The Listsize Capacity of the Gaussian Channel with Decoder Assistance

The listsize capacity is computed for the Gaussian channel with a helper that—cognizant of the channel-noise sequence but not of the transmitted message—provides the decoder with a rate-limited description of said sequence. This capacity is shown to equal the sum of the cutoff rate of the Gaussian channel without help and the rate of help. In particular, zero-rate help raises the listsize capacity from zero to the cutoff rate. This is achieved by having the helper provide the decoder with a sufficiently fine quantization of the normalized squared Euclidean norm of the noise sequence.

Robust Filtering of Sequences with Periodically Stationary Multiplicative Seasonal Increments

We consider stochastic sequences with periodically stationary generalized multiple increments of fractional order which combines cyclostationary, multi-seasonal, integrated and fractionally integrated patterns. We solve the filtering problem for linear functionals constructed from unobserved values of a stochastic sequence of this type based on observations of the sequence with a periodically stationary noise sequence. For sequences with known matrices of spectral densities, we obtain formulas for calculating values of the mean square errors and the spectral characteristics of the optimal filtering of the functionals. Formulas that determine the least favourable spectral densities and the minimax (robust) spectral characteristics of the optimal linear filtering of the functionals are proposed in the case where spectral densities of the sequence are not exactly known while some sets of admissible spectral densities are given.

Particle Filter for Randomly Delayed Measurements with Unknown Latency Probability

This paper focuses on developing a particle filter based solution for randomly delayed measurements with an unknown latency probability. A generalized measurement model that includes measurements randomly delayed by an arbitrary but fixed maximum number of time steps along with random packet drops is proposed. Owing to random delays and packet drops in receiving the measurements, the measurement noise sequence becomes correlated. A model for the modified noise is formulated and subsequently its probability density function (pdf) is derived. The recursion equation for the importance weights is developed using pdf of the modified measurement noise in the presence of random delays. Offline and online algorithms for identification of the unknown latency parameter using the maximum likelihood criterion are proposed. Further, this work explores the conditions that ensure the convergence of the proposed particle filter. Finally, three numerical examples, one with a non-stationary growth model and two others with target tracking, are simulated to show the effectiveness and the superiority of the proposed filter over the state-of-the-art.

Cognitive Computation

Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic. This paper describes two circuits as validating examples. The first one implements a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications. The second one implements decision making in a sensorimotor system: it allows a simple robot to avoid obstacles using Bayesian sensor fusion.

Practical aspects of noise immune data transmission channel design in telecommunication networks

Practical aspects of noise immune data transmission channel design in telecommunication networks are considered. Main accent is made on securing noise immunity and concealment of information transmission, as well as on countering its interception by the rogue elements. It is noted that satisfaction of the mentioned requirements is possible by using spectrum spreading of useful signal bits and deploying data scrambling. Design of telecommunication channel with spectrum spreading and scrambling on the base of pseudo noise coding sequences derived from primitive polynomials of the eighth and fifteenth order that possess good auto and inter correlation properties are proposed. While studying practical aspects of the telecommunication channel design, its structure is put forward that consists of frames counting 256 bits, each of which is spread by using the synthesized pseudo noise sequence. In this case, the second synthesized pseudo noise coding sequence with the length of 32768 chips is used to mark the frame duration and perform additional information scrambling. Computer simulation is employed to study performance of the designed algorithm. Simulation results proved that processing of the additive mixture of the useful signal and interferences, which surpass useful signal two times in their power, by the matched despreading filters permits to confidently determine the frame structure of the information being transmitted by finding frame beginning pulses and determining values of the useful information bits. Analysis of the results obtained during the research permits to assume that designed telecommunication channel can be successfully used while developing noise immune concealed telecommunication networks. It is proposed to carry out further research to study boundary possibilities of the designed telecommunication channel in terms of useful signal to interference ratio.

Filtering of multidimensional stationary sequences with missing observations

The problem of mean-square optimal linear estimation of linear functionals which depend on the unknown values of a multidimensional stationary stochastic sequence is considered. Estimates are based on observations of the sequence with an additive stationary stochastic noise sequence at points which do not belong to some finite intervals of a real line. Formulas for calculating the mean-square errors and the spectral characteristics of the optimal linear estimates of the functionals are proposed under the condition of spectral certainty, where spectral densities of the sequences are exactly known. The minimax (robust) method of estimation is applied in the case where spectral densities are not known exactly while some sets of admissible spectral densities are given. Formulas that determine the least favorable spectral densities and minimax spectral characteristics are proposed for some special sets of admissible densities.

Dynamic properties of internal noise probed by modulating binocular rivalry

Neural systems are inherently noisy, and this noise can affect our perception from moment to moment. This is particularly apparent in binocular rivalry, where our perception of competing stimuli shown to the left and right eyes alternates over time in a seemingly random fashion. We investigated internal noise using binocular rivalry by modulating rivalling stimuli using dynamic sequences of external noise of various rates and amplitudes. As well as measuring the effect on dominance durations, we repeated each external noise sequence twice, and assessed the consistency of percepts across repetitions. External noise modulations with standard deviations above 4% contrast increased consistency scores above baseline, and were most effective at 1/8Hz. A computational model of rivalry in which internal noise has a 1/f (pink) temporal amplitude spectrum, and a standard deviation of 16%, provided the best account of our data, and was able to correctly predict perception in additional conditions. Our novel technique provides detailed estimates of the dynamic properties of internal noise during binocular rivalry, and by extension the stochastic processes that drive our perception and other types of spontaneous brain activity.Significance statementAlthough our perception of the world appears constant, sensory representations are variable because of the ‘noisy’ nature of biological neurons. Here we used a binocular rivalry paradigm, in which conflicting images are shown to the two eyes, to probe the properties of this internal variability. Using a novel paradigm in which the contrasts of rivalling stimuli are modulated by two independent external noise streams, we infer the amplitude and character of this internal noise. The temporal amplitude spectrum of the noise has a 1/f spectrum, similar to that of natural visual input, and consistent with the idea that the visual system evolved to match its environment.