Joint estimation algorithm for multi‐targets’ motion parameters

A fast ground moving target focusing method based on first-order discrete polynomial-phase transform

Determining and visualizing safe motion parameters of a ship navigating in severe weather conditions

The new H.264 (MPEG-4 AVC) video coding standard can achieve considerably higher coding efficiency compared to previous standards. This is accomplished mainly due to the consideration of variable block sizes for motion compensation, multiple reference frames, intra prediction, but also due to better exploitation of the spatiotemporal correlation that may exist between adjacent Macroblocks, with the SKIP mode in predictive (P) slices and the two DIRECT modes in bipredictive (B) slices. These modes, when signaled, could in effect represent the motion of a macroblock (MB) or block without having to transmit any additional motion information required by other inter-MB types. This property also allows these modes to be highly compressible especially due to the consideration of run length coding strategies. Although spatial correlation of motion vectors from adjacent MBs is used for SKIP mode to predict its motion parameters, until recently, DIRECT mode considered only temporal correlation of adjacent pictures. In this letter, we introduce alternative methods for the generation of the motion information for the DIRECT mode using spatial or combined spatiotemporal correlation. Considering that temporal correlation requires that the motion and timestamp information from previous pictures are available in both the encoder and decoder, it is shown that our spatial-only method can reduce or eliminate such requirements while, at the same time, achieving similar performance. The combined methods, on the other hand, by jointly exploiting spatial and temporal correlation either at the MB or slice/picture level, can achieve even higher coding efficiency. Finally, improvements on the existing Rate Distortion Optimization related to B slices within the H.264 codec are also presented, which can lead to improvements of up to 16% in bit rate reduction or, equivalently, more than 0.7 dB in PSNR.

The airborne coherent multi-input multi-output (MIMO) radar benefiting from coherent integration (including intra-channel integration and inter-channel integration) of multi-channel echoes can obtain superior detection performance for high-speed maneuvering targets. However, due to the coupling motion characteristic between multiple airborne platforms and high-speed targets, the range walk (RW) and Doppler walk (DW) will occur within the intra-channel integration. Besides, the inter-channel signals also exist envelope and phase differences, which will lead to the difficulty in multi-channel integration. We make contributions towards addressing these limitations. On the one hand, we establish a three-dimensional (3-D) geometrical model for high-speed maneuvering target detection with airborne coherent MIMO radar and derive the mathematical expression of radar echoes, while the generalized Radon-Fourier transform (GRFT) is utilized to simultaneously correct the RW and DW for acquiring intra-channel integration. On the other hand, we analyze the properties of GRFT outputs and propose a GRFT-domain integration method to achieve the multi-channel coherent integration. More specifically, the proposed method first constructs a set of coupling equations for the estimation of target motion parameters (i.e., position, velocity and acceleration), then, the target's motion parameters can be estimated by Newton-Raphson method and solving the linear equations. Second, in order to settle the envelope and phase differences between multi-channel echoes, the envelope alignment and phase compensation functions of inter-channel echoes are constituted based on the estimated parameters. Finally, the coherent accumulation of multi-channel echoes can be implemented. Numerical simulations demonstrate the effectiveness of the proposed method.

We present a supervised hyperspectral segmentation procedure, consisting of best band analysis (BBA), an initial distance-based segmentation, and level set segmentation enhancement by forcing localized vicinities to be more homogeneous. BBA uses the spectral information divergence (SID) to reduce each pixel's high dimensional data to a scalar value, where the Bhattacharyya distance (BD) is maximized. The initial segmentation is based on feature vectors created from the SID metric. The level set segmentation then enhances areas that do not have spatially homogeneous ground cover. The proposed method is tested on a 72-band compact airborne spectrographic imager (CASI) image of a farm area in northern Mississippi, U.S.A. The proposed method is compared to a BBA-based maximum-likelihood (ML) method. Quantitative results are compared using segmentation and classification accuracies. Results show that both the initial classification using BBA features and the level set enhancement produced high-quality ground cover maps and outperformed the ML method, as well as previous studies by the authors. The ML method resulted in accuracies ges95.5%, whereas the level set segmentation approach resulted in accuracies as high as 99.7%.

This paper proposes a novel strategy for estimating the size of the resulting relation after an equi-join and selection using a regression model. An approximating series representing the underlying data distribution and dependency is derived from the actual data. The proposed method provides an instant and accurate size estimation by performing an evaluation of the series, with no run-time overheads in page faults and space, and with negligible CPU overhead. In contrast, the popular sampling methods incur run-time overheads in page faults (for sampling), CPU time and space. These overheads of sampling methods increase the response time of processing a query. The results of a comprehensive experimental study are also reported, which demonstrate that the estimation accuracy by the proposed method is comparable with that of the sampling methods which are believed to provide the most accurate estimation. The proposed method seems ideal for retrieval-intensive database and information systems. Since the overheads involved in deriving the approximating series are fairly moderate, we believe that this method is also an extremely competent method when moderate or periodical updates are present.

This paper proposes a novel strategy for estimating the size of the resulting relation after an equi-join and selection using a regression model. An approximating series representing the underlying data distribution and dependency is derived from the actual data. The proposed method provides an instant and accurate size estimation by performing an evaluation of the series, with no run-time overheads in page faults and space, and with negligible CPU overhead. In contrast, the popular sampling methods incur run-time overheads in page faults (for sampling), CPU time and space. These overheads of sampling methods increase the response time of processing a query. The results of a comprehensive experimental study are also reported, which demonstrate that the estimation accuracy by the proposed method is comparable with that of the sampling methods which are believed to provide the most accurate estimation. The proposed method seems ideal for retrieval-intensive database and information systems. Since the overheads involved in deriving the approximating series are fairly moderate, we believe that this method is also an extremely competent method when moderate or periodical updates are present.

Accurate determination of the effective reproduction number (Rt) is a very important strategy in the epidemiology of contagious diseases, including coronavirus disease 2019 (COVID-19). This study compares different methods of estimating the Rt of susceptible population to identify the most accurate method for estimating Rt. A secondary study. The value of Rt was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time-dependent (TD), and sequential Bayesian (SB) methods, for Iran, the United States, the United Kingdom, India, and Brazil from June to October 2021. In order to accurately compare these methods, a simulation study was designed using forty scenarios. The lowest mean square error (MSE) was observed for TD and ML methods, with 15 and 12 cases, respectively. Therefore, considering the estimated values of Rt based on the TD method, it was found that Rt values in the United Kingdom (1.33; 95% CI: 1.14-1.52) and the United States (1.25; 95% CI: 1.12-1.38) substantially have been more than those in other countries, such as Iran (1.07; 95% CI: 0.95-1.19), India (0.99; 95% CI: 0.89-1.08), and Brazil (0.98; 95% CI: 0.84-1.14) from June to October 2021. The important result of this study is that TD and ML methods lead to a more accurate estimation of Rt of population than other methods. Therefore, in order to monitor and determine the epidemic situation and have a more accurate prediction of the incidence rate, as well as control COVID-19 and similar diseases, the use of these two methods is suggested to more accurately estimate Rt.

This study reports the construction of a simple and elegant method to improve the detection of brain regions with increased neuronal activity in functional magnetic resonance imaging (fMRI). The main advantages of the method are regularization of voxelwise autoregressive parameters and accurate estimation of parameters and statistical inferences as compared to the conventional restricted maximum likelihood (ReML) method, where each voxel uses the two voxel‐wide (global) hyperparameters. The model is tested for its accuracy and efficiency in analysis of fMRI data for stimulated and real brains of healthy individuals. Receiver operating characteristic curves are used to show that the proposed method is superior to the conventional ReML method. The proposed method will improve the diagnostic accuracy of fMRI studies in the human brain. © 2011 Wiley Periodicals, Inc. Concepts Magn Reson Part A 38: 187–196, 2011.

The micro-Doppler (m-D) provides valuable information for the motion parameters extraction and the target recognition of space targets. To address the issue of estimating the motion parameters of precession warhead targets, a new method based on the m-D spectrum of the top and the bottom of the cone is proposed in this paper. In this method, the m-D features of the cone target are firstly extracted by calculating the first-order moments of the time-frequency distribution of the echo signal. Then, the motion parameters of the target are roughly estimated by the Fourier transformation of the m-D curve. Based on the rough estimation, the search method is employed to estimate the motion parameters of the cone target precisely. The validity of the proposed method is verified by the analysis data.

In this paper, a new method to build an adaptable body shape considering the motion parameters. The proposed method constructs MPEG conforming scalable virtual human by simplifying the body parts which have similar motion parameters while preserving details with characteristic motion parameters. Characteristic motion is classified by its high deformation over time which is extracted from skinning parameter distribution. The proposed method is adaptable with in MPEG framework and is designed to minimize load of adaptation server which keeping compactness as much as possible. It is also targeted to cover devices with small computational power such as PDAs. Example of body simplification is given to show the effectiveness of the proposed method.

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images.

Currently, the design and implementation of reliable robust prosthesis control systems are very important because these systems must work in noisy and interfering environments of various natures. An important factor for the design of these systems is the correct movement parameter estimation of the prosthesis, from the electromyographic signals analysis of the muscles that are involved in the movement of the human body joints. The present work shows the design results and an analysis method for control systems creation of elbow joint prosthesis, which allows diminishing the noise influence and environment interferences and estimating the joint movement parameters. The method defines three processes: sEMG signals treatment; analysis of the dynamic changes of the signals during the muscular contraction, and the identification and estimation of the movement parameters. For the sEMG signals treatment and analysis, it is proposed the decomposition through Wavelet transform with mother function BIOR 3.5. The signals analysis is realized in the time and frequency domain. The recognition and identification stage, as an expert system uses backpropagation neural networks. In a TMS320C6713 board, the C/C++ model codes obtained from each process of the proposed method. The results allow concluding that it is possible to use this method for designing prosthesis control systems capable of diminishing the environmental noise and interference.

Radial acceleration estimation of multiple high maneuvering targets

The phylogeny reconstruction problem is a fundamental problem in computational molecular biology and biochemical physics. Since the number of data sets has grown substantially in recent years, the accuracy and speed of constructing phylogenies become increasingly critical. Numerous studies have demonstrated that the maximum likelihood (ML) method is the most effective method for reconstructing a phylogenetic tree from sequence data. Conversely, tree bisection and reconnection (TBR) is a tree topology rearrangement method that can generate an extensive tree space. In this paper, we propose an enhanced method for reconstructing phylogenetic trees in which the TBR operation is modified and combined with the minimum evolution principle to filter out some unnecessary reconnected positions to reduce the search time. The experiment results demonstrate that the proposed method can assist other algorithms in constructing more accurate trees within a reasonable time.