Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has attracted numerous attention due to its superior per-formance in detecting the weak perturbations along the fiber. Relying on the ...ultra-sensitivity of light phase to the tiny de-formation of optical fiber, Φ-OTDR has been treated as a powerful technique with a wide range of applications. It is fun-damental to extract the phase of scattering light wave accurately and the methods include coherent detection, I/Q de-modulation, 3 by 3 coupler, dual probe pulses, and so on. Meanwhile, researchers have also made great efforts to im-prove the performance of Φ-OTDR. The frequency response range, the measurement accuracy, the sensing distance, the spatial resolution, and the accuracy of event discrimination, all have been enhanced by various techniques. Further-more, lots of researches on the applications in various kinds of fields have been carried out, where certain modifications and techniques have been developed. Therefore, Φ-OTDR remains as a booming technique in both researches and ap-plications.
The density map is an effective manner to infer how many cells a cell image contains, and it carries valuable information. However, a fine-grained density map requires rich spatial information to ...recover the distribution details. In this paper, we propose a cell counting network with two paths, i.e. detail path and context path, which respectively extract spatial details and semantics. The detail path encodes the spatial information with small convolutional kernels. The context path rapidly enlarges the receptive field and extracts multi-scale features with an atrous spatial pyramid pooling. At the end of the two paths, we design a feature fusion module to merge the high-level feature maps from the two paths. To decrease the parameters and computation, we directly upsample the fused feature maps to the input size and decode them to obtain the density map. The proposed model is evaluated on three cell datasets and a popular crowd dataset Shanghaitech Part-B. The experiments illustrated that the proposed model not only achieves superior performance on cell datasets but also generalizes well on the crowd dataset.
Cell counting is a fundamental part of biomedical and pathological research. Predicting a density map is the mainstream method to count cells. As an easy-trained and well-generalized model, the ...random forest is often used to learn the cell images and predict the density maps. However, it cannot predict the data that are beyond the training data, which may result in underestimation. To overcome this problem, we propose a cell counting framework to predict the density map by detecting cells. The cell counting framework contains two parts: the training data preparation and the detection framework. The former makes sure that the cells can be detected even when overlapping, and the latter makes sure the count result accurate and robust. The proposed method uses multiple random forests to predict various probability maps where the cells can be detected by Hessian matrix. Take all the detection results into consideration to get the density map and achieve better performance. We conducted experiments on three public cell datasets. Experimental results showed that the proposed model performs better than the traditional random forest (RF) in terms of accuracy and robustness, and even superior to some state-of-the-art deep learning models. Especially when the training data are small, which is the usual case in cell counting, the count errors on VGG cells, and MBM cells were decreased from 3.4 to 2.9, from 11.3 to 9.3, respectively. The proposed model can obtain the lowest count error and achieves state-of-the-art.
Islanding detection is one of the conditions necessary for the safe operation of the microgrid. The detection technology should provide the ability to differentiate islanded operations from power ...grid disturbances effectively. Given that it is difficult to set the fault threshold using the passive detection method, and because the traditional active detection method affects the output power quality, a microgrid islanding detection method based on the Sliding Window Discrete Fourier Transform (SDFT)-Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) network optimized by an attention mechanism is proposed. In this paper, the inverter output current and voltage at the point of common coupling (PCC) are transformed by the SDFT. The positive sequence, zero sequence, and negative sequence components of voltage and current harmonics are calculated and reconstructed by adopting the symmetrical component method (SCM). Meanwhile, the current and voltage are decomposed into a mono intrinsic mode function (IMF). The symmetric components of voltage, current, and IMFs are used as inputs to the deep learning algorithm. An LSTM with the features extracted to classify islanding and grid disturbance is proposed. By using the attention mechanism to set the weight values of the features of hidden states obtained by the LSTM network, the proportion of important features increases, which improves the classification effect. MATLAB/Simulink simulation results indicate that the proposed method can effectively classify the islanding state under different working conditions with an accuracy level of 98.4% and a loss value of 0.0725 with a maximal detection time of 66.94 ms. It can also reduce the non-detection zone (NDZ) and detection time and has a certain level of noise resistance. Meanwhile, the problem whereby the active method affects the microgrid power quality is avoided without disturbing the current or power of the microgrid.
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This paper proposes a real-time multi-class disturbance detection algorithm based on YOLO for distributed fiber vibration sensing. The algorithm achieves real-time detection of event location and ...classification on external intrusions sensed by distributed optical fiber sensing system (DOFS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). We conducted data collection under perimeter security scenarios and acquired five types of events with a total of 5787 samples. The data is used as a spatial-temporal sensing image in the training of our proposed YOLO-based model (You Only Look Once-based method). Our scheme uses the Darknet53 network to simplify the traditional two-step object detection into a one-step process, using one network structure for both event localization and classification, thus improving the detection speed to achieve real-time operation. Compared with the traditional Fast-RCNN (Fast Region-CNN) and Faster-RCNN (Faster Region-CNN) algorithms, our scheme can achieve 22.83 frames per second (FPS) while maintaining high accuracy (96.14%), which is 44.90 times faster than Fast-RCNN and 3.79 times faster than Faster-RCNN. It achieves real-time operation for locating and classifying intrusion events with continuously recorded sensing data. Experimental results have demonstrated that this scheme provides a solution to real-time, multi-class external intrusion events detection and classification for the Φ-OTDR-based DOFS in practical applications.
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This paper presented a new kind of salinity and temperature dual-parameter sensor based on a fiber ring laser (FRL) with tapered side-hole fiber (SHF) embedded in a Sagnac interferometer. The sensing ...structure is majorly composed of tapered SHF located in the middle of SHF inside the Sagnac interferometer loop structure. The influences of the SHF’s diameters of different tapered in the Sagnac interferometer loop on the FRL sensing system are studied. The presence of air holes in the SHF makes the cladding mode easier to excite, and the interaction between the cladding mode with its surroundings is enhanced, thus having higher salinity sensitivity. Besides, the unique advantages of high resolution, narrower linewidth, and high signal-to-noise ratio (SNR) of fiber laser make the measurement results more accurate. In this experiment, the SHF with different taper diameters was made, and it was found that reducing the diameter of the taper waist diameter could further improve the salinity sensitivity. When the waist diameter was 9.70 μm, the maximum salinity sensitivity of 0.2867 nm/‰ was achieved. Temperature sensing experiments were also carried out. The maximum temperature sensitivity of the FRL sensing system was −0.3041 nm/°C at the temperature range from 20 to 30 °C. The sensor has the characteristics of easy manufacture, good selectivity, and high sensitivity, proving the feasibility of simultaneous measurement of seawater salinity and temperature.
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Data storage is a problem that cannot be ignored in the long-term monitoring of a phase-sensitive optical time-domain reflectometry (Φ-OTDR) system. In this paper, we proposed a data-reduction ...approach for heterodyne Φ-OTDR using an ultra-low sampling resolution and undersampling techniques. The operation principles were demonstrated and experiments with different sensing configurations were carried out to verify the proposed method. The results showed that the vibration signal could be accurately reconstructed from the undersampled 1-bit data. A space saving ratio of 98.75% was achieved by converting 128 MB of data (corresponding to 268.44 ms of sensing time) to 1.6 MB. The proposed method led to a potentially new data-reduction approach for heterodyne Φ-OTDR, which also provided economical guidance for the selection of the data-acquisition device.
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Background
There is no direct evidence of gut microbiota disturbance in children with gastroesophageal reflux disease (GERD). This study aimed to provide direct evidence and a comprehensive ...understanding of gut microbiota disturbance in children with GERD through combined metagenomic and metabolomic analysis.
Methods
30 children with GERD and 30 healthy controls (HCs) were continuously enrolled, and the demographic and clinical characteristics of the subjects were collected. First, 16S rRNA sequencing was used to evaluate differences in the gut microbiota between children with GERD and HC group, and 10 children with GERD and 10 children in the HC group were selected for metagenomic analysis. Nontargeted metabolomic analysis was performed using liquid chromatography/mass spectrometry (LC/MS), and metagenomic and metabolomic data were analyzed together.
Results
There were significant differences in the gut microbiota diversity and composition between children with GERD and HCs. The dominant bacteria in children with GERD were Proteobacteria and Bacteroidota. At the species level, the top three core bacterial groups were
Bacteroides stercoris
,
Bacteroides vulgatus
and
Alistipes putredinis
. The main differential pathways were identified to be related to energy, amino acid, vitamin, carbohydrate and lipid metabolism. LC/MS detected 288 different metabolites in the positive and negative ion modes between children with GERD and HCs, which were mainly involved in arachidonic acid (AA), tyrosine, glutathione and caffeine metabolism.
Conclusion
This study provides new evidence of the pathogenesis of GERD. There are significant differences in the gut microbiota, metabolites and metabolic pathways between HCs and children with GERD, and the differences in metabolites are related to specific changes in bacterial abundance. In the future, GERD may be treated by targeting specific bacteria related to AA metabolism.
This paper presents an efficient and practical design method for a LED based reflector-array lighting module. Improving on previous designs, this method could offer higher design freedom to achieve ...specific illuminance distribution for actual lighting application and deal with the LED light intensity distribution while shortening the design time. The detailed design description of the lighting system is thoroughly investigated. To demonstrate the effectiveness of this method, an ultra-compact reflector-array module, which produces a rectangular illumination area with a large aspect ratio, is specially designed to meet the high-demanding requirements of industrial lighting application. Design results show that most LED emitting energy could be collected into the required lighting region while higher-brightness and better-uniformity are simultaneously available within the focus region. It is expected that this method will have great potential for other lighting applications.
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The Vernier effect created using an incorporated Lyot-Sagnac loop is used to create an ultra-high sensitivity temperature sensor based on a ring laser cavity. Unlike standard double Sagnac loop ...systems, the proposed sensor is fused into a single Sagnac loop by adjusting the welding angle between two polarization-maintaining fibers (PMFs) to achieve effective temperature sensitivity amplification. The PMFs are separated into two arms of 0.8 m and 1 m in length, with a 45° angle difference between the fast axes. The sensor's performance is examined both theoretically and experimentally. The experimental results reveal that the Vernier amplification effect can be achieved via PMF rotating shaft welding. The temperature sensitivity in the laser cavity can reach 2.391 nm/°C, which is increased by a factor of more than eight times compared with a single Sagnac loop structure (0.298 nm/°C) with a length of 0.8 m without the Vernier effect at temperatures ranging from 20 °C to 30 °C. Furthermore, unlike traditional optical fiber sensing that uses a broadband light source (BBS) for detection, which causes issues such as low signal-to-noise ratio and broad bandwidth, the Sagnac loop can be employed as a filter by inserting itself into the fiber ring laser (FRL) cavity. When the external parameters change, the laser is offset by the interference general modulation, allowing the external temperature to be monitored. The superior performance of signal-to-noise ratios of up to 50 dB and bandwidths of less than 0.2 nm is achieved. The proposed sensor has a simple structure and high sensitivity and is expected to play a role in biological cell activity monitoring.
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