Defining an equivalent geometry for Monte Carlo calculations of a high-purity Ge detector with high relative efficiency using a genetic algorithm.

Detailed geometric information of a high-purity Ge (HPGe) detector is a very important issue for Monte Carlo simulation of the detector. Commonly, users have no geometric information about the detector and information given by the manufacturer is not completely valid for simulation. An equivalent geometry of detector, the parameters of which can be used for Monte Carlo simulation, is optimised using a genetic algorithm for a large-volume HPGe detector in this study. A mixed-point gamma calibration standard, emitting 12 useful gamma-radiation energies within 59.5-1836.1 keV, is placed at 74 different locations around the detector for this purpose. A high-quality solution is generated starting from an initial population of randomly-generated detector geometries using a genetic algorithm. Fitness of each geometry is obtained by comparing full energy peak efficiencies computed by Monte Carlo simulation with experimental values for each energy and position. Efficiencies with relative errors less than 5% for high energies and less than 7% for lower energies, except 59.5 keV, are obtained using optimised equivalent geometry parameters for the Monte Carlo simulation. Also, the necessity of using crystal dimensions smaller than real dimensions for Monte Carlo simulations of high-volume HPGe detectors is discussed. In addition, for Monte Carlo simulation of high-volume HPGe detectors, it is demonstrated that the use of smaller crystal dimensions than the real dimensions is necessary to obtain experimentally measured efficiencies of the detector.

A time-dependent Monte Carlo approach to chance coincidence summing correction factor calculation for high-purity Ge gamma-ray spectroscopy.

The chance (random) coincidence correction factor (CCCF) for the counting geometry of a 137Cs point source placed very close to the end cap of a high-purity Ge coaxial detector with 50% relative efficiency was evaluated by a time-dependent Monte Carlo approach. The probability distributions of gamma-ray and X-ray energy depositions in the detector crystal were obtained by use of the MCNPX code. The signal resolving time of the electronic parts, one of the parameters needed for time-dependent Monte Carlo simulation, was evaluated experimentally by the moving-source method. Another parameter also needed for the simulation is the signal pile-up rejection time interval. A random pulse generator was replaced with the detector for this purpose and the value was calculated iteratively by comparing the spectrum obtained experimentally with the spectrum obtained from the time-dependent Monte Carlo simulation of the random pulse generator. A pulse train with a Poisson distribution in time was created, and these parameters with energy deposition probability distributions were used for theoretical determination of the high-count-rate spectrum and the low-count-rate spectrum. The CCCF for the experiment was calculated as 0.92 by our comparing these two theoretical spectra and agrees well with the experimental result, 0.94. Also, the results of paralyzable and nonparalyzable model approaches for dead time calculations were compared with the experimental results.

A neural network-based optimal spatial filter design method for motor imagery classification.

In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN) is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP) algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.

A new versatile underground gamma-ray spectrometry system.

The newest development in IRMM's underground analytical facility is a large lead shield lined with copper that is versatile and can host several detectors of different types. The characteristics and the background performance of the shield are described for four different detector configurations involving HPGe-detectors and NaI-detectors. The shield has been designed to swap detectors, while still maintaining a low background. This enables testing of detectors for other experiments and optimisation of detection limits for specific radionuclides in different projects.

Comparison of background in underground HPGe-detectors in different lead shield configurations.

In underground HPGe-detector systems where the cosmic ray induced background is low, it is often difficult to assess the location of background sources. In this study, background counting rates of different HPGe-detectors in different lead shields are reported with the aim of better understanding background sources. To further enhance the understanding of the variations of environmental parameters, the background as a function of time over a long period was also studied.

Simulation of Eisenmenger syndrome with ventricular septal defect using equivalent electronic system.

BACKGROUND: In this study, we aim to investigate the simulation of the cardiovascular system using an electronic circuit model under normal and pathological conditions, especially the Eisenmenger syndrome. METHODS AND RESULTS: The Eisenmenger syndrome includes a congenital communication between the systemic and pulmonary circulation, with resultant pulmonary arterial hypertension and right-to-left reversal of flow through the defect. When pulmonary vascular resistance exceeds systemic vascular resistance, it results in hypoxaemia and cyanosis. The Westkessel model including Resistor-Inductance-Capacitance pi-segments was chosen in order to simulate both systemic and pulmonary circulation. The left and right heart are represented by trapezoidal shape stiffness for better simulation results. The Eisenmenger syndrome is simulated using a resistance (septal resistance) connected between the left ventricle and right ventricle points of the model. Matlab® is used for the model implementation. In this model, although there is a remarkable increase in the pulmonary artery pressure and right ventricle pressure, left ventricle pressure, aortic pressure, aortic flow, and pulmonary compliance decrease in the Eisenmenger syndrome. In addition, left-to-right septal flow reversed in these diseases. CONCLUSION: Our model is effective and available for simulating normal cardiac conditions and cardiovascular diseases, especially the Eisenmenger syndrome.

Simulation of normal cardiovascular system and severe aortic stenosis using equivalent electronic model.

OBJECTIVE: In this study, we have designed an analog circuit model of the cardiovascular system that is able to simulate normal condition and cardiovascular diseases, such as mitral stenosis, aortic stenosis, and hypertension. Especially we focused on severe aortic stenosis, because it is one of the causes of sudden death in asymptomatic patients. In this study, we aim to investigate the simulation of the cardiovascular system using an electronic circuit model under normal and especially severe aortic valve stenosis conditions. METHODS: The Westkessel model including RLC pi-segments is chosen in order to simulate both systemic and pulmonary circulation. The left and right heart is represented by trapezoidal shape stiffnesses. Aortic capacitance and aortic valve characteristics are chosen nonlinear. Severe aortic stenosis is implemented by changing the value of the serial resistance to the aortic valve. MATLAB software program is used for the model implementation. RESULTS: The results for normal conditions of the given electrical model are similar to the normal cardiovascular physiology. As a result of simulation, a remarkable increase of the left ventricle systolic blood pressure and aortic mean pressure gradient, and decrease of aortic systolic blood pressure are observed in severe aortic valve stenosis. CONCLUSION: In conclusion, our model is effective and available for simulating normal cardiac conditions and cardiovascular diseases, especially severe aortic stenosis.