Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior

Large-scale data collection by means of wireless sensor network and internet-of-things technology poses various challenges in view of the limitations in transmission, computation, and energy resources of the associated wireless devices. Compressive data gathering based on compressed sensing has been proven a well-suited solution to the problem. Existing designs exploit the spatiotemporal correlations among data collected by a specific sensing modality. However, many applications, such as environmental monitoring, involve collecting heterogeneous data that are intrinsically correlated. In this study, we propose to leverage the correlation from multiple heterogeneous signals when recovering the data from compressive measurements. To this end, we propose a novel recovery algorithm---built upon belief-propagation principles---that leverages correlated information from multiple heterogeneous signals. To efficiently capture the statistical dependencies among diverse sensor data, the proposed algorithm uses the statistical model of copula functions. Experiments with heterogeneous air-pollution sensor measurements show that the proposed design provides significant performance improvements against state-of-the-art compressive data gathering and recovery schemes that use classical compressed sensing, compressed sensing with side information, and distributed compressed sensing.Comment: accepted to IEEE Transactions on Communication

COOPEDU IV — Cooperação e Educação de Qualidade

O quarto Congresso Internacional de Cooperação e Educação-IV COOPEDU, organizado pelo Centro de Estudos Internacionais (CEI) do Instituto Universitário de Lisboa e pela Escola Superior de Educação e Ciências Sociais do Instituto Politécnico de Leiria decorreu nos dias 8 e 9 de novembro de 2018, subordinado à temática Cooperação e Educação de Qualidade. Este congresso insere-se numa linha de continuidade de intervenção por parte das duas instituições organizadoras e dos elementos coordenadores e este ano beneficiou do financiamento do Instituto Camões, obtido através de um procedimento concursal, que nos permitiu contar com a participação presencial de elementos dos Países Africanos de Língua Portuguesa, fortemente implicados nas problemáticas da Educação e da Formação. Contou também com a participação do Instituto Camões e da Fundação Calouste Gulbenkian, entidades que sistematizaram a sua intervenção nos domínios da cooperação na área da educação nos últimos anos. A opção pela temática da qualidade pareceu aos organizadores pertinente e actual. Com efeito os sistemas educativos dos países que constituem a Comunidade de países de língua portuguesa têm implementado várias reformas mas em vários domínios mantem-se a insatisfação de responsáveis políticos, pedagogos, técnicos sociais face aos resultados obtidos. Aliás o caminho de procura da Qualidade é interminável porque vai a par da aposta na exigência e na promoção da cidadania e responsabilidade social. As comunicações que agora se publicam estão organizadas em dois eixos: o das Políticas da Educação e Formação e o das dimensões em que se traduzem essas políticas. Neste último eixo encontramos fios condutores para agregarmos as comunicações apresentadas

Modelling and performance assessment of OFDM communication systems in the presence of non-linearities

Includes bibliographical referencesAvailable from British Library Document Supply Centre- DSC:DX220520 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Compressed sensing with side information: Geometrical interpretation and performance bounds

We address the problem of Compressed Sensing (CS) with side information. Namely, when reconstructing a target CS signal, we assume access to a similar signal. This additional knowledge, the side information, is integrated into CS via L1-L1 and L1-L2 minimization. We then provide lower bounds on the number of measurements that these problems require for successful reconstruction of the target signal. If the side information has good quality, the number of measurements is significantly reduced via L1-L1 minimization, but not so much via L1-L2 minimization. We provide geometrical interpretations and experimental results illustrating our findings.Comment: This paper, to be presented at GlobalSIP 2014, is a shorter version of http://arxiv.org/abs/1408.525

Bayesian Compressed Sensing with Heterogeneous Side Information

The classical compressed sensing (CS) paradigm can be modified so as to leverage a signal correlated to the signal of interest, called side information, which is assumed to be provided a priori at the decoder in order to aid reconstruction. In this work, we propose a novel CS reconstruction method based on belief propagation principles, which manages to exploit side information generated from a diverse (or heterogeneous) data source by using the statistical model of copula functions. Through simulations, we demonstrate that the proposed method yields significant reduction in the mean-squared error of the reconstructed signal as compared to state-of-the-art methods in classical compressed sensing and compressed sensing with side information

Measurement matrix design for compressive sensing with side information at the encoder

We study the problem of measurement matrix design for Compressive Sensing (CS) when the encoder has access to side information, a signal analogous to the signal of interest. In particular, we propose to incorporate this extra information into the signal acquisition stage via a new design for the measurement matrix. The goal is to reduce the number of encoding measurements, while still allowing perfect signal reconstruction at the decoder. Then, the reconstruction performance of the resulting CS system is analysed in detail assuming the decoder reconstructs the original signal via Basis Pursuit. Finally, Gaussian width tools are exploited to establish a tight theoretical bound for the number of required measurements. Extensive numerical experiments not only validate our approach, but also demonstrate that our design requires fewer measurements for successful signal reconstruction compared with alternative designs, such as an i.i.d. Gaussian matrix

Internet-of-Things data aggregation using compressed sensing with side information

The Internet-of-Things (IoT) is the key enabling technology for transforming current urban environments into so-called Smart Cities. One of the goals behind making cities smarter is to provide a healthy environment that improves the citizens' quality of life and wellbeing. In this work, we introduce a novel data aggregation mechanism tailored to the application of large-scale air pollution monitoring with IoT devices. Our design exploits the intra- and inter-source correlations among air-pollution data using the framework of compressed sensing with side information. The proposed method delivers significant improvements in the data reconstruction quality with respect to the state of the art, even in the presence of noise when measuring and transmitting the data