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We consider estimation of binary channels with memory where the transition probabilities (channel parameters) from the input to output are determined by prior outputs (state of the channel). While the channel is unknown, we observe the joint input/output process of the channel - we have n i.i.d. input bits and their corresponding outputs. Motivated by(More)
We observe a length-n sample generated by an unknown, stationary ergodic Markov process (model) over a finite alphabet A. Given any string w of symbols from A we want estimates of the conditional probability distribution of symbols following w, as well as the stationary probability of w. Two distinct problems that complicate estimation in this setting are:(More)
Fault tolerance is an important aspect of real-time control systems, due to unavoidable timing constraints. In this paper, the timing problem of a set of concurrent periodic tasks is considered where each task has primary and alternate versions. In the literature, probability of fault in the alternate version of a task is assumed to be zero. Here, a fault(More)
—The adaptive zero-error capacity of discrete memo-ryless channels (DMC) with noiseless feedback has been shown to be positive whenever there exists at least one channel output " disprover " , i.e. a channel output that cannot be reached from at least one of the inputs. Furthermore, whenever there exists a disprover, the adaptive zero-error capacity attains(More)
In the write process of multilevel per cell (MLC) flash memories, an iterative approach is used to mitigate the monotonicity problem. The monotonicity in programming is considered to be the major restriction in MLC flash. In this paper, we are mostly concerned with deriving a mathematical model for iterative programming using the framework of(More)
We observe a length-n sample generated by an unknown, stationary ergodic Markov process (model) over a finite alphabet A. In this paper, we do not assume any bound on the memory of the source, nor do we assume that the source is rapidly mixing. Rather we consider a class M<sub>d</sub> of all Markov sources where for all i &#x2208; &#x2115;, the mutual(More)
A key issue in intelligent demand-side management is the accurate prediction of electricity consumption. This paper presents a dynamic model for short-term special days load forecasting which uses a Recurrent Wavelet Network (RWN). However, initialization of this network encounters a major problem. Thus, a new initialization method is suggested based on(More)
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