Showing 13–24 of 158 results
Gastric Cancer Detection by Two-step Learning in Near-Infrared Hyperspectral Imaging
Abstract: Gastric cancer is one of the most serious cancers that affects and kills many people around the world every year. Early treatment of gastric cancer dramatically improves the survival rate. Endoscopy has become an important tool for early detection. Since invasive gastric cancer or the edge of the invasive gastric cancer is difficult to find by using conventional visible-light endoscopy, near-infrared imaging, which is bringing great progress to the medical field, is focused on in recent years. In order to apply near-infrared hyperspectral imaging (NIR-HSI) in real-time, wavelength feature extraction is important because a large amount of data needs to be analyzed. The purpose of this study is to detect gastric cancer using NIR-HSI and to select a suitable wavelength for the target in the near-infrared region (1000–1600 nm). NIR-HSI was used to take data from six specimens of gastric cancer and each pixel was labeled as normal or tumor on the hyperspectral image based on the histopathological diagnosis. 4 wavelengths were extracted from 95 wavelengths using the least absolute shrinkage and selection operator method. Supervised learning was performed using a support vector machine for both cases using all 95 wavelengths and the case using 4 selected wavelengths. In both cases, the approximate location of the tumor could be identified, indicating that an appropriate wavelength could be selected. We were also able to improve the detection accuracy by creating new supervised data and adding another learner. The detection accuracy was 93.3% for accuracy, 69.8% for sensitivity, and 96.7% for specificity. These results show that gastric cancer can be detected even at four wavelengths. By applying the results of this study to the endoscope system, the possibility of constructing a NIR endoscope system for gastric cancer was suggested.
Keywords: Cancer detection; Gastric cancer; Near-infrared hyperspectral imaging; The least absolute shrinkage and selection operator; Support vector machine
Non-Predefined Life Signs Detection for Disaster Survivors Rescue
Abstract: No one can tell or predict when and where a natural disaster such as an earthquake or tornado will occur and the damages they may cause. Or an overflow of a large amount of water beyond its normal limit (water flood) shallowing city as we used to watch on news TV after the passage of a strong typhoon causing heavy rain. These natural disasters occur every day and anywhere around the globe are not new. And we cannot not prevent them from occurring in spite of the best technology we have in our possession now. But saving lives after their occurring is still possible and the best technology for this is the combination of AUV and the image processing. Image processing is one of the best ever invented technology by human since the course on technology development between scientists for sustainable development of our society. In order word “image processing is the technology that meets the needs of the 21st society we live in without compromising the ability of future generation to meet what they need to make the use of this technology efficiency. This paper proposes non-predefined life signs detection for disaster survivors rescue when a disaster occur and especially during a floodwater. In this research we use the matrix-based pairs of opposing pixels positioned directly around the observed point that belongs to the edge of the life signs target. At first one-dimension matrix for bitmap memorization values of the RGB components of pixel is used. Next these values of the RGB components of pixel color are copied from bitmap to matrix. The number of bytes in a row is rounded up to the nearest number divisible by four. As a result, the method clearly detects all life signs edge made by human using any type of item around them with 95%.
Keywords: Life signs; Image processing; Disaster; Edge detection; Rescue
Seashore Debris Detection Model with KaKaXi Camera Custom Dataset Using Instance Segmentation
Abstract: Marine debris is impacting coastal landscapes majorly by affecting biodiversity, impairing recreational uses, causing losses to fishing industries, maritime industries, etc. Motivated by the need for automatic and cost-effective approaches for debris monitoring and removal, we employed computer vision technique together with deep learning-based model to identify and classify marine debris on several beach locations. This paper provides a comparative analysis of state-of-the-art deep learning architectures and proposed architecture which is used as feature extractor for debris image classification.
The model is being proposed to detect seven categories of marine debris using a custom debris dataset, with the help of instance segmentation and a shape matching network, which can then be cleaned timely and efficiently. The manually constructed dataset for this system is created by annotating fixed KaKaXi camera images using CVAT with seven types of labels. A pre-trained HOG shape feature extractor is being used on LIBSVM along with template matching to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the recorded live debris data. The proposed network resulted in the improvement of misclassification of debris masks for objects with different illuminations, shape, occlusion and viewpoints.
Keywords: debris; fixed camera images; computer vision; instance segmentation; deep learning; template matching; Histogram of Gradients (HOG)
Quantitative Evaluation of Orthodontic Treatment by Moment Measurement Device
Abstract: The purpose of this study is to quantitatively evaluate orthodontic treatment. The forces and moments applied to the teeth during treatment are rarely measured. Therefore, dentists must rely on their own skills, experience, and senses to perform treatment, which may not be sufficient for some patients. To solve this problem, devices have been developed to measure the forces and moments generated in the teeth. However, there are disadvantages, such as the limitation of the direction in which the forces and moments can be measured and the fact that they do not consider the movement of the teeth during treatment. Therefore, we have developed a device that can measure forces and moments in all axes, reproducing the movement of the teeth during treatment. The developed device consists of two teeth model, a force sensor, and a stepping motor. Considering that the teeth move during treatment, this device was incorporated a motor to control the angle of the teeth. A force sensor is used to measure the forces and moments in the three axes applied to the teeth. This device can reproduce the treatment of abnormally tilted teeth until teeth return to normal position. Quantitative evaluation was performed using the device. A comparative study was conducted between the case of treatment with stainless steel wires and the case of treatment with nickel titanium wires. The result showed that nickel titanium wire has many advantages compare the conventional stainless-steel wires. The advantages are agreed with the dentist’s rule of thumb, and the experimental results suggest that this device was able to make a quantitative evaluation of orthodontic treatment.
Keywords: Force and moment measurement device; Movement of teeth; Orthodontic treatment
Emergent Transition from Radial Foraging to Tree-like Raiding Patterns Induced by Complete Following for Pheromone
Abstract: Among ants that show complex and diverse collective actions, army ants are known to raid prey in groups. It has been confirmed that the characteristic pattern in a swarm raid of army ants changes from radial to tree-like with time. There are some simulation studies focusing only on the emergence of tree-like patterns. However, the models adopted in their simulations cannot represent the transition of patterns from radial to tree-like observed in the real world. In this study, we propose an army ant model with a modification of the model adopted by Solé et al. and clarify the conditions of the emergence of the transition. From the experimental simulation results of our model, we deduce that the simple modification of the Solé model can move individuals in eight directions, but it is not enough to express multidirectional to unidirectional convergence. Additionally, our simulation experiments showed that for the pattern transition from radial to tree-like, it is necessary for the individuals to keep moving forward until they get food, and for the returning individual to completely follow the pheromone.
Keywords: army ant model, radial foraging pattern, tree-like raiding pattern, swarm raid, complete following for pheromone.
Speech Recognition Signal Lamp Image Simulation
Abstract: Speech recognition technology is a method of computer sound signal processing, which determines human behavior by analyzing the characteristics of sound signal. It has a wide range of applications in modern science and technology, and is a new frontier science, also is known as intelligent language. With the continuous improvement of people’s requirements for the quality of life, intelligent devices have sprung up in various fields. It is very practical to apply speech recognition technology to smart home reasonably to make home life more comfortable, safe and effective. The recognition of speech signal is primarily completed by preprocessing, feature extraction, training and pattern matching; the user interface is established by using the function of Matlab GUI, and the signal lamp image based on speech recognition is simulated and controlled by using the software.
Keywords: Speech recognition; Endpoint detection; Feature extraction; pattern recognition
Intelligent Ramp Patrol Car based on MSP430
Abstract: This article aims to use the MSP430 single-chip to design an intelligent car, which is suitable for the precision tracking of various inclined slopes. The patrol car uses infrared sensors to collect ramp trajectory information and to adjust the forward direction through the front-axle steering mechanism. The gyroscope collects the car status in real time so that the car is controlled to perform uphill acceleration, downhill deceleration. Such a design allows the patrol car to be stabilized in the ramp with the established route.
Keywords: MPU6050, patrol car, front steering axle, ramp track
Research on Similar Odor Recognition Based on Bid Data Analysis
Abstract: In the common olfactory system odor recognition is processed by the electronic nose collecting sensor data, but the odor data collection of substances is easily affected by the environment and the processing is complicated, which is prone to deviation. This paper proposes a method based on big data analysis. According to the different chemical structure characteristics of different odor substances, the BP neural network is used to build a model to classify and recognize similar odors, and compare it with the traditional PCA+LDA recognition method. The results show that the establishment of a similar odor recognition model can accurately classify substances with similar odors, and the BP neural network algorithm is used to identify different substances with a higher rate of odor recognition. This method is stable and simple, and can provide different ideas for odor identification.
Keywords: Smell recognition; Big data analysis; BP neural network; Similar smell
Text Classification Based on Title Sematic Information
Abstract: With the rapid development of big data technology, text classification plays an important role in practical application, its applications span a wide range of activities such as sentiment analysis, spam detection, etc. Traditionally, we model the relationship between document and label. However, in many scenarios, document have specific relationship with corresponding title. Inspired by this, a text classification model based on title Semantic Information is proposed in this study. In our model, long short-term memory (LSTM)is used to learn title embedding, document embedding is obtained by using promoted LSTM(TS-LSTM) which take into account the title information. The experimental results on the standard text classification datasets show that its performance is better than the existing state-of-the-art text classification algorithms.
Keywords: Text classification; natural language processing; deep learning; LSTM
Design of the Automatic Control System for Restaurant Food Delivery Based On PLC
Abstract: The paper designs an automatic control system for restaurant food delivery based on PLC, including the mechanical structure and automatic control system design. The mechanical structure of the system includes horizontal delivery subsystems and a vertical delivery subsystem. The automatic control system includes PLC control and the human-machine interface, which realizes the entire system’s automation. At the end of the paper, we analyze the whole system’s reliability and economy to reflect the characteristics and practicability of the automatic control system.
Keywords: Food delivery system; PLC control; human-machine interface; reliability
License Plate Recognition Algorithm Based on Convolutional Neural Network
Abstract: In order to improve the problem of unequal suspension positions in the traditional license plate recognition system, this paper introduces the convolutional neural network algorithm into the license plate recognition system, and conducts a series of tests and corrections to meet the current license plate recognition system. This paper proposes for the first time that the flood filling algorithm is applied to the preprocessing of the license plate image, the recognized contour is divided into regions, and then the license plate inclination angle is offset, and rough positioning and cutting are performed to make the vehicle shot from the side The picture can also fully identify the license plate, and finally judge according to the aspect ratio of the license plate and the standard aspect ratio, and get whether the recognized license plate is. The experimental results show that the model utilizes the advantages of convolutional neural network so that the model can recognize classification features more accurately.
Keywords: License plate recognition, Convolutional neural network, Flood filling algorithm
Research and Implementation of FacialNet Based on Convolutional Neural Network
Abstract: Deep learning, artificial intelligence and other cutting-edge technologies are constantly being integrated into people’s daily lives. Even small vending machines that can be seen everywhere in life have begun to use facial payment methods. The detection and recognition of face images is no longer unattainable, but the analysis and recognition of face information and characteristics (gender, age, race, etc.) is still not fully mature, in order to improve the accuracy of face information recognition. In this paper, a face information recognition model is designed. The feature extraction part uses an eight-layer convolutional neural network, and then uses two fully connected modules as the classifiers for gender recognition and age recognition. The experimental results show that the model uses the advantages of the convolutional neural network so that the model can predict the gender and age of the face more accurately.
Keywords: Convolutional neural network, Face recognition, Gender recognition, Age recognition