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In telecommunications engineering degrees, simulations are used to understand better theoretical concepts. However, in courses related to digital communications or signal processing usually these simulations cannot be easily applied to real data or in real scenarios. This limitation does not allow the students to verify that the learned theoretical hypotheses are valid in practice. Nowadays, software defined radio platforms have been proposed to validate theoretical telecommunications concepts in real situations. Therefore, by combining a software defined radio and simulation tools, students can implement the learned notions over real data in real scenarios. This allows students to work under real applications in order to learn by experience and to see that the theoretical concepts are valid in the real world. This paper presents a project involving real-time decoding of automatic dependent surveillance–broadcast (ADS-B) signals, implemented by using a universal software radio peripheral software defined radio and MATLAB®. The universal software radio peripheral allows to receive real automatic dependent surveillance–broadcast signals sent by the aircraft. Then, by using MATLAB® tools, the automatic dependent surveillance–broadcast baseband signals are decoded. The students are guided through a manual to demodulate the real automatic dependent surveillance–broadcast signals and display in a map the aircraft trajectories. The evaluation is based on a competition between the students, considering execution time of their algorithms, and a final report of the project. This project allows to obtain a game-oriented and novel approach for teaching digital communications and signal processing. The implementation of this project in telecommunications engineering courses motivate the students, allowing them to understand better difficult theoretical concepts, and improve their computer, teamwork, and collaboration skills. The originality of the approach is based on the usage of a prototyping platform, combined with simulation tools, for practical application of telecommunications engineering concepts using real signals.

This work presents a methodology to automatically detect and identify manatee vocalizations in continuous passive acoustic underwater recordings. Given that vocalizations of each manatee present a slightly different frequency content, it is possible to identify individuals using a non-invasive acoustic approach. The recordings are processed in four stages, including detection, denoising, classification, and manatee counting and identification by vocalization clustering. The main contribution of this work is considering the vocalization spectrogram as an image (i.e., two-dimensional pattern) and representing it in terms of principal component analysis coefficients that feed a clustering approach. A performance study is carried out for each stage of the scheme. The methodology is tested to analyze three years of recordings from two wetlands in Panama to support ongoing efforts to estimate the manatee population.

The internet of things covers a large number of solutions, among which we have the LoRa technology. The following work present theoretical and technical aspects of the implementation of this technology in a wireless network to monitor environmental parameters. The document covers a brief look of the physical layer of the LoRa network, such as frequency bands and type of modulation, as well as details of components and procedures performed in our network, and results of the performance of the network in terms of signal-to-noise ratio (SNR) as a function of distance.

The purpose of this paper is to share our positive experience about the collaboration launched a few years ago between UTP (Panama) and Bordeaux INP (France) in the field of signal processing. This collaboration involves research and education activities. This has led to numerous internships of French students in Panama, mobilities of researchers, common research papers, and the 1st double diploma signed between France and a country of Central America. Thus, this paper presents the various aspects of the collaboration.

Wireless communication opens up new avenues in the field of intelligent vehicle systems, including monitoring, guidance and warning applications. In order to determine more precisely the feasibility of using the 802.11n standard in vehicular communication systems, this paper describes a process for evaluating the propagation performance of the 802.11n and 802.11p standards. To do that, the antennas are described and simulated using Win Pro Solutions which include Proman, Wallman softwares. The measurements were performed using an urban environment to prove the effectiveness of the simulation analysis

We evaluated the potential of using convolutional neural networks in classifying spectrograms of Antillean manatee (Trichechus manatus manatus) vocalizations. Spectrograms using binary, linear and logarithmic amplitude formats were considered. Two deep convolutional neural networks (DCNN) architectures were tested: linear (fixed filter size) and pyramidal (incremental filter size). Six experiments were devised for testing the accuracy obtained for each spectrogram representation and architecture combination. Results show that binary spectrograms with both linear and pyramidal architectures with dropout provide a classification rate of 94%&ndash, 99% on the training and 92%&ndash, 98% on the testing set, respectively. The pyramidal network presents a shorter training and inference time. Results from the convolutional neural networks (CNN) are substantially better when compared with a signal processing fast Fourier transform (FFT)-based harmonic search approach in terms of accuracy and F1 Score. Taken together, these results prove the validity of using spectrograms and using DCNNs for manatee vocalization classification. These results can be used to improve future software and hardware implementations for the estimation of the manatee population in Panama.

This study deals with an interweave cognitive-radio (CR) system, where an uplink orthogonal frequency-division multiple access system is considered for the primary users (PUs). Our purpose is to estimate the PU carrier frequency offsets (PU-CFOs) as well as the channels to estimate the transmitted symbols. However, in wideband wireless communications, the PU received-signal spectrum usually exhibits a localised fading because of the multipath propagation channel. When the PU faded frequencies are used by the CR system, the PU system is contaminated by a CR narrowband interference (CR-NBI). In that case, if a Kalman filter (KF)-based approach is used for the estimations of the CFOs and the channels, the state-space representation does not necessarily take into account the CR-NBI; this hence has a negative impact on the algorithm performance. Therefore the authors propose to make this solution more robust to the CR-NBI by detecting when it appears and disappears to avoid using the data disturbed by the CR-NBI. The authors’ contribution is to assume that the CR-NBI is because of the transmission of contiguous blocks and to propose various criteria based on the covariance matrix of the KF innovation.

To accommodate the ever-expanding wireless data traffic volumes, mobile network operators are complementing their macrocellular networks by deploying low-power base stations (or small cells) to offload traffic from congested macrocells and to reuse spectrum. To that end, Ultra Dense Network (UDN) deployments provide means to aggressively reuse spectrum, thus providing significant enhancements in terms of system capacity. However, these deployments entail several challenges, including the increased complexity in network planning and optimization. In this paper, we propose a versatile optimization framework for planning UDN deployments. The planning and optimization framework is underpinned by metrics that consider scalability in terms of number of users, cost of densification, and fairness. The proposed methodology is evaluated using a real-world UDN planning case. The numerical results expose a number of interesting insights, including the impact of different bandwidth allocation strategies and spatial service demand distribution on the performance of various network topologies. Specifically, we provide a performance comparison of the optimized UDN topologies versus random (unplanned), regular grid, and heuristically derived UDN topologies. This comparison further underlines the need for flexible network planning and optimization frameworks as different operator performance metrics of interest may require different radio access networks configurations.

En este trabajo proponemos la estimacion de frecuencia en sistemas de redes inteligentes en presencia de desbalances de voltaje. Para esta estimacion proponemos la utilizacion de Modelos Multiples Interactivos (IMM) utilizando dos filtros de Kalman extendidos (EKF) operando en paralelo. Los filtros(EKF) son utilizados para modelar dos modos de operacion que hemos considerado para representar el sistema. Uno de los filtros de EKF permite caracterizar un modo de operacion con variaciones minimas o nulas de frecuencia. El segundo filtro EKF caracteriza un modo de operacion en que se dan una cambios de frecuencia mas significativas. Los resultados demuestran que el algoritmo permite detectar el cambio significativo de frecuencia y combinar las salidas de los filtros EKF de manera que tengamos un tiempo de respuesta adecuado a este cambio, es decir, dando mayor ponderacion el filtro correspondiente al segundo modo. En este trabajo comparamos los resultados del enfoque propuesto contrabajos previos utilizando un solo filtro EKF para demostrar sus ventajas en terminos de error de estimacion y tiempo de respuesta en las cambios de frecuencia

En este trabajo presentamos diversos aspectos de la implementacion del primer acuerdo de doble titulacion en ingenieria entre una institucion de educacion superior de Francia y una en Centroamerica. Entre los temas abordados se destacan la definicion de doble titulacion, las diferencias de los sistemas educativos de ambos paises y en general de Europa y America Latina. Ademas de mostrar los beneficios y el impacto esperado de esta iniciativa, se presentan los retos administrativos y legales que se han presentado durante la implementacion de este programa.

In an uplink transmission of a coded orthogonal frequency division multiple access (C-OFDMA) system, channel estimation, time and frequency synchronization has to be addressed. For this purpose a control data, i.e. a known training sequence called "preamble" and pilot sub-carriers are used. As an alternative to the classic scheme and in order to maximize the data rate, we propose a non-pilot aided estimator based on an iterative architecture that does not require pilot sub-carriers. Our approach combines 1/ the so-called minimum mean square error successive detector to estimate the signal sent by each user 2/ a recursive method estimating the CFOs. Various algorithms such as the extended Kalman filter, the sigma-point Kalman filters and the extended H ? filter are tested and their performances are compared in terms of convergence speed and estimation accuracy. When considering an interleaved OFDMA uplink system over a Rayleigh fading channel, simulation results clearly show the efficiency of the proposed algorithm in terms of CFO estimation and bit error rate performances.

Resumen– La radio inteligencia es un novedoso enfoque en los sistemas de comunicacion inalambrica que surge para solucionar la saturacion actual del espectro. Existen tres esquemas de acceso para la radio inteligencia: interweave, underlay y overlay. El esquema overlay puede permitir mayores velocidades de transmision y ancho de banda mediante la aplicacion de tecnicas de mitigacion o manejo de interferencias. A traves de estas tecnicas se garantiza la calidad y eficiencia en las transmisiones. Este trabajo presenta un estudio y un analisis de estas tecnicas que podran ser implementadas en los futuros sistemas de comunicaciones inalambricos. Se analizan las caracteristicas de algunas de las tecnicas mas comunes de mitigacion de interferencia en los esquemas overlay para sistemas de radio inteligencia. Los resultados de simulacion que se presentan en terminos de error de bit para sistemas overlay que utilizan tecnicas de precodificacion y filtrado espacial muestran el buen desempeno de estas tecnicas en los sistemas de comunicacion inalambricos.

In this work we present an extension of the SIFT algorithm to color images. In the extrema detection stage, an energy level descriptor based on the color tensor of the image is computed and used to locate keypoints candidates. Then, in the description stage, the color gradient magnitude and orientation of the samples around the keypoint are used to compute an orientation histogram to create the keypoint descriptor. A comparative study is carried out between the proposed algorithm and the classic SIFT and the C-SIFT algorithms in several illumination settings. The proposed algorithm presents a better performance in terms of accuracy when objects are poorly illuminated.

This paper deals with a system combining orthogonal frequency division multiplexing (OFDM), interleave-division multiple access (IDMA) and space time block coding (STBC). Less investigated in the literature than multicarrier code division multiple access or OFDMA, OFDM-IDMA can be viewed as a promising technique to solve the problem of bandwidth scarcity. With a specific frame organization to estimate the carrier frequency offsets (CFO) and the channels, we suggest a new receiver architecture. Indeed, based on a modification of the standard IDMA receiver where a CFO correction is no longer necessary unlike common OFDM approaches, we show that the transmitted bits can be still recovered. When considering a STBC-OFDM-IDMA uplink system over different Rayleigh channels, simulation results show the efficiency of the proposed algorithm. These results confirm the utility of this technique as a mid-term solution to solve the constant increase of data rate and user density per cell in cellular systems.

This paper deals with a system combining orthogonal frequency di- vision multiplexing (OFDM) and interleave-division multiple access (IDMA). OFDM makes the system robust against intersymbol inter- ference, whereas IDMA combats the multiple access interference. Nevertheless, unwanted phenomena must be taken into account: 1/ carrier frequency offset (CFO) must be estimated/corrected to guarantee the orthogonality between subcarriers and hence the sym- bol detection in the system. 2/ the conventional iterative IDMA receiver, initially proposed by Ping et al., requires a priori knowl- edge of the multipath channel. Therefore, the CFO and the multipath channel must be estimated "accurately" in an OFDM-IDMA system. Our contribution is to propose a new scheme where a CFO correc- tion is no longer necessary, unlike common OFDM approaches. We show that without a CFO correction, the symbols transmitted by the different users can be recovered by modifying the conventional iterative architecture of the IDMA receiver. When considering an OFDM-IDMA network over different Rayleigh fading channels, simulation results clearly show the efficiency of the proposed algorithm in terms of estimation accuracy and bit error rate.

Multicarrier modulation is the common feature of high-data rate mobile wireless systems. In that case, two phenomena disturb the symbol detection. Firstly, due to the relative transmitter-receiver motion and a difference between the local oscillator (LO) frequency at the transmitter and the receiver, a carrier frequency offset (CFO) affects the received signal. This leads to an intercarrier interference (ICI). Secondly, several versions of the transmitted signal are received due to the wireless propagation channel. These unwanted phenomena must be taken into account when designing a receiver. As estimating the multipath channel and the CFO is essential, this PhD deals with several CFO and channel estimation methods based on optimal filtering. Firstly, as the estimation issue is nonlinear, we suggest using the extended Kalman filter (EKF). It is based on a local linearization of the equations around the last state estimate. However, this approach requires a linearization based on calculations of Jacobians and Hessians matrices and may not be a sufficient description of the nonlinearity. For these reasons, we can consider the sigma-point Kalman filter (SPKF), namely the unscented Kalman Filter (UKF) and the central difference Kalman filter (CDKF). The UKF is based on the unscented transformation whereas the CDKF is based on the second order Sterling polynomial interpolation formula. Nevertheless, the above methods require an exact and accurate a priori system model as well as perfect knowledge of the additive measurementnoise statistics. Therefore, we propose to use the H∞ filtering, which is known to be more robust to uncertainties than Kalman filtering. As the state-space representation of the system is non-linear, we first evaluate the “extended H∞ filter”, which is based on a linearization of the state-space equations like the EKF. As an alternative, the “unscented H∞ filter”, which has been recently proposed in the literature, is implemented by embedding the unscented transformation into the “extended H∞ filter” and carrying out the filtering by using the statistical linear error propagation approach. Multicarrier modulation is the common feature of high-data rate mobile wireless systems. In that case, two phenomena disturb the symbol detection. Firstly, due to the relative transmitter-receiver motion and a difference between the local oscillator (LO) frequency at the transmitter and the receiver, a carrier frequency offset (CFO) affects the received signal. This leads to an intercarrier interference (ICI). Secondly, several versions of the transmitted signal are received due to the wireless propagation channel. These unwanted phenomena must be taken into account when designing a receiver. As estimating the multipath channel and the CFO is essential, this PhD deals with several CFO and channel estimation methods based on optimal filtering. Firstly, as the estimation issue is nonlinear, we suggest using the extended Kalman filter (EKF). It is based on a local linearization of the equations around the last state estimate. However, this approach requires a linearization based on calculations of Jacobians and Hessians matrices and may not be a sufficient description of the nonlinearity. For these reasons, we can consider the sigma-point Kalman filter (SPKF), namely the unscented Kalman Filter (UKF) and the central difference Kalman filter (CDKF). The UKF is based on the unscented transformation whereas the CDKF is based on the second order Sterling polynomial interpolation formula. Nevertheless, the above methods require an exact and accurate a priori system model as well as perfect knowledge of the additive measurementnoise statistics. Therefore, we propose to use the H∞ filtering, which is known to be more robust to uncertainties than Kalman filtering. As the state-space representation of the system is non-linear, we first evaluate the “extended H∞ filter”, which is based on a linearization of the state-space equations like the EKF. As an alternative, the “unscented H∞ filter”, which has been recently proposed in the literature, is implemented by embedding the unscented transformation into the “extended H∞ filter” and carrying out the filtering by using the statistical linear error propagation approach.

In Cognitive Radio (CR) systems, spectrum sensing plays a key role to determine the free frequency bands. However, when the primary-user (PU) signal spectrum exhibits localized fading, PU detection cannot be guaranteed. In addition, as the CR may use the PU faded frequencies, the PU spectrum can be disturbed by a narrow-band interference (NBI) and synchronization algorithms used for the PU carrier frequency offset (CFO) estimation suffer degradations. In this paper, we propose a new scheme that jointly allows the CR-NBI to be detected and the PU-CFOs and the channels to be estimated in an orthogonal frequency division multiple access (OFDMA) system. It combines a sigma point Kalman filter and a test aiming at detecting a variation of the measurement-noise covariance matrix. Simulation results confirm that the proposed algorithm can accurately detect the CR-NBI and estimate the PU-CFOs.