Location-free Spectrum Cartography

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built upon this approach and offer a markedly improved estimation performance than existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.Comment: 14 pages, 12 figures, 1 table. Submitted to IEEE Transactions on Signal Processin

Online Joint Topology Identification and Signal Estimation with Inexact Proximal Online Gradient Descent

Identifying the topology that underlies a set of time series is useful for tasks such as prediction, denoising, and data completion. Vector autoregressive (VAR) model based topologies capture dependencies among time series, and are often inferred from observed spatio-temporal data. When the data are affected by noise and/or missing samples, the tasks of topology identification and signal recovery (reconstruction) have to be performed jointly. Additional challenges arise when i) the underlying topology is time-varying, ii) data become available sequentially, and iii) no delay is tolerated. To overcome these challenges, this paper proposes two online algorithms to estimate the VAR model-based topologies. The proposed algorithms have constant complexity per iteration, which makes them interesting for big data scenarios. They also enjoy complementary merits in terms of complexity and performance. A performance guarantee is derived for one of the algorithms in the form of a dynamic regret bound. Numerical tests are also presented, showcasing the ability of the proposed algorithms to track the time-varying topologies with missing data in an online fashion.Comment: 14 pages including supplementary material, 2 figures, submitted to IEEE Transactions on Signal Processin

Volatile Organic Compounds from Entomopathogenic and Nematophagous Fungi, Repel Banana Black Weevil (Cosmopolites sordidus)

Fungal Volatile Organic Compounds (VOCs) repel banana black weevil (BW), Cosmopolites sordidus (Germar, 1824), the key-pest of banana [Musa sp. (Linnaeus, 1753)]. The entomopathogens Beauveria bassiana (Bb1TS11) and Metarhizium robertsii (Mr4TS04) were isolated from banana plantation soils using an insect bait. Bb1TS11 and Mr4TS04 were pathogenic to BW adults. Bb1TS11, Bb203 (from infected palm weevils), Mr4TS04 and the nematophagous fungus Pochonia clamydosporia (Pc123), were tested for VOCs production. VOCs were identified by Gas Chromatography/Mass Spectrometry–Solid-Phase Micro Extraction (GC/MS-SPME). GC/MS-SPME identified a total of 97 VOCs in all strains tested. Seven VOCs (styrene, benzothiazole, camphor, borneol, 1,3-dimethoxy-benzene, 1-octen-3-ol and 3-cyclohepten-1-one) were selected for their abundance or previous record as insect repellents. BW-starved adults in the dark showed the highest mobility to banana corm in olfactometry bioassays. 3-cyclohepten-1-one (C7), produced by all fungal strains, is the best BW repellent (p < 0.05), followed by 1,3-dimethoxy-benzene (C5). The rest of the VOCs have a milder repellency to BW. Styrene (C1) and benzothiazole (C2) (known to repel palm weevil) block the attraction of banana corm and BW pheromone to BW adults in bioassays. Therefore, VOCs from biocontrol fungi can be used in future studies for the biomanagement of BW in the field.This research was funded by H2020 European Project Microbial Uptakes for Sustainable management of major bananA pests and diseases with project number 727624

Channel Gain Cartography via Mixture of Experts

Author's accepted manuscript.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio

Channel Gain Cartography via Mixture of Experts

In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair across a geographic area of interest, CG maps can be constructed from spatially distributed sensor measurements. Most approaches to build such spectrum maps are location-based, meaning that the input variable to the estimating function is a pair of spatial locations. The performance of such maps depends critically on the ability of the sensors to determine their positions, which may be drastically impaired if the positioning pilot signals are affected by multi-path channels. An alternative location-free approach was recently proposed for spectrum power maps, where the input variable to the maps consists of features extracted from the positioning signals, instead of location estimates. The location-based and the location-free approaches have complementary merits. In this work, apart from adapting the location-free features for the CG maps, a method that can combine both approaches is proposed in a mixture-of-experts framework.Comment: 5 pages, 2 figures, accepted in Globecom 202

Dynamic network identification from non-stationary vector autoregressive time series

Author's accepted manuscript (postprint).© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio

Localization-Free Power Cartography

Author's accepted manuscript (postprint).© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using measurements of spatially distributed sensors. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radio to name a few. Existing spectrum cartography methods necessitate knowledge of sensor locations, but such locations cannot be accurately determined from pilot positioning signals (such as those in LTE or GPS) in indoor or dense urban scenarios due to multipath. To circumvent this limitation, this paper proposes localization-free cartography, where spectral maps are directly constructed from features of these positioning signals rather than from location estimates. The proposed algorithm capitalizes on the framework of kernel-based learning and offers improved prediction performance relative to existing alternatives, as demonstrated by a simulation study in a street canyon.acceptedVersio

Mitogen-Activated Protein Kinase Phosphatase-1 Is Overexpressed in Non-Small Cell Lung Cancer and Is an Independent Predictor of Outcome in Patients

An increase in the activity of the mitogen-activated protein kinases (MAPKs) has been correlated with a more malignant phenotype in several tumor models in vitro and in vivo. A key regulatory mechanism of the MAPKs [extracellular signal-regulated kinase (ERK); c-jun NH(2)-terminal kinase (JNK); and p38] is the dual specificity phosphatase CL100, also called MAPK phosphatase-1 (MKP-1). This study was designed to examine the involvement of CL100/MKP-1 and stress-related MAPKs in lung cancer. EXPERIMENTAL DESIGN: We assessed the expression of CL100/MKP-1 and the activation of the MAPKs in a panel of 18 human cell lines [1 primary normal bronchial epithelium, 8 non-small cell lung cancer (NSCLC), 7 small cell lung cancer (SCLC), and 2 carcinoids] and in 108 NSCLC surgical specimens. RESULTS: In the cell lines, CL100/MKP-1 expression was substantially higher in NSCLC than in SCLC. P-ERK, P-JNK, and P-p38 were activated in SCLC and NSCLC, but the degree of their activation was variable. Immunohistochemistry in NSCLC resection specimens showed high levels of CL100/MKP-1 and activation of the three MAPK compared with normal lung. In univariate analysis, no relationship was found among CL100/MKP-1 expression and P-ERK, P-JNK, or P-p38. Interestingly, high CL100/MKP-1 expression levels independently predicted improved survival in multivariate analysis. JNK activation associated with T(1-2) and early stage, whereas ERK activation correlated with late stages and higher T and N. Neither JNK nor ERK activation were independent prognostic factors when studied for patient survival. CONCLUSIONS: Our data indicate the relevance of MAPKs and CL100/MKP-1 in lung cancer and point at CL100/MKP-1 as a potential positive prognostic factor in NSCLC. Finally, our study supports the search of new molecular targets for lung cancer therapy within the MAPK signaling pathway