Dental caries remains the most prevalent chronic disease in both children and adults. Optical coherence tomography (OCT) is a noninvasive optical imaging modality utilized to image oral samples to diagnose carious lesions, but detecting early stage dental caries with high-level accuracy remains challenging. Deep learning models have been employed to classify OCT images for various healthcare applications. In this paper, human tooth specimens were imaged ex vivo using OCT imaging systems, and a three-class grading system based on deep learning model for detection and classification of carious lesions was developed. This study is a step forward in the development of automated deep learning/OCT imaging system for early dental caries diagnosis.
Tooth surface with pits and fissures is the most prevalent of carious area for suitability of plaque accumulation. Pit and fissure sealing has been proven be effective in preventing and arresting pit-and-fissure occlusal caries lesions of primary and permanent molars in children and adolescents and can greatly affect smooth surface carious lesion reduction. Clinical decision to seal enamel pits and fissures needs to assess caries risk of the tooth. Surface morphology of pit and fissure, judged by dentist’s subjective experience, together with other factors of socioeconomic status of family, dietary habit, caries history, etc, are comprehensively considered. Due to morphological complexity and diversity of tooth surface, the decision lacks objective morphology-based caries-risk assessment of pit and fissure. In the paper, dental plaque-guided evaluation of pit and fissure caries risk based on 3D morphology analysis of occlusal surface is investigated. The 3D point cloud data of tooth surface are obtained from a commercial 3D intra-oral scanner. Pit-andfissure region can be extracted using region growing. Then skeleton of pit and fissure is determined by L1-medial skeleton method. Section profile of pit-and-fissure can then be obtained for morphological analysis. Bearing area curve (BAC) is introduced to evaluate the morphological distribution and five BAC-based parameters are defined as quantitative indices to describe the characteristic of pit-and-fissure morphology. Dental plaque was quantitatively evaluated by image component ratio of fluorescence image. To obtain dental plaque distribution of 3D pit and fissure region, ICP-based contour registration method was proposed to map fluorescence image on 3D occlusal surface. Nonlinear modeling of plaque distribution and morphological feature was explored using RBF neural network. The reported work reveals that 3D morphological parameters can be used as effective predictors for pit and fissure caries risk evaluation.
To solve the heat dissipation problem of LED, a radiator structure based on strip fins is designed and the method to optimize the structure parameters of strip fins is proposed in this paper. The combination of RBF neural networks and particle swarm optimization (PSO) algorithm is used for modeling and optimization respectively. During the experiment, the 150 datasets of LED junction temperature when structure parameters of number of strip fins, length, width and height of the fins have different values are obtained by ANSYS software. Then RBF neural network is applied to build the non-linear regression model and the parameters optimization of structure based on particle swarm optimization algorithm is performed with this model. The experimental results show that the lowest LED junction temperature reaches 43.88 degrees when the number of hidden layer nodes in RBF neural network is 10, the two learning factors in particle swarm optimization algorithm are 0.5, 0.5 respectively, the inertia factor is 1 and the maximum number of iterations is 100, and now the number of fins is 64, the distribution structure is 8*8, and the length, width and height of fins are 4.3mm, 4.48mm and 55.3mm respectively. To compare the modeling and optimization results, LED junction temperature at the optimized structure parameters was simulated and the result is 43.592°C which approximately equals to the optimal result. Compared with the ordinary plate-fin-type radiator structure whose temperature is 56.38°C, the structure greatly enhances heat dissipation performance of the structure.
KEYWORDS: Magnetism, Magnetic sensors, Sensors, Ferromagnetics, Resistance, Chemical elements, Signal detection, Structural design, Intelligent sensors, Iron
Hardness, as one kind of tactile sensing, plays an important role in the field of intelligent robot application such as gripping, agricultural harvesting, prosthetic hand and so on. Recently, with the rapid development of magnetic field sensing technology with high performance, a number of magnetic sensors have been developed for intelligent application. The tunnel Magnetoresistance(TMR) based on magnetoresistance principal works as the sensitive element to detect the magnetic field and it has proven its excellent ability of weak magnetic detection. In the paper, a new method based on magnetic anomaly detection was proposed to detect the hardness in the tactile way. The sensor is composed of elastic body, ferrous probe, TMR element, permanent magnet. When the elastic body embedded with ferrous probe touches the object under the certain size of force, deformation of elastic body will produce. Correspondingly, the ferrous probe will be forced to displace and the background magnetic field will be distorted. The distorted magnetic field was detected by TMR elements and the output signal at different time can be sampled. The slope of magnetic signal with the sampling time is different for object with different hardness. The result indicated that the magnetic anomaly sensor can recognize the hardness rapidly within 150ms after the tactile moment. The hardness sensor based on magnetic anomaly detection principal proposed in the paper has the advantages of simple structure, low cost, rapid response and it has shown great application potential in the field of intelligent robot.
Multiple LED-based spectral synthesis technology has been widely used in the fields of solar simulator, color mixing, and artificial lighting of plant factory and so on. Generally, amounts of LEDs are spatially arranged with compact layout to obtain the high power density output. Mutual thermal spreading among LEDs will produce the coupled thermal effect which will additionally increase the junction temperature of LED. Affected by the Photoelectric thermal coupling effect of LED, the spectrum of LED will shift and luminous efficiency will decrease. Correspondingly, the spectral synthesis result will mismatch. Therefore, thermal management of LED spatial layout plays an important role for multi-LEDs light source system. In the paper, the thermal dissipation network topology model considering the mutual thermal spreading effect among the LEDs is proposed for multi-LEDs system with various types of power. The junction temperature increment cased by the thermal coupling has the great relation with the spatial arrangement. To minimize the thermal coupling effect, an optimized method of LED spatial layout for the specific light source structure is presented and analyzed. The results showed that layout of LED with high-power are arranged in the corner and low-power in the center. Finally, according to this method, it is convenient to determine the spatial layout of LEDs in a system having any kind of light source structure, and has the advantages of being universally applicable to facilitate adjustment.
Mueller matrix is an optical parameter invasively to reveal the structure information of anisotropic material. Dental tissue has the ordered structure including dental enamel prism and dentinal tubule. The ordered structure of teeth surface will be destroyed by demineralization. The structure information has the possibility to reflect the dental demineralization. In the paper, the experiment setup was built to obtain the Mueller matrix images based on the dual- wave plate rotation method. Two linear polarizer and two quarter-wave plate were rotated by electric control revolving stage respectively to capture 16 images at different group of polarization states. Therefore, Mueller matrix image can be calculated from the 16 images. On this basis, depolarization index, the diattenuation index and retardance index of the Mueller matrix were analyzed by Lu-Chipman polarization decomposition method. Mueller matrix images of artificial demineralized enamels at different stages were analyzed and the results show the possibility to detect the dental demineralization using Mueller matrix imaging method.
Quartz crystal chip discussed in this paper is a semitransparent crystal with thickness of 0.1~0.2 mm. Generally these chips are packaged into one block with 100 or 200 pieces. Mostly, the counting job is accomplished by weighing the chips, however, thickness difference of each crystal will lead to the inaccurate counting results. A new counting method with imaging and signal processing is proposed in this paper. At first, the edge images of crystal are acquired, thus edge information will be turned into edge signals, then the signal will be enhanced, the noise will be decreased. At last the accurate amount will be get from these edge signals. This method has good practical value because of contact less, high efficiencies and high accuracy.
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