Compact ellipse shaped patch with ground slotted broadband monopole patch antenna for head imaging applications

This paper represents an ellipse-shaped patch with a ground slotted broadband patch antenna for microwave head imaging systems. The proposed antenna constructs with a simple ellipse shaped square patch and modified slotted plane. The proposed design is very simple to fabricate and is enclosed in a microwave imaging system. The slotted patch, and the partial ground plane improves the antenna's efficiency, operating frequency range, and gain. The size of the proposed antenna is 70 × 60 × 1.5 mm³ with the electrical dimension being 0.277λ × 0.238λ × 0.006λ at a lower frequency of 1.19 GHz and connected to a 50Ω microstrip feeding line. This antenna is printed onto a low-cost FR-4 substrate whose relative permittivity is 4.4, and whose thickness is 1.5 mm. CST and HFSS software have been used for simulation and thereafter successful completion of the measurements and the fabrication. The comprehensive simulation exhibits that this design provides a bandwidth of 2.37 GHz (1.19 – 3.56 GHz) and 100% of the fractional bandwidths (% BW) with the reflection coefficient of <-10 dB. This antenna on FR-4 can produce an average gain of around 3.63 dBi with 5.95 dBi peak gain at whole operation frequencies. The prototype has a peak radiation efficiency of approximately 97% across the active frequency spectrum with 93% of average. The antenna does have an improved fidelity-factor (> 90 %) with a shorter group-delay. Several design modifications have been performed to get perfect, effective, and suitable results for microwave imaging applications. A 3D-realist Hugo head model is fitted with a single antenna and a 9-antenna array component to verify the performance of both the single antenna, and the configured array antenna. The antenna penetrates the brain human tissues satisfactorily. Across the operational range, the specific absorption rate (SAR) attains a limit of <1 W/kg. The analysis of both numeric and experimental evidence clearly indicates that the suggested antenna is ideal for microwave head-imaging implementations.     

Digital image correlation in experimental mechanics and image registration in computer vision: Similarities,differences and complements

Digital image correlation and image registration or matching are among the most widely used techniques in the fields of experimental mechanics and computer vision, respectively. Despite their applications in separate fields, both techniques primarily involve detecting the same physical points in two or more images. In this paper, a brief technical comparison of the two techniques is reviewed, and their similarities and differences as well as complements are presented. It is shown that some concepts from the image registration or matching technique can be applied to the digital image correlation technique to substantially enhance its performance, which can help broaden the applications of digital image correlation in scientific research and engineering practice.     

Self-Contact for Rods on Cylinders

We study self-contact phenomena in elastic rods that are constrained to lie on a cylinder. By choosing a particular set of variables to describe the rod centerline the variational setting is made particularly simple: the strain energy is a second-order functional of a single scalar variable, and the self-contact constraint is written as an integral inequality. Using techniques from ordinary differential equation theory (comparison principles) and variational calculus (cut-and-paste arguments) we fully characterize the structure of constrained minimizers. An important auxiliary result states that the set of self-contact points is continuous, a result that contrasts with known examples from contact problems in free rods.     

Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning

BackgroundMagnetic resonance images with multiple contrasts or sequences are commonly used for segmenting brain tissues, including lesions, in multiple sclerosis (MS). However, acquisition of images with multiple contrasts increases the scan time and complexity of the analysis, possibly introducing factors that could compromise segmentation quality.ObjectiveTo investigate the effect of various combinations of multi-contrast images as input on the segmented volumes of gray (GM) and white matter (WM), cerebrospinal fluid (CSF), and lesions using a deep neural network.MethodsU-net, a fully convolutional neural network was used to automatically segment GM, WM, CSF, and lesions in 1000 MS patients. The input to the network consisted of 15 combinations of FLAIR, T1-, T2-, and proton density-weighted images. The Dice similarity coefficient (DSC) was evaluated to assess the segmentation performance. For lesions, true positive rate (TPR) and false positive rate (FPR) were also evaluated. In addition, the effect of lesion size on lesion segmentation was investigated.ResultsHighest DSC was observed for all the tissue volumes, including lesions, when the input was combination of all four image contrasts. All other input combinations that included FLAIR also provided high DSC for all tissue classes. However, the quality of lesion segmentation showed strong dependence on the input images. The DSC and TPR values for inputs with the four contrast combination and FLAIR alone were very similar, but FLAIR showed a moderately higher FPR for lesion size <100 μl. For lesions smaller than 20 μl all image combinations resulted in poor performance. The segmentation quality improved with lesion size.ConclusionsBest performance for segmented tissue volumes was obtained with all four image contrasts as the input, and comparable performance was attainable with FLAIR only as the input, albeit with a moderate increase in FPR for small lesions. This implies that acquisition of only FLAIR images provides satisfactory tissue segmentation. Lesion segmentation was poor for very small lesions and improved rapidly with lesion size.     

Finitely maxitive T-conditional possibility theory: Coherence and extension

Starting from the axiomatic definition of finitely maxitive T-conditional possibility (where T is a continuous triangular norm), the paper aims at a comprehensive and self-contained treatment of coherence and extension of a possibilistic assessment defined on an arbitrary set of conditional events. Coherence (or consistence with a T-conditional possibility) is characterized either in terms of existence of a linearly ordered class of finitely maxitive possibility measures (T-nested class) agreeing with the assessment, or in terms of solvability of a finite sequence of nonlinear systems for every finite subfamily of conditional events. Coherence reveals to be a necessary and sufficient condition for the extendibility of an assessment to any superset of conditional events and, in the case of T equal to the minimum or a strict t-norm, the set of coherent values for the possibility of a new conditional event can be computed solving two optimization problems over a finite sequence of nonlinear systems for every finite subfamily of conditional events.     

LOCALIZED PATTERNS OF THE CUBIC-QUINTIC SWIFT-HOHENBERG EQUATIONS WITH TWO SYMMETRY-BREAKING TERMS

Homoclinic snake always refers to the branches of homoclinic orbits \mbox{near} a heteroclinic cycle connecting a hyperbolic or non-hyperbolic equilibrium and a periodic orbit in a reversible variational system. In this paper, the normal form of a Swift-Hohenberg equation with two different symmetry-breaking terms (non-reversible term and non-k-symmetry term) are investigated by using multiple scale method, and their bifurcation diagrams are initially studied by numerical simulations. Typically, we predict numerically the existence of so-called round-snakes and round-isolas upon particular two symmetric-breaking perturbations.     

Steganography and authentication in image sharing without parity bits

Recently, a polynomial-based (k, n) steganography and authenticated image sharing (SAIS) scheme was proposed to share a secret image into n stego-images. At the same time, one can reconstruct a secret image with any k or more than k stego-images, but one cannot obtain any information about the secret from fewer than k stego-images. The beauty of a (k, n)-SAIS scheme is that it provides the threshold property (i.e., k is the threshold value), the steganography (i.e., stego-images look like cover images), and authentication (i.e., detection of manipulated stego-images). All existing SAIS schemes require parity bits for authentication. In this paper, we present a novel approach without needing parity bits. In addition, our (k, n)-SAIS scheme provides better visual quality and has higher detection ratio with respect to all previous (k, n)-SAIS schemes.