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Selective acoustic tweezers can focus the acoustic radiation force on a single particle to manipulate it without affecting its neighbors. This has long required highly complex hardware. In this numerical study, we show that pulsed acoustic waves can be used for the selective manipulation of particles using only two pairs of orthogonal transducers. While these tweezers are well-known for their ability to manipulate arrays of particles, we show that selectivity can be achieved by using sequences of acoustic pulses to iteratively construct a combined acoustic potential focused only on the target particle.

Using pulsed acoustic waves could provide a superior selectivity for microscale acoustic tweezers. However, the theory for the radiation force of pulsed acoustic waves has only been recently derived and no numerical implementations are available. In this paper, we present a finite-element implementation of this model to simulate the transient acoustic radiation force on small spheres. We use the model to simulate laser-guided acoustic tweezers and optimize their performance. By enabling numerical simulations of the transient radiation force, this work may accelerate the rational design of pulse-based high-selectivity acoustic tweezers devices.

Acoustic tweezers can manipulate microscopic objects and cells independently of the optical, magnetic and electrical properties of the objects or their medium. However, because ultrasonic waves are attenuated within few millimeters, existing devices must synthesize intricate and powerful acoustic fields in a very narrow footprint immediately close to the manipulated object. Here we show that the design of microscale acoustic tweezers can be considerably simplified by taking advantage of the nonlinear nature of the acoustic trapping force. In our experiment, a featureless piezoelectric crystal coated with a photoacoustic conversion layer is hit by an electric pulse and a spatially modulated laser pulse to generate synchronized electro- and photo- acoustic waves. Interference between these waves creates a hybrid acoustic trapping force 30 times stronger than the laser pulse alone but with the same spatial information. By disentangling the tradeoff between spatial resolution and trapping force that has long held back the development of acoustic tweezers, this field hybridization strategy opens new avenues for cell manipulation in organ on chip and organ printing.

In recent years, neural network-based anomaly detection methods have attracted considerable attention in the hyperspectral remote sensing domain due to the powerful reconstruction ability compared with traditional methods. However, actual probability distribution statistics hidden in the latent space are not discovered by exploiting the reconstruction error because the probability distribution of anomalies is not explicitly modeled. To address the issue, we propose a novel probability distribution representation detector (PDRD) that explores the intrinsic distribution of both the background and the anomalies in original data for hyperspectral anomaly detection in this paper. First, we represent the hyperspectral data with multivariate Gaussian distributions from a probabilistic perspective. Then, we combine the local statistics with the obtained distributions to leverage the spatial information. Finally, the difference between the corresponding distributions of the test pixel and the average expectation of the pixels in the Chebyshev neighborhood is measured by computing the modified Wasserstein distance to acquire the detection map. We conduct the experiments on four real data sets to evaluate the performance of our proposed method. Experimental results demonstrate the accuracy and efficiency of our proposed method compared to the state-of-the-art detection methods.

The technique of detecting multiple dim and small targets with low signal-to-clutter ratios (SCR) is very important for infrared search and tracking systems. In this paper, we establish a detection method derived from maximal entropy random walk (MERW) to robustly detect multiple small targets. Initially, we introduce the primal MERW and analyze the feasibility of applying it to small target detection. However, the original weight matrix of the MERW is sensitive to interferences. Therefore, a specific weight matrix is designed for the MERW in principle of enhancing characteristics of small targets and suppressing strong clutters. Moreover, the primal MERW has a critical limitation of strong bias to the most salient small target. To achieve multiple small targets detection, we develop a hierarchical version of the MERW method. Based on the hierarchical MERW (HMERW), we propose a small target detection method as follows. First, filtering technique is used to smooth the infrared image. Second, an output map is obtained by importing the filtered image into the HMERW. Then, a coefficient map is constructed to fuse the stationary dirtribution map of the HMERW. Finally, an adaptive threshold is used to segment multiple small targets from the fusion map. Extensive experiments on practical data sets demonstrate that the proposed method is superior to the state-of-the-art methods in terms of target enhancement, background suppression and multiple small targets detection.

In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios. Starting from a coarse initial prediction by a multi-scale residual block, we propose a progressively guided alternate refinement network to refine it. Instead of using ImageNet pre-trained backbone network, we first construct a lightweight depth stream by learning from scratch, which can extract complementary features more efficiently with less redundancy. Then, different from the existing fusion based methods, RGB and depth features are fed into proposed guided residual (GR) blocks alternately to reduce their mutual degradation. By assigning progressive guidance in the stacked GR blocks within each side-output, the false detection and missing parts can be well remedied. Extensive experiments on seven benchmark datasets demonstrate that our model outperforms existing state-of-the-art approaches by a large margin, and also shows superiority in efficiency (71 FPS) and model size (64.9 MB)., Comment: ECCV 2020

Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently. However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy model weight. To this end, this paper presents an accurate yet compact deep network for efficient salient object detection. More specifically, given a coarse saliency prediction in the deepest layer, we first employ residual learning to learn side-output residual features for saliency refinement, which can be achieved with very limited convolutional parameters while keep accuracy. Secondly, we further propose reverse attention to guide such side-output residual learning in a top-down manner. By erasing the current predicted salient regions from side-output features, the network can eventually explore the missing object parts and details which results in high resolution and accuracy. Experiments on six benchmark datasets demonstrate that the proposed approach compares favorably against state-of-the-art methods, and with advantages in terms of simplicity, efficiency (45 FPS) and model size (81 MB)., Comment: ECCV 2018

Spin accumulation generated by the anomalous Hall effects (AHE) in mesoscopic ferromagnetic Ni81Fe19 (permalloy or Py) films is detected electrically by a nonlocal method. The reciprocal phenomenon, inverse spin Hall effects (ISHE), can also be generated and detected all-electrically in the same structure. For accurate quantitative analysis, a series of nonlocal AHE/ISHE structures and supplementary structures are fabricated on each sample substrate to account for statistical variations and to accurately determine all essential physical parameters in-situ. By exploring Py thicknesses of 4 nm, 8 nm, and 12 nm, the Py spin diffusion length {\lambda}_Py is found to be much shorter than the film thicknesses. The product of {\lambda}_Py and the Py spin Hall angle {\alpha}_SH is determined to be independent of thickness and resistivity: {\alpha}_SH*{\lambda}_Py= (0.066 +/- 0.009) nm at 5 K and (0.041 +/- 0.010) nm at 295 K. These values are comparable to those obtained from mesoscopic Pt films.

Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain. In tune with such observations, neuromorphic and other unconventional computing platforms have recently started adopting the usage of computational units that generate outputs probabilistically, depending on the magnitude of the input stimulus. In this work, we experimentally demonstrate a spintronic device that offers a direct mapping to the functionality of such a controllable stochastic switching element. We show that the probabilistic switching of Ta/CoFeB/MgO heterostructures in presence of spin-orbit torque and thermal noise can be harnessed to enable probabilistic inference in a plethora of unconventional computing scenarios. This work can potentially pave the way for hardware that directly mimics the computational units of Bayesian inference.