946 resultados para Decoding principle


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The inadequate planning and inefficient management of coastal aquaculture has resulted into serious socioeconomic consequences. These are the displacement of rural communities which traditionally depended on mangroves due to large-scale mangrove conversion for shrimp and fish farming, land subsidence caused by excessive pumping of groundwater for use in aquaculture, financial losses due to disease outbreaks, and public health consequences due to red tide. In order to maximize the socioeconomic benefits of coastal aquaculture the adoption of the principles of sustainable development is recommended. Sustainable development is the management and conservation of natural resource base and the orientation of technological and institutional change in such a manner to ensure the attainment and continued satisfaction of human needs for present and future generations.

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We study the information rates of non-coherent, stationary, Gaussian, multiple-input multiple-output (MIMO) flat-fading channels that are achievable with nearest neighbour decoding and pilot-aided channel estimation. In particular, we analyse the behaviour of these achievable rates in the limit as the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest neighbour decoding and pilot-aided channel estimation achieves the capacity pre-logwhich is defined as the limiting ratio of the capacity to the logarithm of SNR as the SNR tends to infinityof non-coherent multiple-input single-output (MISO) flat-fading channels, and it achieves the best so far known lower bound on the capacity pre-log of non-coherent MIMO flat-fading channels. © 2011 IEEE.

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This paper studies a noncoherent multiple-input multiple-output (MIMO) fading multiple-access channel (MAC). The rate region that is achievable with nearest neighbour decoding and pilot-assisted channel estimation is analysed and the corresponding pre-log region, defined as the limiting ratio of the rate region to the logarithm of the signal-to-noise ratio (SNR) as the SNR tends to infinity, is determined. © 2011 IEEE.

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Understanding the guiding principles of sensory coding strategies is a main goal in computational neuroscience. Among others, the principles of predictive coding and slowness appear to capture aspects of sensory processing. Predictive coding postulates that sensory systems are adapted to the structure of their input signals such that information about future inputs is encoded. Slow feature analysis (SFA) is a method for extracting slowly varying components from quickly varying input signals, thereby learning temporally invariant features. Here, we use the information bottleneck method to state an information-theoretic objective function for temporally local predictive coding. We then show that the linear case of SFA can be interpreted as a variant of predictive coding that maximizes the mutual information between the current output of the system and the input signal in the next time step. This demonstrates that the slowness principle and predictive coding are intimately related.

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A duality transformation principle was proposed for converting a positive order homogeneous vectorfield into a negative order homogeneous vectorfield. The principle also converted a uniformly locally asymptotically stable differential equation into a uniformly bounded differential equation. The duality transformations included the geometric framework for homogeneity and the removal of origin from the state space.

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An achievable rate is given for discrete memoryless channels with a given (possibly suboptimal) decoding rule. The result is obtained using a refinement of the superposition coding ensemble. The rate is tight with respect to the ensemble average, and can be weakened to the LM rate of Hui and Csiszár-Körner, and to Lapidoth's rate based on parallel codebooks. © 2013 IEEE.