Existing time-frequency representations are usually too noisesensitive or has too bad resolution for studying multiple transient signal components of biosonar signals. In this paper we calculate reassigned spectrograms using the first Hermite function as window. The optimal length Hermite function giving the perfectly localized reassignment for a Gaussian function is combined with a novel techn...

In this paper, we are interested in a special class of automata, called scanners. These machines can be considered as a model for computations that require only “local” information. Informally, a scanner is an automaton equipped with a finite memory and a “sliding” window of a fixed length. In a typical computation, the sliding window is moved from left to right on the input, so that the scanne...

Window functions describe, as a function of orbital period, the probability that an existing planetary transit is detectable in one’s data for a given observing strategy. We show the dependence of this probability upon several strategy and astrophysical parameters, such as length of observing run, observing cadence, length of night, and transit duration. The ability to detect a transit is direc...

1 Windowing FPGA Hardware Figure 1 illustrates the hardware required to implement a windowing function. The window ROM contains 1024 words, each 10-bits wide. (The ROM could be changed to RAM at a later date if the window is required to change in real-time.) The window ROM block contains an address counter which cycles through the window locations sequentially. (For symmetrical windows it could...

Sorted Sliding Window Compression (SSWC) uses a new model (Sorted Sliding Window Model | SSWM) to encode strings e cient, which appear again while encoding a symbol sequence. The SSWM holds statistics of all strings up to certain length k in a sliding window of size n (the sliding window is de ned like in lz77). The compression program can use the SSWM to determine if the string of the next sym...

We propose online prediction algorithms for data streams whose characteristics might change over time. Our algorithms are applications of online learning with experts. In particular, our algorithms combine base predictors over sliding windows with different length as experts. As a result, our algorithms are guaranteed to be competitive with the base predictor with the best fixed-length sliding ...

todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. in this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertai...

The cross-correlation algorithm used to compute the local strain components for elastographic imaging requires a minimum radio-frequency data segment length of around 10 wavelengths to obtain accurate and precise strain estimates with a reasonable signal-to-noise ratio. Shorter radio-frequency data segments generally introduce increased estimation errors as the information content in the data s...

Power exponentiation is an important operation in modern cryptography. This operation can be efficiently calculated using the concept of the addition chain. In this paper, two new systematic methods, a Run-length method and a Hybrid method, are proposed to generate a short addition chain. The performance of these two methods are theoretically analyzed and it is shown that the Hybrid method is m...