Shuo-Tsung Chen

Unexpected in-hospital cardiac arrest (IHCA) is defined as when vital signs are measured when entering the emergency department for injury examination, but unexpected cardiac arrest occurs during the stay in the emergency department, requiring immediate emergency treatment to save life. Since unexpected in-hospital cardiac arrest (IHCA) is an emergency medical event, this study selected possible risk factors to collect data based on literature review and analysis, and used the last action of non-IHCA to transfer emergency patients to the Intensive Care Unit (ICU). The data is the control group, and the data is logged in. The collected data were sorted into a total of five correlation modules based on affinity relationships: personal variables, National Early Warning Scoring System (NEWS) scores, laboratory test indicators, Charlson comorbidity index scale, and possible causes of IHCA. Finally, we build a prediction model using Bayes’ Theorem and its classifier.

Unexpected in-hospital cardiac arrest (IHCA) in the emergency department is defined as an unexpected cardiac arrest during the stay in the emergency department with measured vital signs when entering the emergency department, requiring immediate emergency treatment to save a life. Since IHCA is an urgent medical event, especially in the emergency department, this study explored the risk prediction of IHCA events in the emergency department. IHCA not only has a high mortality rate, but is also likely to cause permanent neurological damage. In the emergency environment, due to the complexity and rapid changes in the patient’s condition, traditional assessment tools often fail to identify high-risk cases in a timely manner. In view of this, this study uses both the Power BI visual analysis platform and the binary decision tree model to construct a data-driven risk prediction tool. Power BI analysis successfully presented the dynamic ranking of influencing factors, and the decision tree prediction model showed excellent performance, with an accuracy of 91%, a recall rate of 89%, an F1-score of 89%, and an overall accuracy of 100%; this prediction system is expected to improve the efficiency of emergency medical care, identify high-risk patients in a timely manner, and assist medical staff in intervening in advance and implementing preventive measures. This study provided two different approaches: Power BI and decision tree. Power BI requires no coding and can be used by medical professionals without a programming background, while decision tree is designed for professionals with a programming background. While the structures of Power BI and decision tree differ slightly, they are generally similar and can both serve as intelligent clinical tools.

Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and condition the weak electrical signals produced by the heart. To evaluate sensor performance under realistic conditions, diverse ECG signals with embedded watermarks are generated, enabling an assessment of how effectively the sensor and its signal-conditioning circuitry handle these modified signals. A Variational Autoencoder (VAE) framework is employed to generate the watermarked ECG signals, addressing critical concerns in the digital era, such as data security, authenticity, and copyright protection. Three watermarking strategies are examined in this study: embedding watermarks in the mean (μ) of the VAE’s latent space, embedding them through the latent variable (z), and using post-reconstruction watermarking in the frequency domain. Experimental results demonstrate that watermarking applied through the mean (μ) and in the frequency domain achieves a low Mean Squared Error (MSE) while maintaining stable signal fidelity across varying watermark strengths (α), latent space dimensions, and noise levels. These findings indicate that the mean (μ) and frequency domain methods offer robust performance and are minimally affected by changes in these parameters, making them particularly suitable for preserving ECG signal quality. By contrasting these methods, this study provides insights into selecting the most appropriate watermarking technique for ECG sensor applications. Incorporating watermarking into sensor design not only strengthens data security and authenticity but also supports reliable signal acquisition in modern healthcare environments. Overall, the results underscore the effectiveness of combining VAEs with watermarking strategies to produce high-fidelity, resilient ECG signals for both sensor performance evaluation and the protection of digital content.

Light-emitting electronic devices are widely used at present, and this increases the risk of myopia. Thus, the early identification of myopia and the prevention of further exacerbation has become an essential issue in ophthalmology. Recently, choroidal imaging has been used to assist in the early identification and prevention of high myopia due to the fact that swept-source optical coherence tomography (SS-OCT) is an essential diagnostic tool in ophthalmology. This study presents a novel method to judge high myopia using the SS-OCT image dataset obtained from a university hospital. In order to relate the proposed method to the region of the SS-OCT image, the curvature analysis of an arbitrary segmented curve similar to the region of the SS-OCT is first illustrated by quadratic functions and matrix operations. Next, the curvature formula is derived and then applied to the choroidal curve obtained manually in each patient’s choroidal image. In particular, we applied the curvature analysis and its results to find the maximal curvature and average curvature of each patient’s choroidal curve. Finally, we used the maximal curvature and average curvature to evaluate high myopia. The accuracy of the proposed maximal curvature method and average curvature method in the experimental results to verify the proposed method.

Recommended travel itinerary planning is an important issue in travel platforms or travel systems. Most research focuses on minimizing the time spent traveling between attractions or the cost of attractions. This study makes four contributions to recommended travel itinerary planning in travel platforms or travel systems. The first contribution is to consider recommended travel itinerary planning which can account for attractions, restaurants, and hotels at the same time. Due to the fact that restaurants and hotels can be repeated on the recommended itinerary, the second contribution is to propose an improved ant colony system (ACS) with repeatedly used nodes for the optimization of travel itinerary planning. In the third contribution, the proposed improved ACS allows repeated use of certain nodes without falling into a pattern of infinitely hovering within a certain interval or over certain nodes, through the interactive operation of a Watch List and a Tabu List. In the fourth contribution, the user satisfaction calculation for restaurants and hotels is also added to the travel itinerary planning in order to fully meet the needs of tourists. The experimental results verify the efficiency of the proposed improved ACS.

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30.

This work implements an intelligent forest monitoring system using the Internet of things (IoT) with the wireless network communication technology of a low-power wide-area network (LPWAN), a long range (LoRa), and a narrow-band Internet of things (NB-IoT). A solar micro-weather station with LoRa-based sensors and communications was built to monitor the forest status and information such as the light intensity, air pressure, ultraviolet intensity, CO₂, etc. Moreover, a multi-hop algorithm for the LoRa-based sensors and communications is proposed to solve the problem of long-distance communication without 3G/4G. For the forest without electricity, we installed solar panels to supply electricity for the sensors and other equipment. In order to avoid the problem of insufficient solar panels due to insufficient sunlight in the forest, we also connected each solar panel to a battery to store electricity. The experimental results show the implementation of the proposed method and its performance.

As the Internet of Things (IOT) becomes more widely used in our everyday lives, an increasing number of wireless communication devices are required, meaning that an increasing number of signals are transmitted and received through antennas. Thus, the performance of antennas plays an important role in IOT applications, and increasing the efficiency of antenna design has become a crucial topic. Antenna designers have often optimized antennas by using an EM simulation tool. Although this method is feasible, a great deal of time is often spent on designing the antenna. To improve the efficiency of antenna optimization, this paper proposes a design of experiments (DOE) method for antenna optimization. The antenna length and area in each direction were the experimental parameters, and the response variables were antenna gain and return loss. Response surface methodology was used to obtain optimal parameters for the layout of the antenna. Finally, we utilized antenna simulation software to verify the optimal parameters for antenna optimization, showing how the DOE method can increase the efficiency of antenna optimization. The antenna optimized by DOE was implemented, and its measured results show that the antenna gain and return loss were 2.65 dBi and 11.2 dB, respectively.

Healthcare is an important medical topic in recent years. In this study, the novelty we propose is the intelligent healthcare system using an inequality-type optimization mathematical model with signal-to-noise ratio (SNR) and wavelet-domain low-frequency amplitude adjustment techniques to hide patients’ confidential data in their electrocardiogram (ECG) signals. The extraction of the hidden patient information also utilizes the low-frequency amplitude adjustment. The detailed steps of establishing the system are as follows. To integrate confidential patient data into ECG signals, we first propose a nonlinear model to optimize the quality of ECG signals with the embedded patients’ confidential data including patient name, patient birthdate, date of medical treatment, and medical history. Then, we apply Simulated Annealing (SA) to solve the nonlinear model such that the ECG signals with embedded patients’ confidential data have good SNR, good root mean square error (RMSE), and high similarity. In other words, the distortion of the PQRST complexes and the ECG shape caused by the embedded patients’ confidential data is very small, and thus the quality of the embedded ECG signals meets the requirements of physiological diagnostics. In the terminals, one can receive the ECG signals with the embedded patients’ confidential data. In addition, the embedded patients’ confidential data can be received and extracted without the original ECG signals. The experimental results confirm the efficiency that our method maintains a high quality of each ECG signal with the embedded patient confidential data. Moreover, the embedded confidential data shows a good robustness against common attacks.

Due to the rapid development of sensor technology and the popularity of the Internet, not only has the amount of digital information transmission skyrocketed, but also its acquisition and dissemination has become easier. The study mainly investigates audio security issues with data compression for private data transmission on the Internet or MEMS (micro-electro-mechanical systems) audio sensor digital microphones. Imperceptibility, embedding capacity, and robustness are three main requirements for audio information-hiding techniques. To achieve the three main requirements, this study proposes a high-quality audio information-hiding technology in the wavelet domain. Due to the fact that wavelet domain provides a useful and robust platform for audio information hiding, this study applies multi-coefficients of discrete wavelet transform (DWT) to hide information. By considering a good, imperceptible concealment, we combine signal-to-noise ratio (SNR) with quantization embedding for these coefficients in a mathematical model. Moreover, amplitude-thresholding compression technology is combined in this model. Finally, the matrix-type Lagrange principle plays an essential role in solving the model so as to reduce the carrying capacity of network transmission while protecting personal copyright or private information. Based on the experimental results, we nearly maintained the original quality of the embedded audio by optimization of signal-to-noise ratio (SNR). Moreover, the proposed method has good robustness against common attacks.