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Abstract
The traditional regime of spectrum allocation, in which governments assign frequencies for particular uses, leads to inefficiencies as large portions of available spectrum remain unused. In this article, Dr. Caicedo finds evidence that the scarcity of frequencies is artificial, and argues that technological innovation and new telecommunications business models are being held back as a result. Dr. Caicedo proposes the development of spectrum trading markets, and discusses their economic and technological viability for making spectrum usage more efficient for consumers and wireless service providers.

Abstract
The traditional regime of spectrum allocation, in which governments assign frequencies for particular uses, leads to inefficiencies as large portions of available spectrum remain unused. In this article, Dr. Caicedo finds evidence that the scarcity of frequencies is artificial, and argues that technological innovation and new telecommunications business models are being held back as a result. Dr. Caicedo proposes the development of spectrum trading markets, and discusses their economic and technological viability for making spectrum usage more efficient for consumers and wireless service providers.

Population genomics is a fast-developing discipline with promising applications in a growing number of life sciences fields. Advances in sequencing technologies and bioinformatics tools allow population genomics to exploit genome-wide information to identify the molecular variants underlying traits of interest and the evolutionary forces that modulate these variants through space and time. However, the cost of genomic analyses of multiple populations is still too high to address them through individual genome sequencing. Pooling individuals for sequencing can be a more effective strategy in Single Nucleotide Polymorphism (SNP) detection and allele frequency estimation because of a higher total coverage. However, compared to individual sequencing, SNP calling from pools has the additional difficulty of distinguishing rare variants from sequencing errors, which is often avoided by establishing a minimum threshold allele frequency for the analysis. Finding an optimal balance between minimizing information loss and reducing sequencing costs is essential to ensure the success of population genomics studies. Here, we have benchmarked the performance of SNP callers for Pool-seq data, based on different approaches, under different conditions, and using computer simulations and real data. We found that SNP callers performance varied for allele frequencies up to 0.35. We also found that SNP callers based on Bayesian (SNAPE-pooled) or maximum likelihood (MAPGD) approaches outperform the two heuristic callers tested (VarScan and PoolSNP), in terms of the balance between sensitivity and FDR both in simulated and sequencing data. Our results will help inform the selection of the most appropriate SNP caller not only for large-scale population studies but also in cases where the Pool-seq strategy is the only option, such as in metagenomic or polyploid studies. ; This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (H2020-ERC-2014-CoG-647900). ; Peer reviewed

Introduction/purpose: The frequency hopping spread spectrum (FH-SS) technique assumes the carrier generated by the syntesizer to hop from frequency to frequency over a wide bandwidth, according to a pseudonoise code sequence defined by the code sequence generator. The article presents the history, principles and applications of the FH-SS technique. Both military and commercial applications are discussed. Methods: This article presents an overview of data from the technical literature, with appropriate comments. Results: After presenting the history and principles of the FH-SS technique, the article summarizes its use with examples of military and commercial applications. The importance of using FH-SS in the described applications is highlighted. Conclusion: The FH-SS technique has been successfully implemented in many military and commercial technologies due to its high protection against interference, making communication difficult for reconnaissance and eavesdropping, and its ability to provide code division multiple access.

International audience ; In digital signal filtering, channels with narrow bandwidth need high order digital filter to be selected without introducing modulation errors. If a carrier randomly switches from a channel to another as in military applications, or some civilian communication standards, it is necessary to detect and estimate these jumps before transposing and analyzing signals in the baseband. This paper presents a real time solution to filter narrow band signals with random frequency hopping spread spectrum. The proposed method is based on three steps. Firstly, the detection of Signal Frequency Hopping (SFH) using the Fast Fourier Transform (FFT), an algorithm to estimate the Dominant Frequency Value (DFV) is developed, it is necessary for better refining the original detection, in particular, with modulated signals. Secondly, the estimated frequency value is scaled and used with a Numerically Controlled Oscillator (NCO) in order shift the interest channel to baseband. Thirdly, the transposed channel in base band is selected using low pass Finite Impulse Response (FIR) filters. Whereas, the multi rate filtering techniques guarantee the high selectivity and low orders of these FIR filters. Each of the following stages is described in detail later in this paper, synthesizing these steps leads to the proposed solution, that is validated by using GSM signals. The algorithms are implemented in Field Programmable Gate Array (FPGA) Altera Cyclone III family

International audience ; In digital signal filtering, channels with narrow bandwidth need high order digital filter to be selected without introducing modulation errors. If a carrier randomly switches from a channel to another as in military applications, or some civilian communication standards, it is necessary to detect and estimate these jumps before transposing and analyzing signals in the baseband. This paper presents a real time solution to filter narrow band signals with random frequency hopping spread spectrum. The proposed method is based on three steps. Firstly, the detection of Signal Frequency Hopping (SFH) using the Fast Fourier Transform (FFT), an algorithm to estimate the Dominant Frequency Value (DFV) is developed, it is necessary for better refining the original detection, in particular, with modulated signals. Secondly, the estimated frequency value is scaled and used with a Numerically Controlled Oscillator (NCO) in order shift the interest channel to baseband. Thirdly, the transposed channel in base band is selected using low pass Finite Impulse Response (FIR) filters. Whereas, the multi rate filtering techniques guarantee the high selectivity and low orders of these FIR filters. Each of the following stages is described in detail later in this paper, synthesizing these steps leads to the proposed solution, that is validated by using GSM signals. The algorithms are implemented in Field Programmable Gate Array (FPGA) Altera Cyclone III family

International audience ; In digital signal filtering, channels with narrow bandwidth need high order digital filter to be selected without introducing modulation errors. If a carrier randomly switches from a channel to another as in military applications, or some civilian communication standards, it is necessary to detect and estimate these jumps before transposing and analyzing signals in the baseband. This paper presents a real time solution to filter narrow band signals with random frequency hopping spread spectrum. The proposed method is based on three steps. Firstly, the detection of Signal Frequency Hopping (SFH) using the Fast Fourier Transform (FFT), an algorithm to estimate the Dominant Frequency Value (DFV) is developed, it is necessary for better refining the original detection, in particular, with modulated signals. Secondly, the estimated frequency value is scaled and used with a Numerically Controlled Oscillator (NCO) in order shift the interest channel to baseband. Thirdly, the transposed channel in base band is selected using low pass Finite Impulse Response (FIR) filters. Whereas, the multi rate filtering techniques guarantee the high selectivity and low orders of these FIR filters. Each of the following stages is described in detail later in this paper, synthesizing these steps leads to the proposed solution, that is validated by using GSM signals. The algorithms are implemented in Field Programmable Gate Array (FPGA) Altera Cyclone III family

As wireless applications are growing rapidly in the modern world, this results in the shortage of radio spectrum due to the fixed allocation of spectrum by governmental agencies for different wireless technologies. This problem raises interest to utilize spectrum in a more efficient way, in order to provide spectrum access to other users when they need it. In wireless communications systems, cognitive radio (CR) is getting much attention due to its capability to combat with this scarcity problem. A CR senses the available spectrum band to check the activity of primary users (PU). It utilizes the unused spectral resources by providing access to secondary users (SU). Spectrum sensing (SS) is one of the most critical issues in cognitive radio, and there are various SS methods for the detection of PU signals. An energy detector (ED) based SS is the most common sensing method due to its simple implementation and low computational complexity. This method works well in ideal scenarios but its detection performance for PU signal degrades drastically under low SNR values in the presence of noise uncertainty. Eigenvalue-based SS method performs well with such real-life issues, but it has very high computational complexity. This raises a demand for such a detector which has less computational complexity and can perform well in practical wireless multipath channels as well as under noise uncertainty. This study focuses on a novel variant of autocorrelation detector operating in the frequency domain (FD-AC). The method is applicable to PUs using the OFDM waveform with the cyclic prefix (CP). The FD-AC method utilizes fast Fourier transform (FFT) and detects an active PU through the CP-induced correlation peak estimated from the FFT-domain samples. It detects the spectral holes in the available electromagnetic spectrum resources in an efficient way, in order to provide opportunistic access to SUs. The proposed method is also insensitive to the practical wireless channel effects. Hence, it works well in frequency selective channels. It also has the capability to mitigate the effects of noise uncertainty and therefore, it is robust to noise uncertainty. FD-AC facilitates partial band sensing which can be considered as a compressed spectrum sensing method. This allows sensing weak PU signals which are partly overlapped by other strong PU or CR transmissions. On the other hand, it helps in the reduction of computational complexity while sensing PU signal in the available spectrum band, depending on the targeted sensitivity. Moreover, it has highly increased flexibility and it is capable of facilitating robust wideband multi-mode sensing with low complexity. Its performance for the detection of PU signal does not depend on the known time lag, therefore, it can perform well in such conditions where the detailed OFDM signal characteristics are not known.