k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters

Development of the ALMA-North America Sideband-Separating SIS Mixers

As the Atacama Large Millimeter/submillimeter Array (ALMA) nears completion, 73 dual-polarization receivers have been delivered for each of Bands 3 (84-116 GHz) and 6 (211-275 GHz). The receivers use sideband-separating superconducting Nb/Al-AlOx/Nb tunnel-junction (SIS) mixers, developed for ALMA to suppress atmospheric noise in the image band. The mixers were designed taking into account dynamic range, input return loss, and signal-to-image conversion (which can be significant in SIS mixers). Typical SSB receiver noise temperatures in Bands 3 and 6 are 30 K and 60 K, resp., and the image rejection is typically 15 dB.Comment: Submitted to IEEE Trans. Microwave Theory Tech., June 2013. 10 pages, 21 figure

Non-invasive and transdermal measurement of blood uric acid level in human by electroporation and reverse iontophoresis

The aim of this study was to find out the optimum combination of electroporation (EP) and reverse iontophoresis (RI) on noninvasive and transdermal determination of blood uric acid level in humans. EP is the use of high-voltage electric pulse to create nano-channels on the stratum corneum, temporarily and reversibly. RI is the use of small current to facilitate both charged and uncharged molecule transportation across the skin. It is believed that the combination of these two techniques has additional benefits on the molecules’ extraction across the human skin. In vitro studies using porcine skin and diffusion cell have indicated that the optimum mode for transdermal uric acid extraction is the combination of RI with symmetrical biphasic direct current (current density = 0.3 mA/cm2; phase duration = 180 s) and EP with 10 pulses per second (voltage = 100 V/cm2; pulse width = 1 ms). This optimum mode was applied to six human subjects. Uric acid was successfully extracted through the subjects’ skin into the collection solution. A good correlation (r2 = 0.88) between the subject’s blood uric acid level and uric acid concentrations in collection solutions was observed. The results suggest that it may be possible to noninvasively and transdermally determine blood uric acid levels

Optimal design of allpass digital filters using artificial bee colony

Abstract -This paper applies a novel artificial bee colony algorithm to solve the design problem of allpass digital filters. We wish that the phase response of allpass filter can meet the desired specification. To achieve this aim, the ABC algorithm is utilized to update the related filter coefficients such that certain cost function of the algorithm can be minimized as possible as much. Finally, numerical simulation results will demonstrate the feasibility and effectiveness of the proposed scheme

A transition-constrained discrete hidden Markov model for automatic sleep staging

Abstract Background Approximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic (PSG) recordings including electroencephalograms (EEGs), electrooculograms (EOGs) and electromyograms (EMGs), are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen & Kales (R&K) rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable. Method The EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen features were utilized including temporal and spectrum analyses of the EEG, EOG and EMG signals, and a total of 158 hours of sleep data were recorded. Ten subjects were used to train the Discrete Hidden Markov Model (DHMM), and the remaining ten were tested by the trained DHMM for recognition. Furthermore, the 2-fold cross validation was performed during this experiment. Results Overall agreement between the expert and the results presented is 85.29%. With the exception of S1, the sensitivities of each stage were more than 81%. The most accurate stage was SWS (94.9%), and the least-accurately classified stage was S1 ( Conclusion The results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.</p

Estimation of Left Ventricular Ejection Fraction Using Cardiovascular Hemodynamic Parameters and Pulse Morphological Characteristics with Machine Learning Algorithms

It is estimated that 360,000 patients have suffered from heart failure (HF) in Taiwan, mostly those over the age of 65 years, who need long-term medication and daily healthcare to reduce the risk of mortality. The left ventricular ejection fraction (LVEF) is an important index to diagnose the HF. The goal of this study is to estimate the LVEF using the cardiovascular hemodynamic parameters, morphological characteristics of pulse, and bodily information with two machine learning algorithms. Twenty patients with HF who have been treated for at least six to nine months participated in this study. The self-constructing neural fuzzy inference network (SoNFIN) and XGBoost regression models were used to estimate their LVEF. A total of 193 training samples and 118 test samples were obtained. The recursive feature elimination algorithm is used to choose the optimal parameter set. The results show that the estimating root-mean-square errors (ERMS) of SoNFIN and XGBoost are 6.9 &plusmn; 2.3% and 6.4 &plusmn; 2.4%, by comparing with echocardiography as the ground truth, respectively. The benefit of this study is that the LVEF could be measured by the non-medical image method conveniently. Thus, the proposed method may arrive at an application level for clinical practice in the future