To address the problems of inaccurate dose rate and long-period measurement, this paper presents a nuclear radiation detector with the function of directional determination, as well as the corresponding Bayesian-based strength estimation method. Firstly, a vehicle-mounted radiation detector with large measurement range is designed, together with the steering mechanism and size-differentiated shielding shell, completing the ultra-wide measurement range of 0.15μGy/h~500Gy/h and the precise directional aiming functions. Enlightened by the characteristics of conjugated Gamma distribution, the radiation strength estimation method is established in an iterative manner. By comparing and analyzing the convergence properties and prediction efficiency under different shape parameters γ and scale parameters β, the selection criteria for initial model parameters is clarified. Finally, the proposed nuclear detector and estimation method are applied in the actual radiation source searching task, verifying the real-time, effectiveness, and accuracy of the overall radiation source detection system. The experimental results show that, the measurement method based on conjugated distribution theory can significantly reduce the number of estimated iterations to less than 15 steps, and improve the accuracy of the directional aiming function, ensuring the ability of on-line exploitation and exploration for the mobile system.
Computer vision is a mode task that uses computer to learn from training data and apply the learned experience to specific task. When patient experiences hallucinations, brain will call the memory stored in the hippocampus and process through specific pattern, and brain visual terminal will receive the image that does not exist in the real world. Patients with Parkinson’s disease (PD) for more than 10 years are usually accompanied by hallucinations, which leads to changes in visual information processing and information perception pathway in brain. Electroencephalogram (EEG) is a spontaneous electrophysiological signal of the brain, which can reflect the working mechanism of the brain. In order to explore the changes of brain information perception pathway in PD, we recruited 5 PD patients with hallucinations, 5 healthy elderly and 5 healthy young subjects to participate visual classification experiment. The EEG collected in the experiment was analyzed by spectrum and brain network. The analysis found that the Delta, theta and alpha power of frontal lobe and occipital lobe in PD patients increased, the beta and gamma power decreased, and the brain fell into the internal circulation, the interaction between brain regions decreased. The lack of interaction may be one of the main causes of hallucinations.
KEYWORDS: Brain, Neuroimaging, Diffusion tensor imaging, Diffusion, Statistical analysis, Image classification, Data acquisition, Medical research, Medical imaging, Magnetic resonance imaging
The aim of this study is to research the features of white matter in the brain by Diffusion Tensor Image (DTI) from the patients with mild cognitive impairment (MCI) and using the features to identify MCI and Normal Control (NC) to explore new methods for MCI diagnosis. In this study, 38 brain DTI images of MCI patients and NC were extracted respectively, and the parameters of cerebral white matter fiber tracts were analyzed. Using automatic fiber tract quantification (AFQ) technology, the index values with significant differences between MCI patients and NC were calculated. Support Vector Machine (SVM) model was built to classify MCI patients from NC. We found significant differences in right cortical spinal tract (CST_R), right uncinate fasciculus (UNC_R), left internal fronto-occipital tract (IFOF_L), and Callosum Forceps major (FP) in MCI patients and NC. The classification accuracy, sensitivity and specificity of the training set and test set were 94.73%, 92.11% and 97.36%, respectively. This study demonstrates that there are significant differences in certain fiber tracts in MCI patients compared with NC and using these fiber tract groups can effectively classify the MCI patients and NC, which can provide novel information for MCI white matter decline.
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