- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Compact Fully Metallic Millimeter-Wave Waveguide-Fed Periodic Leaky-Wave Antenna Based on Corrugated Parallel-Plate Waveguides
摘要： A three-dimensional (3-D) finite-difference time-domain (FDTD) algorithm is used in order to simulate ground penetrating radar (GPR) for landmine detection. Two bowtie GPR transducers are chosen for the simulations and two widely employed antipersonnel (AP) landmines, namely PMA-1 and PMN are used. The validity of the modeled antennas and landmines is tested through a comparison between numerical and laboratory measurements. The modeled AP landmines are buried in a realistically simulated soil. The geometrical characteristics of soil’s inhomogeneity are modeled using fractal correlated noise, which gives rise to Gaussian semivariograms often encountered in the field. Fractals are also employed in order to simulate the roughness of the soil’s surface. A frequency-dependent complex electrical permittivity model is used for the dielectric properties of the soil, which relates both the velocity and the attenuation of the electromagnetic waves with the soil’s bulk density, sand particles density, clay fraction, sand fraction, and volumetric water fraction. Debye functions are employed to simulate this complex electrical permittivity. Background features like vegetation and water puddles are also included in the models and it is shown that they can affect the performance of GPR at frequencies used for landmine detection (0.5–3 GHz). It is envisaged that this modeling framework would be useful as a testbed for developing novel GPR signal processing and interpretations procedures and some preliminary results from using it in such a way are presented.
关键词： rough surface,GPR,water puddles,modeling,FDTD,antipersonnel (AP) landmines,roots,dispersive,fractals,Antennas,bowtie,GprMax,grass,vegetation
更新于2025-09-23 15:19:57
-
[IEEE 2019 IEEE Sustainable Power and Energy Conference (iSPEC) - Beijing, China (2019.11.21-2019.11.23)] 2019 IEEE Sustainable Power and Energy Conference (iSPEC) - Demand Response Quantification Strategy for High Photovoltaic Penetration Systems with Thermal Ramp Rate Limitation
摘要： Hidden Markov models (HMMs) have previously been successfully applied to subsurface threat detection using ground penetrating radar (GPR) data. However, parameter estimation in most HMM-based landmine detection approaches is difficult since object locations are typically well known for the 2-D coordinates on the Earth’s surface but are not well known for object depths underneath the ground/time of arrival in a GPR A-scan. As a result, in a standard expectation maximization HMM (EM-HMM), all depths corresponding to a particular alarm location may be labeled as target sequences although the characteristics of data from different depths are substantially different. In this paper, an alternate HMM approach is developed using a multiple-instance learning (MIL) framework that considers an unordered set of HMM sequences at a particular alarm location, where the set of sequences is defined as positive if at least one of the sequences is a target sequence; otherwise, the set is defined as negative. Using the MIL framework, a collection of these sets (bags), along with their labels is used to train the target and nontarget HMMs simultaneously. The model parameters are inferred using variational Bayes, making the model tractable and computationally efficient. Experimental results on two synthetic and two landmine data sets show that the proposed approach performs better than a standard EM-HMM.
关键词： variational Bayes (VB),hidden landmine detection,Ground penetrating radar (GPR),multiple-instance learning (MIL)
更新于2025-09-19 17:13:59
-
[IEEE 2019 International Energy and Sustainability Conference (IESC) - Farmingdale, NY, USA (2019.10.17-2019.10.18)] 2019 International Energy and Sustainability Conference (IESC) - Influence of Photovoltaic Installations on Employability and Education in Senegal
摘要： Hidden Markov models (HMMs) have previously been successfully applied to subsurface threat detection using ground penetrating radar (GPR) data. However, parameter estimation in most HMM-based landmine detection approaches is difficult since object locations are typically well known for the 2-D coordinates on the Earth’s surface but are not well known for object depths underneath the ground/time of arrival in a GPR A-scan. As a result, in a standard expectation maximization HMM (EM-HMM), all depths corresponding to a particular alarm location may be labeled as target sequences although the characteristics of data from different depths are substantially different. In this paper, an alternate HMM approach is developed using a multiple-instance learning (MIL) framework that considers an unordered set of HMM sequences at a particular alarm location, where the set of sequences is defined as positive if at least one of the sequences is a target sequence; otherwise, the set is defined as negative. Using the MIL framework, a collection of these sets (bags), along with their labels is used to train the target and nontarget HMMs simultaneously. The model parameters are inferred using variational Bayes, making the model tractable and computationally efficient. Experimental results on two synthetic and two landmine data sets show that the proposed approach performs better than a standard EM-HMM.
关键词： variational Bayes (VB),hidden landmine detection,Ground penetrating radar (GPR),multiple-instance learning (MIL)
更新于2025-09-19 17:13:59
-
[IEEE 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - HangZhou, China (2018.9.5-2018.9.7)] 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - Reconfigurable Half-Mode Substrate Integrated Waveguide Filter With Wide Out-Of-Band Rejection
摘要： A three-dimensional (3-D) finite-difference time-domain (FDTD) algorithm is used in order to simulate ground penetrating radar (GPR) for landmine detection. Two bowtie GPR transducers are chosen for the simulations and two widely employed antipersonnel (AP) landmines, namely PMA-1 and PMN are used. The validity of the modeled antennas and landmines is tested through a comparison between numerical and laboratory measurements. The modeled AP landmines are buried in a realistically simulated soil. The geometrical characteristics of soil’s inhomogeneity are modeled using fractal correlated noise, which gives rise to Gaussian semivariograms often encountered in the field. Fractals are also employed in order to simulate the roughness of the soil’s surface. A frequency-dependent complex electrical permittivity model is used for the dielectric properties of the soil, which relates both the velocity and the attenuation of the electromagnetic waves with the soil’s bulk density, sand particles density, clay fraction, sand fraction, and volumetric water fraction. Debye functions are employed to simulate this complex electrical permittivity. Background features like vegetation and water puddles are also included in the models and it is shown that they can affect the performance of GPR at frequencies used for landmine detection (0.5–3 GHz). It is envisaged that this modeling framework would be useful as a testbed for developing novel GPR signal processing and interpretations procedures and some preliminary results from using it in such a way are presented.
关键词： rough surface,GPR,water puddles,modeling,FDTD,antipersonnel (AP) landmines,roots,dispersive,fractals,Antennas,bowtie,GprMax,grass,vegetation
更新于2025-09-19 17:13:59
-
A plasmonic nano-biosensor based on two consecutive disk resonators and unidirectional reflectionless propagation effect
摘要： Hidden Markov models (HMMs) have previously been successfully applied to subsurface threat detection using ground penetrating radar (GPR) data. However, parameter estimation in most HMM-based landmine detection approaches is difficult since object locations are typically well known for the 2-D coordinates on the Earth’s surface but are not well known for object depths underneath the ground/time of arrival in a GPR A-scan. As a result, in a standard expectation maximization HMM (EM-HMM), all depths corresponding to a particular alarm location may be labeled as target sequences although the characteristics of data from different depths are substantially different. In this paper, an alternate HMM approach is developed using a multiple-instance learning (MIL) framework that considers an unordered set of HMM sequences at a particular alarm location, where the set of sequences is defined as positive if at least one of the sequences is a target sequence; otherwise, the set is defined as negative. Using the MIL framework, a collection of these sets (bags), along with their labels is used to train the target and nontarget HMMs simultaneously. The model parameters are inferred using variational Bayes, making the model tractable and computationally efficient. Experimental results on two synthetic and two landmine data sets show that the proposed approach performs better than a standard EM-HMM.
关键词： multiple-instance learning (MIL),variational Bayes (VB),Ground penetrating radar (GPR),hidden landmine detection
更新于2025-09-19 17:13:59
-
A CUDA-based GPU engine for gprMax: Open source FDTD electromagnetic simulation software
摘要： The Finite-Difference Time-Domain (FDTD) method is a popular numerical modelling technique in computational electromagnetics. The volumetric nature of the FDTD technique means simulations often require extensive computational resources (both processing time and memory). The simulation of Ground Penetrating Radar (GPR) is one such challenge, where the GPR transducer, subsurface/structure, and targets must all be included in the model, and must all be adequately discretised. Additionally, forward simulations of GPR can necessitate hundreds of models with different geometries (A-scans) to be executed. This is exacerbated by an order of magnitude when solving the inverse GPR problem or when using forward models to train machine learning algorithms. We have developed one of the first open source GPU-accelerated FDTD solvers specifically focussed on modelling GPR. We designed optimal kernels for GPU execution using NVIDIA’s CUDA framework. Our GPU solver achieved performance throughputs of up to 1194 Mcells/s and 3405 Mcells/s on NVIDIA Kepler and Pascal architectures, respectively. This is up to 30 times faster than the parallelised (OpenMP) CPU solver can achieve on a commonly-used desktop CPU (Intel Core i7-4790K). We found the cost-performance benefit of the NVIDIA GeForce-series Pascal-based GPUs – targeted towards the gaming market – to be especially notable, potentially allowing many individuals to benefit from this work using commodity workstations. We also note that the equivalent Tesla-series P100 GPU – targeted towards data-centre usage – demonstrates significant overall performance advantages due to its use of high-bandwidth memory. The performance benefits of our GPU-accelerated solver were demonstrated in a GPR environment by running a large-scale, realistic (including dispersive media, rough surface topography, and detailed antenna model) simulation of a buried anti-personnel landmine scenario.
关键词： GPGPU,Finite-Difference Time-Domain,GPU,CUDA,GPR,NVIDIA
更新于2025-09-11 14:15:04
-
Energy-exergy modeling of solar radiation with most influencing input parameters
摘要： In this study, a new soft computing model Gaussian process regression (GPR) was evaluated for modeling the total solar radiation (TSR) and exergy (Ф) in Hakkari province (the region with the highest sunshine duration), Turkey. For this purpose, meteorological data include average, maximum and minimum temperature (Tave, Tmax, Tmin), relative humidity (H), sea level pressure (P), wind speed (W), and total sunbathing time (TST), wihch were used, and sensitivity analysis was applied for evaluating the results of TSR and Ф modeling. The results showed that all the input variables have significant impact on TSR and Ф modeling. Mean absolute percentage error and coefficient of determination (R2) for TSR and Ф predicted by GPR were 1.51–7.02% and 0.97–0.95, respectively. Application of five-fold cross validation method showed that GPR model is able to predict the TSR and Ф with a small size of data, but for more accuracy, it is suggested to use more than 70% of total data set for training the models. This research showed that GPR has a good ability for modeling the TSR and Ф with high accuracy, and so the engineers can use this method for the TSR and Ф prediction without using the solar radiation or exergy-to-energy ratio.
关键词： solar energy,Solar radiation exergy,Hakkari province of Turkey,Gaussian process regression (GPR),modeling
更新于2025-09-09 09:28:46
-
[IEEE 2018 9th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS) - Odessa, Ukraine (2018.9.4-2018.9.7)] 2018 9th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS) - UWB Signal Processing for the Solving Inverse Scattering Problem of Plane-Layered Media
摘要： The possibilities of using UWB pulse signals GPR for solving inverse scattering problems, especially in the frequency domain, have been investigated. The proposed computational algorithms are based on previously developed iterative schemes for solving inverse scattering problems by a procedure of minimizing the auxiliary residual functional. To carry out computational experiments initial data synthesized by software constructing the Green function for corresponding direct diffraction problems are used. The values of electrical and geometric parameters of plane-layer media were chosen primarily based on the specifics of the problems of nondestructive control of road pavements and other building structures, as well as the problems of biomedical research. The obtained results allow to improve the effectiveness of monitoring methods of the road pavements, subbase and subgrade current condition.
关键词： plane-layered stratified medium,UWB signals,road pavements,inverse scattering problem,GPR
更新于2025-09-09 09:28:46
-
[IEEE 2018 17th International Conference on Ground Penetrating Radar (GPR) - Rapperswil, Switzerland (2018.6.18-2018.6.21)] 2018 17th International Conference on Ground Penetrating Radar (GPR) - An FPGA-based Flexible and MIMO-capable GPR System
摘要： Ground penetrating radar (GPR) has broad applications in non-destructive subsurface imaging. Most GPRs on the market are bistatic devices that illuminate the buried objects using analog pulses with simple Gaussian-like shapes. These GPRs suffer from drift in the scan results and have either a low-resolution or a low depth of scan, which limits their application. High resolution along with an increased depth of scan can be achieved by transmitting maximal length pseudorandom sequences (m-sequences) which enable pulse compression due to their near-ideal autocorrelation properties. In addition, improved object localization and reduced drift can be obtained with the spatial diversity offered by a MIMO transceiver. This paper discusses the design and implementation of a 8×8 MIMO-capable impulse-based GPR that transmits m-sequences generated on a low-cost FPGA platform, performs a quadrature transform on the received signal to reduce computation, and implements sub-sampling to sample the quadrature-converted signals using low-speed ADCs. Preliminary experimental results are also presented.
关键词： multistatic,MIMO,pulse compression,subsampling,m-sequence,GPR,Ground penetrating radar
更新于2025-09-09 09:28:46
-
[IEEE 2018 17th International Conference on Ground Penetrating Radar (GPR) - Rapperswil, Switzerland (2018.6.18-2018.6.21)] 2018 17th International Conference on Ground Penetrating Radar (GPR) - Structural Planes Parameters Extraction Method Based on Borehole Digital Optical Image and GPR
摘要： This paper combines the borehole ground penetrating radar imaging and digital optical borehole imaging, presents an efficient method to recognize and extract the structural planes parameters of rock mass. In this method, it’s necessary to transform the digital optical image in color modeling. Processing such as segmentation and edge refinement are essential steps. Afterwards, matching algorithm is employed by using a transform of sinusoidal curve to extract the fitting parameters. Finally, the image feature data acquired by the borehole GPR image is integrated for verification. With this method, the physical and geometrical characteristics of the rock mass at and around the borehole can be revealed. It can effectively exert the respective characteristics of the two exploration technologies to identify the structural planes in a continuous and rapid way. It has proven highly reliable and efficient.
关键词： structural plane,curve fitting algorithm,borehole digital optical image,identification,borehole GPR image
更新于2025-09-09 09:28:46