Animation Preserving Simplification for MPEG Based Scalable Virtual Human

When multiple targets are within the same radar antenna beam and cannot be separated in the range dimension, the conventional imaging methods cannot be directly used to obtain a focused radar image. In this study, a new joint estimation algorithm for multi-targets' motion parameters is proposed. In this method, the first-order Keystone transform is first applied to correct the range walk of multiple targets simultaneously, and then the Lv's transform is used to estimate the motion parameters of targets including velocity and acceleration. The signal-to-noise ratio threshold for the proposed method is also given. The proposed method is fast and can obtain the accurate parameter estimation without knowing the number of targets and their motion information. Experimental results demonstrate the performance of the proposed algorithm. Comparisons between the proposed method and other methods, the maximum-likelihood method, fractional Fourier transform and discrete polynomial transform, are performed, which show that the proposed method can efficiently obtain the accurate parameter estimation with low computational burden.

Increased uptake of variable renewable generation and further electrification of energy demand necessitate efficient coordination of flexible demand resources to make most efficient use of power system assets. Flexible electrical loads are typically small, numerous, heterogeneous and owned by self-interested agents. Considering the multi-temporal nature of flexibility and the uncertainty involved, scheduling them is a complex task. This paper proposes a forecast-mediated real-time market-based control approach (F-MBC) for cost minimizing coordination of uninterruptible time-shiftable (i.e. deferrable) loads. F-MBC is scalable, privacy preserving, and useable by device agents with small computational power. Moreover, F-MBC is proven to overcome the challenge of mutually conflicting decisions from equivalent devices. Simulations in a simplified but challenging case study show that F-MBC produces near-optimal behaviour over multiple time-steps.

We propose a multistep procedure for the on-line detection and analysis of gravitational wave signals emitted during the coalescence of compact binaries. This procedure, based on a hierarchical strategy, consists of a rough analysis of the gravitational wave signal using adaptive line enhancers (ALE) filters and the controlled random search (CRS) optimization algorithm followed by a refined analysis using the classic matched-filtering technique. The results of simulations for the rough analysis are quite promising both for the relatively small computational power needed and for the robustness of the algorithms used, so that it could be very helpful for gravitational wave detection with very large baseline interferometric detectors like LIGO and VIRGO.

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.

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

A new method is introduced for the estimation of structure and motion parameters in an image sequence. The proposed method uses the correspondences of two straight lines over four frames to establish a set of nonlinear equations in terms of the structure and motion parameters. To solve the simultaneous nonlinear equations numerically, a technique is devised to obtain the initial guess values of the parameters.

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

Range, radial velocity, and acceleration MLE using frequency modulation coded LFM pulse train

A method of determining and visualizing safe motion parameters of a ship navigating in restricted waters

본 논문은 스마트 기기의 비 접촉 사용자 인터페이스를 위한 비전 기반 손동작 인식 기법에 대해 제안한다. 먼저 스마트 기기의 저 용량 메모리와 낮은 계산 능력을 고려하여 컬러 원본 이미지를 그레이 스케일 이미지로 변환하고 해상도를 줄이는 전처리 과정을 수행한다. 다음으로 손의 수평, 수직 움직임을 분리하여 강인하게 손동작을 검출할 수 있도록 수평, 수직 주요 검출 대상 영역을 정의한다. 그리고 연속적으로 획득된 영상의 차분 영상으로부터 손의 움직임에 의해 주요하게 변화되는 픽셀들의 무게 중심을 구하고, 시간에 따라 변화되는 무게 중심 값에 대한 최소 자승 선을 정의하여 손의 움직임 방향을 인식한다. 제안한 방법의 효용성을 검증하기 위해 비전 시스템에 의한 실험을 수행하였다.

Radiologists are required to write free paper text reports for breast screenings in order to assign cancer diagnoses in a later step. The current procedure requires considerable time and needs efficiency. In this paper, to streamline the writing process and keep up with the specific vocabulary, a word prediction tool using neural language models was developed. Consequently, challenges as different languages (English, Dutch), small data sizes and low computational power have been overcome by introducing a novel English-Dutch Radiology Language Modelling process. After defining model architectures, the process involves data preparation, bilevel hyperparameters optimization, configuration transfer and evaluation. The model is able to improve the current workflow and successfully meet the computational constraints, based on both an intrinsic and extrinsic evaluation. Given its flexibility, the model opens the door for future research involving other languages and also an extensive set of real-world applications.

Recently, convolutional auto-encoders (CAE) were introduced for image coding. They achieved performance improvements over the state-of-the-art JPEG2000 method. However, these performances were obtained using massive CAEs featuring a large number of parameters and whose training required heavy computational power.In this paper, we address the problem of lossy image compression using a CAE with a small memory footprint and low computational power usage.In this work, we propose a constrained approach and a new structured sparse learning method. We design an algorithm and test it on three constraints: the classical ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> constraint, the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,∞</inf> and the new ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,1</inf> constraint. Experimental results show that the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,1</inf> constraint provides the best structured sparsity, resulting in a high reduction of memory ( 82 %) and computational cost reduction (25 %), with similar rate-distortion performance as with dense networks.

We propose two techniques for robust motion artifact suppression in ultrasound images based on what is called navigator echoes. The navigator echo approach has been introduced and widely used in the area of magnetic resonance imaging where motion is detected along one of the image directions based on simple registration of one-dimensional projection of the image assuming rigid body motion. In ultrasound imaging, the same concept can be applied to correct for simple shifts (e.g., with linear array probes) or rotations (e.g., with convex array probes) along the width and depth direction. The first technique assumes a rigid body motion model. Hence, simple one-dimensional template matching can be used to obtain the motion parameters as in magnetic resonance imaging. Alternatively, the second technique considers a more general spatially- variant motion model. The motion parameters of this model can be obtained through localized optimization of an information theoretic criterion. The motion model parameters are then employed in the reconstruction thus providing images with substantially reduced artifacts. The proposed method was implemented on an experimental ultrasound system in which each line is obtained from 2-4 acquisitions from interleaved frames. The motion models are compared and their practical implementation for clinical systems is evaluated.

Image based registration of rigid objects has been frequently addressed in the literature to obtain an object's motion parameters. In this paper, a new approach of joint segmentation-rigid registration, within the variational framework of the phase field approximation of the Mumford-Shah's functional, is proposed. The defined functional consists of two Mumford-Shah equations, extracting the discontinuity set of the reference and target images due to a rigid spatial transformation. Multiscale minimization of the proposed functional after finite element discretization provided a sub-pixel, robust algorithm for edge extraction as well as edge based rigid registration. The implementation considerations of the proposed method, including memory usage, convergence rate and effects of parameters selection, was discussed and its efficacy was examined in a comprehensive set of synthetic, phantom and clinical experiments. It was found that the proposed joint segmentation-rigid registration method provides improved results, in comparison with the currently available methods which are often based on maximizing images similarities, especially when the reference and target images are of different qualities. A high registration accuracy was obtained when estimating the knee joint kinematics through MR images taken at different joint configurations. It was concluded that the proposed method can be effectively used in applications where 3D image registration of rigid objects is concerned, e.g. for estimation of the motion parameters of human joints.