Correia, João Fé, Slavisa Tomic, Marko Beko. Erratum: Correia, S.D., et al. Development of a Test-Bench for Evaluating the Embedded Implementation of the Improved Elephant Herding Optimization Algorithm Applied to Energy-Based Acoustic Localization. Computers 2020, 9, 87. Computers. 2021; 10 (4):41.

Chicago/Turabian Style

Sérgio Correia; João Fé; Slavisa Tomic; Marko Beko. 2021. "Erratum: Correia, S.D., et al. Development of a Test-Bench for Evaluating the Embedded Implementation of the Improved Elephant Herding Optimization Algorithm Applied to Energy-Based Acoustic Localization. Computers 2020, 9, 87." Computers 10, no. 4: 41.

Communication
Published: 03 March 2021 in Sensors
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This work addresses the problem of target localization in three-dimensional wireless sensor networks (WSNs). The proposed algorithm is based on a hybrid system that employs angle of arrival (AOA) and received signal strength (RSS) measurements, where the target’s transmit power is considered as an unknown parameter. Although both cases of a known and unknown target’s transmit power have been addressed in the literature, most of the existing approaches for unknown transmit power are either carried out recursively, or require a high computational cost. This results in an increased execution time of these algorithms, which we avoid in this work by proposing a single-iteration solution with moderate computational complexity. By exploiting the measurement models, a non-convex least squares (LS) estimator is derived first. Then, to tackle its nonconvexity, we resort to second-order cone programming (SOCP) relaxation techniques to transform the non-convex estimator into a convex one. Additionally, to make the estimator tighter, we exploit the angle between two vectors by using the definition of their inner product, which arises naturally from the derivation steps that are taken. The proposed method not only matches the performance of a computationally more complex state-of-the-art method, but it outperforms it for small N. This result is of a significant value in practice, since one desires to localize the target using the least number of anchor nodes as possible due to network costs.

ACS Style

Marcelo Costa; Slavisa Tomic; Marko Beko. An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks. Sensors 2021, 21, 1731 .

AMA Style

Marcelo Costa, Slavisa Tomic, Marko Beko. An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks. Sensors. 2021; 21 (5):1731.

Chicago/Turabian Style

Marcelo Costa; Slavisa Tomic; Marko Beko. 2021. "An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks." Sensors 21, no. 5: 1731.

Journal article
Published: 24 February 2021 in IEEE Transactions on Control of Network Systems
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We propose several novel distributed gradient-based temporal difference algorithms for multi-agent off-policy learning of linear approximation of the value function in Markov decision processes with strict information structure constraints (ISC). The algorithms are composed of: 1) local parameter updates based on gradient temporal difference learning with eligibility traces, and 2) linear stochastic time varying consensus schemes over directed graphs. The proposed algorithms differ by their form, definition of eligibility traces, selection of time-scales and the way of incorporating consensus iterations. The main contribution of the paper is a convergence analysis based on the properties of the Feller-Markov processes and the consensus model. We prove that the parameter estimates weakly converge to the corresponding ordinary differential equations with precisely defined invariant sets. It is shown how the adopted methodology can be applied under weaker ISC. The variance reduction effect is demonstrated by analyzing an asymptotic stochastic differential equation. Guidelines for communication network design are provided. The algorithms' properties are illustrated usi

ACS Style

Milos S. Stankovic; Marko Beko; Srdjan S. Stankovic. Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning. IEEE Transactions on Control of Network Systems 2021, PP, 1 -1.

AMA Style

Milos S. Stankovic, Marko Beko, Srdjan S. Stankovic. Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning. IEEE Transactions on Control of Network Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Milos S. Stankovic; Marko Beko; Srdjan S. Stankovic. 2021. "Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning." IEEE Transactions on Control of Network Systems PP, no. 99: 1-1.

Journal article
Published: 09 February 2021 in Applied Sciences
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In a wiretap channel system model, the jammer node adopts the energy-harvesting signal as artificial noise (jamming signal) against the cooperative eavesdroppers. There are two eavesdroppers in the wiretap channel: eavesdropper E1 is located near the transmitter and eavesdropper E2 is located near the jammer. The eavesdroppers are equipped with multiple antennas and employ the iterative block decision feedback equalization decoder to estimate the received signal, i.e., information signal at E1 and jamming signal at E2. It is assumed that E1 has the channel state information (CSI) of the channel between transmitter and E1, and similarly, E2 has the CSI of channel between jammer and E2. The eavesdroppers establish communication link between them and cooperate with each other to reduce the information signal interference at E2 and jamming signal interference at E1. The performance of decoders depends on the signal to interference plus noise ratio (SINR) of the received signal. The power of information signal is fixed and the power of the jamming signal is adjusted to improve the SINR of the received signal. This research work is solely focused on optimizing the jamming signal power to degrade the performance of cooperative eavesdroppers. The jamming signal power is optimized for the given operating SINR with the support of simulated results. The jamming signal power optimization leads to better energy conservation and degrades the performance of eavesdroppers.

ACS Style

AkashKumar Rajaram; Dushnatha Jayakody; Rui Dinis; Marko Beko. Energy Efficient Secure Communication Model against Cooperative Eavesdropper. Applied Sciences 2021, 11, 1563 .

AMA Style

AkashKumar Rajaram, Dushnatha Jayakody, Rui Dinis, Marko Beko. Energy Efficient Secure Communication Model against Cooperative Eavesdropper. Applied Sciences. 2021; 11 (4):1563.

Chicago/Turabian Style

AkashKumar Rajaram; Dushnatha Jayakody; Rui Dinis; Marko Beko. 2021. "Energy Efficient Secure Communication Model against Cooperative Eavesdropper." Applied Sciences 11, no. 4: 1563.

Review
Published: 31 December 2020 in Journal of Sensor and Actuator Networks
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Cryptography is considered indispensable among security measures applied to data concerning insecure means of transmission. Among various existent algorithms on asymmetric cryptography, we may cite Elliptic Curve Cryptography (ECC), which has been widely used due to its security level and reduced key sizes. When compared to Rivest, Shamir and Adleman (RSA), for example, ECC can maintain security levels with a shorter key. Elliptic Curve Point Multiplication (ECPM) is the main function in ECC, and is the component with the highest hardware cost. Lots of ECPM implementations have been applied on hardware targeting the acceleration of its calculus. This article presents a systematic review of literature on ECPM implementations on both Field-Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC). The obtained results show which methods and technologies have been used to implement ECPM on hardware and present some findings of the choices available to the hardware designers.

ACS Style

Arielle Verri Lucca; Guilherme Mariano Sborz; Valderi Leithardt; Marko Beko; Cesar Albenes Zeferino; Wemerson Parreira. A Review of Techniques for Implementing Elliptic Curve Point Multiplication on Hardware. Journal of Sensor and Actuator Networks 2020, 10, 3 .

AMA Style

Arielle Verri Lucca, Guilherme Mariano Sborz, Valderi Leithardt, Marko Beko, Cesar Albenes Zeferino, Wemerson Parreira. A Review of Techniques for Implementing Elliptic Curve Point Multiplication on Hardware. Journal of Sensor and Actuator Networks. 2020; 10 (1):3.

Chicago/Turabian Style

Arielle Verri Lucca; Guilherme Mariano Sborz; Valderi Leithardt; Marko Beko; Cesar Albenes Zeferino; Wemerson Parreira. 2020. "A Review of Techniques for Implementing Elliptic Curve Point Multiplication on Hardware." Journal of Sensor and Actuator Networks 10, no. 1: 3.

Journal article
Published: 03 November 2020 in Computers
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The present work addresses the development of a test-bench for the embedded implementation, validity, and testing of the recently proposed Improved Elephant Herding Optimization (iEHO) algorithm, applied to the acoustic localization problem. The implemented methodology aims to corroborate the feasibility of applying iEHO in real-time applications on low complexity and low power devices, where three different electronic modules are used and tested. Swarm-based metaheuristic methods are usually examined by employing high-level languages on centralized computers, demonstrating their capability in finding global or good local solutions. This work considers iEHO implementation in C-language running on an embedded processor. Several random scenarios are generated, uploaded, and processed by the embedded processor to demonstrate the algorithm’s effectiveness and the test-bench usability, low complexity, and high reliability. On the one hand, the results obtained in our test-bench are concordant with the high-level implementations using MatLab® in terms of accuracy. On the other hand, concerning the processing time and as a breakthrough, the results obtained over the test-bench allow to demonstrate a high suitability of the embedded iEHO implementation for real-time applications due to its low latency.

ACS Style

Sérgio Correia; João Fé; Slavisa Tomic; Marko Beko. Development of a Test-Bench for Evaluating the Embedded Implementation of the Improved Elephant Herding Optimization Algorithm Applied to Energy-Based Acoustic Localization. Computers 2020, 9, 87 .

AMA Style

Sérgio Correia, João Fé, Slavisa Tomic, Marko Beko. Development of a Test-Bench for Evaluating the Embedded Implementation of the Improved Elephant Herding Optimization Algorithm Applied to Energy-Based Acoustic Localization. Computers. 2020; 9 (4):87.

Chicago/Turabian Style

Sérgio Correia; João Fé; Slavisa Tomic; Marko Beko. 2020. "Development of a Test-Bench for Evaluating the Embedded Implementation of the Improved Elephant Herding Optimization Algorithm Applied to Energy-Based Acoustic Localization." Computers 9, no. 4: 87.

Journal article
Published: 02 May 2020 in Sensors
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The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals’ recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applicable for different types of biomedical signals and different applications with under-sampled data. Incomplete sampling/sensing can be considered as a sort of signal damage, where missing data can occur as a result of noise or the incomplete signal acquisition procedure. Many approaches for recovering the missing signal parts have been developed, depending on the signal nature. Here, several approaches and their applications are presented for medical signals and images. The possibility to analyze results using different statistical parameters is provided, with the aim to choose the most suitable approach for a specific application. The instrument provides manifold possibilities such as fitting different parameters for the considered signal and testing the efficiency under different percentages of missing data. The reconstruction accuracy is measured by the mean square error (MSE) between original and reconstructed signal. Computational time is important from the aspect of power requirements, thus enabling the selection of a suitable algorithm. The instrument contains its own signal database, but there is also the possibility to load any external data for analysis.

ACS Style

Stefan Vujović; Andjela Draganić; Maja Lakičević Žarić; Irena Orović; Miloš Daković; Marko Beko; Srdjan Stanković. Sparse Analyzer Tool for Biomedical Signals. Sensors 2020, 20, 2602 .

AMA Style

Stefan Vujović, Andjela Draganić, Maja Lakičević Žarić, Irena Orović, Miloš Daković, Marko Beko, Srdjan Stanković. Sparse Analyzer Tool for Biomedical Signals. Sensors. 2020; 20 (9):2602.

Chicago/Turabian Style

Stefan Vujović; Andjela Draganić; Maja Lakičević Žarić; Irena Orović; Miloš Daković; Marko Beko; Srdjan Stanković. 2020. "Sparse Analyzer Tool for Biomedical Signals." Sensors 20, no. 9: 2602.

Journal article
Published: 30 March 2020 in Electronics
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This article is focused on implementing simultaneous wireless information and power transmission as a physical layer security measure by using artificial noise. A series of high energy precoded symbols is simultaneously transmitted along with the information symbols over a Rayleigh frequency selective fading channel. The high energy precoded symbols act as an artificial noise for the eavesdroppers. The energy symbols are precoded on the basis of a legitimate user’s channel matrix to form a null space vector, which eliminates the interference of energy symbols at the information symbol receiver antennas, while allowing the rectenna to harvest energy from the superimposed information and energy symbols. We analyze the secrecy rate and error rate performance at the receiver under different circumstances, and we show that the performance of the legitimate user can be improved by using the iterative block decision feedback equalization method at the receiver.

ACS Style

AkashKumar Rajaram; Rui Dinis; Dushnatha Nalin K. Jayakody; Marko Beko. Secure Information Transmission with Self Jamming SWIPT. Electronics 2020, 9, 587 .

AMA Style

AkashKumar Rajaram, Rui Dinis, Dushnatha Nalin K. Jayakody, Marko Beko. Secure Information Transmission with Self Jamming SWIPT. Electronics. 2020; 9 (4):587.

Chicago/Turabian Style

AkashKumar Rajaram; Rui Dinis; Dushnatha Nalin K. Jayakody; Marko Beko. 2020. "Secure Information Transmission with Self Jamming SWIPT." Electronics 9, no. 4: 587.

Journal article
Published: 04 March 2020 in IEEE Access
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This work addresses target localization problem in precarious surroundings where possibly no links are line of sight. It exploits the known architecture of available reference points to act as an irregular antenna array in order to estimate the azimuth angle between a reference point and a target, based on distance estimates withdrawn from integrated received signal strength (RSS) and time of arrival (TOA) observations. These fictitious azimuth angle observations are then used to linearize the measurement models, which triggers effortless derivation of a new estimator in a closed-form. It is shown here that, by using fixed network geometry in which target orientation with respect to a line formed by a pair of anchors can be correctly estimated, the localization performance can be significantly enhanced. The new approach is validated through computer simulations, which corroborate our intuition of profiting from inherent information within a network.

ACS Style

Slavisa Tomic; Marko Beko; Milan Tuba. Exploiting Orientation Information to Improve Range-Based Localization Accuracy. IEEE Access 2020, 8, 44041 -44047.

AMA Style

Slavisa Tomic, Marko Beko, Milan Tuba. Exploiting Orientation Information to Improve Range-Based Localization Accuracy. IEEE Access. 2020; 8 (99):44041-44047.

Chicago/Turabian Style

Slavisa Tomic; Marko Beko; Milan Tuba. 2020. "Exploiting Orientation Information to Improve Range-Based Localization Accuracy." IEEE Access 8, no. 99: 44041-44047.

Editorial
Published: 27 February 2020 in Journal of Sensor and Actuator Networks
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Recent and continuous development of mobile computing and user-centric applications has led to a requirement for accurate and low-cost localization and tracking systems

ACS Style

Slavisa Tomic; Marko Beko. Special Issue: Localization in Wireless Sensor Networks. Journal of Sensor and Actuator Networks 2020, 9, 14 .

AMA Style

Slavisa Tomic, Marko Beko. Special Issue: Localization in Wireless Sensor Networks. Journal of Sensor and Actuator Networks. 2020; 9 (1):14.

Chicago/Turabian Style

Slavisa Tomic; Marko Beko. 2020. "Special Issue: Localization in Wireless Sensor Networks." Journal of Sensor and Actuator Networks 9, no. 1: 14.

Journal article
Published: 24 February 2020 in IEEE Access
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Collaborative beamforming (CBF) is a promising technique aimed at improving energy efficiency of communication in wireless sensor networks (WSNs) which has attracted considerable attention in the research community recently. It is based on a fact that beampattern with stable mainlobe can be formed, if multiple sensors synchronize their oscillators and jointly transmit a common message signal. In this paper, we consider application of CBF with one bit of feedback in different communication scenarios and analyze the impact of constraints imposed by simple sensor node hardware, on the resulting signal strength. First, we present a CBF scheme capable of reducing interference levels in the nearby WSN clusters by employing joint feedback from multiple base stations that surround the WSN of interest. Then, we present a collaborative power allocation and sensor selection algorithm, capable of achieving beamforming gains with transmitters that are not able to adjust their oscillators’ signal phase. The performance of the algorithms is assessed by means of achieved beamforming gain which is given as a function of algorithm iterations. The presented results, which are based on numerical simulations and mathematical analysis, are compared with the ideal case without constraints and with negligible noise at the Base Station (BS).

ACS Style

Lazar Berbakov; Goran Dimic; Marko Beko; Jelena Vasiljevic; Zeljko Stojkovic. Collaborative Data Transmission in Wireless Sensor Networks. IEEE Access 2020, 8, 39647 -39658.

AMA Style

Lazar Berbakov, Goran Dimic, Marko Beko, Jelena Vasiljevic, Zeljko Stojkovic. Collaborative Data Transmission in Wireless Sensor Networks. IEEE Access. 2020; 8 (99):39647-39658.

Chicago/Turabian Style

Lazar Berbakov; Goran Dimic; Marko Beko; Jelena Vasiljevic; Zeljko Stojkovic. 2020. "Collaborative Data Transmission in Wireless Sensor Networks." IEEE Access 8, no. 99: 39647-39658.

Journal article
Published: 05 February 2020 in IEEE Access
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The present work proposes a new approach to address the energy-based acoustic localization problem. The proposed approach represents an improved version of evolutionary optimization based on Elephant Herding Optimization (EHO), where two major contributions are introduced. Firstly, instead of random initialization of elephant population, we exploit particularities of the problem at hand to develop an intelligent initialization scheme. More precisely, distance estimates obtained at each reference point are used to determine the regions in which a source is most likely to be located. Secondly, rather than letting elephants to simply wander around in their search for an update of the source location, we base their motion on a local search scheme which is found on a discrete gradient method. Such a methodology significantly accelerates the convergence of the proposed algorithm, and comes at a very low computational cost, since discretization allows us to avoid the actual gradient computations. Our simulation results show that, in terms of localization accuracy, the proposed approach significantly outperforms the standard EHO one for low noise settings and matches the performance of an existing enhanced version of EHO (EEHO). Nonetheless, the proposed scheme achieves this accuracy with significantly less number of function evaluations, which translates to greatly accelerated convergence in comparison with EHO and EEHO. Finally, it is also worth mentioning that the proposed methodology can be extended to any population-based metaheuristic method (it is not only restricted to EHO), which tackles the localization problem indirectly through distance measurements.

ACS Style

Sergio D. Correia; Marko Beko; Slavisa Tomic; Luis A. Da Silva Cruz. Energy-Based Acoustic Localization by Improved Elephant Herding Optimization. IEEE Access 2020, 8, 28548 -28559.

AMA Style

Sergio D. Correia, Marko Beko, Slavisa Tomic, Luis A. Da Silva Cruz. Energy-Based Acoustic Localization by Improved Elephant Herding Optimization. IEEE Access. 2020; 8 (99):28548-28559.

Chicago/Turabian Style

Sergio D. Correia; Marko Beko; Slavisa Tomic; Luis A. Da Silva Cruz. 2020. "Energy-Based Acoustic Localization by Improved Elephant Herding Optimization." IEEE Access 8, no. 99: 28548-28559.

Review
Published: 30 December 2019 in Applied Sciences
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Remarkable progress in radio frequency and micro-electro-mechanical systems integrated circuit design over the last two decades has enabled the use of wireless sensor networks with thousands of nodes. It is foreseen that the fifth generation of networks will provide significantly higher bandwidth and faster data rates with potential for interconnecting myriads of heterogeneous devices (sensors, agents, users, machines, and vehicles) into a single network (of nodes), under the notion of Internet of Things. The ability to accurately determine the physical location of each node (stationary or moving) will permit rapid development of new services and enhancement of the entire system. In outdoor environments, this could be achieved by employing global navigation satellite system (GNSS) which offers a worldwide service coverage with good accuracy. However, installing a GNSS receiver on each device in a network with thousands of nodes would be very expensive in addition to energy constraints. Besides, in indoor or obstructed environments (e.g., dense urban areas, forests, and canyons) the functionality of GNSS is limited to non-existing, and alternative methods have to be adopted. Many of the existing alternative solutions are centralized, meaning that there is a sink in the network that gathers all information and executes all required computations. This approach quickly becomes cumbersome as the number of nodes in the network grows, creating bottle-necks near the sink and high computational burden. Therefore, more effective approaches are needed. As such, this work presents a survey (from a signal processing perspective) of existing distributed solutions, amalgamating two radio measurements, received signal strength (RSS) and angle of arrival (AOA), which seem to have a promising partnership. The present article illustrates the theory and offers an overview of existing RSS-AOA distributed solutions, as well as their analysis from both localization accuracy and computational complexity points of view. Finally, the article identifies potential directions for future research.

ACS Style

Slavisa Tomic; Marko Beko; Luís M. Camarinha-Matos; Luís Bica Oliveira. Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods. Applied Sciences 2019, 10, 272 .

AMA Style

Slavisa Tomic, Marko Beko, Luís M. Camarinha-Matos, Luís Bica Oliveira. Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods. Applied Sciences. 2019; 10 (1):272.

Chicago/Turabian Style

Slavisa Tomic; Marko Beko; Luís M. Camarinha-Matos; Luís Bica Oliveira. 2019. "Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods." Applied Sciences 10, no. 1: 272.