Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications

It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we consider the QPSK equalization as a four-classification task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error rate (SER) with the one-hot coding instead of the MMSE criterion. In addition, we introduce a sliding structure based on the banded approximation of the channel matrix to reduce the network size and aid UDNet to perform well for different-length signals without changing the network structure. Furthermore, we apply the measured at-sea doubly-selective UWA channel and offshore background noise to evaluate the proposed equalizer. Experimental results show that the proposed UDNet performs better with low computational complexity. Concretely, the SER of UDNet is nearly an order of magnitude lower than that of MMSE

Multi-Dimensional Data Analysis Platform (MuDAP): A Cognitive Science Data Toolbox

Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to truncate data dimensions when dealing with data with multiple dimensions. This is not necessarily because of its merit in terms of mathematical algorithm, but partly because it is easy to conduct with commonly accessible statistical software. On the other hand, dimension reduction might not be the best analysis when modeling data with no more than 20 dimensions. Using state-of-the-art techniques, researchers in various research disciplines (e.g., computer vision) classified data with more than hundreds of dimensions with neural networks and revealed the inner structure of the data. Therefore, it might be more sophisticated to process human perception data directly with neural networks. In this paper, we introduce the multi-dimensional data analysis platform (MuDAP), a powerful toolbox for data analysis in cognitive science. It utilizes artificial intelligence as well as network analysis, an analysis method that takes advantage of data symmetry. With the graphic user interface, a researcher, with or without previous experience, could analyze multiple dimensional data with great ease

DPSSD: Dual-Path Single-Shot Detector

Object detection is one of the most important and challenging branches of computer vision. It has been widely used in people’s lives, such as for surveillance security and autonomous driving. We propose a novel dual-path multi-scale object detection paradigm in order to extract more abundant feature information for the object detection task and optimize the multi-scale object detection problem, and based on this, we design a single-stage general object detection algorithm called Dual-Path Single-Shot Detector (DPSSD). The dual path ensures that shallow features, i.e., residual path and concatenation path, can be more easily utilized to improve detection accuracy. Our improved dual-path network is more adaptable to multi-scale object detection tasks, and we combine it with the feature fusion module to generate a multi-scale feature learning paradigm called the “Dual-Path Feature Pyramid”. We trained the models on PASCAL VOC datasets and COCO datasets with 320 pixels and 512 pixels input, respectively, and performed inference experiments to validate the structures in the neural network. The experimental results show that our algorithm has an advantage over anchor-based single-stage object detection algorithms and achieves an advanced level in average accuracy. Researchers can replicate the reported results of this paper

Self-Calibration Method and Pose Domain Determination of a Light-Pen in a 3D Vision Coordinate Measurement System

Light pens for 3D vision coordinate measurement systems are increasingly widely used due to their advantages, such as their small size, convenience of being carried, and widespread applicability. The posture of the light pen is an important factor that affects accuracy. The pose domain of the pen needs to be given so that the measurement system has a suitable measurement range to obtain more qualified parameters. The advantage of the self-calibration method is that the entire self-calibration process can be completed at the measurement site with no auxiliary equipment. After the system camera calibration was completed, we took several pictures of the same measurement point with different poses to obtain the conversion matrix of the picture and subsequently used spherical fitting, the generalized inverse method of least squares, and the principle of position invariance in the pose domain range. The combined stylus tip center self-calibration method calculates the actual position of the light pen probe. The experimental results verify the effectiveness of the method; the measurement accuracy of the system can satisfy basic industrial measurement requirements

Current-Induced Domain Wall Motion and Tilting in Perpendicularly Magnetized Racetracks

Abstract The influence of C insertion on Dzyaloshinskii–Moriya interaction (DMI) as well as current-induced domain wall (DW) motion (CIDWM) and tilting in Pt/Co/Ta racetracks is investigated via a magneto-optical Kerr microscope. The similar DMI strength for Pt/Co/Ta and Pt/Co/C/Ta samples reveals that DMI mainly comes from the Pt/Co interface. Fast DW velocity around tens of m/s with current density around several MA/cm2 is observed in Pt/Co/Ta. However, it needs double times larger current density to reach the same magnitude in Pt/Co/C/Ta, indicating DW velocity is related to the spin-orbit torque efficiency and pinning potential barrier. Moreover, in CIDWM, DW velocity is around 103 times larger than that in field-induced DW motion (FIDWM) with current-generated effective field keeping the same magnitude as applied magnetic field, revealing that the current-generated Joule heating has an influence on DW motion. Interestingly, current-induced DW tilting phenomenon is observed, while this phenomenon is absent in FIDWM, demonstrating that the current-generated Oersted field may also play an essential role in DW tilting. These findings could provide some designing prospects to drive DW motion in SOT-based racetrack memories

Spin-fluctuation-induced sign reversal of the spin Hall angle in Pt100−x Co x alloys near the Curie temperature

The spin Hall effect (SHE), typically emerging in non-magnetic metals with strong spin-orbit couplings, has attracted significant attention for its ability to convert a charge current into a spin current, a key feature in power-efficient spintronic devices. Recently, an enhanced SHE has been detected in the magnetic alloys, where the spin Hall conductivity is strongly modified by the dynamical and thermal spin fluctuations. We find that the spin Hall angle ( ${\theta _{{\text{SH}}}}$ ) in Pt _100− _x Co _x alloys dramatically changes at the Curie temperature, which is positive in the paramagnetic phase akin to Pt, while negative in the ferromagnetic phase. Such intriguing behavior of ${\theta _{{\text{SH}}}}$ stemming from individual and collective fluctuations in the magnetic moments is further substantiated with the full-fledged Monte Carlo simulations. Our work broadens insights into the SHE and highlights the importance of spin fluctuations for the spin-current generation near the ferromagnetic instability point of magnetic alloys