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- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow, Poland (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Application of ANN and ANFIS for detection of brain tumors in MRIs by using DWT and GLCM texture analysis
摘要： In this work we combine different methodologies in order to develop algorithms for Computer-Aided Diagnosis (CAD) for brain tumors from the axial plane (T2 MRI). All methods utilize texture analysis by extracting features from raw data, without post-processing, based on different techniques, such as Gray Level Co-Occurrence Matrix (GLCM), or Discrete Wavelet Transform (DWT) and different classification methods, based on ANN or ANFIS. All of our proposed methodologies are developed, validated and verified on various sub data including 65% non-healthy MRIS. The total used database consists of 202 MRIs from non-healthy patients and 18 from healthy, segmented visually by an experienced neurosurgeon. Combining different subsets of features, our best results are by using 4 GLCM features for a 4 input and two hidden layers ANN, giving sensitivity 100%, specificity 77.8% accuracy 94.3%. It is proved that the input data to train such a CAD are considered to be unbiased if the ratio between healthy/un-healthy tissue MRIs is about 35%/65%, respectively.
关键词： MRI tumor CAD diagnosis,DWT,ANFIS,GLCM,ANN
更新于2025-09-23 15:23:52
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FPGA-Based Implementation of an Artificial Neural Network for Measurement Acceleration in BOTDA Sensors
摘要： In recent years, using distributed fiber-optic sensors based on Brillouin scattering, for monitoring pipelines, tunnels, and other constructional structures have gained huge popularity. However, these sensors have a low signal-to-noise ratio (SNR), which usually increases their measurement error. To alleviate this issue, ensemble averaging is used which improves the SNR but in return increases the measurement time. Reducing the noise by averaging requires hundreds or thousands of scans of the optical fiber; hence averaging is usually responsible for a large percent of the entire system latency. In this paper, we propose a novel method based on artificial neural network for SNR enhancement and measurement acceleration in distributed fiber-optic sensors based on the Brillouin scattering. Our method takes the noisy Brillouin spectrums and improves their SNR by 20 dB, which reduces the measurement time significantly. It also improves the accuracy of the Brillouin frequency shift estimation process and its latency by more than 50% in comparison with the state-of-the-art software and hardware solutions.
关键词： Artificial neural network (ANN),digital signal processing,optical fibers,curve fitting,field-programmable gate arrays (FPGAs)
更新于2025-09-23 15:23:52
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A trial for EBT3 film without batch-specific calibration using a neural network
摘要： This note reports a trial to establish an ANN (artificial neural network) method applying to EBT3 films of different batches without batch-specific calibration. Based on Pytorch (Facebook, https://pytorch.org/), a feed-forward ANN model was built to convert the pixel values of scanned images from different batches into absorbed dose. Films from different batches exposed to X-ray doses were digitized in transmission mode on an Epson 11000XL scanner for training and testing. The calculated dose map of TPS (Radiation Therapy Planning System) was used as a label (the desired output) for the ANN model. To verify the performance and generalization of the ANN method, a cross-validation experiment was performed. Using the trained ANN method, the scanned images were converted into absorbed dose maps, and the converted dose maps have good agreement with the calculated dose maps from TPS. For films irradiated via the sliding window mode, the MSEs (mean square errors) of the trained batches were less than 16.0 cGy and the MSEs of the tested batches were less than 18.0 cGy. For patient intensity-modulated radiotherapy (IMRT) films, the γ(3%, 3 mm) between the dose maps obtained from the trained films and TPS exceeded 97.5%. The γ(3%, 3 mm) between most of the dose maps obtained from the tested films and TPS exceeded 97.0%. This shows that it is feasible to establish a method for EBT3 films from certain batches to convert pixel values into an absorbed dose without batch-specific calibration, and the method can be applied to other cases.
关键词： EBT3 film,Trial,ANN method
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE Industry Applications Society Annual Meeting (IAS) - Portland, OR, USA (2018.9.23-2018.9.27)] 2018 IEEE Industry Applications Society Annual Meeting (IAS) - Performance Based Design of IMD for Single Stage PV Fed Water Pumping
摘要： This paper presents an improved designed induction motor, used for photovoltaic (PV) array fed water pumping system. The overall system is designed without a mechanical sensor to reduce both cost and complexity with simultaneous assurance of optimum power utilization of a PV array. The proposed system consists of an induction motor operated water pump, controlled by field-oriented control (FOC) with artificial neural network (ANN) current control technique. The MPPT (Maximum Power Point Tracking) as well as DC link voltage, is regulated by three-phase voltage source inverter (VSI). The estimation of motor speed eliminates the use of mechanical sensor and makes the system cheaper and robust. A new robust speed adaptive algorithm is presented, which is less dependent on parameters. A detailed study of various factors affecting the efficiency of the motor, is given to improve the behavior of the induction motor drive (IMD) for water pumping. The designed motor is tested on the developed prototype in the laboratory and its suitability is judged through various results under steady state and dynamic conditions of insolation variations.
关键词： Field-Oriented Control (FOC),Speed Adaptation,Finite Element Method,ANN Based Current Control,IMD (Induction Motor Drive),Water pump,PV Array,P&O Based MPPT Algorithm
更新于2025-09-23 15:23:52
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[IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe, Japan (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - Coastal Ocean Wind Speed Estimation Based GNSS-Reflectometry of BeiDou GEO Satellite
摘要： Global Navigation Satellite System-Reflectometry (GNSS-R) is useful for the ocean remote sensing. It has shown promising results as altimetry, Significant Wave Height, and wind speed measurement. To retrieve the information of the ocean surface, the GNSS-R technique receive the direct signal from GNSS satellites and capture the reflected signal by GNSS-R receivers. The difference delay of the reflected signal provided difference characteristics of the ocean including wind speed information. The aim of this paper is to estimate the ocean wind speed in the coastal area using the reflected signal information. This paper used the observed data sets from 3 to 12 January 2014 collected from the Geostationary Earth Orbit (GEO) of Chinese satellite (BeiDou G1) which consist of phase I and Q component and the in situ wind speed measurement collected from buoy station. A Method based on Artificial Neural Network (ANN) technique for wind speed estimation was presented. In addition, Particle Filter (PF) based autoregressive model was used to improve the efficacy of ANN. The performance of proposed technique has evaluated by using the Root Mean Square Error (RMSE) as shown in the experimental result section.
关键词： Particle Filter,GNSS-Reflectometry,Wind Speed Estimation,ANN,BeiDou GEO satellite
更新于2025-09-23 15:22:29
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Modeling of circular fractal antenna using BFO-PSO-based selective ANN ensemble
摘要： Accurate design of miniaturized antenna is constrained by the limited well‐formulated exact mathematical expressions. Demands for smart devices with features like portability, implantability, and configurability have further placed bigger challenges in front of the antenna design engineers or scientists. As a part of the search for various solutions, many innovative approaches have been proposed by various authors in different literatures. Application of soft computing is also another design approach to accurate design of fractal antenna. Here, the authors have attempted to propose a better solution to miniaturized antenna and its design. A fractal antenna based on circular outer geometry has been proposed as a solution to the search of miniaturized antennas, and a particle swarm optimization–based selective artificial neural networks ensemble is developed, which is employed as the objective function of a bacterial foraging optimization algorithm leading to a hybridized algorithm. The developed hybrid algorithm is utilized to develop the proposed antenna at 2.45 GHz. A good agreement of the simulated, desired, and experimental results validates the proposed design approach.
关键词： BFO,ANN ensemble,ISM band,fractal antenna,PSO
更新于2025-09-23 15:22:29
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[IEEE 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) - Kuala Lumpur, Malaysia (2019.8.27-2019.8.29)] 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) - Power Electronic Interface for Low Voltage DC Link Using Photovoltaic Cells with ANN based MPPT
摘要： The aim of this paper is to design and develop low voltage direct current system within building structures to cater to low power load demands like LED lights, BLDC fans, desktops computers etc. The intended system comprises of a solar panel, a buck converter, a battery and a boost converter. The output of boost converter will be 48 V direct current which can cater to loads below 1 kw. The buck converter regulates the output of solar panel to constant voltage, which is stored in a battery and boosted to 48 V direct current as per household requirements. The proposed system will simplify the transmission and distribution of energy by ensuring direct current supply to appliances which are devised to run on the same. This will rule out the need for rectifier within household appliances, thereby reducing their size and cost. The future scope of this project lies in the fact that the entire system can be integrated into a module which can be used in inaccessible and rural areas by dint of a solar panel that will serve as source and the output of module will be 48V which can be directly used for household purposes. The concept of artificial neural network is incorporated for maximum power point tracking, maintaining constant output voltage.
关键词： MPPT,Low Voltage DC,ANN
更新于2025-09-23 15:21:01
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[IEEE 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) - Ghaziabad (2018.2.9-2018.2.10)] 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) - Development of a Decision-Based Neural Network for a Day-Ahead Prediction of Solar PV Plant Power Output
摘要： Day-ahead photovoltaic power prediction is vital for policy making and providing necessary backup capacities. Previous researchers include the implementation of time series, auto-regression and Soft computing techniques like Artificial Neural Networks and Fuzzy Logic. Artificial Neural Networks provides a better fit to complex, non-linear and error-prone data. The paper shows a comparative study of a Radial Basis Neural Network Schema (exact fit), a ‘k-means’ Radial Neural Network, and a Feed Forward Neural Network with Levenberg-Marquardt error backpropagation designed for the prediction of power output at an hourly resolution. The ability of the Neural Network to be trained to adapt to a previous set of data and then interpolate or extrapolate to the new data set has been exploited. The proposed model uses five meteorological variables and uses recorded data collected from the SN Mohanty PV Power Plant. Training of neural network is done on a monthly basis so that normalization constants of variables can be lower and better mapping can be produced. An improved decision-based schematic using Neural Networks is proposed which combines the advantages of both Radial Basis Function (exact fit) and FFNN.
关键词： solar photovoltaic power plant,Radial Basis,Artificial Neural Network,Decision-based,ANN
更新于2025-09-23 15:21:01
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Investigating the capabilities of multispectral remote sensors data to map alteration zones in the Abhar area, NW Iran
摘要： Economic mineralization is often associated with alterations that are identi?able by remote sensing coupled geological analysis. The present paper aims to investigate the capabilities of Advanced Spaceborne Thermal Emission and Re?ection Radiometer (ASTER), Landsat-8 and Sentinel-2 data to map iron oxide and hydrothermally alteration zones in the Abhar area, NW Iran. To achieve this goal, the principal component analysis (PCA) and two machine learning methods including support vector machine (SVM) and arti?cial neural network (ANN) were employed. PCA method was carried out on four bands of all data and then the appropriate principal components were selected to map alterations. Due to the high precision of ASTER data within the short-wave infrared range, these data results are more satisfactory compared with Landsat-8 and Sentinel-2 sensors in detecting hydrothermally alterations through the PCA technique. Based on the obtained maps, the performance of all data types was approximately similar in the detection of iron oxide zones. Our desired data were classi?ed by two methods of SVM and ANN. The results of these algorithms were presented as confusion matrix. According to these results, for hydrothermally alterations, ASTER data showed better performance in both SVM and ANN than other datasets by gaining values greater than 90%. These data did not perform well in the iron oxide zones detection, while Landsat-8 and Sentinel-2 have been more successful. For iron oxide, based on confusion matrix, Landsat-8 data have obtained the values of 78% and 79% through SVM and ANN algorithms, respectively, and also Sentinel-2 has acquired the values of 88.11% and 90.55% via SVM and ANN, respectively. Therefore, to map iron oxide zones, Sentinel-2 data are more favorable than Landsat-8 data. In addition, the ANN algorithm in ASTER data has represented the highest overall accuracy and Kappa coe?cient with the values of 88.73% and 0.8453, respectively.
关键词： Sentinel-2,SVM,Abhar,alteration zones,ANN,ASTER
更新于2025-09-23 15:19:57
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RetoNet: a deep learning architecture for automated retinal ailment detection
摘要： Researchers are trying to tap the immense potential of big data to revolutionize all aspects of societal activity and to assist in having well informed decisions. Healthcare being one such field where proper analytics of available big medical data can lead to early detection and treatment of many ailments. Machine learning played a significant role in the design of automated diagnostic systems and today we have deep learning models in this arena which are outperforming human expertise in terms of predictive accuracy. This paper proposes RetoNet, a convolutional neural network architecture, which is trained and optimized to detect retinal ailment from fundus images with pronounced accuracy and its performance is also proven to be superior to a transfer learning based model developed for the same. Deep learning based e-diagnostic system can be an accurate, cost effective and convenient solution for the shortage of expertise on demand in the healthcare field.
关键词： Convolutional neural network,E-health,Retinal disease detection,ANN,Deep learning
更新于2025-09-19 17:15:36