In practice, since the measurement environments are usually complex, the electromagnetic interference problem has been an important issue for sensor applications, and the fiber sensor can overcome it effectively. this paper proposes a new type of vibration sensor based on fiber Bragg grating (FBG). The vibration sensor with double equal strength cantilever beams structure can further improve the measurement stability. The physical model of FBG vibration sensor was established, and the structural parameters of the double equa-strength cantilever beams were designed and optimized by finite element analysis. Both simulation and experimental tests are included in this paper to illustrate the low-frequency vibration performance of the designed vibration sensor. Among, the sensitivity of the sensor was 0.024 0 pm/(m·s-2）, the natural frequency was 185 Hz, and the linearity was obtained, which further verify the stability and reliability of the sensor vibration frequency measurement.

The whispering gallery mode (WGM) based micro-bottle resonator (MBR) sensor has been proposed and demonstrated for relative humidity measurement by using an agarose gel as a transducer. MBR was fabricated by using a soften- and-compress method to form a 190 μm bulge bottle. The micro-bottle was optically excited by a 3 μm tapered fiber and it exhibits a resonance with a Q factor in an order of 105. The agarose coated MBR produces a good sensing response towards humidity with the sensitivity of 0.107 dB/%RH and linearity of 99.614%. The agarose hydrophilic nature and its changing porosity and refractive index with increasing relative humidity made the coated MBR structure to be more sensitive than the uncoated structure. This humidity sensor has a simple fabrication and is showing good sensitivity, linearity, resolution, response time and operational in a wide humidity range.

The present study reports the titanium dioxide (TiO2) films synthesized from TiCl3 precursor on ITO glass substrates using two electrochemical techniques, namely direct electro-deposition (DE) and pulse electro-deposition (PE). The synthesis potential during the time-on (Ton) period was fixed at -1.5 V. However, the open-circuit potential was applied during the time-off (Toff) period. The effect of the technique of electro-deposition and Toff duration on the properties of TiO2 films and their photoelectron-catalytic activity were investigated. The obtained films were investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-VIS spectrometer, and photocurrent measurement respectively. It is found that the use of the PE technique at different Toff improves the properties of TiO2 films compared to the DE technique. The XRD patterns show the anatase phase with a marked preferential orientation along the (101) direction for all samples. From the SEM analysis, it is seen a significant change from big multigrain agglomerates at DE to a dense film structure and small multigrain agglomerates at different Toff. As the Toff decreases from 3 s to 1 s, the photocurrent response rises and reaches a high value of about 12 mA/cm2. Compared with DE, and under UV light the photocatalytic property of TiO2 film synthesis via PE has been improved in the degradation of methyl orange (MO). Finally, the films deposited at low Toff (Toff =1 s) show a faster degradation of MO.

In the object detection task, how to better deal with small objects is a great challenge. The detection accuracy of small objects greatly affects the final detection performance. Our propose a detection framework WeBox based on weak edges for small object detection in dense scenes, and proposes to train the richer convolutional features (RCF) edges detection network in a weakly supervised way to generate multi-instance proposals. Then through the region proposal network (RPN) network to locate each object in the multi-instance proposals, in order to ensure the effectiveness of the multi-instance proposals, we correspondingly proposed a multi-instance proposals evaluation criterion. Finally, we use faster region-based convolutional neural network (R-CNN) to process WeBox single-instance proposals and fine-tune the final results at the pixel level. The experiments have been carried out on BDCI and TT100K proves that our method maintains high computational efficiency while effectively improving the accuracy of small objects detection.

The current mainstream methods of loop closure detection in visual simultaneous localization and mapping (SLAM) are based on bag-of-words (BoW). However, traditional BoW-based approaches are strongly affected by changes in the appearance of the scene, which leads to poor robustness and low precision. In order to improve the precision and robustness of loop closure detection, a novel approach based on stacked assorted auto-encoder (SAAE) is proposed. The traditional stacked auto-encoder is made up of multiple layers of the same autoencoder. Compared with the visual BoW model, although it can better extract the features of the scene image, the output feature dimension is high. The proposed SAAE is composed of multiple layers of denoising auto-encoder, convolutional auto-encoder and sparse auto- encoder, it uses denoising auto-encoder to improve the robustness of image features, convolutional auto-encoder to preserve the spatial information of the image, and sparse auto-encoder to reduce the dimensionality of image features. It is capable of extracting low to high dimensional features of the scene image and preserving the spatial local characteristics of the image, which makes the output features more robust. The performance of SAAE is evaluated by a comparison study using data from new college dataset and city centre dataset. The methodology proposed in this paper can effectively improve the precision and robustness of loop closure detection in visual SLAM.

Image caption is a high-level task in the area of image understanding, in which most of the models adopt a convolutional neural network (CNN) to extract image features assigning a recurrent neural network (RNN) to generate sentences. Researchers tend to design complex networks with deeper layers to improve the performance of feature extraction in recent years. Increasing the size of the network could obtain features of high quality, but it is not an efficient way in terms of computational cost. A large number of parameters brought by CNN makes the research difficult to apply in human daily life. In order to reduce the information loss of the convolutional process with less cost, we propose a lightweight convolutional neural network, named as Bifurcate-CNN (B-CNN). Furthermore, recent works are devoted to generating captions in English, in this paper, we develop an image caption model that generates descriptions in Chinese. Compared with Inception-v3, the depth of our model is shallower with fewer parameters, and the computational cost is lower. Evaluated on the AI CHALLENGER dataset, we prove that our model can enhance the performance, improving BLEU-4 from 46.1 to 49.9 and CIDEr from 142.5 to 156.6 respectively.

Crowd counting is a challenging task, which is partly due to the multiscale variation and perspective distortion of crowd images. To solve these problems, an improved deep multiscale crowd counting network with perspective awareness was proposed. This network contains two branches. One branch uses the improved ResNet50 network to extract multiscale features, and the other extracts perspective information using a perspective-aware network formed by fully convolutional networks. The proposed network structure improves the counting accuracy when the crowd scale changes, and reduce the influence of perspective distortion. To accommodate various crowd scenarios, data-driven approaches are used to fine-tune the trained convolutional neural networks (CNN) model of the target scenes. The extensive experiments on three public datasets demonstrate the validity and reliability of the proposed method.

In this work, the spectral properties of the photo-induced delayed luminescence (DL) from mesenchymal stem cells (MSCs) and their correlation with cell viability were investigated using the single photon counting combined with band-pass filters. The results show that the DL of MSCs has a broad spectral distribution, which covers from 300 nm to 650 nm at least. The DL spectrum is not evenly distributed, but mainly distributed in the range from 400 nm to 550 nm. In addition, the DL spectral distribution remains stable during the DL decay process. Compared with the DL spectra of MSCs with high viability (>80%), those of MSCs with low viability (<30%) show a significant red-shift, referring to the increase in the proportion of 572—650 nm band and the decrease in the proportions of both 315—436 nm band and 413—500 nm band. Furthermore, the degree of the DL spectral red-shift exhibits a monotonous change as MSCs’ viability decreases, and thus can be used as an important indicator for the cell viability assessment.

A photonic approach is proposed to generate microwave arbitrary waveforms based on the stimulated Brillouin scattering (SBS) effect and the Sagnac loop. The Sagnac loop is utilized to generate the optical signals of ±1st-order and ±3rd-order sidebands. Due to the principle of velocity adaptation, even order optical sidebands are suppressed. Thanks to the SBS effect, the microwave signal consisting of odd order harmonics is generated after the photodetector (PD), which can construct the triangular waveform, the square waveform, or the sawtooth waveform by adjusting the weight of the harmonics.