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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.

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.

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.