Hand pose estimation from monocular depth images is an important and challenging problem for human-computer interaction. Recently deep convolutional networks (ConvNet) with sophisticated design have been employed to address it, but the improvement over traditional methods is not so apparent.To promote the performance of directly 3D coordinate regression, we propose a tree-structured Region Ensemble Network (REN), which partitions the convolution outputs into regions and integrates the results from multiple regressors on each regions. Compared with multi-model ensemble, our model is completely end-to-end training. The experimental results demonstrate that our approach achieves the best performance among state-of-the-arts on two public datasets.
One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW). Effective methods for suppressing BW include the wavelet-based (WT) and the mathematical morphological filtering-based (MMF) algorithms. However, the T waveform distortions introduced by the WT and the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WT to overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinical BW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with other state-of-the-art methods commonly used in the literature. The results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG.
This paper introduces balanced switching schemes to compensate linear and quadratic gradient errors, in the unary current source array of a current-steering digital-to-analog converter (DAC). A novel algorithm is proposed to avoid the accumulation of gradient errors, yielding much less integral nonlinearities (INLs) than conventional switching schemes. Switching scheme examples with different number of current cells are also exhibited in this paper, including symmetric arrays and nonsymmetric arrays in round and square outlines. (a) For symmetric arrays where each cell is divided into two parallel concentric ones, the simulated INL of the proposed round/square switching scheme is less than 25%/40% of conventional switching schemes, respectively. Such improvement is achieved by the cancelation of linear errors and the reduction of accumulated quadratic errors to near the absolute lower bound, using the proposed balanced algorithm. (b) For non-symmetric arrays, i.e. arrays where cells are not divided into parallel ones, linear errors cannot be canceled, and the accumulated INL varies with different quadratic error distribution centers. In this case, the proposed algorithm strictly controls the accumulation of quadratic gradient errors, and different from the algorithm in symmetric arrays, linear errors are also strictly controlled in two orthogonal directions simultaneously. Therefore, the INLs of the proposed non-symmetric switching schemes are less than 64% of conventional switching schemes.
Tactile Sensors
In article number 2200595, Xin‐Jun Liu, Huichan Zhao, and co‐workers present compact multi‐axis tactile sensor for both human and robotic grasping. In this work, a single waveguide fiber embedded in a soft matrix is demonstrated which has anisotropic sensitivity to spatial vector force components. They are capable of differentiating normal and shear forces after a specially‐designed multi‐layer arrangement of these sensitive units in its depth direction.
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