Automatic detection of prohibited items is vital in helping security staff be more efficient while improving the public safety index. However, prohibited item detection within X-ray security inspection images is limited by various factors, including the imbalance distribution of categories, diversity of prohibited item scales, and overlap between items. In this paper, we propose to leverage the Poisson blending algorithm with the Canny edge operator to alleviate the imbalance distribution of categories maximally in the X-ray images dataset. Based on this, we improve the cascade network to deal with the other two difficulties. To address the prohibited scale diversity problem, we propose the Re-BiFPN feature fusion method, which includes a coordinate attention atrous spatial pyramid pooling (CA-ASPP) module and a recursive connection. The CA-ASPP module can implicitly extract direction-aware and position-aware information from the feature map. The recursive connection feeds the CA-ASPP module processed multi-scale feature map to the bottom-up backbone layer for further multi-scale feature extraction. In addition, a Rep-CIoU loss function is designed to address the overlapping problem in X-ray images. Extensive experimental results demonstrate that our method can successfully identify ten types of prohibited items, such as Knives, Scissors, Pressure, etc. and achieves 83.4% of mAP, which is 3.8% superior to the original cascade network. Moreover, our method outperforms other mainstream methods by a significant margin.

We attempt to design and fabricate of a 4×4 Butler matrix for short-millimeter-wave frequencies by using the microfabrication process for a polytetrafluoroethylene (PTFE) substrate-integrated waveguide (SIW) by the synchrotron radiation (SR) direct etching of PTFE and the addition of a metal film by sputter deposition. First, the dimensions of the PTFE SIW using rectangular through-holes for G-band (140-220 GHz) operation are determined, and a cruciform 90 ° hybrid coupler and an intersection circuit are connected by the PTFE SIW to design the Butler matrix. Then, a trial fabrication is performed. Finally, the validity of the design result and the fabrication process is verified by measuring the radiation pattern.

Fine-grained image analysis, such as pixel-level approaches, improves threat detection in x-ray security images. In the practical setting, the cost of obtaining complete pixel-level annotations increases significantly, which can be reduced by partially labeling the dataset. However, handling partially labeled datasets can lead to training complicated multi-stage networks. In this paper, we propose a new end-to-end object separation framework that trains a single network on a partially labeled dataset while also alleviating the inherent class imbalance at the data and object proposal level. Empirical results demonstrate significant improvement over existing approaches.

The microfabrication technique based on synchrotron radiation (SR) direct etching process has recently been applied to construct PTFE microstructures. This paper proposes a PTFE substrate integrated waveguide (PTFE SIW). It is expected that the PTFE SIW contributes to the improvement of the structural strength. A rectangular through-hole is introduced taking the advantage of the SR direct etching process. First, a PTFE SIW for the Q-band is designed. Then, a cruciform 3-dB directional coupler consisting of the PTFE SIW is designed and fabricated by the SR direct etching process. The validity of the PTFE SIW coupler is confirmed by measuring the frequency characteristics of the S-parameters. The mechanical strength of the PTFE SIW and the peeling strength of its Au film are also additionally investigated.

Accurate and automatic quantitative cephalometry analysis is of great importance in orthodontics. The fundamental step for cephalometry analysis is to annotate anatomic-interested landmarks on X-ray images. Computer-aided automatic method remains to be an open topic nowadays. In this paper, we propose an efficient deep learning-based coarse-to-fine approach to realize accurate landmark detection. In the coarse detection step, we train a deep learning-based deformable transformation model by using training samples. We register test images to the reference image (one training image) using the trained model to predict coarse landmarks' locations on test images. Thus, regions of interest (ROIs) which include landmarks can be located. In the fine detection step, we utilize trained deep convolutional neural networks (CNNs), to detect landmarks in ROI patches. For each landmark, there is one corresponding neural network, which directly does regression to the landmark's coordinates. The fine step can be considered as a refinement or fine-tuning step based on the coarse detection step. We validated the proposed method on public dataset from 2015 International Symposium on Biomedical Imaging (ISBI) grand challenge. Compared with the state-of-the-art method, we not only achieved the comparable detection accuracy (the mean radial error is about 1.0-1.6mm), but also largely shortened the computation time (4 seconds per image).

A neural network that outputs reconstructed images based on projection data containing scattered X-rays is presented, and the proposed scheme exhibits better accuracy than conventional computed tomography (CT), in which the scatter information is removed. In medical X-ray CT, it is a common practice to remove scattered X-rays using a collimator placed in front of the detector. In this study, the scattered X-rays were assumed to have useful information, and a method was devised to utilize this information effectively using a neural network. Therefore, we generated 70,000 projection data by Monte Carlo simulations using a cube comprising 216 (6 × 6 × 6) smaller cubes having random density parameters as the target object. For each projection simulation, the densities of the smaller cubes were reset to different values, and detectors were deployed around the target object to capture the scattered X-rays from all directions. Then, a neural network was trained using these projection data to output the densities of the smaller cubes. We confirmed through numerical evaluations that the neural-network approach that utilized scattered X-rays reconstructed images with higher accuracy than did the conventional method, in which the scattered X-rays were removed. The results of this study suggest that utilizing the scattered X-ray information can help significantly reduce patient dosing during imaging.

Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.

In X-ray computed tomography, scattered X-rays are generally removed by using a post-patient collimator located in front of the detector. In this paper, we show that the scattered X-rays have the potential to improve the estimation accuracy of the attenuation coefficient in computed tomography. In order to clarify the problem, we simplified the geometry of the computed tomography into a thin cylinder composed of a homogeneous material so that only one attenuation coefficient needs to be estimated. We then conducted a Monte Carlo numerical experiment on improving the estimation accuracy of attenuation coefficient by measuring the scattered X-rays with several dedicated toroidal detectors around the cylinder in addition to the primary X-rays. We further present a theoretical analysis to explain the experimental results. We employed a model that uses a T-junction (i.e., T-junction model) to divide the photon transport into primary and scattered components. This division is processed with respect to the attenuation coefficient. Using several T-junction models connected in series, we modeled the case of several scatter detectors. The estimation accuracy was evaluated according to the variance of the efficient estimator, i.e., the Cramer-Rao lower bound. We confirmed that the variance decreases as the number of scatter detectors increases, which implies that using scattered X-rays can reduce the irradiation dose for patients.

The microfabrication technique based on SR (Synchrotron Radiation) direct etching process has recently been applied to construct PTFE microstructures. This paper attempts to fabricate an integrated PTFE-filled waveguide Butler matrix for short millimeter-wave by SR direct etching. First, a cruciform 3-dB directional coupler and an intersection circuit (0-dB coupler) are designed at 180 GHz. Then, a 4×4 Butler matrix with horn antennas is designed and fabricated. Finally, the measured radiation patterns of the Butler matrix are shown.

Detection of cavities in X-ray astronomical images has become a field of interest, since the flourishing studies on black holes and the Active Galactic Nuclei (AGN). In this paper, an approach is proposed to detect cavities in X-ray astronomical images using our newly designed Granular Convolutional Neural Network (GCNN) based classifiers. The raw data are firstly preprocessed to obtain images of the observed objects, i.e., galaxies or galaxy clusters. In each image, pixels are classified into three categories, (1) the faint backgrounds (BKG), (2) the cavity regions (CAV), and (3) the bright central gas regions (CNT). And the sample sets are then generated by dividing large images into subimages with a window size according to the cavities' scale. Since the number of BKG samples are far more than the other types, to achieve balanced training sets, samples from the major class are split into subsets, i.e., granule. Then a group of three-convolutional-layer granular CNN networks without subsampling layers are designed as the classifiers, and trained with the labeled granular sample sets. Finally, the trained GCNN classifiers are applied to new observations, so as to estimate the cavity regions with a voting strategy and locate them with elliptical profiles on the raw observation images. Experiments and applications of our approach are demonstrated on 40 X-ray astronomical observations retrieved from chandra Data Archive (CDA). Comparisons among our approach, the β-model fitting and the Unsharp Masking (UM) methods were also performed, which prove our approach was more accurate and robust.

A novel activation method for a B dopant implanted in a Si substrate using a soft X-ray undulator was examined. As the photon energy of the irradiated soft X-ray approached the energy of the core level of Si 2p, the activation ratio increased. The effect of soft X-ray irradiation on B activation was remarkable at temperatures lower than 400°C. The activation energy of B activation by soft X-ray irradiation (0.06 eV) was lower than that of B activation by furnace annealing (0.18 eV). The activation of the B dopant by soft X-ray irradiation occurs at low temperature, although the activation ratio shows small values of 6.2×10-3 at 110°C. The activation by soft X-ray is caused not only by thermal effects, but also electron excitation and atomic movement.

This paper gives an outline of key technologies necessary for science-based agriculture. In order to design future agriculture, present agriculture should be redesigned based on the context of smart agriculture that indicates the overall form of agriculture including a social system while the present precision agriculture shows a technical form of agriculture only. Wireless Sensor Network (WSN) and the various type of optical sensors are assumed to be a basic technology of smart agriculture which intends the harmony with the economic development and sustainable agro-ecosystem. In this paper, the current state and development for the optical sensing for environment and plant are introduced.

A four-pixel-array superconducting transition-edge sensor (TES) microcalorimeter with a mushroom-shaped absorber was fabricated for the energy dispersive spectroscopy performed on a transmission electron microscope. The TES consists of a bilayer of Au/Ti with either a 50-nm or 120-nm thickness. The absorber of 5.0,$mu$m thick is made from a Au layer and its stem is deposited in the center of the TES surface. A Ta$_{2}$O$_{5}$ insulating layer of 100-nm thickness is inserted between the overhang region of the absorber and the TES surface. A selected pixel of the TES microcalorimeter was operated for the detection of Np L X-rays emitted from an $^{241}$Am source. A response of the TES microcalorimeter to L X-rays was obtained by analyzing detection signal pulses with using the optimal filter method. An energy resolution was obtained to be 33,eV of the full width at half maximum value at 17.751,keV of Np L$_{eta 1}$ considering its natural width of 13.4,eV. Response to L X-rays emitted from a mixture source of $^{238}$Pu, $^{239}$Pu and $^{241}$Am was obtained by operating the selected pixel of the TES microcalorimeter. Major L X-ray peaks of progeny elements of $alpha$ decay of Pu and Am isotopes were clearly identified in the obtained energy spectrum. The experimental results demonstrated the separation of $^{241}$Am and plutonium isotopes by L X-ray spectroscopy.

Three-dimensionally assembled TES X-ray microcalorimeter arrays may be utilized for three purposes: (1) to obtain wide X-ray energy coverage of TES microcalorimeters, (2) to distinguish charged particle events from X-ray events, (3) to reconstruct Compton-scattering geometry for hard X-ray Compton cameras. We have designed and fabricated three-dimensionally assembled array of the minimum format i.e. $2 imes 2 imes 2$ array in order to obtain a good energy resolution in a wide energy range of 0.5--20,keV and a high maximum counting rate of 2000,cps for energy dispersive X-ray spectrometer (EDS) system for a transmission electron microscope (TEM). Although we could not obtain required energy resolution because of a problem in the refrigerator system, we confirmed the operation of the three-dimensional array.

Superconducting tunnel junction (STJ) array detectors can exhibit excellent performance with respect to energy resolution, detection efficiency, and counting rate in the soft X-ray energy range, by which those excellent properties STJ array detectors are well suited for detecting X-rays at synchrotron radiation facilities. However, in order to achieve a high throughput analysis for trace impurity elements such as dopants in structural or functional materials, the sensitive area of STJ array detectors should be further enlarged up to more than 10 times larger by increasing the pixel number in array detectors. In this work, for a large STJ-pixel number of up to 1000 within a 10,mm- square compact chip, we have introduced three-dimensional (3D) structure by embedding a wiring layer in a SiO$_{2}$ isolation layer underneath a base electrode layer of STJs. The 3D structure is necessary for close-packed STJ arrangement, avoiding overlay of lead wiring, which is common in conventional two-dimensional layout. The fabricated STJ showed excellent current-voltage characteristics having low subgap currents less than 2,nA, which are the same as those of conventional STJs. An STJ pixel has an energy resolution of 31,eV (FWHM) for C-K$alpha $ (277,eV).

In developing an automatic system of a single tooth length measurement on x-ray image, since a tooth shape is assumed to be straight and curve, an algorithm which can accurately deal with straight and curve is required. This paper proposes an automatic algorithm for measuring the length of single straight and curve teeth. In the algorithm consisting of control point determination, curve fitting, and length measurement, PCA is employed to find the first and second principle axes as vertical and horizontal ones of the tooth, and two terminal points of vertical axis and the junction of those axes are determined as three first-order control points. Signature is then used to find a peak representing tooth root apex as the forth control point. Bezier curve, Euclidean distance, and perspective transform are finally applied with determined four control points in curve fitting and tooth length measurement. In the experiment, comparing with the conventional PCA-based method, the average mean square error (MSE) of the line points plotted by the expert is reduced from 7.548 pixels to 4.714 pixels for tooth image type-I, whereas the average MSE value is reduced from 7.713 pixels and 7.877 pixels to 4.809 pixels and 5.253 pixels for left side and right side of tooth image type-H, respectively.

Thickness of crystalline layer induced by annealing after rubbing at surface of polyimide film for liquid crystal displays was estimated to be 3--5 nm by grazing-incidence X-ray diffractions with multi incident angles. Agreement of thickness of crystalline layer with that of initially oriented layer suggests polymer orientation induced by rubbing proceeds crystallization by annealing. Furthermore, no in-plane smectic ordering in bottom 20,nm region of polyimide film was suggested.

In tooth contour extraction there is insufficient intensity difference in x-ray images between the tooth and dental bone. This difference must be enhanced in order to improve the accuracy of tooth segmentation. This paper proposes a method to improve the intensity between the tooth and dental bone. This method consists of an estimation of tooth orientation (intensity projection, smoothing filter, and peak detection) and PCA-Stacked Gabor with ellipse Gabor banks. Tooth orientation estimation is performed to determine the angle of a single oriented tooth. PCA-Stacked Gabor with ellipse Gabor banks is then used, in particular to enhance the border between the tooth and dental bone. Finally, active contour extraction is performed in order to determine tooth contour. In the experiment, in comparison with the conventional active contour without edge (ACWE) method, the average mean square error (MSE) values of extracted tooth contour points are reduced from 26.93% and 16.02% to 19.07% and 13.42% for tooth x-ray type I and type H images, respectively.

An SiO2/Si-cap/Si0.55Ge0.45 heterostructure was fabricated on p-type Si(100) and strained silicon on insulator (SOI) substrates by low pressure chemical vapor deposition (LPCVD) and subsequent thermal oxidation in an O2 + H2 gas mixture. Chemical bonding features and valence band offsets in the heterostructures were evaluated by using high-resolution x-ray photoelectron spectroscopy (XPS) measurements and thinning the stack layers with a wet chemical solution.

As means to control interface reactions between HfO2 and Ge(100), chemical vapor deposition (CVD) of ultrathin Ta-rich oxide using Tri (tert-butoxy) (tert-butylimido) tantalum (Ta-TTT) on chemically-cleaned Ge(100) has been conducted prior to atomic-layer controlled CVD of HfO2 using tetrakis (ethylmethylamino) hafnium (TEMA-Hf) and O3. The XPS analysis of chemical bonding features of the samples after the post deposition N2 annealing at 300 confirms the formation of TaGexOy and the suppression of the interfacial GeO2 layer growth. The energy band structure of HfO2/TaGexOy/Ge was determined by the combination of the energy bandgaps of HfO2 and TaGexOy measured from energy loss signals of O 1s photoelectrons and from optical absorption spectra and the valence band offsets at each interface measured from valence band spectra. From the capacitance-voltage (C-V) curves of Pt-gate MIS capacitors with different HfO2 thicknesses, the thickness reduction of TaGexOy with a relative dielectric constant of 9 is a key to obtain an equivalent SiO2 thickness (EOT) below 0.7 nm.