Data-Driven Distributed Optical Vibration Sensors: A Review

Distributed optical vibration sensors (DOVS) have attracted much attention recently since it can be used to monitor mechanical vibrations or acoustic waves with long reach and high sensitivity. Phase-sensitive optical time domain reflectometry (Φ-OTDR) is one of the most commonly used DOVS schemes. For Φ-OTDR, the whole length of fiber under test (FUT) works as the sensing instrument and continuously generates sensing data during measurement. Researchers have made great efforts to try to extract external intrusions from the redundant data. High signal-to-noise ratio (SNR) is necessary in order to accurately locate and identify external intrusions in Φ-OTDR systems. Improvement in SNR is normally limited by the properties of light source, photodetector and FUT. But this limitation can also be overcome by post-processing of the received optical signals. In this context, detailed methodologies of SNR enhancement post-processing algorithms in Φ-OTDR systems have been described in this paper. Furthermore, after successfully locating the external vibrations, it is also important to identify the types of source of the vibrations. Pattern classification is a powerful tool in recognizing the intrusion types from the vibration signals in practical applications. Recent reports of Φ-OTDR systems employed with pattern classification algorithms are subsequently reviewed and discussed. This thorough review will provide a design pathway for improving the performance of Φ-OTDR while maintaining the cost of the system as no additional hardware is required

Growth Pattern in Chinese Children With 5α-Reductase Type 2 Deficiency: A Retrospective Multicenter Study

Background5α-reductase type 2 deficiency (5αRD) is an autosomal recessive hereditary disease of the group of 46, XY disorders of sex development (DSD).ObjectiveTo study the growth pattern in Chinese pediatric patients with 5αRD.SubjectsData were obtained from 141 patients with 5αRD (age: 0–16 years old) who visited eight pediatric endocrine centers from January 2010 to December 2017.MethodsIn this retrospective cohort study, height, weight, and other relevant data were collected from the multicenter hospital registration database. Baseline luteinizing hormone (LH), follicle stimulating hormone (FSH), testosterone (T), and dihydrotestosterone (DHT) after human chorionic gonadotropin (HCG) stimulation test were measured by enzyme enhanced chemiluminescence assay. Bone age (BA) was assessed using the Greulich-Pyle (G-P) atlas. Growth curve was constructed based on λ-median-coefficient of variation method (LMS).ResultsThe height standard deviation scores (HtSDS) and weight standard deviation scores (WtSDS) in 5αRD children were in the normal range as compared to normal boys. Significantly higher HtSDS was observed in patients with 5αRD who were <1 year old (t = 3.658, 2.103, P = 0.002, 0.048, respectively), and higher WtSDS in those <6 months old (t = 2.756, P = 0.012). Then HtSDS and WtSDS decreased gradually and fluctuated near the median of the same age until 13 years. WtSDS in 5αRD children from northern China were significantly higher than those from the south (Z = -2.670, P = 0.008). The variation tendency of HtSDS in Chinese 5αRDs was consistent with the trend of stimulating T. HtSDS and stimulating T in the external masculinization score (EMS) <7 group were slightly higher than those in EMS ≥ 7 group without significant difference. Additionally, the ratio of BA over chronological age (BA/CA) was significantly <1 in children with 5αRD.ConclusionChildren with 5αRD had a special growth pattern that was affected by high levels of T, while DHT played a very small role in it. Their growth accelerated at age <1 year, followed by slowing growth and fluctuating height near normal median boys’ height. The BA was delayed in 5αRD children. Androgen treatment, which may be considered anyway for male 5αRD patients with a micropenis, may also be beneficial for growth

A Cell Counting Framework Based on Random Forest and Density Map

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

Discriminating the Nature of Thyroid Nodules Using the Hybrid Method

Prompt and correct diagnosis of benign and malignant thyroid nodules has always been a core issue in the clinical practice of thyroid nodules. Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the nature of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intraobserver variabilities. This paper proposes a novel hybrid approach based on machine learning and information fusion to discriminate the nature of thyroid nodules. Statistical features are extracted from the B-mode ultrasound image while deep features are extracted from the shear-wave elastography image. Classifiers including logistic regression, Naive Bayes, and support vector machine are adopted to train classification models with statistical features and deep features, respectively, for comparison. A voting system with certain criteria is used to combine two classification results to obtain a better performance. Experimental and comparison results demonstrate that the proposed method classifies the thyroid nodules correctly and efficiently

Field curvature correction method for ultrashort throw ratio projection optics design using an odd polynomial mirror surface

This paper presents a field curvature correction method of designing an ultrashort throw ratio (TR) projection lens for an imaging system. The projection lens is composed of several refractive optical elements and an odd polynomial mirror surface. A curved image is formed in a direction away from the odd polynomial mirror surface by the refractive optical elements from the image formed on the digital micromirror device (DMD) panel, and the curved image formed is its virtual image. Then the odd polynomial mirror surface enlarges the curved image and a plane image is formed on the screen. Based on the relationship between the chief ray from the exit pupil of each field of view (FOV) and the corresponding predescribed position on the screen, the initial profile of the freeform mirror surface is calculated by using segments of the hyperbolic according to the laws of reflection. For further optimization, the value of the high-order odd polynomial surface is used to express the freeform mirror surface through a least-squares fitting method. As an example, an ultrashort TR projection lens that realizes projection onto a large 50 in. screen at a distance of only 510 mm is presented. The optical performance for the designed projection lens is analyzed by ray tracing method. Results show that an ultrashort TR projection lens modulation transfer function of over 60% at 0.5 cycles/mm for all optimization fields is achievable with f-number of 2.0, 126° full FOV, <1% distortion, and 0.46 TR. Moreover, in comparing the proposed projection lens’ optical specifications to that of traditional projection lenses, aspheric mirror projection lenses, and conventional short TR projection lenses, results indicate that this projection lens has the advantages of ultrashort TR, low f-number, wide full FOV, and small distortion.Published versio

Study on optical finite impulse response filter

An optical filter is one of the most important devices used in optical system spectrum crunching and optical signal processing. The present optical filters mainly consist of two broad categories: thin film filter and birefringent filter

Effect of Mo Concentration on the Microstructure Evolution and Properties of High Boron Cast Steel

The microstructure evolution, mechanical properties, and tribological properties of high boron cast steel (HBCS) with various Mo concentrations are investigated. The results indicate that Mo addition can significantly modify the microstructure and enhance the comprehensive properties. With the increase of Mo concentration, borides change from the original fish-bone Fe-rich and Cr-rich M2B to dendritic Fe-rich M2B, blocky and cluster-like Cr-rich M2B, and grainy Mo-rich M2B. The hardness of HBCS increases gradually with the increase of Mo content due to the solid solution strengthening and the refinement of M2B. It can be found that all the samples exhibit quasi-cleavage, but the impact toughness increases firstly and reaches the maximum value when the concentration of Mo is 2.10 wt.%, which is the result of the dispersive distribution of M2B rather than the original fish-bone M2B. Subsequently, the impact toughness begins to decrease as the concentration of Mo further increases because of the extensive formation of grainy Mo-rich M2B at the grain boundary. Meanwhile, the wear results reveal that the average friction coefficient and wear ratio decrease with the increase of Mo content, and the wear mechanism changes from abrasive wear and adhesive wear to abrasive wear when the concentration of Mo exceeds 2.10 wt.%

Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology

Inspired by gestalt psychology, we combine human cognitive characteristics with knowledge of radiologists in medical image analysis. In this paper, a novel framework is proposed to detect breast masses in digitized mammograms. It can be divided into three modules: sensation integration, semantic integration, and verification. After analyzing the progress of radiologist’s mammography screening, a series of visual rules based on the morphological characteristics of breast masses are presented and quantified by mathematical methods. The framework can be seen as an effective trade-off between bottom-up sensation and top-down recognition methods. This is a new exploratory method for the automatic detection of lesions. The experiments are performed on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) data sets. The sensitivity reached to 92% at 1.94 false positive per image (FPI) on MIAS and 93.84% at 2.21 FPI on DDSM. Our framework has achieved a better performance compared with other algorithms