Integration of the differential evolution algorithm and the Monte Carlo code to create a spectrometric detector model

A standard procedure for characterizing the high-purity germanium detector (HPGe), manufactured by Canberra Industries Inc [1], is performed directly by the company using patented methods. However, the procedure is usually expensive and must be repeated because the characteristics of the HPGe crystal change over time. In this work, the principles of a technique are developed for use in obtaining and optimizing the detector characteristics based on a cost-effective procedure in a standard research laboratory. The technique requires that the detector geometric parameters are determined with maximum accuracy by the Monte Carlo method [2] in parallel with the optimization based on evolutionary algorithms. The development of this approach facilitates modeling of the HPGe detector as a standardized procedure. The results will be also beneficial in the development of gamma spectrometers and/or their calibrations before routine measurements.

On the Quantum Mechanical Description of the Interaction between Particle and Detector

Quantum measurements of physical quantities are often described as ideal measurements. However, only a few measurements fulfil the conditions of ideal measurements. The aim of the present work is to describe real position measurements with detectors that are able to detect single particles. For this purpose, a detector model is developed that can describe the time dependence of the interaction between a non-relativistic particle and a detector. The example of a position measurement shows that this interaction can be described with the methods of quantum mechanics. At the beginning of a position measurement, the detector behaves as a target consisting of a large number of quantum mechanical systems. In the first reaction, the incident particle interacts with a single atom, electron or nucleus, but not with the whole detector. This reaction and all following reactions are quantum mechanical processes. At the end of the measurement, the detector can be considered as a classical apparatus. A detector is neither a quantum mechanical system nor a classical apparatus. The detector model explains why one obtains a well-defined result for each individual position measurement. It further explains that, in general, it is impossible to predict the outcome of an individual measurement.

Nonlinear Analytical Model of Localized Sub-THz and THz Rectifications in FET Power Detectors

<div>A nonlinear analytical model for THz FET power detectors based on their distributed RC network is presented. This empirical model works well for both drain-unbiased and drain-biased THz FET responses. The physics-based analysis reveals that localized THz rectifications in long channel transistors may be mathematically expressed in the same way as regular RF frequency rectifications of a single lumped device. However, the one lumped FET model can’t work properly at THz frequencies without correct definitions of THz signals on its terminals and independently considers localized rectifications on the source and drain sides. An improved compact one lumped THz FET power detector model with additional Schottky diodes at the source and drain terminals is presented. THz FET detector can also perform a simultaneous self-amplification (active rectification) of the localized THz rectified dc signal when operates in the saturation regime beyond its unity gain frequency. A novel analytical expression for the localized THz dc rectified response is developed for FETs operating in the saturation regime. The presented physics-based model agrees excellently with the measured experimental results of GaAs HEMT transistors at 1.6THz under arbitrary biasing conditions. Many novel electronic designs can be implemented for Millimeter-wave and THz technologies based on the physical FET's nonlinear nature in this frequency range</div>

Nonlinear Analytical Model of Localized Sub-THz and THz Rectifications in FET Power Detectors

<div>A nonlinear analytical model for THz FET power detectors based on their distributed RC network is presented. This empirical model works well for both drain-unbiased and drain-biased THz FET responses. The physics-based analysis reveals that localized THz rectifications in long channel transistors may be mathematically expressed in the same way as regular RF frequency rectifications of a single lumped device. However, the one lumped FET model can’t work properly at THz frequencies without correct definitions of THz signals on its terminals and independently considers localized rectifications on the source and drain sides. An improved compact one lumped THz FET power detector model with additional Schottky diodes at the source and drain terminals is presented. THz FET detector can also perform a simultaneous self-amplification (active rectification) of the localized THz rectified dc signal when operates in the saturation regime beyond its unity gain frequency. A novel analytical expression for the localized THz dc rectified response is developed for FETs operating in the saturation regime. The presented physics-based model agrees excellently with the measured experimental results of GaAs HEMT transistors at 1.6THz under arbitrary biasing conditions. Many novel electronic designs can be implemented for Millimeter-wave and THz technologies based on the physical FET's nonlinear nature in this frequency range</div>

Spatial–Temporal Evolution of Vegetation NDVI in Association with Climatic, Environmental and Anthropogenic Factors in the Loess Plateau, China during 2000–2015: Quantitative Analysis Based on Geographical Detector Model

In the Loess Plateau (LP) of China, the vegetation degradation and soil erosion problems have been shown to be curbed after the implementation of the Grain for Green program. In this study, the LP is divided into the northwestern semi-arid area and the southeastern semi-humid area using the 400 mm isohyet. The spatial–temporal evolution of the vegetation NDVI during 2000–2015 are analyzed, and the driving forces (including factors of climate, environment, and human activities) of the evolution are quantitatively identified using the geographical detector model (GDM). The results showed that the annual mean NDVI in the entire LP was 0.529, and it decreased from the semi-humid area (0.619) to the semi-arid area (0.346). The mean value of the coefficient of variation of the NDVI was 0.1406, and it increased from the semi-humid area (0.1165) to the semi-arid area (0.1926). The annual NDVI growth rate in the entire LP was 0.0079, with the NDVI growing faster in the semi-humid area (0.0093) than in the semi-arid area (0.0049). The largest increments of the NDVI were from grassland, farmland, and woodland. The GDM results revealed that changes in the spatial distribution of the NDVI could be primarily explained by the climatic and environmental factors in the semi-arid area, such as precipitation, soil type, and vegetation type, while the changes were mainly explained by the anthropogenic factors in the semi-humid area, such as the GDP density, land-use type, and population density. The interactive analysis showed that interactions between factors strengthened the impacts on the vegetation change compared with an individual factor. Furthermore, the ranges/types of factors suitable for vegetation growth were determined. The conclusions of this study have important implications for the formulation and implementation of ecological conservation and restoration strategies in different regions of the LP.

Calculation of the Coupling Coefficient of Electrons in Laser Induced Plasma for Samples of Writing Ink Elements Using LIBS Technique

The technique of laser breakdown spectroscopy (LIBS) was employed for samples of writing inks under the influence of a Nd:YAG laser pulse 1064 nm with a pulse duration of 10 ns on different targets of writing ink models. The plasma parameters were also calculated, which are the temperature and density of electrons, assuming local thermodynamic equilibrium conditions (LTE) and using a spectral detector model (View spectra 2100) for the spectral range (200nm - 900nm). The results showed differences in the values of the pairing coefficient of electrons in the plasma. Produced due to the laser pulse used as well as in the plasma parameters mentioned, which can be applied in plasma spectroscopy for forensic sciences in detecting forgery in documents and tracking the performance and phenomena of the plasma formed due to the laser pulse.

Measuring the Thermal De-Broglie Wavelength in Laser-produced Plasma of different Samples of Writing Inks Elements Using LIBS Spectroscopy

In this paper, the technique of laser pulse breakdown spectroscopy (LIBS) under the influence of the pulse Nd:YAG laser of 1064nm wavelength and with a pulse time of 10ns was used on different samples of writing ink models. In this work, the de-Broglie wavelength was measured. After calculating the electron temperature and assuming the local thermal equilibrium conditions (LTE), and using a spectral detector model (View spectra 2100) within the spectral range (200nm-900nm), the results after performing the analysis showed differences in the D-Broglie thermal wavelength of the plasma. The formation and temperature of the electron, which can be applied in plasma spectroscopy processes in many sciences, including the field of forensic evidence, to detect forgery in documents and documents.

AN INTELLIGENT CBMIR SYSTEM FOR DETECTION AND LOCALIZATION OF LUNG DISEASES

The diagnosis of lung diseases is a complicated and time-consuming task for radiologists. Often radiologists struggle with accurately diagnosing lung diseases, They use Commonly CT imaging signs (CISs) which common appear in CT lung nodules in the diagnosis of lung diseases. Computer-aided diagnosis systems (CAD) can automatically diagnose and detect these signs by analyzing CT scans, which will reduce radiologists workload. The diagnosis and recognition efficiency and accuracy can be improved by using content-based medical image retrieval (CBMIR). This paper proposes a new intelligent CBMIR method to retrieve CISs helping in diagnosing and recognize lung diseases by using deep Convolutional Neural Network (CNN). Fine-tuned YOLOv4 (You Only Look Once) object detector model are proposed to fast detect and efficiently localize signs in real-time. The proposed CBMIR system can be applied as a useful and accurate medical instrument for diagnostics. The experimental results show an average detection accuracy of CT signs lung diseases as high as 92% and a mean average precision (MAP) of 0.92 is achieved using the proposed technique. Also, it takes only 0.1 milliseconds for the retrieval process. The proposed system presents high improvement as compared to the other system. It achieved better precision of retrieval results and the fastest run of the retrieval time.