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The development of spintronic emitters of broadband THz pulses relies on designing heterostructures where processes of laser-driven spin current generation and subsequent spin-to-charge current conversion are the most efficient. An interface between ferromagnetic and nonmagnetic layers in the emitter is one of the critical elements. Here, we study experimentally single-cycle THz pulse generation from a laser-pulse excited Pt/Co emitter with a composition gradient interface between Pt and Co and compare it with the emission from a conventional Pt/Co structure with an abrupt interface. We find that the gradient interface enhances the efficiency of optics-to-THz conversion by a factor of two in a wide range of optical fluences up to 3 mJ cm$^{-2}$. We reveal that this enhancement is caused by a pronounced increase in transmittance of the laser-driven spin-polarized current through the gradient interface compared to the abrupt one. Furthermore, we find that such a transmission deteriorates with laser fluence due to the spin accumulation effect.

Barcode magnetic nanowires typically comprise a multilayer magnetic structure in a single body with more than one segment type. Interestingly, owing to selective functionalization and novel interactions between the layers, barcode magnetic nanowires have attracted significant attention, particularly in the field of bioengineering. However, an analysis of their magnetic properties at the individual nanowire level remains challenging. With this background, herein, we investigated the characterization of magnetic nanowires at room temperature under ambient conditions based on magnetic images obtained via wide-field quantum microscopy with nitrogen-vacancy centers in diamond. Consequently, we could extract critical magnetic properties, such as the saturation magnetization and coercivity, of single nanowires by comparing the experimental results with those of micromagnetic simulations. This study opens up the possibility for a versatile characterization method suited to individual magnetic nanowires., Comment: main text (20 pages, 5 figures), supplementary materials (9 pages, 8 figures)

Metallic barcode nanowires (BNWs) composed of repeating heterogeneous segments fabricated by template-assisted electrodeposition can offer extended functionality in magnetic, electrical, mechanical, and biomedical applications. We can consider such nanostructures as a three-dimensional system of magnetically interacting elements with magnetic behavior strongly affected by complex magnetostatic interactions. This study discusses the influence of geometrical parameters of segments on the character of their interactions and the overall magnetic behavior of the array of BNWs having alternating magnetization. By controlling the applied current densities and the elapsed time in the electrodeposition, we regulate the dimension of the Fe-Au BNWs. We show that the Fe and Au segments are made of Fe-Au alloys with high and low magnetization. With the help of micromagnetic simulations, we discover and analyze the three types of magnetostatic interactions in the BNW arrays. As a result, we demonstrate that the dominating type of interaction depends on the geometric parameters of the Fe and Au segments and the interwire and intrawire distances., Comment: 29 pages, 7 figures

Latest CNN-based object detection models are quite accurate but require a high-performance GPU to run in real-time. They still are heavy in terms of memory size and speed for an embedded system with limited memory space. Since the object detection for autonomous system is run on an embedded processor, it is preferable to compress the detection network as light as possible while preserving the detection accuracy. There are several popular lightweight detection models but their accuracy is too low for safe driving applications. Therefore, this paper proposes a new object detection model, referred as YOffleNet, which is compressed at a high ratio while minimizing the accuracy loss for real-time and safe driving application on an autonomous system. The backbone network architecture is based on YOLOv4, but we could compress the network greatly by replacing the high-calculation-load CSP DenseNet with the lighter modules of ShuffleNet. Experiments with KITTI dataset showed that the proposed YOffleNet is compressed by 4.7 times than the YOLOv4-s that could achieve as fast as 46 FPS on an embedded GPU system(NVIDIA Jetson AGX Xavier). Compared to the high compression ratio, the accuracy is reduced slightly to 85.8% mAP, that is only 2.6% lower than YOLOv4-s. Thus, the proposed network showed a high potential to be deployed on the embedded system of the autonomous system for the real-time and accurate object detection applications., Comment: in Chinese language

Spin-orbit torque manifested as an accumulated spin-polarized moment at nonmagnetic normal metal, and ferromagnet interfaces is a promising magnetization switching mechanism for spintronic devices. To fully exploit this in practice, materials with a high spin Hall angle, i.e., a charge-to-spin conversion efficiency, are indispensable. To date, very few approaches have been made to devise new nonmagnetic metal alloys. Moreover, new materials need to be compatible with semiconductor processing. Here we introduce W-Ta and W-V alloys and deploy them at the interface between $\beta$-W/CoFeB layers. First, spin Hall conductivities of W-Ta and W-V structures with various compositions are carried out by first-principles band calculations, which predict the spin Hall conductivity of the W-V alloy is improved from $-0.82 \times 10^3$ S/cm that of W to $-1.98 \times 10^3$ S/cm. Subsequently, heterostructure fabrication and spin-orbit torque properties are characterized experimentally. By alloying $\beta$-W with V at a concentration of 20 at%, we observe a large enhancement of the absolute value of spin Hall conductivity of up to $-(2.77 \pm 0.31) \times 10^3$ S/cm. By employing X-ray diffraction and scanning transmission electron microscopy, we further explain the enhancement of spin-orbit torque efficiency is stemmed from W-V alloy between W and CoFeB.

Classification of EEG-based motor imagery (MI) is a crucial non-invasive application in brain-computer interface (BCI) research. This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based MI classification that outperforms the state-of-the-art methods. The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network, which showed to be highly efficient and accurate for time-series classification. Also, the proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing. Furthermore, this paper proposes a novel data augmentation method for EEG signals to enhance the accuracy, at least by 3%, and reduce overfitting with limited BCI datasets. The proposed model outperforms all the state-of-the-art methods by achieving the average accuracy of 88.4% and 88.6% on the 2008 BCI Competition IV 2a (four-classes) and 2b datasets (binary-classes), respectively. Furthermore, it takes less than 0.025 seconds to test a sample suitable for real-time processing. Moreover, the classification standard deviation for nine different subjects achieves the lowest value of 5.5 for the 2b dataset and 7.1 for the 2a dataset, which validates that the proposed method is highly robust. From the experiment results, it can be inferred that the EEG-Inception network exhibits a strong potential as a subject-independent classifier for EEG-based MI tasks., Comment: Provisionally Accepted by Journal of Neural Engineering

For deployment on an embedded processor for autonomous driving, the object detection network should satisfy all of the accuracy, real-time inference, and light model size requirements. Conventional deep CNN-based detectors aim for high accuracy, making their model size heavy for an embedded system with limited memory space. In contrast, lightweight object detectors are greatly compressed but at a significant sacrifice of accuracy. Therefore, we propose FRDet, a lightweight one-stage object detector that is balanced to satisfy all the constraints of accuracy, model size, and real-time processing on an embedded GPU processor for autonomous driving applications. Our network aims to maximize the compression of the model while achieving or surpassing YOLOv3 level of accuracy. This paper proposes the Fire-Residual (FR) module to design a lightweight network with low accuracy loss by adapting fire modules with residual skip connections. In addition, the Gaussian uncertainty modeling of the bounding box is applied to further enhance the localization accuracy. Experiments on the KITTI dataset showed that FRDet reduced the memory size by 50.8% but achieved higher accuracy by 1.12% mAP compared to YOLOv3. Moreover, the real-time detection speed reached 31.3 FPS on an embedded GPU board(NVIDIA Xavier). The proposed network achieved higher compression with comparable accuracy compared to other deep CNN object detectors while showing improved accuracy than the lightweight detector baselines. Therefore, the proposed FRDet is a well-balanced and efficient object detector for practical application in autonomous driving that can satisfies all the criteria of accuracy, real-time inference, and light model size.

A misalignment of LiDAR as low as a few degrees could cause a significant error in obstacle detection and mapping that could cause safety and quality issues. In this paper, an accurate inspection system is proposed for estimating a LiDAR alignment error after sensor attachment on a mobility system such as a vehicle or robot. The proposed method uses only a single target board at the fixed position to estimate the three orientations (roll, tilt, and yaw) and the horizontal position of the LiDAR attachment with sub-degree and millimeter level accuracy. After the proposed preprocessing steps, the feature beam points that are the closest to each target corner are extracted and used to calculate the sensor attachment pose with respect to the target board frame using a nonlinear optimization method and with a low computational cost. The performance of the proposed method is evaluated using a test bench that can control the reference yaw and horizontal translation of LiDAR within ranges of 3 degrees and 30 millimeters, respectively. The experimental results for a low-resolution 16 channel LiDAR (Velodyne VLP-16) confirmed that misalignment could be estimated with accuracy within 0.2 degrees and 4 mm. The high accuracy and simplicity of the proposed system make it practical for large-scale industrial applications such as automobile or robot manufacturing process that inspects the sensor attachment for the safety quality control.

This paper presents a novel automatic calibration system to estimate the extrinsic parameters of LiDAR mounted on a mobile platform for sensor misalignment inspection in the large-scale production of highly automated vehicles. To obtain subdegree and subcentimeter accuracy levels of extrinsic calibration, this study proposed a new concept of a target board with embedded photodetector arrays, named the PD-target system, to find the precise position of the correspondence laser beams on the target surface. Furthermore, the proposed system requires only the simple design of the target board at the fixed pose in a close range to be readily applicable in the automobile manufacturing environment. The experimental evaluation of the proposed system on low-resolution LiDAR showed that the LiDAR offset pose can be estimated within 0.1 degree and 3 mm levels of precision. The high accuracy and simplicity of the proposed calibration system make it practical for large-scale applications for the reliability and safety of autonomous systems., Comment: prepost for IEEE journal

To stabilize the non-trivial spin textures, e.g., skyrmions or chiral domain walls in ultrathin magnetic films, an additional degree of freedom such as the interfacial Dzyaloshinskii-Moriya interaction (IDMI) must be induced by the strong spin-orbit coupling (SOC) of a stacked heavy metal layer. However, advanced approaches to simultaneously control IDMI and perpendicular magnetic anisotropy (PMA) are needed for future spin-orbitronic device implementations. Here, we show an effect of atomic-scale surface modulation on the magnetic properties and IDMI in ultrathin films composed of 5d heavy metal/ferromagnet/4d(5d) heavy metal or oxide interfaces, such as Pt/CoFeSiB/Ru, Pt/CoFeSiB/Ta, and Pt/CoFeSiB/MgO. The maximum IDMI value corresponds to the correlated roughness of the bottom and top interfaces of the ferromagnetic layer. The proposed approach for significant enhancement of PMA and IDMI through the interface roughness engineering at the atomic scale offers a powerful tool for the development of the spin-orbitronic devices with the precise and reliable controllability of their functionality., Comment: 35 pages, 11 figures