binary signal) is xt = sign(V + zt – θt) where.

ambiguities are encountered in the models because the translation of events into edges is not formal enough. In this paper, we propose a theory of binary signal which allows us to describe the time behavior of the inputs and the outputs of any logical system. We especially describe an extended Boolean Algebra

Met lit. opg., reg. ISBN 90-6144-198-6 SISO 656.2 UDe 519.71.001.26 NUGI832 Trefw.: regelsystemen; optimalisering.- iii-A new extremum hunting method, using a correlation analysis hunting method, is proposed in this paper. A pseudo-random binary signal has been used as probe signal and the hunting

Abstract — We consider a distributed beamforming problem in which nodes are restricted to sending binary phases to a receiver that has access to a one-bit feedback channel. Our simplified model allows us to prove lower bounds, as well as explore algorithmic techniques and analyses. We demonstrate

Binary Signal Compression using DCT (Discrete Cosine Transform) signs is presented. This is quite new technique for signal compression. Even a random binary signal utilized in a computer is included in our aim. The presented method enables us to make more compact signal from an usual compressed

Abstract not found

Experimental realization of binary signals transmission based on synchronized lorenz circuits

In this paper, an experimental study of practical realization to transmit binary signals using chaos is presented. In particular, we use a Generalized Hamiltonian systems approach to synchronize two unidirectionally coupled Lorenz circuits. Experimental results are in close accordance

Abstract — Multiple access interference (MAI) in CDMA sys-tem is related to the correlation of the transmitted signals from multiple users. In this work, we investigate the correlations of binary signal sets over fading channels, particularly the Gold and Kasami signal sets. A lower bound

We study learning in a noisy bisection model: specifically, Bayesian algorithms to learn a target value V given access only to noisy realizations of whether V is less than or greater than a threshold θ. At step t = 0, 1, 2,..., the learner sets threshold θt and observes a noisy realization of sign(V − θt). After T steps, the goal is to output an estimate ˆ V which is within an ε-tolerance of V. This problem has been studied, predominantly in environments with a fixed error probability q < 1 2 for the noisy realization of sign(V − θt). In practice, it is often the case that q can approach 1 2, especially as θ → V, and there is little known when this happens. We give a pseudo-Bayesian algorithm which provably converges to V. When the true prior matches our algorithm’s Gaussian prior, we show near-optimal expected performance. Our methods extend to the general multiple-threshold setting where the observation noisily indicates which of k ≥ 2 regions V belongs to. 1