Since 2002, we have been carrying out programs in developing Saturated Iron-core Superconducting Fault Current Limiters (SISFCLs). Innovative technologies were adopted in designing the iron-core and the magnetization circuit, making our SISFCL more efficient, useful, and reliable. With our technology, a 35 kV SISFCL with dry-type electrical insulation and a 220 kV SISFCL with oil electrical insulation were manufactured and installed for live-grid operation in 2008 and in 2012 respectively. Operation data and test results show that the performances of the devices meet design expectations and utility's needs. Our SISFCL is applicable for both transmission and distribution networks and can satisfy a wide range of fault current limiting impedance requirements.

As the basic tools for neuroscience research, invasive neural recording devices can obtain high-resolution neuronal activity signals through electrodes connected to the subject’s brain. Existing wireless neural recording devices are large in size or need external large-scale equipment for wireless power supply, which limits their application. Here, we developed an ultra-low-noise, low power and miniaturized dual-channel wireless neural recording microsystem. With the full-differential front-end structure of the dual operational amplifiers (op-amps), the noise level and power consumption are notably reduced. The hierarchical microassembly technology, which integrates wafer-level packaged op-amps and the miniaturized Bluetooth module, dramatically reduces the size of the wireless neural recording microsystem. The microsystem shows a less than 100 nV/$\sqrt{\mathrm{Hz}}$ ultra-low noise level, about 10 mW low power consumption, and 9 × 7 × 5 mm${}^3$ small size. The neural recording ability was then demonstrated in saline and a chronic rat model. Because of its miniaturization, it can be applied to freely behaving small animals, such as rats. Its features of ultra-low noise and high bandwidth are conducive to low-amplitude neural signal recording, which may help advance neuroscientific discovery.

Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty. A fast and accurate spike sorting approach named HTsort is proposed for high-density multi-electrode arrays in this paper. Several improvements have been introduced to the traditional pipeline that is composed of threshold detection and clustering method. First, the divide-and-conquer method is employed to utilize electrode spatial information to achieve pre-clustering. Second, the clustering method HDBSCAN (hierarchical density-based spatial clustering of applications with noise) is used to classify spikes and detect overlapping events (multiple spikes firing simultaneously). Third, the template merging method is used to merge redundant exported templates according to the template similarity and the spatial distribution of electrodes. Finally, the template matching method is used to resolve overlapping events. Our approach is validated on simulation data constructed by ourselves and publicly available data and compared to other state-of-the-art spike sorters. We found that the proposed HTsort has a more favorable trade-off between accuracy and time consumption. Compared with MountainSort and SpykingCircus, the time consumption is reduced by at least 40% when the number of electrodes is 64 and below. Compared with HerdingSpikes, the classification accuracy can typically improve by more than 10%. Meanwhile, HTsort exhibits stronger robustness against background noise than other sorters. Our more sophisticated spike sorter would facilitate neurophysiologists to complete spike sorting more quickly and accurately.

This paper presents a novel closed-loop control strategy that can be applied to quadrotor trajectory tracking to improve the control accuracy and stability. Inspired by intelligent creatures, the composite control strategy combines the open-loop mode and closed-loop control to overcome some disadvantages of oscillation and overshoot under feedback control mechanisms. The open-loop mode was realized by the quadrotor maneuverability model, which is proposed to build the bridge between the flight control commands and the desired acceleration. Then, a real-time control and acquisition platform with high-precision positioning, low-delay communication, and asynchronous distribution was built to collect the UAV’s real-time flight trajectory data for training the maneuverability model and to control the quadrotors asynchronously. Based on the platform and the trained model, the composite control mode on the closed-loop of the Proportional–Integral–Derivative (PID) control and the open-loop of the Long Short-Term Memory (LSTM) maneuverability model was implemented and verified. The experimental results show that the open-loop control strategy has a better advantage in the unity of time and space over the existing techniques for improving the UAV dynamic obstacle avoidance capability.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely employed to examine brain functional connectivity (FC) alterations in various neurological disorders. At present, various computational methods have been proposed to estimate connectivity strength between different brain regions, as the edge weight of FC networks. However, little is known about which model is more sensitive to Alzheimer’s disease (AD) progression. This study comparatively characterized topological properties of rs-FC networks constructed with Pearson correlation (PC), dynamic time warping (DTW), and group information guided independent component analysis (GIG-ICA), aimed at investigating the sensitivity and effectivity of these methods in differentiating AD stages. A total of 54 subjects from Alzheimer’s Disease Neuroimaging Initiative (ANDI) database, divided into healthy control (HC), mild cognition impairment (MCI), and AD groups, were included in this study. Network-level (global efficiency and characteristic path length) and nodal (clustering coefficient) metrics were used to capture groupwise difference across HC, MCI, and AD groups. The results showed that almost no significant differences were found according to global efficiency and characteristic path length. However, in terms of clustering coefficient, 52 brain parcels sensitive to AD progression were identified in rs-FC networks built with GIG-ICA, much more than PC (6 parcels) and DTW (3 parcels). This indicates that GIG-ICA is more sensitive to AD progression than PC and DTW. The findings also confirmed that the AD-linked FC alterations mostly appeared in temporal, cingulate, and angular areas, which might contribute to clinical diagnosis of AD. Overall, this study provides insights into the topological properties of rs-FC networks over AD progression, suggesting that FC strength estimation of FC networks cannot be neglected in AD-related graph analysis.

Flexible capacitive pressure sensors are widely used in many applications due to their simple structure and low cost. However, high sensitivity and wide linear range are often incompatible. To solve this problem, five kind of capacitance structures based on interdigital microneedle array electrode (IMAE) with different needle spacing and needle height are simulated and fabricated in this work. The two face to face bonded microneedle arrays (MNAs) in the IMAE are fabricated by template casting with PDMS as substrate. Silver nanowires (AgNWs) dispersion liquids with different concentration are spin-coated on the MNA to serve as conductive electrode. PDMS doped with AgNWs is used as dielectric adhesive layer to increase the dielectric constant. The AgNWs coated IMAE can increase the effective electrode area and deformation capacity dramatically when compared to the flat metal electrode. The optimized IMAE sensor with needle height of 600 μm and needle spacing of 800 μm makes a maximal sensitivity of 0.059 kPa ^-1 from 0 to 60 kPa (linearity of 6.57%). The linear measuring range of the IMAE sensor is much wider than that of the sensor based on the flat electrode. By introducing NaHCO3 induced air gap into the dielectric layer, the maximum sensitivity can be improved to 0.203 kPa ^-1 from 0 to 3 kPa. The IMAE sensor has a fast response time (125/250 ms) and can maintain performance without degradation through 2,500 test cycles. It shows good performance in wide-range pressure monitoring such as micro-mass (160 mg) detection, pulse monitor, twist and gesture measurement.