Ready for the design of voting rules?

The design of fair voting rules has been addressed quite often in the literature. Still, the so-called inverse problem is not entirely resolved. We summarize some achievements in this direction and formulate explicit open questions and conjectures.Comment: 10 page

A bayesian approach to adaptive detection in nonhomogeneous environments

We consider the adaptive detection of a signal of interest embedded in colored noise, when the environment is nonhomogeneous, i.e., when the training samples used for adaptation do not share the same covariance matrix as the vector under test. A Bayesian framework is proposed where the covariance matrices of the primary and the secondary data are assumed to be random, with some appropriate joint distribution. The prior distributions of these matrices require a rough knowledge about the environment. This provides a flexible, yet simple, knowledge-aided model where the degree of nonhomogeneity can be tuned through some scalar variables. Within this framework, an approximate generalized likelihood ratio test is formulated. Accordingly, two Bayesian versions of the adaptive matched filter are presented, where the conventional maximum likelihood estimate of the primary data covariance matrix is replaced either by its minimum mean-square error estimate or by its maximum a posteriori estimate. Two detectors require generating samples distributed according to the joint posterior distribution of primary and secondary data covariance matrices. This is achieved through the use of a Gibbs sampling strategy. Numerical simulations illustrate the performances of these detectors, and compare them with those of the conventional adaptive matched filter

Echo Cancellation : the generalized likelihood ratio test for double-talk vs. channel change

Echo cancellers are required in both electrical (impedance mismatch) and acoustic (speaker-microphone coupling) applications. One of the main design problems is the control logic for adaptation. Basically, the algorithm weights should be frozen in the presence of double-talk and adapt quickly in the absence of double-talk. The optimum likelihood ratio test (LRT) for this problem was studied in a recent paper. The LRT requires a priori knowledge of the background noise and double-talk power levels. Instead, this paper derives a generalized log likelihood ratio test (GLRT) that does not require this knowledge. The probability density function of a sufficient statistic under each hypothesis is obtained and the performance of the test is evaluated as a function of the system parameters. The receiver operating characteristics (ROCs) indicate that it is difficult to correctly decide between double-talk and a channel change, based upon a single look. However, detection based on about 200 successive samples yields a detection probability close to unity (0.99) with a small false alarm probability (0.01) for the theoretical GLRT model. Application of a GLRT-based echo canceller (EC) to real voice data shows comparable performance to that of the LRT-based EC given in a recent paper

Determinants of agricultural protection from an international perspective: The role of political institutions

"This paper explores the role of political institutions in determining the ability of agriculture to avoid taxation in developing countries or attract government transfers in industrialized countries. The utilized model is based on a probabilistic voting environment, wherein rural districts are less ideologically committed than urban districts in industrialized countries, and the reverse is true in developing countries. As a consequence, in industrialized (developing) countries rural (urban) districts are pivotal in determining the coalition that obtains a majority, whereas urban (rural) districts are pivotal within the majority itself. In bargaining at the level of the legislature, this generates a conflict between a government that tends to favor rural (urban) districts, and a parliamentary majority that is dominated by urban (rural) concerns. As district size grows and the electoral system converges to a purely proportional system, both of these biases are attenuated. Overall, we see opposing nonlinear relationships between district size and agricultural subsidies on the one hand and district size and taxation on the other. In developing countries, taxation of agriculture first increases and then decreases with district magnitude; in industrialized countries, agricultural subsidization first increases and then decreases with district magnitude. Moreover, the impact of district magnitude on the level of agricultural subsidization is attenuated in presidential versus parliamentary systems, while the level of agricultural taxation is amplified in presidential systems. In the present paper, these findings are first theorized and then empirically confirmed by a cross-country analysis of data from 37 countries over a 20-year period." from authors' abstractpolitical economy of agricultural protectionism, Agricultural policies, Urban-rural differences, political institutions,

Design a photovoltaic system based on maximum power point tracking under partial shading

Photovoltaic systems have been given special attention given their long-term potential advantages. Solar panels can produce maximum power at specific operating points called maximum power points (MPP). Solar panels must work at this particular stage in order to ensure that solar panels produce maximum power and maximize efficiency. The performance of the solar photovoltaic unit is strongly affected by the level of radiation, heat and partial shading condition. The partial shedding condition is one of vectors that can affect the PV cell performance. To overcome on this problem, this project proposes photovoltaic system based on maximum power point tracking of partial shading condition. The MPPT algorithm has many methods like P&O and PSO. P&O it had limitation that is not capable to cover the multi-peaks curves. Beside that the PSO method is more effective in partial shading condition. The voltage and current of MSX60 PV module are subjected to various insolation conditions. The Particle Swarm Optimization (PSO) algorithm based MPPT has been implemented to track maximum power partial shading condition. So, in normal condition the power reach 245 W which is higher than the power under partial shading condition that reach 100 W. The PV module is designed using MATLAB/SIMULINK. The accurateness of this simulator is verified with PV module, the result is practiced during normal condition and under partial shading condition meanwhile, multiple curves of I-V and P-V will produce during normal condition and partial shading condition

Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio

Multimedia content especially videos is expected to dominate data traffic in next-generation mobile networks. Caching popular content at the network edge has emerged to be a solution for low-latency content delivery. Compared with the traditional wireless communication, content delivery has a key characteristic that many signals coexisting in the air carry identical popular content. They, however, can interfere with each other at a receiver if their modulation-and-coding (MAC) schemes are adapted to individual channels following the classic approach. To address this issue, we present a novel idea of content adaptive MAC (CAMAC) where adapting MAC schemes to content ensures that all signals carry identical content are encoded using an identical MAC scheme, achieving spatial MAC alignment. Consequently, interference can be harnessed as signals, to improve the reliability of wireless delivery. In the remaining part of the paper, we focus on quantifying the gain CAMAC can bring to a content-delivery network using a stochastic-geometry model. Specifically, content helpers are distributed as a Poisson point process, each of which transmits a file from a content database based on a given popularity distribution. It is discovered that the successful content-delivery probability is closely related to the distribution of the ratio of two independent shot noise processes, named a shot-noise ratio. The distribution itself is an open mathematical problem that we tackle in this work. Using stable-distribution theory and tools from stochastic geometry, the distribution function is derived in closed form. Extending the result in the context of content-delivery networks with CAMAC yields the content-delivery probability in different closed forms. In addition, the gain in the probability due to CAMAC is shown to grow with the level of skewness in the content popularity distribution.Comment: 32 pages, to appear in IEEE Trans. on Wireless Communicatio

Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery

This paper studies a new Bayesian unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of so-called endmembers. These endmembers are supposed to be random in order to model uncertainties regarding their knowledge. More precisely, we model endmembers as Gaussian vectors whose means have been determined using an endmember extraction algorithm such as the famous N-finder (N-FINDR) or Vertex Component Analysis (VCA) algorithms. This paper proposes to estimate the mixture coefficients (referred to as abundances) using a Bayesian algorithm. Suitable priors are assigned to the abundances in order to satisfy positivity and additivity constraints whereas conjugate priors are chosen for the remaining parameters. A hybrid Gibbs sampler is then constructed to generate abundance and variance samples distributed according to the joint posterior of the abundances and noise variances. The performance of the proposed methodology is evaluated by comparison with other unmixing algorithms on synthetic and real images

Change detection in multisensor SAR images using bivariate gamma distributions

This paper studies a family of distributions constructed from multivariate gamma distributions to model the statistical properties of multisensor synthetic aperture radar (SAR) images. These distributions referred to as multisensor multivariate gamma distributions (MuMGDs) are potentially interesting for detecting changes in SAR images acquired by different sensors having different numbers of looks. The first part of the paper compares different estimators for the parameters of MuMGDs. These estimators are based on the maximum likelihood principle, the method of inference function for margins and the method of moments. The second part of the paper studies change detection algorithms based on the estimated correlation coefficient of MuMGDs. Simulation results conducted on synthetic and real data illustrate the performance of these change detectors

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