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Terahertz, located between microwave and infrared, is in the intersection of electronics and photonics. It has the advantages of safety, fingerprint spectrum, broadband and penetrability. It can be widely used in biomedical, non-destructive testing, radar, communication, military, aerospace and other fields. Terahertz detectors are an integral part of terahertz applications, but due to limitations in material properties, terahertz detectors are slow to develop. As a new two-dimensional material, 3D graphene has an ultra-high absorption in terahertz, which is very suitable as a terahertz detector. Here we design and fabricated 3D grapheme THz detectors with low NEP, high responsivities and fast response speed. The results show that these kind of photothermoelectric detectors are expected to be a new generation of high performance detectors in terahertz region.

The lower extremity rehabilitation exoskeleton provides a priceless opportunity for paraplegics to stand up and walk normally. In this paper, a framework for human-exoskeleton interaction with sEMG interface and electrotactile feedback is presented. Firstly, the exoskeleton recognises human motion intention through the Long Short Term Memory (LSTM) Neural Network based on surface electromyogram (sEMG). Secondly, electrotactile based on spatiotemporal coding scheme feeds back the exoskeleton states to the sensory skin, as a compensation for paraplegic's absent proprioception in lower limbs. Thirdly, physiological information, i.e. muscle fatigue, of arms is monitored and quantified with sEMG. Three healthy subjects participate the experiments including the motion intention recognition experiment, electrotactile perception experiment and online exoskeleton experiment. The online exoskeleton experiments demonstrate the feasibility and efficiency of the proposed framework. Furthermore, it is proved experimentally that the performance of collaborative control in rehabilitation human-exoskeleton interaction can be facilitated via the framework.

Motor imagery brain-computer interface system based on Electroencephalogram (EEG) is an effective way to help the disabled recover part of their motor abilities. However, decoding the movement intention contained in the EEG signal accurately presents many challenges. In this paper, we propose a time-frequency decomposition-based weighted ensemble learning (TFDWEL) method, which aims to improve the classification performance of motor imagery EEG signals. The TFDWEL method divides the EEG signal into multiple subsets, and uses four time-frequency processing methods to extract the time-frequency sub-bands of each subset. Then the feature extraction model and classifier model of each subset trained by the common spatial pattern (CSP) algorithm and the support vector machine (SVM) algorithm are used to build a set of base learners. The least square error estimation method is used to learn the weight of each base learner, and finally the weighted summation method is used to obtain the final decision. The classification performance of the TFDWEL method is evaluated on the BCI Competition IV Data Set 2b, and the results show that the classification accuracy of 81.58% can be obtained. Superior classification performance indicates that the TFDWEL method can be used in further research to help the rehabilitation of the disabled.

Air quality has been one of the problems of global changes in recent years. Therefore, taking the main urban districts of Chongqing as the research area, we analyzed the spatial-temporal pattern and the driving factors by inverse distance weighting method and geographically weighted regression model. The results showed that: (1) The air quality of the research area was best in summer and worst in winter. And the air quality in the south is generally better than that in the north. (2) The temporal variation of air quality is significantly correlated with precipitation, air pressure and wind speed, and precipitation has the greatest influence on air quality; (3) The spatial distribution of air quality shows a strong trend of spatial aggregation and the geographically weighted regression model constructed in the study has certain predictive significance for air quality.

Hand gesture recognition are widely applied in game industry and rehabilitation engineering. Some recognition methods based on physiological data are attracting attention. In this paper, a novel method for dynamic hand gesture recognition based on sounds of wrist tendon is proposed. Bone conduction sensors which automatically filter the ambient noise is utilized to collect the wrist tendon sounds of three types of dynamic hand gestures. Continuous Wavelet Transform(CWT) is utilized extract the features of sounds and Support Vector Machine(SVM) is utilized to train the classifier model. Five healthy subjects participated the experiment and the average test accuracy of three classes achieves 92.33%. The proposed method provides an effective perspective for hand gesture recognition.

The advent of myoelectric control schemes provides promising chances for locomotion empowerment and restoration of those with disabilities. Despite substantial efforts have been made into advancing sEMG-based motion recognition, it may be a little tricky to determine appropriate muscles and features for people with muscle disorders or different muscle use preferences. To mitigate it, an advantageous sEMG channel and feature selection method based on ReliefF algorithm was proposed. Related experiments were conducted on a eight able-bodied subject database to showcase the feasibility and efficiency of the proposed approach, that is, considerably high classification performance was maintained with the original feature set reduced by more than half. Ulteriorly, we also investigated the influences of different number of neighbors or features on classification accuracy for ascertaining the optimal values. The strengths of our proposed method lie in not only customizing channel and feature selection for individual users, but also offering preliminary insight for a general mapping mechanism between human muscles and corresponding motions.

In this paper, an improved motion planning scheme is proposed for surgical robot control with multiple active constraints, including joint constraints, joint velocity constraints and remote center of motion constraints. It introduces an improved recurrent neural network (RNN) to optimize the online motion planning respect to multiple constraints. The demonstrated surgical operation trajectory is derived using teaching by demonstration. An improved motion planning scheme using the novel recurrent neural network is then designed to achieve the accurate task tracking under the multiple constraints. The general quadratic performance index is adopted to represent the constraints. Finally, the effectiveness of the proposed algorithm is demonstrated using KUKA LWR4+ robot in a lab setup environment.

Most of the human spinal cord injuries result in lower-limb disability. To integrate the brain’s holistic motion intention with sEMG’s control reliability for lower-limb motion neurorehabilitation with the exoskeleton, an ingenious hierarchical fusion strategy based on EEG and sEMG for ambulation with obstacle crossing task was proposed. Specifically, EEG delivers senior task-specific commands to execute refined locomotion while the specific implementation is determined by sEMG. The practical feasibility of ridge regression-based EEG decoding was proved firstly via offline and online methods involving 3 subjects, with subsequent online verification of the proposed strategy yielding higher than 85% of average accuracy. Furthermore, one subject was invited to wear the exoskeleton for the practical application of the strategy.

A series of previous work has found that the environmental constraint (EC), which is the natural result of the contact of the robot and the interacting objects, is immensely helpful for the realization of high-precision robotic tasks. However, due to the existence of multifarious errors, such as mechanical error, modeling error and sensing error, there would be discrepancy between the actual constraints and the ideal models. In such case, it is hard to realize manipulation with EC-based strategies. Inspired by human, a preliminary framework which aims at the integration of the coarse sensing information and the environmental constraints is proposed for robotic manipulation. By mapping the sensing information into the new space, where the environmental constraint can be formally described, the region that integrates the sensing information and the environmental constraint is constructed and the conditions to achieve high-precision manipulation are derived. Based on the conditions, a motion planning strategy is proposed to achieve the required task. The effectiveness of this strategy is verified by case studies.

The motor imagery (MI) based on electroencephalogram (EEG) via non-invasive Brain Computer Interface (BCI) is a promising interactive method to provide effective communication between the disabled and rehabilitation robotic device. Interactive efficiency of MI-based BCI largely relies on the performance of subject’s motor imagery. In this work, we proposed a simple and effective paradigm based on visual and haptic modalities for a high-quality initialized and calibrated MI-BCI model. Five healthy male subjects participated in motor imagery experiments with two paradigms. The proposed paradigm utilized mixed cues based on the visual and haptic modalities where the haptic cues were provided by the mini vibration motors, and a traditional paradigm utilized the ordinary arrow visual cues. The analysis of the topographic maps was conducted to illustrate the differences of the EEG in both paradigms. The average classification accuracy of motor imagery in the proposed paradigm improved about 14% compared to the traditional paradigm. Furthermore, a subject completed the qualitative verification experiment of the trained model on a rehabilitation lower-limb exoskeleton which proved the feasibility of the proposed paradigm.

The adaptive modulation can improve the reliability of the underwater acoustic (UWA) communication systems. However, the large propagation delay of the UWA channel causes a lag of the channel state information (CSI) feedback. In order to overcome this problem, this paper studies the prediction algorithm of UWA time-varying sparse channel based on orthogonal frequency division multiplexing (OFDM) communication system/ The prediction is divided into two parts: prediction of channel amplitude gain and prediction of multipath delay, based on recursive least squares (RLS) algorithm To verify the performance of the prediction algorithm, the data of the sea experiment done in the South China Sea in 2014 was used for analysis. The results prove the feasibility of time-varying sparse channel prediction and the advantage of the prediction algorithm compared to the original algorithm

In this paper we experimentally realize high-order photonic topological insulator as a two-dimensional triangular distorted Kagome lattice that supports two types of topological states: 1D topological edge states and 0D high-order topological states localized at the corners. Using near-field scanning technique we demonstrate both topological edge states and topological corner state in microwave frequency spectrum. The experimental results are in perfect agreement with numerical simulations of finite structure.

Dementia commonly occurs in the elderly and often manifests as cognitive impairments in the sense of orientation, memory and learning but the cause of this disorder is unknown. In recent years, scholars or therapists have engaged in various medical rehabilitation studies that have shifted the focus to the field of virtual reality (VR) due to the rapid development of this and other technologies. The rehabilitation system proposed in this study is solely for patients with Alzheimer’s disease. This system is used to evaluate patients by referring to the Efficient Self-Assessment of Diagnostic Scale for Alzheimer’s Disease and Cognitive Function, thereby providing targeted and scientific guidance for follow-up treatment and rehabilitation. Experimental results prove that the proposed rehabilitation system is easily accepted by the elderly, and the elderly demonstrate a relaxed and pleasant disposition during the testing process. It overcomes the shortcomings of traditional paper–pencil cognitive tests. Given its good performance, this system demonstrates a good initial response and has certain research and application prospects. Research on its diagnosis and rehabilitation is still required.

The new human-robot interaction technology based on biological signals is an advanced method which can reflect the wearer’s motion intentions. This paper presents a hybrid control method for biped lower limb exoskeleton robot with the surface electromyography (sEMG) signals. Compared with physical signals, sEMG has a better performance of identifying the intention of user’s movement for the interaction with robots. In this paper, the Fisher Discriminant Analysis (FDA) of motion recognition method is applied to discriminating three-class gait movements of biped robot using sEMG which are collected from human arms. Considering the system’s uncertainties and disturbances, the adaptive sliding mode control strategy is designed to approximate the nonlinear dynamic system of biped robot, which can ensure the robustness capacity and high control precision. The simulation results exhibit the affection of the proposed approach.

Due to high dynamic range, high overlap, low concentricity and limited computation of the driverless intelligent vehicles, it is hard to implement global path planning and local adjustment under multi-objects and multi-obstacle state. To deal with the above problems, this paper presents a fresh idea that is to transfer the main object of intelligent drive from the car to the road and by using dynamic virtual scees and edge computation to construct the smart road which applies the smart car to the road. Based on this, this paper proposes a path planning method that integrates the vision and satellite convolutional neural network for automatic drive system and explores the distortion of visual image and the redundant processing mechanism of automatic drive. What's more, this paper structures the multi-dimensional data compensatory strategies in view of convolutional neural network and boosts the optimal identification of signs in intelligent roads for the intelligent car. Ultimately, this paper is to achieve intelligent global path planning by fusing satellite navigation and vision navigation.

Along with the development of the programmable logic device, the FPGA application handles to provide a new solution for high speed digital video image processing. It makes full use of the parallel and flexibility of FPGA, improves the speed of image processing, not only reduces the cost, but also makes the real-time image processing to obtain a satisfactory effect. The video image encryption system is based on Altera DE2–70 development platform. The system mainly consists of image acquisition input module, image encryption processing module and an image display output module. Based on the contrast analysis of Arnold transform and Knight-tour transform these two video image encryption algorithm, selected a complex scrambling encryption algorithm that combines Arnold transform and knight-tour transform to handle encryption and decryption of video image Program and debug the algorithm in the Nios II IDE environment. Through debugging of system hardware module and software program, enables it to achieve the requirements of digital video image encryption processing design. The test results show that, the video encryption system achieved in the DE2–70 development platform is in good reliability and portability, taking up less resource, lower cost and has wider application prospects.