A Data Hiding Method Based on Partition Variable Block Size with Exclusive-or Operation on Binary Image

In this paper, we propose a high capacity data hiding method applying in binary images. Since a binary image has only two colors, black or white, it is hard to hide data imperceptible. The capacities and imperception are always in a trade-off problem. Before embedding we shuffle the secret data by a pseudo-random number generator to keep more secure. We divide the host image into several non-overlapping (2n+1) by (2n+1) sub-blocks in an M by N host image as many as possible, where n can equal 1, 2, 3 , …, or min(M,N). Then we partition each sub-block into four overlapping (n+1) by (n+1) sub-blocks. We skip the all blacks or all whites in each (2n+1) by (2n+1) sub-blocks. We consider all four (n+1) by (n+1) sub-blocks to check the XOR between the non overlapping parts and center pixel of the (2n+1) by (2n+1) sub-block, it embed n 2 bits in each (n+1) by (n+1) sub-block, totally are 4*n 2 . The entire host image can be embedded 4×n 2×M/(2n+1)×N/(2n+1) bits. The extraction way is simply to test the XOR between center pixel with their non-overlapping part of each sub-block. All embedding bits are collected and shuffled back to the original order. The adaptive means the partitioning sub-block may affect the capacities and imperception that we want to select. The experimental results show that the method provides the large embedding capacity and keeps imperceptible and reveal the host image lossless

R-process beta-decay neutrino flux from binary neutron star merger and collapsar

This study investigates the antineutrinos production by $\beta$-decay of $r$-process nuclei in two astrophysical sites that are capable of producing gamma-ray bursts (GRBs): binary neutron star mergers (BNSMs) and collapsars, which are promising sites for heavy element nucleosynthesis. We employ a simplified method to compute the $\beta$-decay $\bar\nu_e$ energy spectrum and consider two representative thermodynamic trajectories for $r$-process simulations, each with four sets of $Y_e$ distribution. The time evolution of the $\bar\nu_e$ spectrum is derived for both the dynamical ejecta and the disk wind for BNSMs and collapsar outflow, based on approximated mass outflow rates. Our results show that the $\bar\nu_e$ has an average energy of approximately 3 to 9~MeV, with a high energy tail of up to 20 MeV. The $\bar\nu_e$ flux evolution is primarily determined by the outflow duration, and can thus remain large for $\mathcal{O}(10)$~s and $\mathcal{O}(100)$~s for BNSMs and collapsars, respectively. For a single merger or collapsar at 40~Mpc, the $\bar\nu_e$ flux is $\mathcal{O}(10-100)$~cm$^{-2}$~s$^{-1}$, indicating a possible detection horizon up to $0.1-1$~Mpc for Hyper-kamiokande. We also estimate their contributions to the diffuse $\bar\nu_e$ background. Our results suggest that although the flux from BNSMs is roughly 4--5 orders of magnitude lower than that from the regular core-collapse supernovae, those from collapsars can possibly contribute a non-negligible fraction to the total diffuse $\bar\nu_e$ flux at energy $\lesssim 1$~MeV, with a large uncertainty depending on the unknown rate of collapsars capable of hosting the $r$-process.Comment: 13 pages, 7 figure

Hemispheric dispersion of radioactive plume laced with fission nuclides from the Fukushima nuclear event

Radioactivities of particulate 131I and 137Cs released from the Fukushima nuclear accident were monitored in a regional aerosol network including two high mountain sites (central Taiwan and Tibetan Plateau). The results were integrated with data measured elsewhere around the world, with special focus on the mid-latitudes. The hemispheric transport of the Fukushima radiation clouds (FRCs) by the westerlies took $18 days, displaying an exponential-like decrease eastward, with a dilution factor of at least five orders of magnitude following a full circuit around the globe. The initial two waves of FRCs may travel at different atitudes: the first one at$3–4 km, whereas the second one up to 5 km or more. 131I and 137Cs were fractionated during transport, with 137Cs concentrated in the shallower layer, susceptible to depositional removal, while 131I moving faster and higher. This accident may be exemplified to identify some atmospheric processes on the hemispheric scale

Climatology and Change of Extreme Precipitation Events in Taiwan Based on Weather Types

Taiwan\u27s most significant natural hazards are caused by hydrological extremes resulting from excessive precipitation. The threat of extreme precipitation is posed by several different types of weather patterns that affect Taiwan. This study examined the bi‐decadal changes in rainfall by defining an extreme precipitation occurrence (EPO) for a range of event durations from 1 to 24 hr. Three major weather types affecting EPO in Taiwan were identified from 1993 to 2015: the front type consisting of either a frontal zone or convective systems developing with an apparent Meiyu cloudband, diurnal rainfall events when no apparent synoptic features are present, and a tropical cyclone (TC) type according to the maximum sustained wind radius of a TC. Results show that TC‐type events have the greatest overall contribution to EPO at longer (\u3e6 hr) durations. Diurnal/afternoon convection events contribute most to the shorter (\u3c3 hr) duration EPO, while frontal/Meiyu systems prevail in the medium (3–6 hr) duration. EPO of almost all durations have experienced an increase, with the 3‐ and 12‐hr EPO having increased by 4.6 days each over the 23 years. However, apparent decadal‐scale variability exists in these EPO associated with the decreasing tendency of EPO after the mid‐2000s, particularly the longer duration (\u3e6 hr) EPO associated with the TC‐type events in summer. The distinction between EPO trends for the entire island of Taiwan and for the Taipei metropolitan area alone (northern Taiwan, population of 7 million) were compared, and an intriguing interannual variation is reported in the TC‐type EPO associated with the TC season 1 year to a year and half just before an El Niño–Southern Oscillation event. The analysis here provides refined statistical distributions of extreme rainfall, and these can contribute to the revision of governmental definitions for weather disasters that are used in mitigation and response strategies

A way to learn : the service learning between Indonesia (SCU) and Taiwan (FJU)

Gills and Maclellan (2010) conducted a 1999-2009 systemic literature review service learning in nursing education, outcomes suggest that students who participate in international programs having an increase in self-perceived cultural competency, encouraging lifelong commitment to continue serving, developing students into a positive force of change in healthcare abroad and within their own communities. The purpose of this study is in depth to analyze nursing students\u27 learning process through the service learning. Design: Action research method was used and a total number of 6 nursing students were participated in this study. One Taiwanese and one Indonesia students as a team shared a bed and lived with Indonesia family for 11 days. During this period students logs, field notes, reflection sheets and team working records as raw materials together to be used as content analysis, the reliability and validity is based on Lincoln and Guba (1985) proposed vetting reliability and validity of qualitative research methods. Results: After intensively living and working with Indonesia students and family in this international service learning program, the results come up as an agricultural process. It is improving students\u27 cross-culture communication in spread period. Follow by cultivated period that participants established cross-cultural sensitivities and culture respect. In the final harvest period, through the serving activities, participant fulfilled the role as givers and receivers among Indonesia partners and families and able to understand the meaning of love. In conclusion, this study confirmed the previous studies outcomes and showed the dynamic interaction amongst nursing students who joined service learning, and enhanced students understanding among the cultures, with the respect on inner or outer levels

Impact of body-mass factors on setup displacement in patients with head and neck cancer treated with radiotherapy using daily on-line image guidance

BACKGROUND: To determine the impact of body-mass factors (BMF) before radiotherapy and changes during radiotherapy on the magnitude of setup displacement in patients with head and neck cancer (HNC). METHODS: The clinical data of 30 patients with HNC was analyzed using the alignment data from daily on-line on-board imaging from image-guided radiotherapy. BMFs included body weight, body height, and the circumference and bilateral thickness of the neck. Changes in the BMFs during treatment were retrieved from cone beam computed tomography at the 10th and 20th fractions. Setup errors for each patient were assessed by systematic error (SE) and random error (RE) through the superior-inferior (SI), anterior-posterior (AP), and medial-lateral (ML) directions, and couch rotation (CR). Using the median values of the BMFs as a cutoff, the impact of the factors on the magnitude of displacement was assessed by the Mann–Whitney U test. RESULTS: A higher body weight before radiotherapy correlated with a greater AP-SE (p = 0.045), SI-RE (p = 0.023), and CR-SE (p = 0.033). A longer body height was associated with a greater SI-RE (p = 0.002). A performance status score of 1 or 2 was related to a greater AP-SE (p = 0.043), AP-RE (p = 0.015), and SI-RE (p = 0.043). Among the ratios of the BMFs during radiotherapy, the values at the level of mastoid tip at the 20(th) fraction were associated with greater setup errors. CONCLUSIONS: To reduce setup errors in patients with HNC receiving RT, the use of on-line image-guided radiotherapy is recommended for patients with a large body weight or height, and a performance status score of 1–2. In addition, adaptive planning should be considered for those who have a large reduction ratio in the circumference (<1) and thickness (<0.94) over the level of the mastoid tip during the 20(th) fraction of treatment

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology; this research solves the problem that the original cutting technology cannot extract certain single teeth; and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN); which can identify caries and restorations from the bitewing images. Moreover; it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image; which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization; (2) a dental image cropping procedure to obtain individually separated tooth samples; and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks; namely; AlexNet; GoogleNet; Vgg19; and ResNet50; experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%; respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion

A Low Power Fully Integrated Analog Baseband Circuit with Variable Bandwidth for 802.11 a/b/g WLAN

Abstract: This paper presents experimental results of an analog baseband circuit with variable bandwidth for WLAN direct conversion receiver in UMC 0.18um CMOS process. A seventh order chebyshev lowpass filter with triple bandwidth is used in the analog baseband circuit. The bandwidth is selectable from 7.56MHz, 19.5MHz, or 26.5MHz. The circuit adopts the servo loop for dc offset cancellation. It also has a gain range from 20dB to 60 dB with 10 dB steps while only dissipating 22.248mW. In addition, an automatic frequency tuning loop (ATL) is reported to achieve the bandwidth accuracy of the filter