Comparison of Microwave Imaging Algorithms for Short-Range Scenarios

Three dimensional(3-D)imaging algorithms with irregular planar multiple-input-multiple-output(MIMO)arrays are discussed and compared with each other.Based on the same MIMO array,a modified back projection algorithm(MBPA)is accordingly proposed and four imaging algorithms are used for comparison,back-projection method(BP),back-projection one in time domain(BP-TD),modified back-projection one and fast Fourier transform(FFT)-based MIMO range migration algorithm(FFT-based MIMO RMA).All of the algorithms have been implemented in practical application scenarios by use of the proposed imaging system.Back to the practical applications,MIMO array-based imaging system with wide-bandwidth properties provides an efficient tool to detect objects hidden behind a wall.An MIMO imaging radar system,composed of a vector network analyzer(VNA),a set of switches,and an array of Vivaldi antennas,have been designed,fabricated,and tested.Then,these algorithms have been applied to measured data collected in different scenarios constituted by five metallic spheres in the absence and in the presence of a wall between the antennas and the targets in simulation and pliers in free space for experimental test.Finally,the focusing properties and time consumption of the above algorithms are compared.

Study of the response of 10B-doped MCP to wide-energy range neutrons from eV to MeV

Neutron-sensitive microchannel plates(nMCPs)have applications in neutron detection,including energy spectrum measurements,neutron-induced cross sections,and neutron imaging.10B-doped MCPs(B-MCPs)have attracted significant attention owing to their potential for exhibiting a high neutron detection efficiency over a large neutron energy range.Good spatial and temporal resolutions are useful for neutron energy-resolved imaging.However,their practical applications still face many technical challenges.In this study,a B-MCP with 10 mol%10B was tested for its response to wide-energy neutrons from eV to MeV at the Back-n white neutron source at the China Spallation Neutron Source.The neutron detection efficiency was calibrated at 1 eV,which is approximately 300 times that of an ordinary MCP and indicates the success of 10 B doping.The factors that caused the reduction in the detection efficiency were simulated and discussed.The neutron energy spectrum obtained using B-MCP was compared with that obtained by other measurement methods,and showed very good consistency for neutron energies below tens of keV.The response is more complicated at higher neutron energy,at which point the elastic and nonelastic reactions of all nuclides of B-MCP gradually become dominant.This is beneficial for the detection of neutrons,as it compensates for the detection efficiency of B-MCP for high-energy neutrons.

Discoloration Range and Shroud Image Depth Values Cannot Be Satisfied by the Same Proton Energy

The radiative hypothesis has been revisited showing other characteristics, produced by the protons used as dyes in total disagree with the ones of the Body Image that appears on the Shroud of Turin. Our investigations highlight that for the protons to reach 3.7 cm in air, the distance that measures the range of discoloration effects, must be emitted with an energy of about 1.5 MeV using Wilson and Brobeck’s empirical formula and 1.35 MeV using Bethe’s. This last formula provides a result closer to reality. Bethe shows that the penetration depth is greater than that calculated empirically. Such a value of proton energy (1.35 MeV) makes it possible to satisfy the discoloration effects range for the Shroud but it is incompatible with a depth of penetration in linen that is only 200 nm. Moreover, using the same subatomic particles, we obtained on the colored linen a distribution of energy represented by regression but not linear. Thus, also the possible I(z) correlation, between color intensity and body-sheet distance, which should be due to the oxidizing action of protons, does not agree with that extracted from the Shroud of Turin.

Fast recognition using convolutional neural network for the coal particle density range based on images captured under multiple light sources

A method based on multiple images captured under different light sources at different incident angles was developed to recognize the coal density range in this study.The innovation is that two new images were constructed based on images captured under four single light sources.Reconstruction image 1 was constructed by fusing greyscale versions of the original images into one image,and Reconstruction image2 was constructed based on the differences between the images captured under the different light sources.Subsequently,the four original images and two reconstructed images were input into the convolutional neural network AlexNet to recognize the density range in three cases:-1.5(clean coal) and+1.5 g/cm^(3)(non-clean coal);-1.8(non-gangue) and+1.8 g/cm^(3)(gangue);-1.5(clean coal),1.5-1.8(middlings),and+1.8 g/cm^(3)(gangue).The results show the following:(1) The reconstructed images,especially Reconstruction image 2,can effectively improve the recognition accuracy for the coal density range compared with images captured under single light source.(2) The recognition accuracies for gangue and non-gangue,clean coal and non-clean coal,and clean coal,middlings,and gangue reached88.44%,86.72% and 77.08%,respectively.(3) The recognition accuracy increases as the density moves further away from the boundary density.

Range anomaly suppression based on neighborhood pixels detection in ladar range images

Research on the range anomaly suppression algorithm in laser radar （ladar） range images is significant in the application and development of ladar. But most of existing algorithms cannot protect the edge and linear target well while suppressing the range anomaly. Aiming at this problem, the differences among the edge, linear target, and range anomaly are analyzed and a novel algo- rithm based on neighborhood pixels detection is proposed. Firstly, the range differences between current pixel and its neighborhood pixels are calculated. Then, the number of neighborhood pixels is detected by the range difference threshold. Finally, whether the current pixel is a range anomaly is distinguished by the neighbor- hood pixel number threshold. Experimental results show that the new algorithm not only has a better range anomaly suppression performance and higher efficiency, but also protects the edge and linear target preferably compared with other algorithms.

A Method for Registering Multiple Range Images

This paper presents an algorithm for registering multiple range images, which is efficient and robust in the presence of noise and occlusion. The registration algorithm is an integration of the iterative closest point(ICP) algorithm with random sampling and least squares(LS) estimator. It iterates the processes of random sampling with conditional reject, estimation of motion parameters by using ICP algorithm and evaluation of the estimation. The algorithm first creates an octree spline of object surface to quickly compute point to surface distance and its closest point using trilinear interpolation. The advantages of the proposed method are the reduction of computational cost and robustness to outliers.

Distortion-Free Data Embedding Scheme for High Dynamic Range Images

Distortion-free data embedding is a technique which can assure that not only the secret data is correctly extracted but also the cover media is recovered without any distortion after secret data is extracted completely. Because of these advantages, this technique attracts the attention of many researchers. In this paper, a new distortion-free data embedding scheme for high dynamic range （HDR） images is proposed. By depending on Cartesian product, this scheme can obtain higher embedding capacity while maintaining the exactly identical cover image and stego image when using the tone mapping algorithms. In experimental results, the proposed scheme is superior to Yu et aL＇s scheme in regard to the embedding rate——an average embedding rate of 0.1355 bpp compared with Yn et aL＇s scheme （0.1270 bpp）.

Shape recovery using high dynamic range images

An effective method for object shape recovery using HDRIs （high dynamic range images） is proposed. The radiance values of each point on the reference sphere and target object are firstly calculated, thus the set of candidate normals of each target point are found by comparing its radiance to that of each reference sphere point. In single-image shape recovery, a smoothness operation is applied to the target normals to obtain a stable and reasonable result; while in photometric stereo, radiance vectors of reference and target objects formed due to illuminations under different fight source directions are directly compared to get the most suitable target normals. Finally, the height values can be recovered from the resulting normal field. Because diffuse and specular reflection are handled in an unified framework with radiance, our approach eliminates the limitation presented in most recovery strategies, i.e., only Lambertian model can be used. The experiment results from the real and synthesized images show the performance of our approach.

Edge detection of range images using genetic neural networks

Due to the complexity and asymmetrical illumination, the images of object are difficult to be effectively segmented by some routine method. In this paper, a kind of edge detection method based on image features and genetic algorithms neural network for range images was proposed. Fully considering the essential difference between an edge point and a noise point, some characteristic parameters were extracted from range maps as the input nodes of the network in the algorithm. Firstly, a genetic neural network was designed and implemented. The neural network is trained by genetic algorithm, and then genetic neural network algorithm is combined with the virtue of global optimization of genetic algorithm and the virtue of parallel computation of neural network, so that this algorithm is of good global property. The experimental results show that this method can get much faster and more accurate detection results than the classical differential algorithm, and has better anti-noise performance.

Fast Scheme for Projective Geometric Correction and Edge Blending Based on High Dynamic Range Images

With the development of graphic processing unit(GPU)power,it is now possible to implement geometric correction and edge blending functions on a single computer.However,the processing resources consumed by the geometric correction and edge blending phases still burden the system and slow down the main application considerably.A new platform independent scheme is proposed,minimizing the negative influence on performance.In this scheme,parameters for geometric correction and edge blending are firstly defined in an interactive way and recorded as a 32-bit high dynamic range(HDR) image,which is then used by high level shading language(HLSL) codes embedded in the main application as a lookup table,greatly reducing the computational complexity and enhancing flexibility.

Relational graph location network for multi-view image localization

In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.

High Resolution SAR Image Algorithm with Sample Length Constraints for the Range Direction

The traditional Range Doppler(RD)algorithm is unable to meet practical needs owing to the limit of resolution.The order of fractional Fourier Transform(FrFT)and the length of sampling signals affect SAR imaging performance when FrFT is applied to RD algorithm.To overcome the above shortcomings,the purpose of this paper is to propose a high-resolution SAR image algorithm by using the optimal order of FrFT and the sample length constraints for the range direction.The expression of the optimal order of SAR range signals via FrFT is deduced in detail.The initial sample length and its constraints are proposed to obtain the best sample length of SAR range signals.Experimental results demonstrate that,when the range sampling-length changes in a certain interval,the best sampling-length will be obtained,which the best values of the range resolution,PSLR and ISLR,will be derived respectively.Compared with traditional RD algorithm,the main-lobe width of the peak-point target of the proposed algorithm is narrow in the range direction.While the peak amplitude of the first side-lobe is reduced significantly,those of other side-lobes also drop in various degrees.

Novel reference range selection method in InISAR imaging

Aiming at the reference range selection for different antennas in interferometric inverse synthetic aperture radar （InlSAR） systems, this paper proposes a respective focusing （RF） method. The reference ranges for echoes of different antennas are selected respectively for RF, which is different from the traditional uniform focusing （UF） with the same reference range applied to all the antennas. First, a comparison between UF and RF for InlSAR signal model considering the ranging error is given. Compared with RF, UF has an advantage in overcoming the ranging error differences between different antennas. Then the influence of ranging error upon the interferometric imaging with RF is investigated particularly, and it is found that the ranging error differences between different antennas are far smaller than the wavelength, which is advantageous to imaging. By comparing the capabilities of inter- ferometric imaging between RF and UF, it is concluded that RF is a better choice in conquering problems such as image mismatching and phase ambiguity even with ranging errors. Simulations demonstrate the validity of the proposed method.

SEGMENTATION OF RANGE IMAGE BASED ON KOHONEN NEURAL NETWORK

This paper presents an unsupervised range image segmentation based on Kohonen neural network. At first, the derivative and partial derivative of each point are calculated and the normal in each points is gotten. With the character vectors including normal and range value, self-organization map is introduced to cluster. The normal analysis is used to eliminate over-segmentation and the last result is gotten. This method avoid selecting original seeds and uses fewer samples, moreover computes rapidly. The experiment shows the better performance.

Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method

The range accuracy of three-dimensional（3D） ghost imaging is derived. Based on the derived range accuracy equation, the relationship between the slicing number and the range accuracy is analyzed and an optimum slicing number（OSN）is determined. According to the OSN, an improved 3D ghost imaging algorithm is proposed to increase the range accuracy. Experimental results indicate that the slicing number can affect the range accuracy significantly and the highest range accuracy can be achieved if the 3D ghost imaging system works with OSN.

Lookup Table Hough Transform for Real Time Range Image Segmentation and Featureless Co-Registration

The paper addresses range image segmentation, particularly of data recorded by range cameras, such as the Microsoft Kinect and the Mesa Swissranger SR4000. These devices record range images at video frame rates and allow for acquisition of 3-dimensional measurement sequences that can be used for 3D reconstruction of indoor environments from moving platforms. The role of segmentation is twofold. First the necessary image co-registration can be based on corresponding segments, instead of corresponding point features (which is common practice currently). Secondly, the segments can be used during subsequent object modelling. By realisising that planar regions in disparity images can be modelled as linear functions of the image coordinates, having integer values for both domain and range, the paper introduces a lookup table based implementation of local Hough transform, allowing to obtain good segmentation results at high speeds.

Analysis of Wide-Bandgap Material OPFET UV Detectors for High Dynamic Range Imaging and Communication Applications

The ultraviolet (UV) photoresponses of Wurtzite GaN, ZnO, and 6H-SiC-based Optical Field Effect Transistor (OPFET) detectors are estimated with an in-depth analysis of the same considering the generalized model and the front-illuminated model for high resolution imaging and UV communication applications. The gate materials considered for the proposed study are gold (Au) and Indium-Tin-Oxide (ITO) for GaN, Au for SiC, and Au and silver dioxide (AgO2) for ZnO. The results indicate significant improvement in the Linear Dynamic Range (LDR) over the previously investigated GaN OPFET (buried-gate, front-illuminated and generalized) models with Au gate. The generalized model has superior dynamic range than the front-illuminated model. In terms of responsivity, all the models including buried-gate OPFET exhibit high and comparable photoresponses. Buried-gate devices on the whole, exhibit faster response than the surface gate models except in the AgO2-ZnO generalized OPFET model wherein the switching time is the lowest. The generalized model enables faster switching than the front-illuminated model. The switching times in all the cases are of the order of nanoseconds to picoseconds. The SiC generalized OPFET model shows the highest 3-dB bandwidths of 11.88 GHz, 36.2 GHz, and 364 GHz, and modest unity-gain cut-off frequencies of 4.62 GHz, 8.71 GHz, and 5.71 GHz at the optical power densities of 0.575 μW/cm2, 0.575 mW/cm2, and 0.575 W/cm2 respectively. These are in overall, the highest detection-cum-amplifi-cation bandwidths among all the investigated devices. The same device exhibits the highest LDR of 73.3 dB. The device performance is superior to most of the other existing detectors along with comparable LDR, thus, emerging as a high performance photodetector for imaging and communication applications. All the detectors show considerably high detectivities owing to the high responsivity values. The results have been analyzed by the photovoltaic and the photoconductive effects, and the series resistance effects and will aid in conducting further research. The results are in line with the experiments and the commercially available software simulations. The devices will greatly contribute towards single photon counting, high resolution imaging, and UV communication applications.

3D Laser Imaging Reconstruction of Scenes Using New Photometric Gaussian Beam Range Equation

In this article the problem of three-dimensional （3-D） reconstruction of scenes from terrestrial laser rangefinder data is processed, we present the simulations results of radiometric range function of the laser range finder imaging system for better optimization of maximum range detection with a given SNR （Signal-to-noise ratio）. The study proposes a new reformulation of the radiometric Gaussian beam range equation on the basis of the photodetector active area. The adjustment of the optical and geometrical characteristics of the laser range finder so defined can be used to minimize the signal span to avoid the saturation of the detector cell and improve the ranging measurements （accuracy and range） for a 3D imaging application.

High Dynamic Range Image Fusion Based on Wavelet

With the developpment of image fusion technology and the maturity of wavelet theory, wavelet transform with its good time-frequency characteristics stands out in the field of image fusion. On the basis of wavelet transforms theory, this article proposes a high dynamic range imaging confusion method which combines with wavelet decomposition. First, perform a wavelet multi-scale decomposition to the two registered source image; then conduct wavelet inverse transform to the decomposed images. This paper focuses on the characteristics of high frequency and low frequency domain after wavelet decomposition,using different fusion methods in each of the frequency domain, finally obtain the fused image through inverse wavelet transform image reconstruction. The simulation results and evaluation index results show that, compared with other similar methods, this method is better in retaining the original image's details information, and improves the quality of fusion image.

RESEARCH ON 3 D RANGE SENSOR SYSTEM

The structure of 3 D range sensor system for unmanned vehicle is introduced.Several key technologies are described in detail.The ultrasonic signal is processed from different angles.Image edges are extracted by the multiple scale method.Image noises are recognized and cleared by using multilayer forward neural network with fuzzy logic.The method of information fusion for multiple sensors is discussed.This paper also gives some ultrasonic signal curves and image results.