Multitask Diffusion Adaptation over Networks

Adaptive networks are suitable for decentralized inference tasks, e.g., to monitor complex natural phenomena. Recent research works have intensively studied distributed optimization problems in the case where the nodes have to estimate a single optimum parameter vector collaboratively. However, there are many important applications that are multitask-oriented in the sense that there are multiple optimum parameter vectors to be inferred simultaneously, in a collaborative manner, over the area covered by the network. In this paper, we employ diffusion strategies to develop distributed algorithms that address multitask problems by minimizing an appropriate mean-square error criterion with $\ell_2$-regularization. The stability and convergence of the algorithm in the mean and in the mean-square sense is analyzed. Simulations are conducted to verify the theoretical findings, and to illustrate how the distributed strategy can be used in several useful applications related to spectral sensing, target localization, and hyperspectral data unmixing.Comment: 29 pages, 11 figures, submitted for publicatio

Diffusion LMS for clustered multitask networks

Recent research works on distributed adaptive networks have intensively studied the case where the nodes estimate a common parameter vector collaboratively. However, there are many applications that are multitask-oriented in the sense that there are multiple parameter vectors that need to be inferred simultaneously. In this paper, we employ diffusion strategies to develop distributed algorithms that address clustered multitask problems by minimizing an appropriate mean-square error criterion with $\ell_2$-regularization. Some results on the mean-square stability and convergence of the algorithm are also provided. Simulations are conducted to illustrate the theoretical findings.Comment: 5 pages, 6 figures, submitted to ICASSP 201

A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation

We consider a multitask estimation problem where nodes in a network are divided into several connected clusters, with each cluster performing a least-mean-squares estimation of a different random parameter vector. Inspired by the adapt-then-combine diffusion strategy, we propose a multitask diffusion strategy whose mean stability can be ensured whenever individual nodes are stable in the mean, regardless of the inter-cluster cooperation weights. In addition, the proposed strategy is able to achieve an asymptotically unbiased estimation, when the parameters have same mean. We also develop an inter-cluster cooperation weights selection scheme that allows each node in the network to locally optimize its inter-cluster cooperation weights. Numerical results demonstrate that our approach leads to a lower average steady-state network mean-square deviation, compared with using weights selected by various other commonly adopted methods in the literature.Comment: 30 pages, 8 figures, submitted to IEEE Journal of Selected Topics in Signal Processin

Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

Entrepreneurship, Economic Growth and Policy in Emerging Economies

Entrepreneurship has emerged as an important element in the organization of economies. This emergence did not occur simultaneously in all developed countries. Differences in growth rates are often attributed to differences in the speed with which countries embrace entrepreneurial energy. This led to the political mandate to promote entrepreneurship. Hence, a clear and organized view is needed of what the determinants and consequences of entrepreneurship are. The present contribution tries to provide this view with a particular view on emerging economies. Entrepreneurship, its drivers and its consequences can be best understood using the model of the entrepreneurial economy which explains the functioning of the modern economy. This model differs from that of the earlier managed economy. Policies in emerging economies should aim at combining the two models.entrepreneurship, small firms, economic growth, economic development, policy

Central and East European countries: innovation leapfrog versus “path dependence”?

Strengthening the EU economic and political environment would positively affect the global development. Albeit, building the internal life in the enlarging European society per se represents quite a challenging task. Increasing diversity engenders a huge variety of never seen before problems, which must be responded in appropriate manner. Within this multifold complexity, and despite the variety of issues related to elaboration of a new economic agenda for the member state countries this is specifically innovation path of economic development which would be emphasized as the main underlying route for approaching the prosperous future. This article highlights some problems and prospects of CEEC’ (Central and East European countries) innovation driven development.EU enlargment; comparative advantage, innovation capabilities; innovation driven path of economic development