In the era of Industry 5.0, the rapid development of the Internet of Things (IoT) is expected to extend its applications to broader human–computer interaction (HCI) and human–machine connectivity. With the increasing number of healthcare groups, there is an urgent need to develop embodied neuromorphic intelligent human–machine connectivity products based on these technologies. However, it remains a critical challenge to address the influencing factors of such product designs and how to quantify interaction efficacy. This study proposes a product framework combining IoT with embodied neuromorphic intelligence and conducts a user study. A cognitive rehabilitation product was developed using Leap Motion technology, with gesture recognition difficulty as a design variable, and product efficacy was quantified using a combination of brain–computer interaction and multisource interactive feedback. Fifteen elderly and fifteen young participants engaged in puppet control tasks under resting, simple, and complex conditions. The study compared brain activation levels, brain network connectivity, and eight behavioral indicators. The results demonstrated that the difficulty level significantly affects interaction efficacy. This research reveals neurological changes in the rehabilitation process of healthcare groups and opens new directions for the design and efficacy evaluation of embodied neuromorphic intelligence in HCI rehabilitation products through IoT and big data analytics, thereby advancing the development of Healthcare Industry 5.0.

Computing units for nonlinear complex functions are indispensable in deep neural network training processors. However, the existing computing units for nonlinear complex functions have low utilization efficiency and poor agreement and precision. In this article, we propose a multifunction computing unit for training deep neural networks by reusing computing resources based on a piecewise linear (PWL) method to improve computing density. Based on the state-of-the-art segmentor of PWL method, multiple nonlinear functions are divided into the fewest segments with the same bit width of computation. In hardware implementation, the reconfigurable technique is implemented on multiple functions while reusing computing resources including the multiplier and adder. The application-specific integrated circuit (ASIC) implementation results reveal that the architecture with reuse reduces the area by 44.50% and the power by 43.71% at the same frequency, when compared with the architecture without reuse.

SUMMARY: We present a fast particle fusion method for particles imaged with single-molecule localization microscopy. The state-of-the-art approach based on all-to-all registration has proven to work well but its computational cost scales unfavorably with the number of particles N, namely as N2. Our method overcomes this problem and achieves a linear scaling of computational cost with N by making use of the Joint Registration of Multiple Point Clouds (JRMPC) method. Straightforward application of JRMPC fails as mostly locally optimal solutions are found. These usually contain several overlapping clusters that each consist of well-aligned particles, but that have different poses. We solve this issue by repeated runs of JRMPC for different initial conditions, followed by a classification step to identify the clusters, and a connection step to link the different clusters obtained for different initializations. In this way a single well-aligned structure is obtained containing the majority of the particles. RESULTS: We achieve reconstructions of experimental DNA-origami datasets consisting of close to 400 particles within only 10 min on a CPU, with an image resolution of 3.2 nm. In addition, we show artifact-free reconstructions of symmetric structures without making any use of the symmetry. We also demonstrate that the method works well for poor data with a low density of labeling and for 3D data. AVAILABILITY AND IMPLEMENTATION: The code is available for download from https://github.com/wexw/Joint-Registration-of-Multiple-Point-Clouds-for-Fast-Particle-Fusion-in-Localization-Microscopy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Unbalanced three-phase voltages, harmonics and DC offset to the grid voltage will affect the speed and accuracy of a grid-connected inverter when estimating the frequency and phase angle of the grid voltage. Existing synchronization methods were difficult in accurately estimating the phase angle of the three-phase voltages. To solve these problems, this paper proposes a synchronous control method for three-phase grid-connected inverters using the energy operator. First, the cascade delay signal cancellation algorithm is used to eliminate harmonics and DC offset of the grid voltage, and the grid voltage is unitized to eliminate the adverse effects of unbalanced amplitudes. After that the energy operator is applied to obtain the frequency and the phase angle of the thee-phase input voltages. Therefore, the inverter and the grid voltage can be accurately synchronized even under unbalanced grid voltage conditions with harmonic distortion and DC offset. Finally, simulation results are used to verify the effectiveness of the synchronous control method proposed in this paper.

The weak grid with high line impedance and low short-circuit capacity leads to a large number of background harmonics and fluctuation of the grid voltage, and it is difficult for the inverter to synchronize with the weak grid. In order to overcome these problems, a novel fast and accurate frequency-locked loop(FLL) method is proposed. Firstly, an improved quadrature signal generator(QSG) is utilized to effectively eliminate the impact of the DC offset on the accuracy of frequency and phase detection. Secondly, low-pass filters(LPF) with specific harmonic filtering capabilities are used so that the system has good dynamic response speed and anti-interference ability. Finally, the grid frequency is calculated by the Teager energy operator(TEO) algorithm without any parameter adjustment, and use Park transform to obtain grid phase information. It can be known from the modeling that the FLL system is a type I system with an excellent dynamic response speed and stability margin. The simulation results verify the effectiveness of the proposed method when the grid voltage fluctuates or distorted.

Semantic segmentation of remote sensing ship targets is one of the most challenging works in image processing, especially for small and multi‐scale ship target detection. To solve these problems, an efficient method based on convolutional neural networks (CNN) to detect ship targets is proposed. This method introduces the attention model to the network to enhance the characteristics of small targets and combines atrous convolution with traditional CNN to increase the receptive field. To preserve the information lost by pooling, the proposed method uses the passthrough layer method to retain more features and concatenate the high‐ and low‐resolution features. To verify the effectiveness of the method proposed in this study, the performance was evaluated by using precision, recall, F1‐Score, mean intersection‐over‐union (IoU), and pixel accuracy measurements. These performances are all higher than the traditional semantic segmentation network SegNet. Mean IoU increases to 0.783 and pixel accuracy increases to 0.935. This method can conclusively identify ship targets in remote sensing images and has a certain reference value for remote sensing target detection.

For intelligent vehicles, multi-sensor fusion is of great importance to perceive traffic environment with high accuracy and robustness. In this paper, we propose two effective methods, i.e. spatio-temporal evidence generating and independent vision channel, to improve multi-sensor track-level fusion for vehicle environmental perception. The spatio-temporal evidence includes instantaneous evidence, tracking evidence and tracks matching evidence to improve existence fusion. Independent vision channel leverages the specific advantage of vision processing on object recognition to improve classification fusion. The proposed methods are evaluated by using the multi-sensor dataset collected from real traffic environment. Experimental results demonstrate that the proposed methods can significantly improve the multi-sensor track-level fusion in terms of both detection accuracy and classification accuracy.