One-step Search Research Articles

Improved Hybrid Block-Based Motion Estimation for Inter-frame Coding

Digital video technology has been increasingly needed in various fields, such as telecommunications, entertainment, medicine. Therefore, video compression is required. Motion estimation methods help in improving video compression efficiency by effectively removing the temporal redundancy between successive frames. Several block-based motion estimation (BME) algorithms are being suggested to reduce the coding process’s computational complexity. This paper proposes a new rapid hybrid (BME) algorithm established on the primary search point prediction and advance ending search point strategies. It combines rough adaptive search and effective local search. The coarse search introduces a new motion vector (MV) prediction technique that utilizes the macro-blocks (MBs) Spatio-temporal correlations to optimize the traditional adaptive-rood-pattern search algorithm (ARPS) and speeding up the whole process without affecting the accuracy. In the accurate local search, the cross-formed search pattern using a one-step search (OSS) block matching algorithm is employed, to estimate the actual (MV) with less computation time and further speed up the search efficiency. Exhaustive experiments are performed to demonstrate the present algorithm’s performance over the benchmark schemes concerning specific assessment criteria for results, including the peak signal-to-noise ratio (PSNR), computational complexity and computational gain. The results show that the proposed algorithm is efficient and reliable; it can always give better performance over diamond search (DS) and (ARPS). The conducted test shows an increased performance of search speed while preserving the visual quality of the motion-compensated images, and it achieves 59.76–88.03 speed improvement over (DS) and 20.98–72.06 over (ARPS) for different video sequences. Besides, the suggested method (ARP-OSS) provides the best result compared to (DS) and (ARPS) in terms of time complexity for analyzing all the video samples.

Efficient Detection of Chirp Signals Based on the Fourth-Order Origin Moment of Fractional Spectrum

A novel detection algorithm for chirp signals based on the fourth-order origin moment of fractional spectrum (OMFrS) is presented. The fourth-order OMFrS of a chirp signal in discrete form is first deduced. By using the rough search result and the symmetric property of the fourth-order OMFrS of the chirp signal, the difference between the theoretical optimal transform angle and the detected result is calculated, and a more accurate estimation is obtained. Compared to the existing one-step search and two-step search methods, the proposed algorithm achieves better accuracy with high detection speed. Simulation results were given to prove the advantages of this new approach.

Computational resources to filter gravitational wave data with P-approximant templates

The prior knowledge of the gravitational waveform from compact binary systems makes matched filtering an attractive detection strategy. This detection method involves the filtering of the detector output with a set of theoretical waveforms or templates. One of the most important factors in this strategy is knowing how many templates are needed in order to reduce the loss of possible signals. In this study, we calculate the number of templates and computational power needed for a one-step search for gravitational waves from inspiralling binary systems. We build on previous works by first expanding the post-Newtonian waveforms to 2.5-PN order and second, for the first time, calculating the number of templates needed when using P-approximant waveforms. The analysis is carried out for the four main first-generation interferometers, LIGO, GEO600, VIRGO and TAMA. As well as template number, we also calculate the computational cost of generating banks of templates for filtering GW data. We carry out the calculations for two initial conditions. In the first case we assume a minimum individual mass of 1 M⊙ and in the second, we assume a minimum individual mass of 5 M⊙. We find that, in general, we need more P-approximant templates to carry out a search than if we use standard PN templates. This increase varies according to the order of PN-approximation, but can be as high as a factor of 3 and is explained by the smaller span of the P-approximant templates as we go to higher masses. The promising outcome is that for 2-PN templates, the increase is small and is outweighed by the known robustness of the 2-PN P-approximant templates.

Hierarchical search strategy for the detection of gravitational waves from coalescing binaries.

The detection of gravitational waves from coalescing compact binaries would be a computationally intensive process if a single bank of template wave forms (i.e., a one-step search) is used. We present, in this paper, a method which leads to a large reduction in the computational power required as compared to a one-step search. This method is a hierarchical search strategy involving two template banks. We show that the computational power required by such a two-step search, for an on-line detection of the one-parameter family of Newtonian signals, is $\frac{1}{8}$ of that required when an on-line one-step search is used. This reduction is achieved when signals having a strength of \ensuremath{\sim}8.8 are required to be detected with a probability of \ensuremath{\sim}0.95 and an average of one false event per year. We present approximate formulas for the detection probability of a signal and the false alarm probability. We investigate the effect of statistical correlations on these probabilities and incorporate these effects whereever possible. Our numerical results are specific to the noise power spectral density expected for the initial LIGO.

Optimal Search for the Global Maximum of Functions with Bounded Seminorm

This paper deals with methods seeking the global maximum of a real valued function defined over a real interval $[a,b]$ with prescribed seminorm, i.e. the integral of the square of the derivative. An optimal one-step search algorithm is devised, which provides the minimum guaranteed value of the Lebesgue measure of the set of possible values of the global maximum after evaluating a given information operator at the next point. At each step of the algorithm the value of the function at a new point $x_n $ and the se minorm taken over the interval $[a,x_n ]$ are evaluated. In order to obtain the optimal algorithm, the interpolatory envelope of a certain class of functions is constructed utilizing the minimum seminorm properties of the first order spline functions. A simple search method, which is near optimal, is also described.