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In order to solve the problem of limited driving speed of piezoelectric actuators when the piezoelectric components are small and the driving frequency is low. This paper proposes a bi-directional piezoelectric stick-slip actuator, which mainly utilizes the combination of flexible drive foot and lever amplification mechanism to realize the displacement amplification function, thus improving the driving performance. Firstly describe the structure and working principle of the proposed actuator, and carries out theoretical analysis and mathematical modeling of the flexible stator. Then the driving process of the proposed actuator is simulated and analyzed with COMSOL Multiphysics simulation software, and the feasibility of the driver structure is preliminarily verified. Finally, establish an experimental system to test the basic characteristics of the prototype. The experimental results confirm that under the piezoelectric stacks elongation of $5\mu $ m, the driving frequency of 610Hz, the maximum drive speed of the actuator is 11.21mm/s. The maximum load capacity is 0.78N and the forward and backward displacement deviation within 10 cycles is $0.99\mu $ m under the locking displacement of $10\mu $ m. The proposed stick-slip actuator is capable of realizing a large driving speed when the elongations of the piezoelectric stacks are small and the driving frequency is low.

The InP high-electron-mobility transistor (HEMT) is employed in cryogenic low-noise amplifiers (LNAs) for the readout of faint microwave signals in quantum computing. The performance of such LNAs is ultimately limited by the properties of the active $\mathrm {In_{x}Ga_{1-x}As}$ channel in the InP HEMT. In this study, we have investigated the noise performance of 100-nm gate-length InP HEMTs used in cryogenic LNAs for amplification of qubits. The channel indium content in the InP HEMTs was 53, 60 and 70%. Hall measurements of the epitaxial materials and dc characterization of the InP HEMTs confirmed the superior transport properties of the channel structures. An indirect method involving an LNA and small-signal noise modeling was used for extracting the channel noise with high accuracy. Under noise-optimized bias, we observed that the 60% indium channel InP HEMT exhibited the lowest drain noise temperature. The difference in LNA noise temperature among InP HEMTs became more pronounced with decreasing drain voltage and current. An average noise temperature and average gain of 3.3 K and 21 dB, respectively, for a 4–8 GHz three-stage hybrid cryogenic LNA using 60% indium channel InP HEMTs was measured at a dc power consumption of $108 ~\mu \text{W}$ . To the best of the authors’ knowledge, this is a new state-of-the-art for a C-band LNA operating below 1 mW. The higher drain noise temperature observed for 53 and 70% indium channels InP HEMTs can be attributed to a combination of thermal noise in the channel and real-space transfer of electrons from the channel to the barrier. This report gives experimental evidence of an optimum channel indium content in the InP HEMT used in LNAs for qubit amplification.

Accurate water extraction from urban remote sensing images holds great significance in assisting the formulation of river and lake management policies and ensuring the sustainable development of urban water resources. However, urban high-resolution remote sensing images encompass complex spatial and semantic information, which leads to disparities between the extracted water body features based on local and global information, consequently affecting the accuracy of urban water extraction. To tackle this issue, an attention-based encoder–decoder network was proposed. In this network, the backbone employing atrous convolution (AC) facilitated the acquisition of low-level and high-level features of urban remote sensing images at various scales. Integrated with the attention mechanism, the encoder–decoder structure extracted global features in both the spatial and channel domains. Subsequently, these two types of features were merged to yield the urban water segmentation. Moreover, considering both intersection over union and class weights, a joint loss function (JLF) was introduced to further enhance the accuracy of urban water extraction. Experimental results demonstrated the strong performance of the proposed method on both GID and LoveDA datasets.

Manipulation of light-matter interactions has great potential applications in optical modulators and optical switches. However, the strong coupling of anapole-cavity modes has not been involved yet. Here, we firstly propose a simple Si-based metasurface hybrid system to realize tunable strong coupling of anapole-Fabry-Pérot (FP) cavity via inserting a Si square nanocubes metasurface inside an FP nanocavity. A remarkable spectral splitting behavior is achieved with large Rabi splitting of 192 meV due to the strong resonant coupling between the anapole and FP cavity modes. The induced two hybrid energy states are respectively dominated by the anapole mode and cavity mode and can be tuned by the length of FP nanocavity and the thickness of SiO2 spacer, respectively. This Si-based metasurface system opens an avenue for designing optoelectronic devices that can manipulate light-matter interactions at nanoscales.

Upper limb tremor is a prominent symptom of both Parkinson’s disease and essential tremor. Its kinematic parameters overlap substantially for these two pathological conditions, thus leading to high rate of misdiagnosis, especially for community doctors. Several groups have proposed various methods for improving differential diagnosis. These prior studies have attempted to identify better kinematic parameters, however they have mainly focused on single limb features including tremor intensity, tremor frequency, and tremor variability. In this paper, we propose a wearable system for multi-segment assessment of upper limb tremor and differential diagnosis of Parkinson’s disease versus essential tremor. The proposed system collected tremor data from both wrist and fingers simultaneously. From this data, we extracted multi-segment features in the form of phase relationships between limb segments. Using support vector machine classifiers, we then performed differential diagnosis from the extracted features. We evaluated the performance of the proposed system on 19 Parkinson’s disease patients and 12 essential tremor patients. Moreover, we also assessed the performance cost associated with reducing task load and sensor array size. The proposed system reached perfect accuracy in leave-one-out cross validation. Task reduction and sensor array reduction were associated with penalties of 2% and 9-10% respectively. The results demonstrated that the proposed system could be simplified for clinical applications, and successfully applied to the differential diagnosis of Parkinson’s disease versus essential tremor in real-world setting.

The finger tapping test is a widely-used and important examination in the Movement Disorder Society Clinical Diagnosis for Parkinson’s Disease. However, finger tapping motion could be affected by age, medication, and other conditions. As a result, Parkinson’s disease patients with mild sign and healthy people could be rated as similar scores on the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale, making it difficult for community doctors to perform diagnosis. We therefore propose a three-dimensional finger tapping framework to recognize mild PD patients. Specifically, we first derive the three-dimensional finger-tapping motion using a self-designed three-dimensional finger-tapping measurement system. We then propose a three-dimensional finger-tapping segmentation algorithm to segment three-dimensional finger tapping motion. We next extract three-dimensional pattern features of motor coordination, imbalance impairment, and entropy. We finally adopted the support vector machine as the classifier to recognize PD patients. We evaluated the proposed framework on 49 PD patients and 29 healthy controls and reached an accuracy of 94.9% for the right hand and 89.4% for the left hand. Moreover, the proposed framework reached an accuracy of 95.0% for the right hand and 97.8% for the left hand on 17 mild PD patients and 28 healthy controls who were both rated as 0 or 1 on the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale. The results demonstrated that the proposed framework was less sensitive to traditional features and performed well in recognizing mild PD patients by involving three-dimensional patter features.

Vehicle detection systems play a significant role in intelligent transportation systems (ITSs). At present, vehicle detection is realized either by very costly inductive loops, by video cameras that require good weather conditions and high computing resources, or by recently developed magnetic sensors that only obtain limited detection accuracy in congested traffic conditions. To overcome these challenges, this paper develops a passive radar system that is robust to bad weather conditions, low cost and radio silent for vehicle detection. The device is installed at the side of the road under crowded traffic conditions. In the developed system, cross-correlation (CC) operation is applied to obtain suboptimal detection gain and computational complexity. To decrease the impact of interference that is independently and identically distributed (i.i.d.) across isolated frequency channels and that of the Doppler effect produced by a mobile vehicle, a multichannel joint detection scheme based on multichannel fusion (MCF) is proposed and implemented. Furthermore, to achieve high resolution, an illuminator of opportunity (IO) with wide bandwidth is selected, and the system is then designed to receive a digital video broadcast-satellite (DVB-S) signal with a bandwidth of 37.5 MHz over multiple channels simultaneously. Simulation results show that the proposed MCF achieves better performance than the single-channel-based CC, and the advantage can be enhanced by increasing the number of channels for joint detection. Field test results further verify the novelty of the MCF and the developed passive radar prototype.

High-resolution satellite images contain valuable road semantic information, but the occlusion of vegetation and buildings and the sparse distribution and heterogeneous appearance of roads limit the accuracy of road extraction models. In this article, we propose a novel method for extracting roads using an ensemble learning model with a postprocessing stage. The network weights and biases of our proposed deep learning model are transmitted through the random combination of layers of different submodels during forward and backward propagation. In the gradient descent process, a superior loss function is designed to solve the problem of class imbalance caused by road sparseness, and more attention is given to hard classification samples to extract narrow and covered roads. In addition, we solve road disconnection issues in the results obtained with the neural network by extracting and analyzing the geometric structures and feature points of the roads. Experiments on two challenging datasets of remote sensing imagery show that the proposed method performs better than other models and can extract road information from complex scenes.

When the coils of wireless power transmission system based on a traditional topology is physically offset, the coupling coefficient between the coils will change significantly, which further leads to drastic changes in the output power of the system. This problem limits the application and promotion of wireless power transmission. In order to solve the problem of output power imbalance caused by coils' offset, this paper takes the wireless power transmission system of LCC-S type as an example, and proposes an optimization scheme for anti-offset performance based on the optimization design of compensation topology parameters. This scheme uses two freely adjustable variables K1 and K2 to optimize the design of the compensation network parameters of the wireless power transmission system, so that the system can automatically adjust the transmit current of the primary coil to maintain relatively stable output power. Finally, we build a 120W experimental prototype. When the lateral offset percentage δ of the coil is less than 35%, the maximum fluctuation rate of the output power of the system is kept within 10%, and the efficiency of the whole machine is kept above 81%.

How to select candidate lines to improve the quality and to reduce the computation burden of the transmission expansion planning is an important issue, considering the trend of multi-regional transmission network interconnection. In this paper, a method consisting of five stages to select candidate corridors is proposed, which can provide an optimal candidate set for system planners. In the first and second stage, existing corridors are reinforced to eliminate congestion and to satisfy N-k security criteria. In the third stage, candidate buses are identified to specify the scope of new possible corridors. In the fourth stage, the difference of local marginal price is applied to select new corridors. In the fifth stage, residual possible corridors are checked by using a linear relaxation technique to enhance the performance of the candidate set, and to reduce the required computation time. The proposed method is verified on the revised IEEE 24-bus test system and revised IEEE RTS-96-bus test system.