Far-Field Radar Cross-Section Determination from Near-Field 3-D Synthetic Aperture Imaging with Arbitrary Antenna Scanning Surfaces

*This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.<div><br></div><div>In this study, we propose a generalized algorithm for far-field radar cross-section determination by using 3-D synthetic aperture imaging with arbitrary antenna scanning surfaces. This method belongs to a class of techniques called image-based near-field-to-far-field transformation. The previous image-based approaches have been formulated based on a specific antenna-scanning trajectory or surface, such as a line, plane, circle, cylinder, and sphere; majority of these approaches consider 2-D radar images to determine the azimuth radar cross-section. We generalize the conventional image-based technique to accommodate an arbitrary antenna-scanning surface and consider a 3-D radar image for radar cross-section prediction in both the azimuth and zenith directions. We validate the proposed algorithm by performing numerical simulations and anechoic chamber measurements.<br></div>

Far-Field Radar Cross-Section Determination From Near-Field 3-D Synthetic Aperture Imaging With Arbitrary Antenna Scanning Surfaces

In this study, we propose a generalized algorithm for far-field radar cross-section determination from 3-D synthetic aperture imaging with arbitrary antenna scanning surfaces. This method belongs to the class of techniques called image-based near-field to far-field transformation. The previous image-based approaches were formulated based on a specific antenna scanning surface or trajectory such as a line, a plane, a circle, a cylinder, and a sphere--and the majority of them considered 2-D radar images to determine the azimuth radar cross-section. We generalize the conventional image-based technique to accommodate an arbitrary antenna scanning surface, and consider a 3-D radar image for radar cross-section prediction in both the azimuth and zenith directions. We demonstrate the proposed algorithm via numerical simulations and anechoic chamber measurements.

Far-Field Radar Cross-Section Determination From Near-Field 3-D Synthetic Aperture Imaging With Arbitrary Antenna Scanning Surfaces

In this study, we propose a generalized algorithm for far-field radar cross-section determination from 3-D synthetic aperture imaging with arbitrary antenna scanning surfaces. This method belongs to the class of techniques called image-based near-field to far-field transformation. The previous image-based approaches were formulated based on a specific antenna scanning surface or trajectory such as a line, a plane, a circle, a cylinder, and a sphere--and the majority of them considered 2-D radar images to determine the azimuth radar cross-section. We generalize the conventional image-based technique to accommodate an arbitrary antenna scanning surface, and consider a 3-D radar image for radar cross-section prediction in both the azimuth and zenith directions. We demonstrate the proposed algorithm via numerical simulations and anechoic chamber measurements.

Unsupervised water scene dehazing network using multiple scattering model

In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmospheric multiple scattering model is proposed. Unlike previous image dehazing methods, our method uses the unsupervised neural network and the atmospheric multiple scattering model and solves the problem of difficult acquisition of ideal datasets and the effect of multiple scattering on the image. In our method, in order to embed the atmospheric multiple scattering model into the unsupervised dehazing network, the unsupervised dehazing network uses four branches to estimate the scene radiation layer, transmission map layer, blur kernel layer and atmospheric light layer, the hazy image is then synthesized from the four output layers, minimizing the input hazy image and the output hazy image, where the output scene radiation layer is the final dehazing image. In addition, we constructed unsupervised loss functions which applicable to image dehazing by prior knowledge, i.e., color attenuation energy loss and dark channel loss. The method has a wide range of applications, with haze being thick and variable in marine, river and lake scenes, the method can be used to assist ship vision for target detection or forward road recognition in hazy conditions. Through extensive experiments on synthetic and real-world images, the proposed method is able to recover the details, structure and texture of the water image better than five advanced dehazing methods.

Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model

As an important export, cleanliness control on edible bird’s nest (EBN) is paramount. Automatic impurities detection is in urgent need to replace manual practices. However, effective impurities detection algorithm is yet to be developed due to the unresolved inhomogeneous optical properties of EBN. The objective of this work is to develop a novel U-net based algorithm for accurate impurities detection. The algorithm leveraged the convolution mechanisms of U-net for precise and localized features extraction. Output probability tensors were then generated from the deconvolution layers for impurities detection and positioning. The U-net based algorithm outperformed previous image processing-based methods with a higher impurities detection rate of 96.69% and a lower misclassification rate of 10.08%. The applicability of the algorithm was further confirmed with a reasonably high dice coefficient of more than 0.8. In conclusion, the developed U-net based algorithm successfully mitigated intensity inhomogeneity in EBN and improved the impurities detection rate.

Neural Twins Talk and Alternative Calculations

Inspired by how the human brain employs more neural pathways when increasing the focus on a subject, we introduce a novel twin cascaded attention model that outperforms a state-of-the-art image captioning model that was originally implemented using one channel of attention for the visual grounding task. Visual grounding ensures the existence of words in the caption sentence that are grounded into a particular region in the input image. After a deep learning model is trained on visual grounding task, the model employs the learned patterns regarding the visual grounding and the order of objects in the caption sentences, when generating captions. We report the results of our experiments in three image captioning tasks on the COCO dataset. The results are reported using standard image captioning metrics to show the improvements achieved by our model over the previous image captioning model. The results gathered from our experiments suggest that employing more parallel attention pathways in a deep neural network leads to higher performance. Our implementation of Neural Twins Talk (NTT) is publicly available at: https://github.com/zanyarz/NeuralTwinsTalk .

An Adaptive Reversible Data Hiding Scheme Using AMBTC and Quantization Level Difference

Hiding a message in compression codes can reduce transmission costs and simultaneously make the transmission more secure. This paper presents an adaptive reversible data hiding scheme that is able to provide large embedding capacity while improving the quantity of modified images. The proposed scheme employs the quantization level difference (QLD) and interpolation technique to adaptively embed the secret information into pixels of each absolute moment block truncation coding (AMBTC)-compressed block, except for the positions of two replaced quantization levels. The values of QLD tend to be much larger in complex areas than in smooth areas. In other words, our proposed method can obtain good performance for embedding capacity and still meets the requirement for better modified image quality when the image is complex. The performance of the proposed approach was compared to previous image hiding methods. The experimental results show that our approach outperforms referenced approaches.

Photographic Text-To-Image Synthesis via Multi-Turn Dialogue Using Attentional GAN

The process of generating an image that depicts naturalness is not so easy. To address such problem this paper introduces a novel approach to synthesize a photo-realistic image from the caption. The user can adjust the image highlights turn-by-turn according to the caption. This leads to the integration of natural intelligence. For this, the input passed to dialogue state tracker to extract context feature. Then the generator produces an image. If image is not as per expectations then user gives another dialogue, but the system takes both recent input and previous image to generate a new one. In such a manner, user gets a chance to visualize as per the imagination. We performed extensive experiments on two datasets CUB and COCO to generate a realistic image each turn and obtained the results: Inception Score (IS) of 4.38 ± 0.05, R-precision of 67.96 ± 5.27 % on CUB dataset and IS of 26.12 ± 0.24, R-precision of 91.00 ± 2.31 % on COCO dataset. Further, the work could be enhance to synthesize HQ image, voice integration, and video generation from stories and so on. This research is limited to 256x256 image in each turn.

The Dilemma of Scientific Demarcation and Its Possible Approach

From logicism to historicism, philosophers of science have put forward different standards of scientific demarcation according to their own scientific views. However, these standards encounter problems either in theory or in practice, and then fall into difficulties, thus moving towards relativism. Philosophy of scientific practice has reversed the previous image of science with scientific practice and pointed out the temporality, dynamics and locality of science. Therefore, the scientific boundary under this approach also has the above characteristics. Besides, the scientific boundary constructed by the scientific image is developmental and features temporary stability and effectiveness. Scientific demarcation is not a purely epistemological problem, but also a practical one.

Estimation of the foliar area of jambolão (Syzygium cumini (L.) Skeels) by different methods.

The jambolan as it is popularly known as Syzygium cumini (L.) Skeels, is of Indian origin. Studies in various areas require estimation of leaf area over the growing cycle. Leaf area is an important aspect in the analysis of photosynthetic efficiency of plants. The objective of the present study was to evaluate S. cumini leaf area determination methods based on measurements by caliper, leaf area integrator and previous image calibration in ImageJ Software, comparing both methodologies. The present study was developed at Mato Grosso State University Carlos Alberto Reys Maldonado - UNEMAT, Alta Floresta campus - MT. Fifty leaves were analyzed, which were numbered from 1-50 in length (C) and width (L) with the aid of a digital caliper and leaf area integrator (Leaf area integrator LI-COR 3100), for measuring leaf area (AF), after the sheets were scanned on an HP Photosmart C4680 flatbed scanner and processed using ImageJ software. Given the data found, it was found that the method used via digital imaging was accurate and showed a high correlation with manual measurement, indicating that the morphological characteristics of leaf area, length and width of Jambolão leaves can be estimated through analysis and analysis. Digital image processing.