Liquid biopsy is an emerging in vitro diagnostic technology in recent years, which can detect and analyze various life information carried by screening tumor cells from the peripheral blood of patients. It is convenient, less invasive and has high detection accuracy. Circulating tumor cells (CTCs), as one of the important biomarkers of liquid biopsy, play an important role in early diagnosis, efficacy evaluation and prognosis monitoring of cancer disease. However, the level of CTCs in blood is very low, usually only 1~10 CTCs per milliliter of human whole blood, and there are tens of billions of blood cells. Therefore, the enrichment and screening of CTCs are key problems in liquid biopsy, and the analysis of the life information carried by CTCs is very significant. This paper summarizes the classical separation methods and focuses on the detection techniques of CTCs.

Plasmonics provide us with an opportunity to achieve highly localized optical field in subwavelength scale and manipulate light artificially. Combining it with nonlinear optics gives birth to a new research field of nonlinear plasmonics. Among them, plasmonic second-harmonic generation (SHG) is of particular interest in both theory and application owing to its extraordinary characteristics. In this paper, we introduce origin of second harmonic generation from plasmonic structures based on the hydrodynamic model, and summarize the development and application of them, including enhancement of SHG from plasmonic resonances, enhanced SHG based on magnetic Lorentz force, manipulations of SHG radiation direction and frequency, and their applications in structure detection, sensing, and imaging. As a close remark, we provided an outlook for emerging platforms that are benefiting the plasmonic SHG.

The metasurfaces can be regarded as a two-dimensional metamaterial, which can control the light field distribution in the surface domain. The research of active metasurfaces as the new design requirements has become the hot research field. Therefore, the precise modulations of the phase, polarization, intensity and other dimensions of the light field by using the active metasurfaces, as well as the modulations of the interactions between the light field and matters, are the frontier research of the integrated development of basic physics and multi-disciplinary science. In this paper, we systematically introduce the latest research progress in the multi-dimensional modulations and applications of light field such as structural color, polarization conversion, perfect absorption and image processing. Finally, some research frontiers and potential applications of active metasurface are prospected.

Graphene is a new member of the carbon nanomaterial family, and it has many special properties in electronics and optics. Moreover, the performance of the fiber-optic sensors can be improved if their surfaces are modified with a layer of graphene. However, the conventional methods, including liquid phase transfer and vapor deposition methods, for modifying the fiber surface with a layer of graphene have some drawbacks, such as high-cost, difficult implementation, and generating toxic gas. In this paper, a new method for modifying the fiber surface with graphene is proposed, which enables to extract graphene fragments from the graphene dispersion waterand form an accumulation layer of graphene over the fiber surface. Experimental results demonstrate that the accumulation horizon over the surface of a tilted fiber Bragg grating (TFBG) is stable, and it can improve the sensitivity of the TFBG sensor for refractive index measurement with 28%. Furthermore, the experimental results also show that the TFBG with a graphene layer can be made to be a glucose sensor, through attaching pyrene-1-boronic acidon the graphene, enabling a measurement of glucose concentration.

Micro/nano structures with multilayer features have been widely applied in the fields of display, semiconductor, MEMS. The existing methods based on phase analysis of interferogram including white-light interferometry and spectral interferometry utilize the phase information of interferogram formed by multilayer reflected light and reference light to achieve multilayer three-dimensional reconstruction. However, when the number of dielectric layers is large, the interference signals of adjacent dielectric layers will interfere each other, which seriously affect the measurement accuracy. Also, there are various limitations in the measurement of complex structures with large steps and high curvatures. The measurement method based on the modulation evaluation of structured light is a unique way in the field of complex micro-nano structure measurement due to its non-contact, high precision, high adaptability. The principle of structured light to synchronously realize the three-dimensional reconstruction of multi-layer structure is applied in this paper. By imaging different dielectric layers, the modulation degree distribution of different layer structures is analyzed, and finally the multi-layer structure and its thickness distribution are synchronously measured in three dimensions.

Starting from semiconductor technologies, low dimensional nanomaterials and heterostructured composites have attracted extensive attention due to their unique structures, excellent properties and broad potential applications. The appearance of transition metal dichalcogenide family of materials, brings new solutions for engineering bandgap, which cannot be achieved by conventional 2D graphene materials. In this paper, synthesis techniques of heterostructures with MoS2 and metal particles, low dimensional carbon materials, MXenes are discussed. Then, the applications of MoS2 materials and their heterojunction structures in the field of new energy vehicle and optoelectronics are reviewed. Finally, the future application of MoS2 materials and their Van der Waals heterostructures and plasmonic techniques in the optoelectronics field is briefly discussed.

Multi-core transmission system is expected to solve the problem of high-capacity data transmission in the future. However, in the whole communication system, the relay of multi-core signal faces many difficulties, such as the imbalance of channel gain, the beam splitting coupling of multi-core pump light and so on. The uniform coupling between pump light and gain multi-core fiber core determines the efficiency of multi-core fiber gain, the balance of corresponding core gain and the utilization of system energy efficiency, which has become one of the key problems of multi-core system amplification. Based on this, this paper designs a metalens coupling device based on the end face of the optical fiber. By properly optimizing the distribution of the metalens structure, the pump light can focus on the focal plane of the coupled multi-core optical fiber. Compared with the space optical coupling optical fiber system, the waste of pump light is reduced. The theoretical design and numerical simulation results show that the light in the optical fiber cladding can be focused on the end face of the optical fiber, so that the pump light can be shunted and focused on the core, and the total light intensity utilization rate is more than 90%, which provides a solution for the subsequent active optical fiber to realize the core pump.

In order to overcome the limitation of the traditional terahertz tomography system which can only be applied to flat samples, a robot-based terahertz tomography system is built by combining an industrial robot and a reflective terahertz time-domain spectroscopy system. Using the robot graphical offline programming technology to plan the scanning path, it can be ensured that the terahertz wave is perpendicular incident on the sample surface and the maximum reflective signal can be received. The deconvolution algorithm is used to process the terahertz time domain signal to improve the time resolution capability of the system. The rubber material with the shape of a curved surface is selected as the sample, and the normal vector of each sampling point on the sample surface is calculated by the principal component analysis method, and the three-dimensional reconstruction of the sample surface and internal structure is carried out with the time-of-flight imaging technology, which verifies the feasibility of the system.

Layered two dimensional transition metal sulfides are a kind of important two dimensional layered semiconductor materials. MoTe2 has attracted much attention because of its small band gap and high carrier mobility. In this paper, the optical pump-wide spectrum terahertz time-domain spectroscopy system is used to study the wide spectrum terahertz transmission spectrum of MoTe2 thin films with few layers, the dynamic process of photogenerated carriers at picosecond scale and the change of photoconductivity. The transmittance spectra of MoTe2 thin films with thin layers in the range of 0.2~7.2 THz are measured by using the method of gallium phosphide crystal detection. The absorption peaks at 3.6, 4.6, 6.4 and 7 THz are found. The lifetime of photogenerated carriers in MoTe2 thin films is about 1.6 ps. The terahertz spectra of MoTe2 thin films are measured on picosecond time scale by using 800 nm pulsed light excitation. On this basis, the real and imaginary parts of the variation of photoconductivity under different pump-probe time differences are further studied. It is found that the real part of the conductivity change decreases from about 600 Ω-1cm-1 to 300 Ω-1cm-1 with the increase of the delay time, and the imaginary part of the conductivity change about 200 Ω-1cm-1 does not change significantly with the pump-probe time difference. This result indicates that the thin MoTe2 films with few layers have higher conductivity and longer exciton lifetime.

The spiking neural network, which is called the third generation of artificial neural network, is the best neuromorphic algorithm to imitate human brain. Compared with the traditional artificial neural networks, the spiking neural networks are hardware-friendly and highly energy-efficient. Furthermore, the optical spiking neural networks are high speed, low energy consumption, low delay, high parallelism, and anti-electromagnetic interference, compared with the electronic spiking neural networks. The origin of optical spiking neural networks are introduced in this paper. The research advances, existing problems and challenges are illustrated from the aspect of the optical spiking neurons, framework of optical spiking neural networks and the corresponding training algorithms. The perspectives of the optical spiking neural networks are also analyzed.

Large-scale equipment manufacturing, spacecraft formation flying in space, and other fields are facing large-range, high-speed and high-precision absolute distance measurement requirements. The traditional interferometric phase method and time-of-flight method can no longer meet the measurement requirements. With the advent of optical frequency combs, the high-resolution characteristics of the optical frequency combs in the time and frequency domains can be used for ranging. After solving the problem of range expansion, the dual optical comb ranging has become an optical ranging method that integrates large-scale, fast measurement, and high precision. The article describes the development process of optical comb ranging, expounds the problems and solutions encountered in the development process of dual-comb ranging, such as range expansion, noise suppression, air refractive index compensation, and light source miniaturization. It provides theoretical guidance for the improvement of dual optical comb ranging system.

Beam shifts refer to the small deviation of the reflection point or transmission point in the process of reflection or transmission of the beam, which violates the prediction of geometric optics, including Goos-H nchen, Imbert-Fedorv shifts, Goos-H nchen angular shifts and Imbert-Fedorv Angular shifts. The research on beam shifts continues to develop with the advancement of science and technology, which not only enriches our understanding of light fluctuations and quantum nature, but also further deepens our understanding of the internal physical mechanism of new materials, thereby promoting us to the unknown to explore the physical world. This paper presents the discovery of beam shifts, its theoretical explanation and a summary of its research progress.

Because of advantages such as simple structure, ease of manufacture and high sensitivity, all-fiber interferometer has been applied in many sensing fields in recent years. In this paper, an all-fiber interferometer is proposed, which is based on a multimode fiber spliced between two single mode fibers with a certain of transverse offset. And it is applied for twist sensing. Experimental results show that the sensitivities are, through intensity interrogation method, -0.225 dB/(°·m) and 0.148 dB/(°·m) for measuring twist in clockwise and anti-clockwise directions, respectively. The sensitivities are, through wavelength interrogation method, -0.259 nm/(°·m) and 0.222 nm/(°·m) in twist directions of clockwise and anti-clockwise, respectively. The linearity, either using intensity or wavelength interrogations, is higher than 97% for the twist measurement from 0 to 180°. The proposed twist sensor has advantages of simple structure and good performance, making it a good choice for engineering applications, such as shape sensing and structural health monitoring. Furthermore, it can also be used for other measurements like temperatureand curvature.

With the growing demand for electricity from all industries, the scale of China's power grid is also rising. The historical data of online operation of the power grid has increased explosively, and it is a great challenge to effectively utilize the historical data to achieve stable assessment of the online operation status of the power grid system in the context of big energy data. In the context of big energy data, this paper realizes automatic mining of grid dispatch operation characteristics and rules by using data mining methods and establishing association analysis models, and finally uses five months of online historical data from a provincial dispatch center for offline analysis. Based on the discovered grid operation rules, it is shown that the model established in this paper can improve the online analysis of the grid, and the effectiveness of the proposed method is verified.

Fault identification, classification and location of distribution network are of great significance to clear the faults quickly and ensure the safe operation of the grid. This paper is based on the big data-driven method, and proposes two algorithms: choosing the least PMU measurements and choosing the best buses from the areas, which are based on evaluation of categorical feature and have differences in their buses selection strategies. Using decision tree classifier and random forest ensemble classifier to verify the performance of the algorithms, Algorithm 1 achieves a maximum accuracy of 91% when using only 7% of the buses, and Algorithm 2 achieves a maximum accuracy of 85% when using data from 16% of the nodes. The experimental results show that the algorithms proposed in this paper can classify and locate feeder faults without necessary measurements of all nodes. In the case of low observability, the propossed methods are achievable for fault identification and location, with the advantages of low number of buses measurements and high accuracy.

Power grid load forecasting is of great significance to ensure power quality. A method of using time series analysis to predict the load change of power grid in the next 72 hours is proposed in this paper, which enables the power grid dispatching department to understand the change trend of user load in the short term, so as to adopt the corresponding dispatching strategy to ensure the power quality. In addition, a statistical study on the load category of users is also made. Thus, the load type that accounts for a large proportion in the peak load period is found out, and users are encouraged to transfer the use of this type of load to non peak hours, which is beneficial to realize the peak shaving of load curve and ensure the safe operation of power grid. Finally, the data collected by the power grid company and the intelligent internet of things system are tested and verified, and the results also show the effectiveness of this method.

The abnormal data of power system can affect the safe and stable operation of power system. Traditional abnormal data detection methods can no longer achieve effective identification and judgment of massive power system operation data. In this paper, a strategy fusion method of grid operation outlier detection is proposed, which combines machine learning algorithm and statistical algorithm to quickly determine the time period when the abnormal data appears through machine learning, and then uses statistical algorithm to effectively judge the grid operation outliers. The proposed method is applied to grid operation data containing a variety of electricity consumption ends, and the experimental results are compared with those of three traditional methods. The experimental results show that the F1-score of the proposed strategy fusion outlier detection method is higher than the other three compared methods, and is significant compared to the other three methods. The method proposed in this paper can identify grid operation anomaly data quickly and effectively, and the effect is significantly higher than the other three traditional methods.

The development of smart grid technology has injected new vitality into the power grid. Through this technology, the power grid is not only integrated to realize the internal communication of the system, but also the control center can monitor the needs of users and send a notice to customers through SMS and e-mail during peak hours to remind users that the power consumption cost will increase. In order to help users better realize the rational distribution of energy consuming equipment, the existing smart grid system is improved and a controller with a single controller to control all loads is added. The controller can detect the peak time demand and reduce the load use by turning off the unnecessary load. The priority is set by the user according to the control strategy algorithm. The algorithm schedules the load use by creating multiple possible plan vectors for the user, so as to never increase the demand. In addition, the controller can also monitor the use of electric energy and create an important sequence between loads, so as to realize the intelligent control on the user side.

Permanent magnet synchronous motor control is a common control problem in practical industrial production. Using the development mode of fast control prototype, the modeling and simulation of permanent magnet synchronous motor control is completed in MATLAB, and the simulation program is transformed into an embedded executable program through automatic code generation by using the relevant hardware support package of MATLAB, and loaded into Xilinx series ZedBoard. The real-time speed control of permanent magnet synchronous motor in open and closed loop is realized. The difference of speed response waveform between actual motor and virtual motor is very small, and the speed error in closed-loop state is less than 0.1°/s. The development method has the characteristics of less programming workload, simple man-machine interaction and real-time monitoring.